Yacine Boulaftali

Lamia Lamrani

Marie-Catherine Rouzaud

St Phane Loyau

Martine Jandrot-Perrus

Marie-Christine Bouton

The mouse dorsal skinfold chamber as a model for the study of thrombolysis by intravital microscopy

Keywords: Dorsal skinfold chamber, intravital microscopy, thrombolysis, thrombosis, tPA '

Although intravital microscopy models of thrombosis in mice have contributed to dissect the mechanisms of thrombus formation and stability, they have not been well adapted to study long-term evolution of occlusive thrombi. Here, we assessed the suitability of the dorsal skinfold chamber (DSC) for the study of thrombolysis and testing of thrombolytic agents by intravital microscopy. We show that induction of FeCl -induced occlusive thrombosis is achievable in microvessels of DSCs, and that thrombi formed in DSCs can be 3 visualized by intravital microscopy using brightfield transmitted light, or fluorescent staining of thrombus components such as fibrinogen, platelets, leukocytes, and von Willebrand factor. Direct application of control saline or recombinant tissue-plasminogen activator (rtPA) to FeCl -produced thrombi in DSCs did not affect thrombus size or induce recanalization. However, in the presence 3 of hirudin, rtPA treatment caused a rapid dose-dependent lysis of occlusive thrombi, resulting in recanalization within 1 hour after treatment. Skin hemorrhage originating from vessels located inside and outside the FeCl3-injured area was also observed in DSCs of rtPA-treated mice. We further show that rtPA-induced thrombolysis was enhanced in plasminogen activator inhibitor-1-deficient (PAI-1 / ) mice, and dropped considerably as the time between occlusion and treatment application increased. Together, our results -show that by allowing visualization and measurement of thrombus lysis and potential bleeding complications of thrombolytic treatments, the DSC provides a model for studying endogenous fibrinolysis and for first-line screening of thrombolytic agents.

Furthermore, using this system, we found that thrombin and clot aging impair the thrombolytic action of rtPA towards FeCl 3 -produced thrombi.

INTRODUCTION

Intravital microscopy models of thrombosis have been valuable tools for dissecting the molecular and cellular interactions involved in thrombus formation. As early as in 1882, Bizzozero discovered platelets and their central role in thrombus formation using intravital microscopy on the guinea pig mesentery . Since this founding study, intravital microscopy models of thrombosis have contributed to [START_REF] Bizzozero | Ueber einen neuen Forrnbestandteil des Blutes und dessen Rolle bei der Thrombose und Blutgerinnung[END_REF] demonstrate the pro-or anti-thrombotic properties of several endogenous proteins and drugs, including the ADAMTS13 metalloprotease[2 , protease nexin-1 , tumor necrosis factor-alpha and the anti-von Willebrand factor A1-domain aptamer ARC177 . These models

] [3 ] [4 ] [5 ]
have also provided information regarding the role of various factors including thrombin inhibitor and fibrin(ogen) in regulating thrombus stability and propensity to embolize in blood flow . Thus, although intravital microscopy models of thrombosis are generally [6 -8 ] performed on the cremaster muscle and mesentery, whose vascular beds differ from more clinically relevant sites of thrombosis, they have proven highly beneficial. However, these models have not been well adapted for studying longer term evolution of occlusive thrombi and their response to drugs administered after vessel occlusion. The interest of adapting these models to thrombolysis becomes particularly evident when one considers the therapeutic potential of thrombolytics. Indeed, many cardio-and cerebrovascular events are a consequence of occlusive thrombosis whose treatment involves administration of not only antithrombotics but also thrombolytics.

Except for rare models of thrombotic coronary occlusion induced in large animals like dogs , , thrombolysis studies are mostly [9 10 ] performed in mouse models of stroke and of carotid artery thrombosis . These models provide information on global outcomes [11 -13 ] such as infarct size and survival rates. However, besides time to reperfusion, they give little or no dynamic information relative to thrombus evolution. Thus, by allowing live visualization and monitoring of several parameters such as blood flow, thrombus size, stability, and composition, intravital microscopy could benefit the field of thrombolysis/fibrinolysis research and complement current thrombolysis models.

The accessibility and transparency of the exposed mesentery and cremaster muscle have made their microvasculature the sites of predilection for the induction of thrombosis and subsequent analysis by intravital microscopy. However, these models involve acute surgical preparations that limit the observation and manipulation times of isolated vessel segments to only a few hours in anesthetized animals, which is incompatible with longer-term follow-up of the evolution of an occlusive thrombus. The dorsal skinfold chamber (DSC) is a long-term intravital microscopy technique that has been used in several research areas including cancer, wound healing , and

[14 ] [START_REF] Harder | Evolution of ischemic tissue injury in a random pattern flap: a new mouse model using intravital microscopy[END_REF] ischemia-reperfusion but not thrombolysis. In DSCs, observation of isolated vessels can be performed for several weeks, a period [START_REF] Nolte | Microcirculatory models of ischaemia-reperfusion in skin and striated muscle[END_REF] which should largely allow follow-up of an occlusive thrombus, and contrasts with the limited observation time window of acute preparations, such as the exposed mesentery or cremaster muscle. Another advantage of the DSC is that it enables precise spatial and temporal delivery of drugs directly to the target site. For these reasons, the DSC may provide a model of choice to study the evolution of a thrombus and to test potential thrombolytic agents by intravital microscopy. Here, using ferric chloride (FeCl )-induced occlusive 3 thrombosis, we show that the assets of the DSC make it an interesting model suitable for the study of fibrinolysis and for the evaluation of thrombolytics.

MATERIAL AND METHODS

Mice

C57BL/6J female mice were purchased from C.E.R.J (France). Mice deficient in plasminogen activator inhibitor-1 (PAI-1 / ) on --C57BL/6J background were a gift from Pr MC Alessi (Mediterranean University, Aix-Marseille, France). All experimental procedures involving the use of mice were approved by the Animal Care and Use Committee of the Claude Bernard Institute (Paris, France).

Dorsal skinfold chamber

DSCs (APJ trading Co., Ventura, CA) were implanted in 10 to 12 week-old mice (20 to 25 g body weight) anesthetized by intraperitoneal injection of 100 mg/kg ketamine and 10 mg/kg xylazine in saline solution as previously described with minor [START_REF] Lehr | Dorsal skinfold chamber technique for intravital microscopy in nude mice[END_REF] modifications. Briefly, a patch of dorsal hair was removed using electric clippers and hair remover cream, and two titanium frames were positioned so as to sandwich the extended double layer of skin. One layer of betadine-cleaned skin was completely removed in a circular area of 12 mm in diameter and the edges of the cut skin were cauterized using a pen-like electric cauterizer. The remaining layer, consisting of epidermis, subcutaneous tissue, and striated skin muscle, was covered with a 12-mm glass coverslip incorporated in one of the frames. Following surgery, mice were injected subcutaneously with buprenorphine (0. Thrombus size before, and 1 hour after, thrombolytic treatment was calculated as the intravascular surface area covered by rhodamine 6G-labeled platelets and leukocytes at the site of injury.

For the visualization of fibrinolysis, rat fibrinogen (Sigma) was coupled to Alexa Fluor 488 succinimidyl ester following the manufacturer s instructions (Molecular Probes Europe BV). Uncoupled dye was separated from the conjugate on a G-25 Sephadex 3 12

(Pharmacia, Piscataway, NJ) chromatographic column followed by centrifugation of the flow-through fraction using a 50 kDa-cut off centrifugal filtration device (Vivaspin Sartorius, Aubugne, France). The conjugate was then injected intravenously (10 mg/kg) into mice ,

bearing DSCs five minutes prior to the induction of FeCl injury.

3

The occurrence of hemorrhage within DSCs, evidenced by the presence of extravasated red blood cells, was assessed visually, under the microscope, at 24 hours post-treatment. Data acquisition and analysis was done using Axiovision software (Carl Zeiss MicroImaging GmbH, Germany).

Assessment of vascular permeability after FeCl -induced injury 3

Five minutes before applying a filter paper saturated with 15 FeCl to microvessels in DSCs, Alexa 594-conjugated bovine serum % 3 albumin (BSA, Invitrogen) was injected intravenously (10 mg/kg),and monitored for extravasation under the microscope.

