Yves-Henri Sanejouand
email: yves-henri.sanejouand@univ-nantes.fr

Are most human specific proteins encoded by long non-coding RNA ?

Keywords: Human-specific proteins, Primates, Uniprot, RNAcentral, IUPred, AlphaFold

By looking for a lack of homologues in a reference database of 27 well-annotated proteomes of primates and 52 well-annotated proteomes of other mammals, 170 putative human-specific proteins were identified. Among them, only 2 are known at the protein level and 23 at the transcript level, according to Uniprot. Though 21 of these 25 proteins are found encoded by an open reading frame of a long non-coding RNA, 60% of them are predicted to be at least 90% globular, with a single structural domain. However, there is a near complete lack of structural knowledge about these proteins, with no tridimensional structure presently available in the Protein Databank and a fair prediction for a single of them in the AlphaFold Protein Structure Database. Moreover, the knowledge about the function of these possibly key proteins remains scarce.

Introduction

Each time a new genome is sequenced, genes coding for proteins with no known homologue are found, even when genomes of closely related species are available, like in the case of primates [START_REF] Toll-Riera | Origin of primate orphan genes: a comparative genomics approach[END_REF][START_REF] Cai | Relaxed purifying selection and possibly high rate of adaptation in primate lineage-specific genes[END_REF][START_REF] Ruiz-Orera | Origins of de novo genes in human and chimpanzee[END_REF] or Drosophila [START_REF] Domazet-Loso | An evolutionary analysis of orphan genes in Drosophila[END_REF][START_REF] Wang | Duplication-degeneration as a mechanism of gene fission and the origin of new genes in Drosophila species[END_REF][START_REF] Lange | Structural and functional characterization of a putative de novo gene in drosophila[END_REF].

In the former case, the hypothesis that humanspecific proteins may prove involved in the behavioral or anatomical peculiarities of the human species, like the size of its brain [7] or its functions [START_REF] Li | A human-specific de novo protein-coding gene associated with human brain functions[END_REF][START_REF] An | De novo genes with an lncRNA origin encode unique human brain developmental functionality[END_REF], seems worthy of consideration. So, in the present study, taking advantage of both the high quality of the annotation of the human proteome [START_REF] Amaral | The status of the human gene catalogue[END_REF] and of the availability of a significant number of well-annotated primate proteomes [START_REF] Marques-Bonet | Sequencing primate genomes: what have we learned?[END_REF][START_REF] Juan | Current advances in primate genomics: novel approaches for understanding evolution and disease[END_REF], an extensive search of human-specific proteins was performed, focusing noteworthy on what can be predicted about their tridimensional structure, with the idea that such a knowledge could provide hints about their function or their origin.

To do so, like in a previous study [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF], a reference database was set up. Then, two popular protein structure prediction methods were applied to the putative human-specific proteins thus found, namely, IUPred [START_REF] Dosztányi | Prediction of protein disorder based on IUPred[END_REF] and AlphaFold [START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF]. While the former provides a qualitative prediction, stating whether a polypeptide is expected to adopt a globular fold or not, the later is able to predict its tridimensional structure at an atomic level of detail [START_REF] Jones | The impact of AlphaFold2 one year on[END_REF]. However, as already underlined previously [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF][START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF], informations other than the sole sequence of the polypeptide are usually required.

1

Methods

Choice of a reference database

The definition of what a species-specific protein is depends upon what is known about the proteomes of the closest species at a given time. For studying species-specific proteins, as well as for the sake of reproducibility, it is thus important to choose a well-defined reference system [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF], that is, a set of reference proteomes. Moreover, since the quality of the annotation of a proteome can vary significantly, both from a proteome to another and as a function of time, these proteomes have to be as well annotated as possible.

To do so, 27 Uniprot reference proteomes [START_REF]Uniprot: the universal protein knowledgebase[END_REF] of primates were picked, that is, all those that have at least a standard level of annotation 1 , according to the Complete Proteome Detector [START_REF]Uniprot: the universal protein knowledgebase in 2021[END_REF]. Note that 10 of them are high-value outliers, meaning that they have significantly more identified proteins than closely taxonomically related species.

For these 27 reference proteomes, complete BUSCO predictions of single-copy orthologs [START_REF] Simão | BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs[END_REF] are found for 94% (median value) of the cases. However, the percentage of short (less than 50 amino-acid residues long) proteins vary widely from a proteome to another, ranging between 0.1% (Sapejus apella) and 3.5%, being over 1% in the case of three species only, namely, Pongo abelii, Pan troglodytes and Macaca fascicularis.

Overall, there are between 19229 (Chlorocebus sabaeus) and 50207 (Macaca fascicularis) proteins per proteome (40000 ± 7500, on average) in our reference database, with a total of 1.083.746 proteins, including known isoforms.

Figure 1 shows the phylogenetic tree of the primate species considered herein, according to the TimeTree webserver, version 5 [START_REF] Kumar | TimeTree 5: An expanded resource for species divergence times[END_REF].

Search of homologues

For each of the 20,449 human proteins at least 30 amino-acid residues long associated to a given gene 2 , homologues in the reference database were looked for using BLAST [START_REF] Altschul | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[END_REF] version 2.6.0+, two proteins being assumed to be homologous when the E-value of their pairwise alignment is lower than 1 On June 2023. 2 As found in Uniprot, on February 2023. 10 -6 [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF][START_REF] Lobley | Inferring function using patterns of native disorder in proteins[END_REF][START_REF] Lucas | Next-generation sequencing of flowsorted wheat chromosome 5D reveals lineagespecific translocations and widespread gene duplications[END_REF]. Note that, in order to avoid an overestimation of the number of specific proteins, due to the filtering of low-entropy segments, that is, of segments of restricted amino-acid composition, composition-based statistics [START_REF] Schäffer | Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements[END_REF] was not considered (-comp based stats 0).

Non coding RNAs

For each human-specific protein found, that is, for each human protein with no homologue in the reference database, its possible encoding by a human non-coding RNA (ncRNA), as found in the RNAcentral database [START_REF] Rnacentral | RNAcentral: an international database of ncRNA sequences[END_REF], version 22, was checked, by comparing its sequence to those obtained by translating all at least 90 nucleotides long nonoverlapping open reading frames. Note that it was assumed that both start and stop codons are standard ones, while it is known that peptides encoded by ncRNAs can have atypical stop codons [START_REF] Dragomir | FuncPEP: a database of functional peptides encoded by non-coding RNAs[END_REF].

