This paper presents a compact multi-physical model for lithium-ion batteries used in electric vehicles (EVs). The proposed framework combines an equivalent circuit for electrical behavior, a lumped thermal model, and a semi-empirical ageing model separating calendar and cycling effects. The model is parameterized and validated using experimental data from Kokam NMC-based High Energy (HE) and High Power (HP) cells. Results show high accuracy in voltage, temperature, and degradation prediction, with relative errors below 2%. The model structure offers a good balance between accuracy and computational cost, making it suitable for EV performance evaluation, ageing prediction, and battery management system integration.

The purpose of this paper is to investigate the challenges that arise while doing an analysis of electric machines throughout the course of a complete driving cycle. Because of the complicated nature of this process, which is caused by the large number of operational points, the use of specialist equipment and the expenditure of a considerable amount of time are both requirements. This study's overarching goal is to improve the efficacy of electric machine design by investigating clustering strategie. The purpose of this study is to investigate various methods of clustering in order to create an electric machine design that is more effective. The proposed approach employs clusters of operating points to identify chosen Representative points or RPs to construct electric machines with the highest possible efficiency within a given operating range. The study suggests the automated k-Means method for cluster analysis and RPs detection. To test the effectiveness of the proposed method, the study conducted an electromagnetic design study and analysis of the internal permanent magnet machine (IPM) for the WLTP driving cycle. As a result, the operating point set was reduced to only eight points, allowing for an assessment of the k-Means technique's efficiency. This study has implications for researchers and practitioners seeking to improve electric machine design and efficiency in the automotive industry.

This paper focuses on the optimization of the sizing of the on-board storage system in an electric vehicle. The source studied is composed of a combination of two Li-ion battery technologies (a high-power density battery and another high energy density). The interest of this hybridization will be illustrated by comparing the performance of this solution with that of a conventional source (high power battery). A study on the various possible hybridizations is detailed. Following this study, a report is drawn up on the interest of each hybridization according to constraints related to the type of mission, desired autonomy, weight, volume and lifespan of the storage system. The conclusion of this work led us to the need for a very precise choice of the auxiliary storage system. The latter must ensure sufficient autonomy to respect the constraints linked to the aging of the source.

This paper investigates the impact of battery technology on the electric motor's optimization process for an electric vehicle application. Matlab and Ansys Electronics are used to conduct the simulations. The needed autonomy is estimated for the WLTC driving cycle using a dynamic vehicle model while considering the storage system mass calculated with a connected sizing algorithm. The Motor model is constructed using the finite element soft-ware Ansys electronics. The genetic algorithm will determine its geometrical parameters while considering the new power and torque demands, including the storage system weight. The comparison of the optimization results was carried out for four battery technologies that have promising characteristics for an automotive application. The results discussed active material cost and performances evaluated for the entire selected driving cycle.

The embedded storage system is the principal part in the electric vehicle. The vehicle's autonomy and price influence depend on the chosen source technology. For that, we must optimize the sizing and the ageing of the storage system using heavy-duty models. These last ones must take into account the battery behavior and the system cost. The development of an accurate multi-physical lithium battery model can be an extremely time-consuming process because of the complexity of the battery electrochemical phenomena that could occur during an automotive application. In this paper, we will start by introducing the proposed dynamic battery model, and submit the possibility of building an accurate dynamic design while highlighting the key objective of our study, which is the optimization algorithms performances comparison in terms of precision and computing time.