Evaluation of the impact of time of administration of rtPA on thrombolysis efficiency

In order to determine the influence of time-to-treatment on the efficiency of rtPA-induced thrombolysis, a thrombolytic cocktail composed of 40 M rtPA and 50 M hirudin in 40 L saline was applied directly to the occluded vessels either within 1 hour or at 4 hours

μ μ μ
after occlusion had occured. The incidence of recanalization and the evolution of thrombus size were then determined and compared at 1 hour after administration of the thrombolytic treatment.

Statistics

Data are expressed as means SEM and were compared by the non-parametric Mann-Whitney test. For incidences of recanalization ± and of tissue hemorrhage, individual groups were compared by a sequence of 2 2 one-sided Fisher s exact test using GraphPad Prism × ' software (San Diego, CA). P values < 0.05 were regarded as statistically significant.

RESULTS

Induction of occlusive thrombosis by FeCl in dorsal skinfold chambers 3

To produce occlusive thrombosis in vessels of subcutaneous striated muscle in DSCs, we used ferric chloride (FeCl )-induced injury 3 as it is among the most commonly used and well characterized models of thrombosis in exposed vessels . After removing the cover [START_REF] Eckly | Mechanisms underlying FeCl -induced arterial thrombosis 3[END_REF] glasses of DSCs, a filter paper strip saturated with FeCl was applied directly to the exposed vessels for 3 minutes (Supplemental Fig. 1).

3

The chamber was then rinsed with saline, and blood flow and thrombus formation in injured vessels were monitored by standard intravital transmitted brightfield videomicroscopy. Thrombus formation, assessed by the formation of white intravascular aggregates, started immediately after application of filter papers drenched with aqueous solutions at 5, 10, or 15 FeCl . While exposure to 5 or 10 FeCl Although thrombi formed in DSCs after FeCl injury were easily visualized in brightfield microscopy, more precise observation of 3 thrombus formation and composition in DSCs was also achievable in classical epifluorescence microscopy ( ). In fact, Fig. 1A rhodamine-6G-labeled platelets and leukocytes, red calcein-labeled platelets, Alexa fluor 594-conjugated fibrinogen, and Alexa Fluor 488-conjugated anti-mouse VWF polyclonal antibody, all accumulated at the site of injury, enabling higher contrast imaging of the thrombus (

). However, while incorporation of fluorescent anti-VWF IgG, fibrinogen, or platelets, led to labelling of distinct and/or Fig. 1A overlapping thrombus areas, staining of both circulating platelets and leukocytes by intravenous injection of rhodamine-6G, led to global labelling of the thrombus, as compared to brightfield images ( ).

Fig. 1A

DSCs enable evaluation of spontaneous thrombolysis and testing of thrombolytic drugs after occlusive thrombosis

To determine the feasibility of thrombolysis studies in DSCs, we then compared the incidence of recanalization and thrombus size evolution in occluded vessels (ranging from 100 170 m diameter) from mice treated with saline or rtPA, the most potent thrombolytic μ agent currently in clinical use. application of the treatments was chosen over intravenous injection in order to optimize their In situ delivery to the target site by avoiding circulation half-life issues and delivery limitations due to the complete absence of flow in occluded vessels. Because application was made in the extravascular space, we first verified that diffusion of proteins between intra-and extravascular spaces was possible at sites of FeCl -induced injury. To assess vascular permeability, Alexa 594-conjugated BSA was 3 injected intravenously 5 min prior to exposure of microvessels to 15 FeCl , and monitored for extravasation by intravital microscopy.

% 3
Progressive leakage of fluorescent BSA into the extravascular space was seen in the FeCl -exposed area within minutes after removing the Both saline and rtPA application was performed within 30 minutes after vessel occlusion (Supplemental Fig. 1). Recanalization did not occur in any control or rtPA-treated mice either at 1 hour ( ) or at 24 hours (not shown) following treatment. Furthermore, there Fig. 2A was no change in thrombus size at 1 hour after treatment with saline or rtPA (

). These results show that endogenous Fig. 2B andC fibrinolysis following FeCl injury is a slow process and confirm the previously reported resistance of FeCl -produced occlusive thrombi 3 3 to thrombolysis by tPA .

[11 ]

Because FeCl -induced occlusive thrombosis has been shown to be highly dependent on thrombin , we hypothesized that 3 [18 -22 ] ongoing thrombin activity at the site of endothelial injury could interfere with thrombolysis and mask the action of rtPA. To test this possibility, hirudin was administered directly in DSCs together with saline or rtPA in order to stop the thrombotic process. While the addition of hirudin did not change the incidence of recanalization in saline-treated control mice, it improved considerably the efficacy of rtPA treatment. In fact, in the presence of 50 M hirudin, thrombus size was reduced in a dose-dependent manner by rtPA and It should be noted that although we monitored the surface area covered by rhodamine-6G-labeled platelets and leukocytes to estimate the evolution of global thrombus size, rtPA treatment induced a reduction in the surface covered by both fluorescently-labeled cells and fibrinogen ( ). Also, intravital visualization of rtPA-induced thrombolysis revealed that the reduction in thrombus size involved both Fig. 3 progressive clot lysis and embolization of large thrombus chunks subsequently to recanalization (Supplemental Movie).

All these data show that, by allowing intravital observation of thrombus morphology and blood flow, as well as precise spatial and temporal administration of drugs, DSCs can be used for the evaluation of spontaneous thrombolysis and testing of thrombolytic in vivo activity.

Thrombolysis experiments in DSCs enable evaluation of hemorrhagic risk associated with thrombolytic therapy

When evaluating a thrombolytic agent, it is important to not only consider its thrombolytic properties, but also its potential hemorrhagic complications. For this reason, we monitored the mice for signs of skin hemorrhage in the 24h following thrombolytic treatment. Skin hemorrhage was only observed within DSCs of mice treated with rtPA (

) and a statistically significant Fig. 4A andB increase in the proportion of bleeding mice was observed in the groups that received the highest rtPA regimen, with no difference between hirudin-treated and -untreated mice (

). Hemorrhage in DSCs of rtPA-treated mice originated from vessels located within and Fig. 4B outside the FeCl -injured area, suggesting that both quiescent and injured vessels are susceptible to bleeding when exposed to high 3 extravascular concentrations of rtPA (

). Furthermore, it is noteworthy that among the 15 mice showing skin bleeding after rtPA Fig. 4A treatment, 7 had FeCl -occluded vessels that did not recanalize. Altogether, these results indicate that rtPA can cause bleeding even when 3 failing to induce recanalization and show that the hemorrhagic complications of thrombolytic agents can be evaluated in DSCs.

RtPA-induced lysis of FeCl -produced occlusive thrombi in DSCs is enhanced in plasminogen activator inhibitor-1-deficient mice 3

The release of fibrinolysis inhibitors by activated platelets also contributes to the resistance of platelet-rich thrombi to thrombolysis. PAI-1 is considered to be the primary physiological inhibitor of both tPA and urokinase-type plasminogen activators, and has been shown to be a potent inhibitor of tPA-induced thrombolysis . This implicates that rtPA-induced thrombolysis should be enhanced in PAI-1 /

[11 ] -mice. Thus, to further characterize our model, we compared the sensitivity of WT and PAI-1 / mice to thrombolysis induced by rtPA.

--

There was no significant difference between WT and PAI-1 / mice in the occlusive thrombus size ( ) or in the incidence of vessel --Fig. 5A recanalization or the rate of thrombolysis in response to 20 M rtPA and 50 M hirudin (not shown). This suggests that a concentration of μ μ 20 M of rtPA may be high enough to overcome inhibition by PAI-1. In contrast, the incidence of vessel recanalization and the rate of

WT mice (

). These results confirm the role of PAI-1 in the resistance of platelet-rich thrombi to thrombolysis and show that the Fig. 5B D -DSC is also suitable for studying the fibrinolytic system.

Resistance to rtPA-induced lysis of FeCl -produced occlusive thrombi increases with time following occlusion 3

A review of several clinical studies has suggested that thrombus resistance to lysis by rtPA could develop over time . In order to [START_REF] Grines | Fibrinolytic therapy: is it a treatment of the past?[END_REF] determine whether the thrombolytic activity of rtPA is indeed influenced by clot aging , we compared the ability of rtPA to lyse in vivo FeCl -induced occlusive thrombi and to achieve recanalization, when applied within 1 hour (early treatment) or 4 hours (late treatment) 3 after occlusion. While the size of the occlusive thrombi was reduced by over 50 1 hour after early rtPA treatment, it remained unaffected % by late treatment ( ). Concomitantly, the incidence of recanalization was considerably decreased when rtPA was applied at 4 hours Fig. 6A as compared to 1 hour post-occlusion, with only 1 out of 9 versus 9 out of 11 occluded vessels being recanalized at 1 hour post-treatment, respectively (

). These results indicate that the thrombolytic efficacy of rtPA against platelet-rich occlusive thrombi decreases when Fig. 6B the interval between occlusion and treatment lengthens, and that our method can be used to determine whether the thrombolytic action of a drug is time-dependent or not.