Protein globularity

The degree of globularity of each human-specific protein found, that is, the percentage of the protein length predicted to be globular, as well as their number of globular domains, was estimated using the standalone version of IUPred [START_REF] Dosztányi | Prediction of protein disorder based on IUPred[END_REF][START_REF] Pajkos | The origin of discrepancies between predictions and annotations in intrinsically disordered proteins[END_REF]. Note 2 that IUPred performs its predictions by considering the local sequential environment of each aminoacid residue within 2-100 residues in either direction. Note also that, at variance with most recent methods [START_REF] Necci | Critical assessment of protein intrinsic disorder prediction[END_REF], IUPred does not make use of evolutionary data, which are expected to miss in the case of species-specific proteins.

Structure prediction

Predictions of tridimensional structures were picked in the AlphaFold Protein Structure Database [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF]. These structures were obtained with the AlphaFold2 artificial intelligence from Deep-Mind [START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF], an algorithm whose predictions proved to be the most accurate of all the submissions for over 90% of the targets of the CASP14 blind prediction experiment [START_REF] Jones | The impact of AlphaFold2 one year on[END_REF].

Note that AlphaFold2 can also be used for predicting if a protein has disordered segments [START_REF] Pajkos | The origin of discrepancies between predictions and annotations in intrinsically disordered proteins[END_REF][START_REF] Ruff | AlphaFold and implications for intrinsically disordered proteins[END_REF]. Indeed, AlphaFold2 provides an estimate of the accuracy of its prediction for the position of each amino-acid residue, coined pLDDT 3 , values over 90% corresponding to a high quality and values below 50% to a poor one [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF]. Herein, the overall quality of the prediction of the structure of a protein is assumed to be given by the average of the quality of the prediction of the position of its residues ( pLDDT ).

Results

How many human-specific proteins ?

The need to use a large enough reference database was assessed as follows. When proteins of Homo sapiens not found in the proteome of its closest relative, namely, Pan troglodytes, are looked for, 347 of them are identified. Note that this number is lower than a previous estimate obtained ten years ago, namely, 634 [START_REF] Ruiz-Orera | Origins of de novo genes in human and chimpanzee[END_REF], maybe as the result of the improvement of the annotation of the human proteome [START_REF] Amaral | The status of the human gene catalogue[END_REF]. Indeed, when the search was performed the other way around [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF], 1036 chimpanzee-specific proteins 4 were found.

As a matter of fact, as shown in Figure 2, when the number of proteomes in the reference database 3 Standing for predicted local-distance difference test. 4 847 singletons. is increased, the number of human-specific proteins keeps on dropping, down to 193. Interestingly, a few species have significantly higher contributions to this downsizing, like the fourth (Pongo abelii ) and tenth (Macaca fascicularis), further suggesting that their proteomes are more complete than the others. But while the proteome of Macaca fascicularis is indeed the largest in our reference database, the size of the proteome of Pongo abelii, with 39491 proteins, is slightly below the average. Indeed, its level of annotation is just considered standard, according to the Complete Proteome Detector [START_REF]Uniprot: the universal protein knowledgebase in 2021[END_REF].

As shown in Figure 3, while 89% of human proteins have homologues in all 27 proteomes of our reference database, 836 of them have homologues in all but one, 298 in all but two, etc, suggesting that the annotation of several reference proteomes is far from being complete.

Of course, if the annotation of the 27 proteomes considered were improved, or if more primate proteomes were added, the number of proteins found specific to the human species would keep on dropping. In order to partially take this expected trend into account, homologues of the 193 proteins found above were looked for in 52 Uniprot reference proteomes of other mammalian species, these other proteomes being chosen based on their high level of annotation, being all high-value outliers5 , according to the Complete Proteome Detector [START_REF]Uniprot: the universal protein knowledgebase in 2021[END_REF].

Homologues were indeed found for 23 (12%) of them. However, for more than half of these cases (13/21) homologues were found in a single mammalian species only, as if the annotation of these proteins were intrinsically difficult. A possible reason is that these proteins are often short, with an average length of 106 ± 54 amino-acid residues.

How many well-known ones ?

In Uniprot, the degree of knowledge, that is, the type of evidence that supports the existence of a protein is quantified through a number ranging between one (known at the protein level) and five (uncertain).

Among the 170 human-specific proteins identified above, only two are known at the protein level (see Table 1), according to Uniprot, namely, PACMP, the Poly-ADP-ribosylation-amplifying and CtIP-maintaining micropeptide [START_REF] Zhang | Micropeptide PACMP inhibition elicits synthetic lethal effects by decreasing CtIP and poly(ADP-ribosyl)ation[END_REF], and SDIM1, the Stress-responsive DNAJB4-interacting membrane protein 1 [START_REF] Lei | A novel neuron-enriched protein SDIM1 is down regulated in Alzheimer's brains and attenuates cell death induced by DNAJB4 over-expression in neuro-progenitor cells[END_REF]. Such a result is in sharp contrast with the fact that 90% of the human proteome is nowadays known at this level [START_REF] Adhikari | A high-stringency blueprint of the human proteome[END_REF]. Note that PACMP is short (44 residues) and, as such, could have escaped annotation in the proteomes considered above. As a matter of fact, PACMP was included in Uniprot quite recently 6 .

How many globular ones ?

From the above results, it is tempting to speculate that most human-specific genes do not code for proteins and may instead be involved, for instance, in the regulation of gene expression [START_REF] Nahon | Birth of "human-specific" genes during primate evolution[END_REF]. However, it has recently been shown that lncRNAs show similar coding potential and sequence constraints than evolutionary young protein coding sequences [START_REF] Ruiz-Orera | Long non-coding RNAs as a source of new peptides[END_REF]. Actually, up to 3330 human lncRNAs were found bound to ribosomes with active translation elongation [START_REF] Lu | A hidden human proteome encoded by "non-coding" genes[END_REF].

