This study introduces an efficient methodology for evaluating the performance of electric motors in electric vehicles, focusing on the trade-off between simulation accuracy and computational cost. Three standardized driving cycles—Worldwide Harmonized Light Vehicles Test Procedure (WLTP), New European Driving Cycle (NEDC), and Federal Test Procedure (FTP)—are employed to represent realistic operating conditions. The performance of an Interior Permanent Magnet Synchronous Motor (IPMSM) is analyzed through high-fidelity electromagnetic simulations using Ansys Maxwell. For each cycle, full-scale Finite Element Analysis (FEA) is performed across all torque-speed operating points to obtain detailed efficiency and performance insights. Although this exhaustive approach ensures high accuracy, it is computationally demanding and unsuitable for iterative design or optimization. To address this, the Energy Center of Gravity (ECG) clustering method is introduced to reduce the simulation workload. By analyzing the energy distribution across each driving cycle, ECG identifies a small set of energy-representative operating points that effectively capture the motor’s typical load behavior. These selected points are then simulated, and the resulting efficiency estimates are compared with those from the full-cycle FEA. The comparison shows that the ECG method maintains high accuracy—with efficiency deviations under 2%—while reducing simulation time from hours to just minutes. This demonstrates the method’s effectiveness in achieving a balance between precision and efficiency. The proposed approach offers a scalable and practical solution for performance evaluation and early-stage optimization of electric vehicle powertrains, enabling faster design iterations without sacrificing result fidelity.

Quadcopter drones are increasingly used in a wide range of applications. However, their development remains limited by key challenges such as power supply constraints, short flight durations, and the high weight-to-energy ratio of onboard batteries. Critical factors such as endurance, mass, cost, and fuel consumption are closely linked to the performance of the energy storage system. This study proposes an optimal sizing methodology for quadcopter drone power systems, aiming to maximize flight autonomy across various sizes. A comparative analysis is conducted to evaluate different energy configurations, including battery-only systems, fuel cell-only systems, and hybrid configurations that integrate both technologies. The objective is to highlight the benefits of hybridization inn in terms of fuel efficiency, cost-effectiveness, and weight reduction over a range of mission durations. over various mission durations. The study identifies the most suitable energy combination to enhance autonomy while adhering to structural and weight constraints inherent to drone platforms. Furthermore, the influence of drone size on flight duration is analyzed. Results show that, up to a certain threshold, larger drones can accommodate more energy storage, thus enabling longer flight times.

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.

Future electric car issues that need to be addressed (EV) are mainly the power/energy densities of the energy storage systems and the electric motor performances. Interior Permanent-Magnet Synchronous Machines (IPMSM) are commonly utilized in today's electrical automobiles because they have a wider constant power speed range and a better power/torque density, a compact structure, a higher efficiency than induction machines. Magnetic steel sheets are frequently utilized in IPMSM, and the usage of low-iron-loss materials in IPMSM to increase performance has recently been investigated. This paper deals with a study on the influence of Different magnetic steel sheets technologies on the design of IPMSM for automotive applications. The designed IPMsynchronous motors results of the simulation In terms of efficiency, torque and output power, various magnet material components are compared to one another.

Photovoltaic systems are one of methods of using solar energy, which is one of the most renewable sources. The growth of photovoltaic systems has led to many technical problems, including control; low efficiency and stable provided power. Therefore, we propose in this paper a detailed study of a hybrid system consisting of a PV generator as a primary source and batteries as auxiliary one. Maximum Power Point Tracking (MPPT) algorithm is used to control the boost converter in the objective to increase the PV efficiency, between load and PV generator. This structure can be used as a power supply for standalone system or a solar electric vehicle.The main contribution in this paper is to propose a hybrid FLC/PI supervisor for DC bus voltage control. It should satisfy the load power requirement via the DC-DC bidirectional converter control with optimizing energy transfer. The obtained results using this controller are compared with PI and FLC controllers in term of robustness, stability and dynamic performances. It is confirmed that the proposed control can improve greatly the dynamic performances and robustness of the hybrid PV system.

The aim of this paper is to develop multi-physical model to validate the performance on power management strategies developed in our laboratory. The different parts of this model (electrical, thermal and ageing models) will be described and validated using experimental results. In our case, the hybrid energy storage system is composed with two Li-ion cells, High energy modules and high power modules. The obtained results prove the good modelling of the two technologies with just 2% of error between the experimental data and the model data.

A comparative study of two Hybrid Energy Storage Systems (HESS) for automotive applications in terms of weight, volume and cost is undertaken. Main source is a High Energy (HE) density battery while secondary source can be an Ultra High Power (UHP) battery or a Super capacitor (SC). Simulation results show that gains in weight and volume are obtained when HESS uses an UHP secondary source instead of a SC. Drive range is found to have no effect on HESS sizing. Sizing algorithm is used to find an “optimal” solution that improves electric vehicles performance.

This paper deals with a comparison study between two hybrid systems composed by batteries with fuel cell and super capacitors with fuel cell. A sizing algorithm is used to define the optimal sizes of the hybrid source. The comparison between the two systems is based on the weight, volume and cost. The batteries prove best performances in case of sizing according to the energy. However, the super capacitors provide the optimal sizes of the hybrid source in case of sizing according to the total recovered power. It is noted that the hybridization of the HP batteries with the fuel cell is an interesting solution to make up the Energy Storage System for automotive applications with high drive range.

This paper focalizes on sizing of hybrids sources composed with Fuel cells FCs and battery pack. Also an experimental validation of energy management of FC/Battery electric vehicle is tested. The system is composed of a fuel cell system as the main source and batteries as an assisting one. This last one is connected to a bidirectional DC/DC converter, and a DC/DC boost converter is associated to the fuel cell stack. To increase the power efficiency and to achieve the best performances of the hybrid source, an online EM strategy is used to share the power between the main and the auxiliary source by determining the power profile of each one. This strategy is based on frequency separation; it takes into account the slow dynamics of FC, fuel consumption and the batteries limits. Also, it is needed to make these results as a reference to be compared with other strategies which are currently under development in our laboratory. In the objective to verify the efficiency of the proposed approach, both simulation and experimental results leads to confirm its efficiency, the robustness and stability regrading dynamic performances during power demand, and regenerative braking, fuel consumption.