Discussion

Here, we demonstrate the feasibility and interest of studying thrombolysis by intravital microscopy in DSCs. In fact, using regular brightfield and epifluorescence microscopy, we were able to visualize and measure thrombus lysis, vessel recanalization, and hemorrhagic complications, following application of rtPA to FeCl -produced occlusive thrombi. We further show that the rate of rtPA-induced in situ 3 thrombolysis and the incidence of recanalization are enhanced in PAI-1-deficient mice, and decrease considerably as the interval between occlusion and treatment application lengthens. Thus, in addition to providing a suitable model for testing thrombolytic agents , the in vivo DSC can also be used to study the mechanisms of thrombolysis resistance.

Notably, in our experimental model, a single topical application of hirudin and rtPA was sufficient to achieve recanalization of occluded vessels in DSCs. In contrast, to be effective in mouse models of stroke and of carotid artery thrombosis, rtPA has to be administered as a bolus followed by continuous systemic intravenous infusion , , . This time-and product-consuming constraint [11 24 25 ] requiring extra-surgical procedures and equipment is imposed by the short circulation half-life of rtPA . In patients, delivery issues can [START_REF] Jang | Differential sensitivity of erythrocyte-rich and platelet-rich arterial thrombi to lysis with recombinant tissue-type plasminogen activator. A possible explanation for resistance to coronary thrombolysis[END_REF] be bypassed through catheter directed administration of thrombolytic agents directly into the clot, a strategy that has proven its -in situ superiority over systemic infusion in the treatment of stroke , limb arterial occlusion , acute deep vein thrombosis , , or acute

[27 ] [28 ]
[29 30 ] massive pulmonary embolism , . Because of vessel size-related limitations, this method is difficult to achieve in mice. Thus, by [31 32 ] enabling delivery of drugs directly to the thrombosis site, the easy access to occluded vessels through the window of DSCs in situ represents an interesting advantage for assessing the thrombolytic properties of a compound, in absence of circulation half-life issues, a strategy that could help saving money and drugs. This possibility of delivering high doses of drugs may also be of interest in cases in situ of decreased ability of human recombinant proteins to interact with the mouse fibrinolytic system. Such a situation is well illustrated by the low catalytic efficacy of human rtPA in activating mouse plasminogen , , , which forces the use of rtPA doses 10 times higher in [11 33 34 ] rodents than in humans , , . However, delivery to the thrombus of drugs applied in the extravascular space requires passage of [24 25 35 ] these molecules through the vascular wall. Thus, this convenient way of drug administration can only be used with agents capable of crossing the layers of the blood vessel wall. In the present study, we show that FeCl -injured vessels are permeable to proteins with a 3 molecular weight of up to at least ~ 70,000 Da, a cut-off that enabled the visualization of the combined effect of the 7,000 Da hirudin and 72,000 Da rtPA. For agents of higher molecular weight, one should verify that the injured vascular wall is permeable enough to permit their diffusion to the thrombus. If not, classical systemic intravenous injection should then be used as the route of administration.

Intravascular drug injection may also be preferable in cases of incomplete vessel occlusion, where residual blood flow and pressure could impair the diffusion of extravascularly-applied drugs to the thrombus.

In addition, further insights into FeCl -induced occlusive thrombosis have come from this study. Despite the absence of change in the 3 occlusive thrombus size and the apparent arrest of the thrombotic process after occlusion, our results show that ongoing thrombin activity at the site of FeCl -induced severe injury continuously interferes with the thrombolytic activity of rtPA, the current gold standard 3 thrombolytic drug used in patients. In fact, blocking of thrombin activity by hirudin at the site of FeCl -induced occlusive thrombosis was 3 an absolute requirement for visualization of the thrombolytic activity of rtPA. Dependence on anticoagulants for successful lysis of FeCl 3 -produced thrombi by rtPA was previously suggested by the study of Zhu , in which heparin was co-infused with rtPA to favour et al. [11 ] its thrombolytic activity. FeCl has been shown to cause de-endothelialization , , resulting in exposure of thrombogenic basement 3 [6 18 ] membrane components and subsequent thrombosis in a tissue factor-and thrombin-dependent manner , . Therefore, and as [8 18 -22 ] supported by our results, after FeCl -induced endothelial denudation, the thrombogenic stimulus at the site of injury is highly likely to 3 persist and to counter any attempt of thrombolysis unless inhibited or until healing of the endothelial monolayer has occurred. Thus, failure of potential thrombolytic agents to lyse FeCl -produced occlusive thrombi, when not co-administered with a coagulation inhibitor, may be 3 due more to the severity of this particular thrombosis model than to an absence of thrombolytic activity.

Interestingly, coagulation-related resistance to thrombolysis by rtPA has not been reported in embolic models of thrombosis that are commonly used in experimental stroke research. In fact, in these models, as in clinical situations, administration of rtPA alone is sufficient to achieve significant recanalization rates , . In contrast to FeCl -produced vessel occlusion, embolic models of thrombosis do not high dose of thrombin . Therefore, while FeCl causes vascular injury and the exposure of pro-coagulant substances that persist long [START_REF] Orset | Mouse model of in situ thromboembolic stroke and reperfusion[END_REF] 3 after the injurious stimulus is removed, embolic models are characterized by a thrombogenic stimulus that is highly limited in time and by the absence of endothelial denudation. The discrepancy regarding rtPA efficacy depending on the thrombosis model considered, underlines the influence of the model but more importantly, of the associated thrombus composition and environment, on the success of thrombolysis using a specific agent. In fact, it is now well established that the pathways of thrombus formation vary with the model of thrombosis used and the organ concerned , . Therefore, although here we used severe FeCl injury to demonstrate the feasibility of thrombolysis [20 37 ] 3 experiments in DSCs, one could combine the DSC with another intravital microscopy model of thrombosis such as Rose Bengal-, laser-, or mild FeCl -induced injury, in order to determine on which type of thrombus a given thrombolytic agent may work best. This becomes 3 particularly interesting when one considers the fact that, as shown in the present study, staining and analysis of specific thrombus components can be performed in DSCs.

Another important result of this study is that successful dissolution by rtPA of platelet-rich occlusive thrombi formed in a thrombin-rich environment at a site of vascular injury is time-dependent, as shown by the decreased efficacy of rtPA administered late after occlusion. This result supports the generally well-accepted clinical observation that fresh clots are more susceptible to thrombolysis than aged clots. Previous studies have suggested that several platelet-dependent and independent mechanisms could contribute to the progressive increased resistance of platelet-rich thrombi to thrombolysis by rtPA. These factors include the release of inhibitors of plasminogen activators by activated platelets , , , shielding of tPA binding sites by thrombin-activable fibrinolysis inhibitor [11 [START_REF] Boulaftali | Platelet protease nexin-1, a serpin that strongly influences fibrinolysis and thrombolysis[END_REF] mechanisms lead to decreased clot permeation to rtPA, reduced rtPA binding to fibrin, and direct inhibition of rtPA enzymatic activity.

Notably, we found that rtPA can cause bleeding even when failing to induce recanalization. This observation corroborates clinical observations in stroke and myocardial infarction. Indeed, while rtPA has been shown to be more effective for recanalizing infarct vessels when administered early in stroke and in myocardial infarction patients , the risk of parenchymal hemorrhage has been shown to [43 ] [23 ] be similarly increased in stroke patients whether treated early or late with rtPA . The reduced ability of rtPA to lyse aged thrombi, [START_REF] Lees | Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials[END_REF] combined with a stable hemorrhagic risk over time, is very likely to contribute to the decreased benefits of thrombolysis observed as the interval between ischemic event onset and rtPA administration increases in both stroke patients and patients with acute myocardial [START_REF] Lees | Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials[END_REF] infarction , . Also, the fact that rtPA application caused bleeding from vessels located both within and outside the FeCl -injured [23 45 -47 ] 3 area, suggests that, if present in the extravascular space (for instance in case of blood-brain barrier breakdown), rtPA can damage microvessels other than the ones initially occluded or injured. This deleterious action of rtPA may involve the ability of the tPA/plasminogen system to degrade directly, or indirectly activation of matrix metalloproteinases, most of the major protein via components of the extracellular matrix and vessel wall.