It is thus necessary to assess the coding potential of lncRNAs. A straightforward way to do it is to predict how globular encoded proteins are expected to be [START_REF] Papadopoulos | Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution[END_REF]. As shown in Table 1, according to IUPred [START_REF] Dosztányi | Prediction of protein disorder based on IUPred[END_REF], 12 human-specific proteins known at the transcript level and encoded by lncRNA (60% of them) are predicted to be at least 90% globular, with a single structural domain. Note that all four human-specific proteins that are not known to be encoded by lncRNA (SDIM1, CATR1, YV004 and YK004) are predicted to be at least 83% globular, also with a single structural domain. On the other hand, since nearly 30% of regions within the proteome are expected to be disordered [START_REF] Ruff | AlphaFold and implications for intrinsically disordered proteins[END_REF], other human-specific lncRNA could also encode genuine proteins.

What about their structure ?

No homolog was found in the Protein Databank [START_REF] Kouranov | The RCSB PDB information portal for structural genomics[END_REF] for the human-specific proteins identified above. [START_REF] Rnacentral | RNAcentral: an international database of ncRNA sequences[END_REF]. b According to Uniprot. c Percentage of the protein length predicted to be globular, according to IUPred [START_REF] Dosztányi | Prediction of protein disorder based on IUPred[END_REF]. d PACMP was included in Uniprot after the release of the fourth version of the AlphaFold database [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF].

However, thanks to artificial intelligence algorithms, major progresses have recently been witnessed in the field of protein structure prediction [START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF][START_REF] Jones | The impact of AlphaFold2 one year on[END_REF]. Moreover, such predictions have been performed on a large scale. Furthermore, they are nowadays available in public databases [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF].

As a matter of fact, as illustrated in Figure 4, the tridimensional structure of most human proteins has been predicted with a high level of confidence by AlphaFold2 [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF], the average pLDDT being over 90 for nearly 40% of them, and over 70 for more than 70% of them. Note that the structures of only 14% of human proteins are predicted with a very low confidence ( pLDDT below 50).

However, as shown in Table 1, among the 25 human-specific proteins known either at the protein or transcript level, AlphaFold2 is able to make a fair prediction ( pLDDT = 83) in the case of a single of them only, namely, CATR1, the CATR tumorigenic conversion 1 protein [START_REF] Li | Cloning and sequencing of CATR1. 3, a human gene associated with tumorigenic conversion[END_REF][START_REF] Li | Epstein-Barr virus growth-transformed cells are converted to malignancy following transfection of a 1.3kb CATR1 antisense construct independent of a change in the level of c-myc expression followed by a 8;14 chromosomal translocation[END_REF]. Note that its predicted structure is fairly simple, with a single, long α-helical segment (see Figure 5).

Given that the structure of most proteins predicted to be globular by IUPred are poorly pre- dicted by AlphaFold2 (Table 1), this result suggests that AlphaFold2 is of little help for predicting the structure of proteins with no known homolog [START_REF] Varadi | AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with highaccuracy models[END_REF], as for instance illustrated in a previous study of 362 eukaryotic proteomes [START_REF] Sanejouand | On the unknown proteins of eukaryotic proteomes[END_REF].

Conclusion

By looking for a lack of homologues in a reference database of 27 well-annotated proteomes of primates and 52 well-annotated proteomes of other mammals, 170 human-specific proteins were identified. However, most of them are coined dubious in Uniprot, casting doubts on 23 others that are just predicted. Indeed, given the efforts made in order to complete the annotation of the human proteome [START_REF] Amaral | The status of the human gene catalogue[END_REF], it is less and less likely to have human proteins that are not known at the transcript level [START_REF] Adhikari | A high-stringency blueprint of the human proteome[END_REF].

21 of the 25 human-specific proteins known either at the protein or transcript level are found encoded by an open reading frame of a lncRNA (Table 1). While a single of them, namely, PACMP, the Poly-ADP-ribosylation-amplifying and CtIPmaintaining micropeptide, is known at the protein level [START_REF] Zhang | Micropeptide PACMP inhibition elicits synthetic lethal effects by decreasing CtIP and poly(ADP-ribosyl)ation[END_REF], twelve others are predicted to be at least 90% globular, with a single structural domain, suggesting that most, if not all, human-specific proteins are encoded by lncRNA.

As a matter of fact, some de novo genes have already been found to have a lncRNA origin [START_REF] An | De novo genes with an lncRNA origin encode unique human brain developmental functionality[END_REF][START_REF] Ruiz-Orera | Long non-coding RNAs as a source of new peptides[END_REF][START_REF] Ruiz-Orera | Evolution of new proteins from translated sORFs in long non-coding RNAs[END_REF]. Such lncRNA could come from intergenic open reading frames [START_REF] Papadopoulos | Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution[END_REF], which in turn may pop up The structure of the human specific protein CATR1, as predicted by AlphaFold2 [START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF], with a fair level of confidence ( pLDDT =83). The darker the higher the level of confidence. Drawn with Chimera [START_REF] Pettersen | UCSF chimera: a visualization system for exploratory research and analysis[END_REF]. randomly, becoming new functional proteins when they happen to confer selective advantages [START_REF] Ruiz-Orera | Evolution of new proteins from translated sORFs in long non-coding RNAs[END_REF].

Figure 1 :

 1 Figure 1: The phylogenetic tree of the primate species with well annotated proteomes considered herein.

Figure 2 :

 2 Figure 2:Number of human specific proteins, as a function of the number of primate proteomes in which homologues of the human proteins were looked for. Primate proteomes are ranked according to the time of divergence between the primate and the human species, as provided in the Time-Tree database, Pan troglodytes being on the left and Protolemur simus on the right.

Figure 3 :

 3 Figure 3: Number of human proteins with homologues found in a given number of primate proteomes. 193 human proteins have no homologue in the proteomes of the 27 primates considered.

Figure 4 :

 4 Figure 4: Number of human proteins with a given quality of 3D structure prediction, according to Al-phaFold2. Quality ( pLDDT ) over 90 means: very high model confidence. Below 50: very low.