In conclusion, we show that the DSC provides a method suitable for the study of endogenous thrombus degradation and for first-line screening of new thrombolytics. Using this method, in addition to confirming the negative impact of PAI-1 on rtPA-induced in vivo thrombolysis, we showed that thrombin activity and clot aging impair the thrombolytic efficacy of rtPA, and that rtPA-induced bleeding can occur in the absence of recanalization. While supporting several clinical observations, our results show that thrombolysis experiments in DSCs can provide information on clinically relevant aspects of thrombolytic treatment and could be used in complement to more complicated experimental settings such as stroke and myocardial infarction models.

% 3 % 3 solution 3 μ

 333 led to only partial occlusion of the injured vessels, 15 FeCl led to complete occlusion of vessels of 100 to 200 m diameter, % which usually occurred within one hour. For this reason, in the rest of the study, 15 FeCl was used to induce occlusive thrombosis in % 3 DSCs.

μ 3 μ

 3 recanalization was observed in 85.7 of vessels from mice treated with 20 M rtPA and in all vessels from mice treated with 40 M rtPA, % μ μ at 1 hour post-treatment ( ). These results indicate that thrombin activity at the site of FeCl -induced injury persists after Fig. 2A C -3 occlusion and that neutralizing the ongoing coagulation process is required to enable successful thrombolysis by rtPA following FeCl 3 -induced occlusive thrombosis. Further increase in hirudin concentration to 100 M did not change the rate of thrombolysis induced by 40 μ M of rtPA while it was reduced for hirudin concentrations below 50 M (not shown). This suggests that in the experimental settings μ μ chosen (occlusive thrombosis induced by 3 min exposure to 15 FeCl ), 50 M hirudin represents a saturating concentration, sufficient to % neutralize the local thrombin activity.

μ

  thrombolysis in response to a suboptimal concentration of 10 M rtPA and 50 M hirudin, were increased in PAI-1 / mice compared to μ μ --

Figure 3

 3 Figure 3 Visualization of both platelets and fibrin dissolution following thrombolytic treatment Micrographs of a representative occlusive thrombus stained by Alexa488-fibrinogen and rhodamine-6G before and at 15 and 30 minutes after treatment with 40 M rtPA and 50 M hirudin. The vessel edges are highlighted in white. The green fluorescence signal outside the vessel μ μcorresponds to leakage of Alexa488-fibrinogen from the FeCl -injured vessel in the extravascular space.

Figure 4

 4 Figure 4 Effect of rtPA treatment on tissue hemorrhage Representative images of the skin from control (left panels) and rtPA-treated (right panels) mice. The upper right panel shows bleeding in A. the vicinity of the FeCl -injured area (yellow stain) in the skin of an rtPA-treated mouse. The lower right panel shows bleeding distant from 3 the FeCl -injured area in the skin of an rtPA-treated mouse. Bar 200 m. Proportion of mice presenting skin hemorrhage within 24 hours 3

μ 3 Numbers

 3 above the bars indicate the number of mice with skin bleeding/number studied in each group.

Real-time intravital imaging of thrombus formation and thrombolysis

	fulfilling the criteria of intact microcirculation and showing no signs of inflammation and infection such as skin redness, swelling, or pus
	were used for thrombosis and thrombolysis experiments. The mean red blood cell (RBC) velocity and shear rate in skin vessels (100 150 -	μ
	m diameter) typical of those used in this study were of 2072 321 m.s and 206 35 s , respectively. A detailed description of the ± μ 1 -± 1 -
	measurement of shear rate in skin microvessels is given in the supplemental material.
	Mice bearing DSCs were anesthetized (100 mg/kg ketamine, 10 mg/kg xylazine) and vessels in DSCs were exposed by removing the
	chamber cover glass. Vascular injury in DSCs was then induced by placing a filter paper strip (1 0.5 mm) saturated with 5, 10, or 15 ×	%
	FeCl (Sigma, St. Louis, MO) over vessels ranging from 100 to 200 m diameter for 3 min. The filter paper was then removed, DSCs 3 μ
	rinsed with saline, and thrombus formation following injury was examined in real-time by monitoring the accumulation of rhodamine 6G-
	labeled platelets and leukocytes (3 mg rhodamine 6G/kg mouse, Sigma, St. Louis, MO), Alexa 594 human-conjugated fibrinogen (10
	mg/kg mouse, Invitrogen Life Technologies, Paisley, UK), Alexa 488-conjugated sheep anti-VWF IgG (10 mg/kg, Abcam, Cambridge,
	UK), or red calcein (Invitrogen)-labeled platelets (600 10 washed platelets per mouse), using a fluorescence microscope (Axio Observer, × 6
	Carl Zeiss MicroImaging) with a 5 objective connected to a CCD camera (Hamamatsu). All fluorescent markers were administered ×
	intravenously into the retro-orbital sinus. Red and green fluorescent signals were visualized using filter sets 43 (excitation BP 550/25 nm;
	beam splitter FT 570 nm; emission BP 605/70 nm) and 38 (excitation BP 470/40 nm; beam splitter FT 495 nm; emission BP 525/50 nm),
	respectively.
	Platelet deposition and thrombus growth in injured vessels were monitored until vessel occlusion, defined as the complete arrest of
	blood flow for at least 5 min. During observation, DSCs were kept open and regularly superfused with saline to prevent drying of the
	tissue. Within twenty minutes after occlusion, thrombolysis was induced by topical application of 40 L of recombinant μ
	tissue-plasminogen activator (rtPA) (Actilyse, Boehringer Ingelheim, Germany) in saline, supplemented or not with 50 M hirudin μ
	(Serbio, Asnieres, France), applied directly to the occluded vessels. Recanalization, defined as partial or complete restoration of flow
	beyond the occluded area, was examined in real-time for at least 1 hour following treatment.

1 mg/kg) and then again 8 12 h later. The animals tolerated the chambers well and showed no signs of discomfort. After a 24 h period of recovery from surgery, preparations

  [35 36 ]

			3
	rely on	in situ	endogenous thrombin generation but on intravascular injection of either a thrombus prepared	, ex vivo [35 36 ]	or of a single

12

Fig. 1B 3 to approximately 70,000 Da.

12

12

12

12

12

Acknowledgements:

We thank Pr Marie-Christine Alessi and Dr Delphine Bastelica for providing us with PAI-1 / mice. We also thank Pr. Antonino Nicoletti

--

and Dr Bertrand Lapergue for helpful discussion and reading of the manuscript, and Dr Mary Osborne-Pellegrin for her help in editing.

This study was supported by Institut National de la Sant et de la Recherche M dicale and by grants from La Fondation Financial support :

pour la Recherche M dicale and from La Fondation de France (grant n 2009002497).

C.

Thrombus surface area at 1 hour following various thrombolytic treatments. Results are expressed as percentages of the thrombus surface area just after occlusion. n 6 7 mice per group.

= -

Comparison of rtPA-induced thrombolysis between wild-type and plasminogen activator inhibitor-1-deficient mice After inducing occlusive thrombosis in up to 3 vessels per mouse, the efficiency of a thrombolytic treatment by 10 M recombinant μ tissue-plasminogen activator (rtPA) and 50 M hirudin was compared between wild-type and plasminogen activator inhibitor-1-deficient μ (PAI-1 / ) mice. Initial occlusive thrombus size in wild-type and PAI-1 / mice before thrombolytic treatment. n 6 mice per group.

--

A.

--=

B.

Incidence of vessel recanalization in wild-type and PAI-1 / mice at 1 hour and 2 hours after rtPA treatment. Numbers above the bars = -


M Hinaje

W Kaewmanee

S Raël

B Davat

New PEMFC behaviour law

Keywords: Proton exchange membrane fuel cell, steady state model, polarisation curve, semi-empirical model, polarisation resistance

This paper presents a new steady state model which contains fewer polarisation curve fitting parameters compared to other semi-empirical models. The established model is also defined at no load operation and can be fitted perfectly to the static polarisation curve for a wide range of cell currents. The model links the fuel cell voltage to the current by a simple function to which various useful derivative laws can be applied. As a result, many analytical expressions, for example, polarisation resistance or power density, can be evaluated directly from the model.

Introduction

Polymer electrolyte membrane fuel cells (PEMFCs) are the attractive solution for zero emission energy productions. They are the promising alternative sources for stationary and transported applications. To improve the efficiency of the fuel cell system, designers need a simple but accurate model of the fuel cell. Unfortunately, the mentioned model is not yet existence.