Figure 5 :

 5 Figure 5:The structure of the human specific protein CATR1, as predicted by AlphaFold2[START_REF] Jumper | Applying and improving AlphaFold at CASP14[END_REF], with a fair level of confidence ( pLDDT =83). The darker the higher the level of confidence. Drawn with Chimera[START_REF] Pettersen | UCSF chimera: a visualization system for exploratory research and analysis[END_REF].

4 Table 1 :

 41 The 25 human specific proteins known either at the protein or transcript level. Most of them come from an open reading frame of a long non coding RNA. Proteins predicted to be 100% globular are in bold. The only protein with a fair quality of 3D structure prediction is underlined.

	Uniprot Identifier	Protein length	RNA Identifier a	Starting basis	Degree of knowledge b	Globularity c (domains)	pLDDT
	PACMP	44	URS0002617ABC	139	Protein	0 (0)	ND d
	SDIM1	146	No	-	Protein	100 (1)	29
	Q68DW6	58	URS00008B55A1	409	Transcript	100 (1)	61
	CATR1	79	No	-	Transcript	100 (1)	83
	CF195	127	URS00025F035E	356	Transcript	97 (1)	29
	YV004	128	No	-	Transcript	83 (1)	41
	A4AS1	129	URS000233F457	1298	Transcript	0 (0)	49
	YS039	131	URS0002546BBE	2824	Transcript	100 (1)	36
	HCP5	132	URS00025BFBD4	372	Transcript	100 (1)	39
	YP023	132	URS000259F674	8	Transcript	61 (2)	39
	CA220	134	URS00025BEBB1	359	Transcript	100 (1)	39
	CL036	138	URS00025D0BC1	219	Transcript	100 (1)	49
	FEAS1	138	URS00009BBF20	179	Transcript	100 (1)	40
	YI001	140	URS00025BBEEA	315	Transcript	0 (0)	38
	YP033	140	URS0002573B74	1427	Transcript	100 (1)	31
	YK004	145	No	-	Transcript	100 (1)	38
	YS045	151	URS0002339A91	77	Transcript	93 (1)	41
	YF010	163	URS00025B1F38	1769	Transcript	60 (2)	34
	YB035	174	URS00025A88CD	314	Transcript	100 (1)	28
	YV008	177	URS000258F499	37	Transcript	42 (1)	26
	IDAS1	188	URS0000A912F7	1	Transcript	99 (1)	36
	YT009	211	URS0000E60B4D	490	Transcript	30 (1)	41
	CQ077	243	URS00021231B2	527	Transcript	91 (1)	25
	CK072	251	URS00025C6C2F	242	Transcript	87 (1)	27
	YU004	302	URS0000EBC2BD	89	Transcript	23 (1)	26
	a In the RNAcentral database

On February 2023.

On October 2023, 12 th .On the other hand, while 23 of them are known at the transcript level (Table1), 23 others are just predicted. Strikingly, 142 of them (71%) are coined uncertain, being annotated as dubious CDS or gene predictions, possible pseudogenes, etc.Among the 25 human-specific proteins known either at the protein or transcript level, 21 (84%) are considered as being uncharacterized, meaning that they do not have any known or putative function.On the other hand, 21 of them are found encoded by an open reading frame of a long non-coding RNA (lncRNA). Note that the growth of the number of reported non-coding RNA genes has been rapid, suggesting that the human catalogue may, in this respect, prove incomplete[START_REF] Amaral | The status of the human gene catalogue[END_REF].


Siccardi Alexandre
email: alexandre.siccardi@keep-motion.com

Joubert Charles

Martin Christian

Makki Ali

Dassonville Louis

Gaspar Benjamin

Optimized integration of a modular motor driver in a low voltage high power, open winding synchronous machine

This paper provides an optimization method for integrated modular motor driver (IMMD) with openwinding synchronous motors. This method can be used on different topologies of open winding machines and can be applied to others stator configurations. The concept of IMMD as well as different integration works are presented. All possible inverter integration methods are described with three of them experimented in the literature. Finally, an application of the optimization process is detailed. It allows to optimize the use of the integrated modular inverter volume, while respecting the physical, thermal, and mechanical constraints of the motor and the components.

Introduction

For several years, sales of electric vehicles have been growing rapidly over the world. The higher power density requirements have challenged the manufacturing methods of permanent magnet synchronous motors (PMSM). In these applications, the supply voltage is limited by the battery and the speed can exceed 6,000 rpm for an ever-increasing torque demand. The resulting high phase currents force manufacturers to increase the conductor cross-section. This implies a reduction in the winding turn number and a lower fill factor [START_REF] Runde | ISCAD -Design, control and car integration of a 48 volt high performance drive[END_REF], which leads to higher losses. In addition, the increased wire diameter is responsible for additional Joule losses due to the skin effect. Existing solutions consisting of dividing the windings into several parallel wires imply technological difficulties for large-scale industrialization (burying in the slots and soldering the connections).

Several solutions are possible to overcome the winding challenges and gain power density. The most common method, doubling the battery voltage, reduces the current in the motor windings and increases the motor power in the fluxweakening region. However, the motor design becomes more complicated because of the additional insulation required to limit partial discharges. Another method found in mid high-end cars is to use multiple motors to power the vehicle such as the Tesla Model S dual motor or the Koenigsegg Gemera.

However, another method is to increase the number of phases in the motor. In applications where the supply voltage is limited, polyphase motors are a solution to reduce the phase current. In this case the diameter of each conductor is reduced, which increases the copper fill factor of the stator and thus the efficiency of the motor. In addition, the increase in the number of phases in PMSMs leads to better reliability [START_REF] Dassonville | Availability analysis and quantification of electrical multiphase machines[END_REF].

To further improve the reliability of multiphase motors, the windings of each phase are divided into subgroups and driven by different converters. The most reliable system is achieved when each winding is supplied independently by an inverter. However, such electric motors must be powered by specific converters. These converters can be integrated into the motor to improve the power density of the drive system and are called Integrated Modular Motor Drives (IMMD).