The straightforward method to create the fuel cell model is to develop the physics model that is to say coupled partial differential equations model. However, the fuel cell system is usually incorporated with many auxiliary systems such as compressors, cooling system and flow control valves, so that, the physics model, which is constructed from physics, electrochemical and thermodynamic laws of each system element, will be very complicated and difficult to use.

The empirical or semi-empirical fuel cell models are the other option to the designer. This kind of model is usually a simple mathematical formula with some curve fitting parameters which form a relationship between the interested variables, in this case the terminal voltage and current of the fuel cell. The drawback of this model is that it usually contains the logarithmic function which makes the model undefined at zero current operation.

This paper presents a new empirical model of PEMFC. The proposed model contains only two curve fitting parameters which is less than the usual empirical (or semi-empirical) models may have. Further more, there is no logarithmic function in the model, so that, it is continuous and differentiable.

The fuel cell models

At present, there are three main types of PEMFC models which are the mass transport, the empirical equivalent electrical circuit model and the empirical mathematic model.

Mass transport model

This kind of model describes entire physical phenomena of electrochemical reactions, such as the transports of gases, water, protons and current, that lead to the relationships amongst fuel cell voltages, currents, temperature, and material parameters [1]- [3]. Several aspects are involved in this kind of model, for example, multi-species diffusions through gas diffusion layers, electrochemical reactions in the activation layers, and water and protons transport through the membrane. All these phenomena are strongly coupled and thermally dependent [4]. Some parameters, for example the current density, are space dependent which make them not just difficult to solve but also require a high computational cost. Moreover, this detailed model contains many adjustable parameters.

This kind of model is necessary to predict and analyse the static or transient behaviour of a fuel cell, or just a part of them by means of computer simulations in different running conditions. However, the model is very difficult to understand and is not well suited for control applications.

Empirical equivalent electrical circuit model

The empirical model is a model that has no solid link between the model and the physical system. The model always needs an experimental result from the real system to adjust the model parameters. The circuit model is one form of the empirical model of the PEMFC. The circuit in Fig. 2 is a common and practical circuit model for a PEMFC [5]- [7]. It consists of three resistors where the r m represents the membrane resistance, r a and r c stand for anode and cathode charge transfer resistance due to the hydrogen oxidation and oxygen reduction, respectively. C dl,a and C dl,c are the double layer capacitances at the electrode/membrane interfaces. Finally, Z w,a and Z w,c are the diffusion impedances of the gas diffusion layer in the anode and the cathode side, respectively. Normally, these parameters are identified by using an electrochemical impedance spectroscopy (EIS) [START_REF] Barsoukov | Impedance Spectroscopy: Theory, Experiment, and Applications[END_REF].

The feature of the model is the low computational cost and its use in control applications. However, as shown in figure 1, there ares a lot of parameter to estimate.

Empirical mathematic model

Some of them are based on a strong, though simplified, physical description of the MEA behaviour [START_REF] Williams | [END_REF] [10]. The advantage is that they allow the experimental determination of important parameters governing the electrode processes. In this sense this type of model is used to confirm the physical interpretation of PEM single cell behaviour in contrast to a simple prediction of a polarisation curve. However, contrary to our model, those semiempirical models are typically intended for single PEMFC and not for stacks.

Other models are quite simple compared to the pure theoretical models previously described. The empirical equation is used to establish the relation between voltage and current of the fuel cell [11], [12]. For example, one of the most preferred models is:

RI ) I ln( b U U o - - = (1) ) I ln( b U U o r o - = (2)
where r U is the reversible cell voltage, b is the Tafel slope, and I o is the exchange current density and R is the ohmic resistance.

Kim et al. [13] proposed the improved version of this expression by adding a term that would correct the observed difference between the voltages computed from (1) and the measured one. This difference has been fixed with the following exponential function:

) nI exp( m U = ∆ (3)
where m and n are constant. Then, (1) can be rewritten as:

) nI exp( m RI ) I ln( b U U o - - - = (4)
This kind of model is quite accurate in a wide operating range. However, as it can be seen in ( 4), the relation is undefined for no-load operations (i.e. I=0) and there are five parameters that have to be determined.

The proposed static model

The new static model, which was established in this work, is simpler than those described above as it has only two parameters to be determined. Moreover, this model is defined at I=0 at atmospheric pressure and for a given temperature stack, it is continuous, differentiable and simple, as follows:

α aI U U o + = 1 (5)
where o U is the open circuit voltage (OCV) and I is the fuel cell stack current. As seen in ( 5), α aI has no unit, this implies that the parameter "a" can be expressed as α h I , where I h is equivalent to an electrical current and its unit is cancelled with I. Then, (5) can be rewritten as:

α         + = h o I I U U 1 (6)
where α and h I are the only two parameters to be determined. We can try to link the parameter h I to a fuel cell characteristic parameter, for instance to the open circuit voltage. By rearranging and applying the logarithmic function on both sides, (6) can be rewritten as:

      - =         U U U ln I I ln h 0 α (7)
From (7), we can deduce that the current h I is equal to I at U = U O /2. However, this is just a theoretical value because a healthy stack cannot safely reach this static operating point. By the experimental results and using the non-integer power law function, the polarisation curve is fitted by a least squares method.

As mentioned, the other useful behaviour laws can be derived from the proposed model, for instance the function of the current-electric power curve and the current-polarisation resistance curve will be shown here.

Since the electric power of the fuel cell is simply the product of the voltage and the current, the function of the current-power derived form the model is:

α         + = h o I I I U P 1 (8)
The last example is the derivation of the current-polarisation resistance curve. Generally, the fuel cell voltage, U, can be expressed as a function of the steady state open circuit voltage (U 0 ) where the voltage drops due to the cathode and anode activations ( a c η , η

) and the membrane ( m η ) as follow:

m c a o η η η U U - - - = (9)
Each term of the voltage drop can be considered as a result from the parasitic resistance of each part.

The fuel cell resistance is a sum of those resistances [14] and the steady state fuel cell voltage can be rewritten as:

I R U U p o - = (10)
This resistance is usually measured using the EIS method. The analytical expression of the fuel cell in (6) enables us to evaluate p R in (10) as follow:

α α         +         = h h o p I I I I I U R 1 (11)

Experimental setup and results

For model verification, the experiments are performed on a sixteen cell stack. The stack temperature is assumed to be homogenous through the system. The details of the experimental set up is shown in Fig. 2. The parameters are given in Table I. Only the inlet air is humidified. A comparison between the proposed model in ( 6) and the experimental result for two different humidification conditions has shown in Fig. 3. The values of I h and α are obtained using a least squares method and given in Table II. It should be noted that the coefficients I h and α depend on the humidification of the inlet gas. Fig. 3. Comparison between the polarisation curves from ( 6) and the experimental results

For the current-power characteristic function in [START_REF] Barsoukov | Impedance Spectroscopy: Theory, Experiment, and Applications[END_REF], the function provides an accurate result compared to the experimental curves as shown in Fig. 4. Since the mass transport limitation does not appear under the normal fuel cell operation, the power curve under normal operating range is roughly a linear function of the current. The evaluated polarisation resistances versus current of the fuel cell at different relative humidity of the inlet air are plotted in Fig. 5. The computed polarisation resistances from (11) are definitely corresponding to the ones obtained experimentally. Therefore, the model is in good agreement with experimental results. Since, the parameters are determined under constant and homogeneous temperature and pressures, several additional experiments are needed when temperature or pressure are changed.

Conclusion

In this work, a new PEMFC steady state model has been established. This model contains less fitting parameters than the usual ones, but presents many advantages for control application. The important property of the model is the validity at open circuit operation, in contrast to the other model that is undefined when the current approaches to zero. Further more, the model is differentiable which is preferred by electrical engineers. Various analytical expressions can be derived from the model, the expressions of current-electrical power and current-polarisation resistance are derived here as examples. The curve of the cell resistance from the expression is in accordance with the resistance at the low frequency measured by EIS method without any extrapolation [15].

Fig. 1 .

 1 Fig. 1. An Equivalent circuit of a PEM fuel cell

Fig. 2 .

 2 Fig. 2. PEMFC test bench

Fig. 4 .

 4 Fig. 4. Comparison between the current-power curves from (8) and the experimental results

Fig. 5 .

 5 Fig. 5. Measured and computed polarisation resistances.

Table I

 I Fuel Cell Parameters

		Table II
	Model parameters for different cathode relative humidity
	RHc	37%	63%
	I h [A]	449.7	483.2
	α	0.338	0.350


S Caux

D Wanderley-Honda

D Hissel

M Fadel

On-line Energy Management for HEV based on Particle Swarm Optimization

Keywords: Particle Swarm Optimization, Hybrid Electric Vehicle, Multi-source System. I

This study considers a Hybrid Electrical Vehicle supplied by a Fuel Cell stack and supercapacitors used as Storage Element. In such an application, real time energy management is of paramount importance in order to increase autonomy and be able to deal on-line with perturbed power demand. Many offline power flow optimization principles are available but online algorithms are preferred and should be derived for optimal management of the instantaneous power splitting between the different available power sources. Based on particle swarm optimization algorithm, this study defines the parameters tuning of such algorithm. The final power splitting allows not only recovering energy braking but also is robust to some disturbances occurring during the trip. The solution provides good-quality and high-robustness results in a certain class of mission profile and power disturbance.

INTRODUCTION

Replacing oil-based engines, and dealing with renewable sources is a challenge because the power distribution structure change drastically. Each decentralized production has its own properties and capacities.

In transport applications when zero emission is foreseen, Hybrid Electrical Vehicles are composed with a main sources of Energy (or Power) and storage elements to be able to store and to restitute an additional energy (or power) when requested [START_REF] Solero | Fuel cell HEV's assisted by ultracapacitor and battery storage system[END_REF]. In transport applications, it is obvious that energy braking recovery should be performed and the stored energy should be provided to the powertrain not only to respond to some high power requests but also to manage the global efficiency of the electrical system.

Maintaining the global efficiency at a maximum level with a dedicated energy management strategy allows autonomy to increase, minimizing the consumption on a given mission profile, but allows also increasing the durability of the different electrical and/or electrochemical components (Fuel Cell, Supercaps or Batteries…).

As for stationary applications, a transportation application can be considered with power demand: 1-known, 2known with perturbations or 3-unknown. Considering nothing is known in advance about the power that has to be delivered in the future, it's obvious that no energy optimization can be performed; a certain level of knowledge of the power demand is also requested. With this consideration two categories of optimization principles can be listed [START_REF] Salmasi | Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends[END_REF]:

-off line global optimization -on line 'partial' optimization In fact when the whole profile to be followed is known and when the different elements are already characterized, it is possible to compute a criterion to be minimized off-line by existing algorithms that work well. There is no need of dynamic consideration at this decision level, the computation search an optimal splitting not so quickly with regards to electric or fluid mechanics time constants. All controlled elements (Fuel Cell Current/Pressure/Flow and Supercaps Current/Voltage) are considered able to follow this (details can be found in [START_REF] Caux | Modelling and Control of a Fuel cell System and storage elements in transport Applications[END_REF]).

In this category, Dynamic Programming (D.P.) is one of the most used algorithm. Its principle is based on the Bellman's principle [START_REF] Bellman | Dynamic Programming[END_REF] and starts computation from the end of the profile to find an optimal path reaching the beginning and leaving the optimization criterion as low as possible. Then, the founded path must be replayed. Some considerations should be given to having an accurate solution but problems arise when constraints are added and when computer time computation is limited.

Optimal control is also use to replay a sequence of control computed optimally off-line using Pontryagin principle [START_REF] Delprat | Control of a parallel hybrid powertrain: Optimal control[END_REF]. This technique provides good results when the criterion can be expressed linearly and thus derived. Constraints are also not obvious to include and some parameters are hard to be tuned to obtain the solution.

In both cases when the power request changes, the sequence to be replayed is not the one's that has been considered for the optimization process and implies, in most cases, an increase in the forecast fuel consumption.

Real time energy management strategies are mainly based on linguistic rules or artificial intelligence [START_REF] Langari | Intelligent energy management agent for a parallel hybrid vehicle -part I: System architecture and design of the driving situation identification process[END_REF], [START_REF] Moreno | Energy-Management System for a Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks[END_REF]. Commonly used in HEV, logical rules are quite easy to define, when considering the management of 2 or 3 sources. These rules impact directly on the consumption and must be optimized [START_REF] Gao | Energy management strategy based on fuzzy logic for a fuel cell hybrid bus[END_REF], [START_REF] Tekin | Energy management strategy for embedded fuel cell system using fuzzy logic[END_REF]. Fuzzy-Logic supervisor, Neural Networks [START_REF] Moreno | Energy-Management System for a Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks[END_REF], System with Multi-Agent [START_REF] Langari | Intelligent energy management agent for a parallel hybrid vehicle -part I: System architecture and design of the driving situation identification process[END_REF] or adding Genetic Algorithm [START_REF] Hankache | Real Time Fuzzy Energy Management of Fuel Cell and Ultracapacitor Powertrains[END_REF] are also used. All methods suffer from the same problems: complexity is increasing and all methods are based on learning patterns.

In this paper a stochastic on-line optimization principle is used. Particle Swarm is a method to explore a given space where an optimal solution is sought. In this case several iterations will ensure that the optimal solution is found. But the computing time can be limited (when the number of iteration is fixed, or when the number of considered particles is low for example). Therefore, a set of parameters can be tuned to reach the solution while keeping a low computation time. When the power splitting should be refreshed with a certain periodicity, this kind of algorithm can provide a sub-optimal solution or even reach the optimal one. In part II, the HEV powertrain is described and all characteristics of the different onboard sources are given. In part III, the problem formulation is detailed to minimize the criterion and to respect all listed constraints. In part IV attention is focused on advantages of the proposed particle swarm algorithm. Part V presents the complete study of parameters tuning and results obtained using different actual profiles, with or without perturbations. Analysis, limitation and conclusion are given in Part VI.

II. PROBLEM DESCRIPTION

A. Power flows and Energy Onboard

In this paper a classical Hybrid Electrical Vehicle is considered. Different power trains can be defined and sizing of all elements should be optimized as well [START_REF] Hatti | Dynamic neural network controller model of PEM fuel cell system[END_REF], [START_REF] Ehsani | Propulsion system design of electric and hybrid vehicles[END_REF]. To not treat 'systemic' problem, a given hybridization is fixed here and only two electrical sources are linked to the same DC bus with their own choppers. Fig1 describes the considered serial electrical structure. It can be noticed that an electrical node appears between the different subsystems.

The Hydrogen Tank, the Fuel Cell and its ancillaries (Compressor, Valves and local control...) and the non reversible chopper are grouped constituting the Fuel Cell System and considered as the primary sources -FCS. The moto-propulsion group is here simplified because only the electrical power demand is considered.

With this consideration if the powertrain architecture changes (boggy or wheels, synchronous or DC machine…) only the electrical power demand should be computed when leaving all the other elements in the optimization algorithm.

B. Efficiency of the main elements

Algorithms, solving the optimal energy management problem are fed with data describing the efficiency of all elements. Each source has its own behavior meaning that it presents a different efficiency depending on the power delivered [START_REF] Tekin | Energy optimization of a Fuel Cell Generator : Modelling and experimental results[END_REF], [START_REF] Fontes | Interaction between fuel cells and power converters influence of currentharmonics on a fuel cell stack[END_REF]. Local controls are considered effective. Fuel Cell Stack stay at a given temperature and pressure and hygrometry are supposed to be well-maintained [START_REF] Naso | Evaluation of the overall efficiency of a low pressure proton exchange membrane fuell cell power unit[END_REF], [START_REF] Caux | Air Loop Model and Control[END_REF]. Homogenous current repartition in the FCS allows dealing with a global FCS efficiency behavior FC The optimal use of the energy braking recovery is a good way to minimize the vehicle consumption. Using the considered power profile, an a priori sizing as been made and is given in Table I. The two power demands used are presented in fig 5 -INRETS is an urban profile for a personal vehicle and fig 6 -ESKISEHIR is the name of a power profile measured on a tramway line in Turkey [START_REF] Caux | Air Loop Model and Control[END_REF], [START_REF] Caux | Modelling and Control of a Fuel cell System and storage elements in transport Applications[END_REF]. This sizing is made to let the power splitting explore all possible solutions and let emerge if possible solutions using only FCS or only SSE, or all combination to supply the power demand. No 'systemic' hybridization linked to size or weight is considered in this approach [START_REF] Ehsani | Propulsion system design of electric and hybrid vehicles[END_REF], [START_REF] Fontes | Interaction between fuel cells and power converters influence of currentharmonics on a fuel cell stack[END_REF] to provide efficient algorithms whatever the HEV characteristic is.