In the literature, many works present prototypes of multiphase motors powered by IMMD [START_REF] Brown | Power Converter Design for an Integrated Modular Motor Drive[END_REF][START_REF] Shea | Hardware integration for an integrated modular motor drive including distributed control[END_REF][START_REF] Wang | Integrated Modular Motor Drive Design With GaN Power FETs[END_REF][START_REF] Verkroost | Simultaneous DC-Link and Stator Current Ripple Reduction With Interleaved Carriers in Multiphase Controlled Integrated Modular Motor Drives[END_REF][START_REF] Van Damme | A holistic DC link architecture design method for multiphase integrated modular motor drives[END_REF][START_REF] Schiestl | Development of a High-Power Density Drive System for Unmanned Aerial Vehicles[END_REF]. However, none of them were designed to independently drive the coils of an open wound permanent magnet synchronous motor (OW-PMSM), with a full bridge inverter. Using a singlephase inverter per winding with a distributed control law in an OW-PMSM achieves the highest level of reliability for the drive train [START_REF] Schiestl | Development of a High Power Density Drive System for Unmanned Aerial Vehicles[END_REF].

Benefits and challenges of IMMD

Introduced in [START_REF] Jahns | The past, present, and future of power electronics integration technology in motor drives[END_REF], the integrated motor driver (IMD) concept emerged in the late 20th century. Today, this technology is widely used in low-to medium-power fan applications, but it faces difficulties in being implemented in other variable speed applications. Compared to a conventional variable speed drive, the integration of electronics into the motor offers many advantages, such as higher power density. Nevertheless, IMD faces two main challenges, which are thermal management and component miniaturization. The optimization of thermal management can be done in 3 aspects:

• Utilization of electronic components that can operate at high temperatures.

• Reduction of Joule losses in the power electronics of the inverter.

• Improvement of the heat transfer between the components and the heatsink to evacuate the losses.

The other factor limiting integration is the size of the power components. In voltage source inverters, DC bus decoupling capacitors ensure a stable DC bus voltage, especially during transistors switching. These capacitors can represent up to 30% of the total volume of the inverter [START_REF] Su | A segmented traction drive system with a small dc bus capacitor[END_REF]. The reduction of the required capacitance is therefore of great interest for the integration of the inverter into the motor. This can be done by using an interleaving method or by increasing the number of phases in the case of a multiphase motor. Another possibility is to increase the switching frequency to reduce the required capacity. However, this method, in addition to increasing the switching frequency, is limited by the capacitor technology. When the switching frequency is increased beyond a frequency threshold, the required operating current overcomes the capacitance, and the capacitor volume no longer decreases. By working on these key points, the size of the power circuit can be minimized, and the power density of the electronics increased.

Electronic integration in the motor

Mechanical parts that constitute an electric motor are the rotor, stator, shaft, bearings, stator sleeve, and front and rear end caps. All these parts are essential to the proper operation of an electric motor. To integrate the inverter electronics into the motor and keep the length of the coil head leads as short as possible, electronics can be integrated in the area represented by the green cylinder in figure 1. The modular inverter to be integrated consists of several identical modules. Integration volume can therefore be divided into equal parts, shown in blue in figure 1, each part being a slice of the cylinder defined by the polar pitch of the stator windings. The orthonormal reference (𝑥 ⃗, 𝑦 ⃗, 𝑧 ⃗) of an inverter module is defined with 𝑥 ⃗ collinear to 𝑟 ⃗ the radius of the cylindrical reference (𝑟 ⃗, 𝜃 ⃗ , 𝑧 ⃗) of the integration volume. The PCBs can be mounted in all three planes of the reference frame (𝑥 ⃗, 𝑦 ⃗, 𝑧 ⃗ ) either inside the stator diameter or outside (over the sleeve). In the first case, the motor diameter is a constraint, and the integration of the electronics will impact the overall length of the machine, in the second case it is the opposite. There are therefore six different ways of integrating the electronics into the motor, as represented in figure 2. Methods A, B and C in the first row are the methods of integration inside the stator diameter that increase the length of the machine, while methods D, E and F in the second row are those outside the stator that increase the diameter. The three columns represent, in order, the orientation of the boards in the (𝑥 ⃗, 𝑦 ⃗), (𝑦 ⃗, 𝑧 ⃗) and (𝑥 ⃗, 𝑧 ⃗) planes.

Method A was introduced in [START_REF] Brown | Power Converter Design for an Integrated Modular Motor Drive[END_REF] and used in [START_REF] Shea | Hardware integration for an integrated modular motor drive including distributed control[END_REF] and [START_REF] Wang | Integrated Modular Motor Drive Design With GaN Power FETs[END_REF]. The PCBs of the inverter modules are mounted in a circle above the backplane in the (𝑥 ⃗, 𝑦 ⃗) plane. In this work, the stator is connected in a star configuration, so each winding is fed from a half-bridge. With this method, the cooling of the power components can be done through the rear end cap, or through an additional end cap above the IMMD. Multiple PCBs can be stacked along the 𝑧 ⃗ -axis to increase the area of a module. However, the A method is limited by the area of a module, which depends on the pole pitch and the stator diameter. The use of this method is only effective if the area of the power board and the heat sink fit on the area of a module. This method is therefore suitable for motors with few poles in the stator and low powers. In method B, the boards are mounted on the (𝑦 ⃗, 𝑧 ⃗) plane in the stator diameter. The first board is fixed on the stator sleeve, being able to benefit from efficient heat dissipation. The area of the PCBs can be increased by extending them in the 𝑧 ⃗ direction, but the width is limited by the pole pitch and the stator diameter. It is possible to stack the boards in the 𝑥 ⃗ direction, the stacking causes a reduction in the width of the PCBs since the radius of the circle on which these boards are placed decreases. Beyond a certain number, stacking is no longer possible since the width of a board no longer allows for the placement of components. Due to the strong constraints on the width of the PCBs, the B method does not achieve a high-power density by volume for IMMD. Method C has been used in [START_REF] Runde | ISCAD -Design, control and car integration of a 48 volt high performance drive[END_REF], [START_REF] Schiestl | Development of a High-Power Density Drive System for Unmanned Aerial Vehicles[END_REF] and [START_REF] Rubey | DC-Link Current Harmonics Minimization in ISCAD Multi-Phase Inverters with Interleaving[END_REF]. The PCBs are mounted internally on stator on the (𝑥 ⃗, 𝑧 ⃗) plane. Unlike methods A and B, this method cannot use mechanical parts to dissipate heat from the power components. Therefore, the heatsink must be integrated into the module, which makes thermal management more complex. The length of the PCBs can be increased in the 𝑧 ⃗-axis, while the width is determined by the pole pitch and diameter of the stator, the height of the heatsink, and the spacing between the boards. It is possible to stack several cards, however the width of the pole pitch is a constraint on the number of cards that can be stacked. This method is therefore more complex to implement than the previous methods, but it provides a large routing and dissipation area for the module. Method D is like the method A, the boards are mounted in the same plane (𝑥 ⃗⃗⃗⃗ , 𝑦 ⃗) outside the stator. Unlike method A, the area of the boards can be adapted by increasing the overall diameter of the machine. In this configuration, the cooling can be done on the front and rear end plate. This method is most effective on motors with short lengths and large diameters such as discoid motors. Indeed, even though the PCBs can be stacked in the 𝑧 ⃗ direction, the IMMD design is more complex when the number of stacked boards is large. In the case of a short discoid machine where the overall diameter is not a constraint, it is possible to realize large area PCBs where the stacking allows the entire length of the stator to be used. This allows a high-power density to be achieved.