Considering a given requested power profile (cf. figures 5 and 6), this paper proposes to respond to the following question: what is the optimal power splitting in order to obtain a maximal efficiency of the vehicle (and thus the lowest fuel consumption) on the driving cycle? Using the complete knowledge about these profiles allows running off-line optimal algorithm (based for example on Dynamic Programming DP). Consumption given in table II shows that more than 30% of consumption can be earned on a given profile using optimally the storage element, instead off no storage capability if Fuel Cell is alone. These results are only provided here as reference and dynamic programming is well known [START_REF] Bellman | Dynamic Programming[END_REF] but only computable off-line and not in real time. The energy management problem is formulated here as a global dynamic optimization problem under constraints. The hydrogen consumption is quantified as a cost function to be minimized. The cost function is evaluated over a defined period of time.

The system's dynamic equation is:

( ) ( )

S E t P t = -&

Where the energy level stored E is the state variable and the power S P the control variable.

The cost function to minimize is the "total consumed energy" of hydrogen Using the previously defined efficiency, the cost criterion is therefore:

∫ = )) ( ( ) ( 2 η (1)
( ) ( ) ( ) FC FC FC P t P t γ η = ( 2
)
The system is subject to non linear constraints of inequality related to the constraints linked to the design of the stack, the storage element power and state of charge resumed in (3):

( )

_
min max E E t E ≤ ≤ (3)
Satisfying the power demand imposes an equality constraint (4):

0 SE FC dem P P P + - = (4)
An additional condition ( 5) is imposed artificially in order to ensure that the state of charge is maintained at the end of the cycle and to facilitate the optimization procedure and cycling the power demand without any more consideration from an energy management point of view.

( ) ( )

f i E t t =
(5)

A. Off line principles : Dynamic Programming (D.P.) and Optimal Control (O.C. )

The principle of Dynamic Programming (D.P.) is based on the Bellman's principle [START_REF] Bellman | Dynamic Programming[END_REF]. This principle can be resumed saying: if an optimal path reaches a path which is optimal the total path is optimal. So the algorithm starts computation from the end of the profile to find an optimal path reaching the beginning and letting the considered optimization criterion as low as possible during the optimal path. Therefore the power demand is sampled both in time domain and in energy level. The algorithm computes at each time t k all possible solution linking level E k-1 to optimal level E k minimizing the criterion. All results and paths must be stored and the optimal path is selected only when the algorithm reaches the origin k=0 with the fixed initial condition E(0). So, it is clear that accuracy depends on the sampling and when the profile is long or when the energy level is divided in little steps, the computation time is exploding. Problems arise critically when non linear constraints are added and when computer time computation must be limited to respect to real time power reference generation.

Off-line optimization provides the optimal power splitting which provides the minimal criterion if replayed with the same power to follow. So the whole profile must be known in advance and unchanged to stay optimal and this solution is commonly used to give the optimal reference only for comparison purposes with other algorithms.

It can be noticed that Optimal Control (O.C.) theory is another way to compute the optimal control off-line and to be replayed. This technique using Pontryagin principle provides good results when the criterion can be expressed linearly and thus derived [START_REF] Delprat | Control of a parallel hybrid powertrain: Optimal control[END_REF]. This optimization based on the minimization/cancelation of the Hamiltonian of the criterion shows linear and derivation assumption problems. Constraints are difficult to include, and some parameters dealing with Lagrange Multiplier are hard to be tuned to obtain the solution.

In both cases, when the power demand changes, the sequence to be replayed is not the correct one and let the fuel consumption increase and sometimes the offline sequence provides a worse result than even the control proposed considering no optimization at all. All the study should be done again even if the disturbance has little magnitude or is only local.

B. Online principles : Rules-Fuzzy-AI

Based on expert knowledge, logical rules are easy to establish. In fact it is easy to say the system should use the FC when SE is empty or should use both FC and SE when power is high and SE in its average State Of Charge etc.

These rules are impacting directly on the consumption and the expert should come up with a way to pass from one rule to the other. So the fuzzy approach is a solution to define each rule, the membership functions and the universe of discourse [START_REF] Gao | Energy management strategy based on fuzzy logic for a fuel cell hybrid bus[END_REF]. Moreover position of all membership functions can be optimized on a given profile. In fact, Genetic Algorithm [START_REF] Caux | Modelling and Control of a Fuel cell System and storage elements in transport Applications[END_REF] or Neural Network [START_REF] Moreno | Energy-Management System for a Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks[END_REF] can 'learn' the profile and the optimization is still made off-line on a given profile but the supervisor build in this way is able to propose an optimal or near-optimal solution even if the profile is not exactly the one known.

Artificial intelligence and expert analyzes should be mixed to have not only an accurate optimal solution, respecting constraints but also an algorithm keeping under control the complexity and the computer time requested to reach the solution in real time.

C. On-line stochastic : Particle swarm

Considering that computing all solutions is not possible online and in real time (with actual processor).

Considering that rule-based algorithm must still be optimized offline and using them online can sometimes not respect important constraints, the idea is here to use both advantages using particle swarm optimization principle. Particle Swarm Optimization (PSO) principle is based on ( 6) and ( 7):

( ) (
)

1 1 2 2 2 1 1 1 1 + + + ⊗ + ⊗ = - ⊗ ⊗ + - ⊗ ⊗ + ⊗ = k k k k k k k v d x c x x p r b x p r b v a v r r r r r r r r r r r r r r r r ) 7 ( ) 6 (
Where v r is a vector of particles speed, x r their positions and ⊗ represents terms by terms vector multiplication [START_REF] Hankache | Real Time Fuzzy Energy Management of Fuel Cell and Ultracapacitor Powertrains[END_REF], [START_REF] Kisacikoglu | Load sharing using fuzzy logic control in a fuel cell/ultracapacitor hybrid vehicle'[END_REF].

This stochastic algorithm explores randomly the space of solution and depending on the number of particles and the number of iterations made, engineer can fix accuracy and limit the requested computation time.

This evolution ( 6) is based on bee behavior (random flights of bee swarm) and to the optimal solution after some iteration k-> infinity, the swarm conserves the better value seen in the previous iteration p1 and the best value ever seen p2 to update velocities and thus particle positions. Attracting coefficients a, b, c, d ensure exploration and convergence form (zigzagging, oscillating, exponentially converging…). In this considered classical case (no abrupt optimal or sub-optimal minima) values found in literature are used: a=0.729, b=1.494 and without loss of generally c=1, d=1 and r1 and r2 are randomly chosen in [0 1], [START_REF] Hankache | Real Time Fuzzy Energy Management of Fuel Cell and Ultracapacitor Powertrains[END_REF], [START_REF] Kisacikoglu | Load sharing using fuzzy logic control in a fuel cell/ultracapacitor hybrid vehicle'[END_REF].

IV. PARTICLE SWARM AND ON-LINE HEV ENERGY MANAGEMENT

In the HEV online energy management problem, the number of particles nb_part, the number of iteration nb_it, and the number of swarm nb_swarm should be fixed. Moreover to limit the number of calculus and the computing time, the problem should be reduced to an optimization in a given window size, so, the whole power profile demand should be divided. The criterion is minimized on the window size and consumption is minimized optimally using energy levels E k and E k+1 equal to the one computed by Dynamic Programming algorithm. It can be noticed that this approach mixes the offline optimization and the online minimization which can be seen as an adaptation capacity. In fact, if no disturbance occurs, particle swarm optimization should converge to the same path as Dynamic Programming. In case of disturbance, the rejection is ensured by particle swarm to locally change the path and return, if possible, to the optimal energy level E k+1 that ensure an optimal ending (with no more disturbance).

All set of parameters can be tested on a profile in a first step to be roughly defined. The correct set should ensure almost 50% of swarm funding the optimal path and delivering a result in a time lower than the refreshing reference period t k+1 -t k .

A. Simulation based tuning

Running some simulations with different sets of parameters allows establishing the values shown in Table III. Clearly, the higher are the nb_swarm or nb_it or nb_part higher is the computation time but the higher is the probability to find the best path. If 3 on 5 swarms have found the optimal path means the path is found with 60% of chance, therefore the path can be considered always found. It can also be seen that using 5

swarms and 20 particles is correct because this set provides the optimal path with 60% of chance in a low and feasible actual computing time. Lower values provide less than 50% path found and are considered too risky to be used in the real time optimization problem.

The code should be also optimized so values given in Table III

B. Validation on actual profiles

Using sampling time of ∆t=2s, ∆E=1kWs, the power demand is satisfied and state of charge of the storage element is shown in Fig 8.

In this figure with no perturbation on the profile, it can be noticed that D.P. path is the same as the one found in each Particular swarm window, the curves obtained with fuzzy logic is only provided to see the possible different energy managements and details can be seen in [START_REF] Delprat | Control of a parallel hybrid powertrain: Optimal control[END_REF], the use of the proposed P.S.O. on a different profile is analyzed in the next section.

It has to be noticed that both optimization provides quite the same consumption of energy on the whole profile (Part. Swarm/D.P.= 10362kW.s and Fuzzy= 10358kW.s) but fuzzy leaves some power demands not furnished (due to saturation at low SOC) and is only shown for comparison purposes. Storage element is considered to be charged at 900kWs and respect its min and max S.O.C. during the trip and allows managing optimally the energy on board. Previous sections demonstrate that particular swarm algorithm is capable of finding the optimal, windows by windows, using the offline energy level computed for example with D.P on a given profile as a reference. This section tests the proposed algorithm on different profiles and with an unknown perturbation added artificially to characterize the robustness of such an approach.

A. First comparison

In fact, depending on the power profile, parameter adjustment must be made. Of course, nb_part and nb_it are linked to the convergence velocity and thus the possibility to adapt the particle speed quickly to the profile variation. So using ESKISEHIR profile instead of INRETS with the set of parameter nb_part=50, nb_it=50, nb_swarm=5 is not sufficient. By analyzing where the problems are located, it was noticed that no solution where found in some specific windows (the other are computed without any problem). For these windows the power variation is greater as in INRETS profile and is shown in Fig9. This profile presents some section with a higher power variation so the nb_part or nb_it must be increased to be able to find the optimal path. So as for many other methods, analyzing the power demand in terms of max power, mean power and power variation demand, not only provides sizing information but also information to tune accurately optimization parameters in the algorithm. Using nb_part=20, nb_it=50, nb_swarm=7 is convenient for both profiles. The same study should be made on other profiles to be sure to be able to optimize the criterion.

Even if the optimization is not optimal, the particle swarm algorithm provides a solution better than optimization following offline references. Adding a perturbation on a given profile may justify the adaptation ability of particle swarm algorithm.

B. Performance and robustness comparison

To verify the possible adaptation of the proposed algorithm, a perturbation is added to the known power demand INRETS limited to the 50 th first point (50s) Fig11. In fig 11 a non expected positive power demand is added during 5s (higher acceleration required for example due to the road traffic). In fig 11bis the disturbance is proposed to be negative to simulate an unexpected braking. The perturbation occurs during 5s which is the size of the optimization window; tests could be made for longer disturbances but are out of scope of this paper. From the start point 0 to point 50, with no disturbance, the optimal energy consumption is 790kWs, with positive disturbance used, the consumption is 1404kWs and when the unexpected braking energy is used (negative disturbance) the consumption is 592kWs.

Depending on the position and here mainly on the perturbation magnitude, it is possible or not to optimize the criterion in a given window size. In the case under study, the profile does not present high power demands after the disturbance point [25-28] and the energy recovered can not be delivered in the foreseen windows. Moreover, if the energy stored is quickly used in this phase, the Fuel Cell power may decrease, decreasing its efficiency, thus a bad criterion is obtained. Using a window twice larger (Fig 11b), the algorithm can find a solution to return to the energy state imposed respecting constraints of the system.

Increasing the optimization window size means increasing the computation time and the necessity to verify real time constraints. nb_swarm=1 solves partially this issue but 4 tests on 10 provides no optimal path (only with an increase of fuel consumption limited to 1%). This last study means that an adaptation is requested on line when a disturbance is detected to be able to switch the strategy (different set of parameters for the particular swarm -resumed in Table IV) to obtain a good result. Of course, a prediction or a statistical data-base can be used to classify the different possible disturbances as well as the requested analysis to classify the profile.

VI. CONCLUSIONS

Particle swarm optimization is an efficient solution for on-line energy management for Hybrid Electrical Vehicle. It did not pass over common problems in such application: the profile must be studied and classified to find accurate set of tuning parameters. Performances are better when references are provided with off-line global optimization. Disturbances must be predicted or a degree of freedom must be used to cancel their influences.

This approach is not really an Artificial Intelligence approach using learning phase, but using optimal references, the particular swarm optimization algorithm is able to compute, in a limited computation time, the optimal path in terms of fuel consumption.

Particles are able to compute in real time the optimal path with some capacity to reject disturbances and in each case to adapt the optimal path to a sub-optimal path respecting real time computing constraints.

Implementation on an actual vehicle should be made in a next step in order to validate the approach.

AKNOWLEGMENT INRETS: to provide actual measurements obtained in actual vehicle and trip, and interests given in energy management in transport application.

FEMTO-ST / FCLAB: to provide actual data obtained on fuel cell test bench developed in Belfort (France) and collaboration in this study.

Fig 2 :

 2 Fig 2 : Multi-source system problem formulation

η

  , described in Fig 3. Storage element is commonly made with several commercial supercaps in serial and parallel to obtain the System Storage Element (SSE). Balance between supercaps elements is also considered effective and just one constant global equivalent resistance Rsc and capacitance Csc are considered with losses only depending on losses in chopper, therefore SSE global efficiency SE η is given in Fig 4.

  available on DCbus (W) Total Fuel cell pack efficiency

Fig 3 :Fig 4 :

 34 Fig 3: Fuel Cell system global efficiency curve

Fig 3 and 4

 4 Fig 3 and 4 provide the efficiency maps to be considered by the algorithm to compute the efficiency of the

Fig. 5 .Fig. 6 .

 56 Fig. 5. INRETS : Power profile of a hybrid vehicle in urban area

Energy 2 HE

 2 consumed is the integration of power P FC in the fuel cell side (not in the DCbus side where there is the power demand Ps) so the efficiency of the Fuel Cell Stack also the fuel cell behavior provided by fig3) is introduced to 'modelized' the losses of energy passing through the FCS.

  Fig7 shows the different loops requested by P.S.O. algorithm (defined with nb_swarm, nb_it values), and also presents the parameters that define the complexity of the problem to be solved (window'size, nb_part). The values of each particle change iteratively to found the optimal path in the windows defined by [Ek E k+1 ]. A certain number of particle nb_part, is fixed randomly at different energy level at each time in a window [tk tk+1]. The sampling time ∆t and window's size increase the computation time. All particles are free to explore all energy level from [0 100%] with a step of about 1%, this ∆E is chosen to be as accurate as possible taking into account the possible chopper and source capacity to maintain E k and also to preserve a realistic computation time.

Fig 7 :

 7 Fig 7 : set of parameters of the particle swarm algorithm

  Energy management State of Charge -INRETS Profile -∆E=1kWs et ∆t=2s of Charge (Energy level) (kW.s) Part. Swarm/D.P. Fuzzy Energy management State of Charge -INRETS Profile -∆E=1kWs et ∆t=2s of Charge (Energy level) (kW.s) Part. Swarm/D.P. Fuzzy

Fig 8 :

 8 Fig 8 : Energy management State of Charge -INRETS Profile -∆E=1kWs et ∆t=2s

Fig 9 :

 9 Fig 9 : Power variation occurrence in different ranges of magnitude

Fig 11 :

 11 Fig 11 : Initial profile, sequence selected and first disturbance injected Dist=50kW (case dist>0).

Fig

  Fig 11b : second disturbance type : Dist=-40kW (Case dist<0)

TABLE I SYSTEM

 I

	POWER AND ENERGY CONSTRAINTS
	Symbol	Quantity Value
	PSEmin	-60kW
	PSEmax	60 kW
	PFCmin	0 kW
	PFCmax	70 kW
	SOEmin	400 kW.s
	SOEmax	1600 kW.s

  should be used only in a relative way.

	NB: program executed in Matlab® R2007a on a Windows XPpro environment dedicated to calculus
	(processor: PowerEdge 6850, Quadri Xeon, 3.2GHz, 32Go Ram, 2x150 Go Scsi UTRA 320).

TABLE IV Set

 IV

		of parameters and robustness
		Ideal	Dist >0	Dist<0
	Window size (nb points)	5	5	12
	nb_swarm	7	1	7
	nb_part	20	20	100
	nb_it	50	50	50