In method E presented in [START_REF] Verkroost | Simultaneous DC-Link and Stator Current Ripple Reduction With Interleaved Carriers in Multiphase Controlled Integrated Modular Motor Drives[END_REF], the PCBs are mounted on the outside of the stator, in the (𝑦 ⃗, 𝑧 ⃗) plane. The boards can be the same length as the stator, providing a large area. It is possible to stack the boards to increase the total area of a module, and unlike method B, the stacked PCBs will have a greater width. The cooling can be done by the stator sleeve or by an external one. Finally in method F, the PCBs are mounted outside on stator on the (𝑥 ⃗, 𝑧 ⃗ ) plane. As with method C, each module must embed its own heat sink to evacuate heat from the power board. For methods E and F, the length of the stator limits the length of the boards, which is not the case for method D. A machine with a very short length such as a discoid motor may be penalizing for integrating an IMMD according to methods E and F. These methods will be more suitable for machines with longer lengths, where the overall diameter is not a constraint. However, in an electric motor, the power is proportional to the length of the stator and the square of its diameter. In the case where the design of the motor and the IMMD go together, it will be more interesting to increase the stator diameter and reduce its length. Therefore, the F method is at a disadvantage compared to the C method.

Optimizing integration

IMMD integration optimization consists in identifying the best integration method, i.e., the one that maximizes the power density of the motor + inverter assembly. First, the electrical and mechanical characteristics of the electric motor must be identified. This information can be obtained from an existing machine or determined during the design of the motor. Optimizing IMMD integration in parallel with the design of the motor optimizes the whole system power density of the IMMD.

In general, an IMMD combines various electrical functions such as: switching power devices (bridge legs), monitoring and control functions, filtering, instrumentation .... An example of an inverter block diagram is shown in figure 3. All these functions are interconnected, determining the necessary routing area for an IMMD module. These functions can be distributed over several PCBs interconnected by connectors. The more boards and interconnections there are, the more connectors there will be between the PCBs, which increases the required routing area and electronic volume. For the C and F methods, the height of these connectors influences the width of the PCBs. Some functions have placement constraints. For example, the switching cells must be placed on the power board, which is itself on the heatsink. Also, the DC bus capacitors and drivers should be as close as possible to the transistors to reduce the effects of parasitic inductance.

The optimization process, shown in Figure 4, is an iterative method where the IMMD integration can be tested for each method to determine which method is feasible and achieves the best power density. The preliminary steps are the IMMD electrical design based on the motor requirements and the calculation of the available integration volume. From the electrical design, electronic components are selected, and the required PCB placement area is determined. For each method, the PCB dimensions that can be integrated are calculated. PCB dimensions can be written as a function whose variables are motor parameters such as stator diameter, number of poles and connector height between the PCBs. If the area required for electronics routing is not achievable, then the IMMD cannot be integrated using this method. Next step is the design of the heat sink that will dissipate the inverter losses. The power to be dissipated includes the losses of the power components and the losses in the power PCB traces. For methods where mechanical parts of the motor are used for heat dissipation, it should be verified that they can transfer the additional heat generated by the inverter and modified if necessary. For methods C and F, the heat sink must be specifically designed. Heatsink dimensions can have an impact on the power board size. The process is iterative, as the length and width of the power board can be modified to achieve optimal space utilization for the power board and heat sink. If the heatsink cannot be integrated into the defined volume, there are several ways to improve it:

• Reduce the Joule losses of the inverter.

• Increase the operating temperature of the transistors.

• Increase the integration volume of the IMMD.

• Change the integration method.

Once the heatsink integration and power dissipation has been verified, the last step is to complete the inverter routing by integrating the remaining components to the mezzanine boards.

If the surface area of the PCBs is not sufficient for routing, it is possible to increase one of the IMMD integration volume dimensions if it is not constrained. This increased surface area benefits both the heat sink and the power board. The heat dissipation is therefore potentially more efficient since the exchange surface is increased. Depending on the additional surface obtained, it is possible to rearrange the functions between the boards to minimize the electronics volume expansion. Similarly, if dimensions have been changed in the last step, it is possible to repeat the previous steps to attempt to reduce the IMMD's volume. When the optimization process is complete, the power density volume results of the different IMMD integration methods can be compared to select the method that achieves the best power density. The optimization process allows (regardless of the integration method, electric motor, and inverter characteristics) to minimize the volume by the IMMD while respecting the physical and thermal limits of the components. A combination of methods can be used, for example, using both the D and F methods. The steps of the optimization process will remain the same, it will only be necessary, for each board and for the heatsink, to define the chosen integration method and to calculate their respective dimensions.

Application

The integration method presented in this document has been applied to the IMMD design for an OW-PMSM of 85 kW at peak power supplied by a 48 V battery. The IMMD is composed of 22 modules. To control the OW-PMSM, each module will integrate a microcontroller with a control algorithm and a heatsink. To minimize the IMMD volume, the converter will be divided into 2 boards: One for the control circuit and one for power components (as close as possible from the heatsink). The proposed IMMD is therefore built from perfectly identical modules. The mechanical power and torque curves obtained from the finite element simulation of the polyphase OW-PMSM are presented in Figure 5. According to the results, each converter will have to provide a nominal electrical power of 2.2 kW and a maximum power of 4.1 kW for 30 seconds which represent a maximal current of 150 ARMS. From a mechanical point of view, the entire IMMD must fit in the motor diameter which is egal to 190 mm. Therefore, the selected integration methods are A, B and C from the figure1.

Converter electrical design

The DC-AC converter has been designed to minimize losses, i.e., maximize efficiency, while ensuring module integration and motor control.

The converter circuit diagram is presented in figure 6. The battery voltage V0 is fixed at 48V while the output current IOUT and output voltage VOUT are fixed by the motor operating point. The converter control, is realized using unipolar modulation (UPWM), detailed in figure 7. This type of modulation allows to double the switching frequency seen by the load, by shifting the switching time by 180 degrees between the two bridge legs. As a result, the output current harmonics are at higher frequencies and therefore better filtered by the The ripple torque of the motor will be consequently reduced. Power losses in the converter has been calculated using the method detailed in [START_REF] Graovac | MOSFET power losses calculation using the data-sheet parameters[END_REF]. This method provides a mathematical tool to calculate the power losses in MOSFET-based power converter. The transistor chosen for this converter are the BSC019N08NS5 (Si technology) because they have the lowest RDS(on) MOSFET in TDSON-8 package and a low QG which reduce switching losses. To optimize the losses, the converter uses two MOSFET in parallel for each commutation cells. As the conduction losses are proportional to the square of the drain current IDS, the conduction losses are reduced. However, the space for placing the power component is increased. The use of two transistors in parallel reduces the total losses by 35% compared to a single MOSFET.

The power losses of the converter have been calculated different operating points. The results, shown in figure 8, are plotted as a function of the output current. The conduction losses represent 1.5 W per MOSFET at the nominal point and 7.6 W at maximal power. As the switching losses depending on both the MOSFET drain current and the switching frequency, they are calculated for different frequency. Using a low switching frequency will increase the efficiency of the inverter but may degrade the efficiency, control, and performance of the motor due to the strong current ripples. Furthermore, a lower switching frequency results in a higher capacitance for the DC-link capacitor for a given bus voltage ripple. The total power losses in the inverter are 22 W for the nominal operating point and 85 W for the maximal operating point for a switching frequency of 10 kHz. To ensure a stable supply voltage, the DC-link capacitor is sized to ensure a maximum ripple bus voltage of 1% in the worst case. The sizing of the DC-link capacitors can be done analytically based on the modulation index and load phase angle [START_REF] Vujacic | Evaluation of DC voltage ripple in single-phase H-bridge PWM inverters[END_REF]. In a single-phase inverter, the input current IIN has three relevant components: a DC component, a low frequency AC current at twice the fundamental frequency and a high frequency component at the switching frequency. To filter the low frequency component, the required capacitance, and consequently the size, is usually high because of the low modulation frequency. However, the large number of phase-shifted converters, operating simultaneously, reduces the required capacitance to filter the input current [START_REF] Nie | Multiphase VSI DC-link capacitor considerations[END_REF]. Sw @20kHz Sw @30kHz

A Fourier analysis has been done on the total current in the IMMD DC bus. This current is equal to the sum of all the converter input currents IIN.

The waveforms for the input current obtained in the circuit detailed earlier are shown in Figure 9a and the IMMD DC bus current is shown in Figure 9b. The low frequency current at twice the modulation frequency has disappeared by connecting all the converters to the same voltage supply. The resulting current has a DC component and even harmonics, whose first harmonic is equal to twice the switching frequency. Consequently, the capacitance value to filter these harmonics will be greatly reduced. For a switching frequency of 20 kHz, the first current harmonic on the bus will be 40 kHz due to the UPWM modulation. The DC bus ripple voltage is highest for a load phase angle φ=0 ° and a modulation index m=0.5. In this case, the RMS value of the ripple current in one converter at maximum power is 57.5 ARMS, for a current IDC=53 A. The required voltage ripple is achieved for a converter DC-link capacitance of 730µF. As each board embed a part of the total DC-link capacitor, the total capacitance of the IMMD DC-link is equal to the sum of each converter DC-link capacitance.

Similarly, capacitors must be sized to provide a current equal to the IDC LINK current divided by the number of converters.

Integration method selection

The dimensions of the components obtained during the electrical design are used to select the integration method. The choice of the method meets the following requirements:

• Place all the components of the different electronic circuits. • Integrate a heatsink capable of dissipating all losses. • Maximise the power density.

The integration capabilities of electronic boards for methods A, B and C have been studied. The mechanical integration constraints are those of the OW-PMSM studied, i.e., 22 identical modules in a diameter of 190 mm. The constraints used for the electronics are the following: The module will consist of a control board, a 5 mm thick heatsink and a power board, mounted on the heatsink. The estimated placing area is respectively 3500 mm² and 5000 mm² for the control and the power board. The results of the 3 integration methods are detailed in figure 10. Fig. 10 Comparison of PCB sizes and IMMD volume depending on the integration method. The volume of method A was not calculated because it does not allow to integrate the converter.

Method

Method A was not selected because the PCB areas are too small to integrate the power converter. Method B allow a sufficient surface area for the power board but there is a lot of space wasted for the control board. Method C reaches the surface objectives for the control and power board and has the lowest volume. Method C is therefore the most suitable method for the IMMD integration in the motor. This method will be used to realize the converter realization.

IMMD design integration

Based on the number of stator poles and stator radius, the integration volume of an IMMD module can be defined. The integration plan of the boards is detailed in figure 11. The heatsink and PCBs are parallel to the z-axis and the dimensions are bounded by the maximum distance between the opposite side of the triangle and the hypotenuse. The position of the module elements on the 𝑦 ⃗-axis impacts their maximum length. The further the elements are far from the base of the triangle, the more the length decreases. The board length L in 𝑧 ⃗ -axis can be calculated from the integration radius R, the PCB height h in 𝑦 ⃗ -axis and the converter number n using the formula (1). The calculated dimensions are 65x100 mm for the power board and 51x70 mm for the control board.

𝐿 = 𝑅 - ℎ tan ( 2𝜋 𝑛 ) -ℎ tan ( 𝜋 2𝑛 ) (1)
Fig. 11 Integration layout of an IMMD module with the method C.

PCB traces losses calculation

The power board, designed to integrate the MOSFETs, a current sensor, power connectors and DC-link capacitors, is shown in Figure 12. The power board is an IMS PCB to benefit from a low thermal resistance between the transistors and the cold plate. To obtain sufficient layers on the power board, the capacitors are assembled on a double layer board, soldered directly to the power board.

To evaluate the losses in the traces, the impedance of the traces of the power PCB is characterized and a frequency analysis of the currents in each mesh of the circuit is performed. The method used to calculate the losses in the tracks is described in Figure 13. The impedance calculation is performed by FEM on the 3D CAD model of the power board. Each trace impedance is calculated as a function of frequency, to consider the skin effect and the proximity of the traces. For each traces m, the harmonics h of the currents flowing in it were extracted and the impedance of the trace was interpolated for these frequencies f. The total losses in the power PCB are the sum of the losses of each track m for each current harmonic h (2). The losses in an m trace for a frequency f corresponding to the harmonic h of current I are the product of the resistance and the square of the current.

𝑃 𝑡𝑟𝑎𝑐𝑒𝑠 = ∑ ∑ 𝑅 𝑚,ℎ (𝑓). 𝐼 𝑚,ℎ (𝑓) 2 ℎ 𝑚

(2) Fig. 12 3D CAD of the IMMD power boards. The trace losses have been added to the convert losses. The total amount of losses to be dissipated by the converter is 103 W at maximum power and 26 W at rated power, for a switching frequency of 10 kHz.

Thermal design

Power losses dissipation was evaluated by constructing the equivalent thermal circuit of the IMMD module, shown in Figure 14. Each thermal resistance of the different material layers in the system is modelled by a resistance. To simplify the model, it is assumed that heat is transferred vertically to the fluid and that there is no horizontal heat transfer in the layers except in the Al layer corresponding to the PCB substrate which is made of Aluminium. The copper thermal resistance of the PCB traces was separated into two resistances RTH CU1 and RTH CU2. RTH CU1 is the thermal resistance associated with the copper surface under the MOSFETs to add the contribution of the trace losses PTRACES, to the MOSFET losses PINV. The layers corresponding to resistors RTH JC, RTH SN and RTH CU1 have the same area S1 corresponding to the contact area of the transistor. The resistors in the RTH ISO, RTH AL, RTH TIM and RTH CP layers use an S2 area which corresponds to the PCB surface. The resistor Rcu2 uses an area which is the difference between the areas S2 and S1. The thermal resistance RTH CP was obtained by performing a CFD calculation of the heat sink. The exchange coefficient obtained is 1720 W.m -2 .K -1 for a pressure of 260 Pa. With these parameters, the junction temperature of the transistors does not exceed 70 °C in steady state for a fluid temperature of 40°C. Even by multiplying the switching frequency by three, the junction temperature does not exceed 90 °C. The dimensioned heatsink therefore allows all the losses of the inverter to be evacuated while maintaining an acceptable temperature for the operation of the components.

IMMD final design

The IMMD designed using integration method C is shown in Figure 15. The converters are connected to the motor via a backplane to which the stator coils are soldered. Customised plug-in connectors have been developed to connect the modules to the backplane. The DC bus of the IMMD is made of a laminated busbar, on which additional capacitors for the DC-link are soldered. A resolver board is integrated on the stator backplane to measure the rotor position for motor control. The total length of the IMMD is 160mm including connectors. The total volume of the IMMD is therefore 4534 cm 3 , which represents a power density of 19.8 W/cm 3 for the motor power.

Conclusion

An optimization method to design an IMMD for OW-PMSM has been presented. The electrical design of the inverter and the surface area required to implement it led to the selection of method C as the most suitable method for integration. Losses calculation for the selected transistors was performed using a calculation method based on the transistor parameters from their datasheet. Additional losses in the tracks of the power board were calculated from the CAD model of the power board. A total of 103 W of losses for the maximum operating point was calculated. According to the thermal model used, the losses represent a maximum temperature difference of 30 °C for a switching frequency of 10 kHz. These electrical and thermal designs allow the IMMD to fits within the stator diameter for a maximum length of 160 mm. This represents a power density associated with the motor of 19.8 W/cm 3 .

Reference

Fig. 1

 1 Fig. 1 Volume available for IMMD integration in green and volume for integration of an IMMD module in the case of an open winding motor in blue.

Fig. 2

 2 Fig.2The six approaches of integrating an IMMD into an electric motor.

Fig. 3

 3 Fig. 3 Block diagram of an IMMD converter module.The next step is the power board routing of the IMMD module. This board should ideally contain power MOSFETs (or other switching cells), DClink capacitors, power connectors for DC and AC side, current sensor and eventually MOSFET drivers. If the area is insufficient, components can be moved to a mezzanine board.

Fig. 4

 4 Fig. 4 Proposed IMMD design optimization method.

Fig. 5

 5 Fig. 5 Mechanical output power and Torque for nominal (65 ARMS) and maximal (150 ARMS) phase current versus rotor speed.

Fig. 6

 6 Fig. 6 Circuit diagram of one IMMD converter.

Fig. 7 a

 7 Fig. 7 a) Modulation and carrier waveforms for UPWM and b) Output voltage VOUT waveform.

Fig. 8

 8 Fig. 8 Detailed losses in a switching cell as a function of current and for different switching frequencies.

Fig. 9 a

 9 Fig. 9 a) Waveforms of the input current and its three components: the DC current IIN DC, the double fundamental frequency harmonic IIN H0 and the high frequency ripple current IC. b) Waveforms of the total current IIMMD in the IMMD with its average value and the current ripple IDC LINK.

Fig. 13

 13 Fig.[START_REF] Graovac | MOSFET power losses calculation using the data-sheet parameters[END_REF] Method used to evaluate the losses in the power PCB traces.

Fig. 14

 14 Fig. 14 Equivalent thermal circuit of the power PCB and heatsink.

Fig. 15 3D

 15 Fig. 15 3D CAD of the designed IMMD for the OW-PMSM.