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.

The significance of Bodes Ideal Transfer Function (BITF) in control theory has motivated researchers to explore new ways to harness its beneficial properties. In this work, we present a novel control strategy that integrates Input-Output Feedback Linearization (IOFL) with a Fractional Integral Controller (FIC) to achieve enhanced system performance. The proposed approach is applied to a Two-Input Two-Output (TITO) nonlinear system, where IOFL is first utilized to linearize and decouple the system into two independent pure integrator subsystems. Subsequently, a newly designed fractional-order integral controller, based on BITF, is implemented to ensure precise reference tracking. To validate the effectiveness of this method, we apply it to a TITO coupled tank (TITOCT) process and compare its performance with Nonlinear Continuous-Time Generalized Predictive Control (NCGPC).

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.

Accurate real-time estimation of battery State of Charge (SOC) and State of Health (SOH) is essential for electric vehicle (EV) performance and safety. We propose a hybrid framework combining deep learning with physics insights using LFP cell data. Our core model is a series CNN-TCN-DNN network trained on raw voltage/current signals; replacing measured temperature with cumulative charge ($\int I d t$) as an input improves accuracy. The model is tested under input noise and bias to ensure robustness. For example, it consistently achieves SOC errors below 2% MAPE and more accurate SOH tracking, enabling more reliable range prediction.

In this research, we develop a thermal energy storage system utilizing phase change materials (PCM) to integrate in electric vehicles (EVs). This system is designed to provide heating for the passenger compartment during winter by integrating it into the existing heat pump in order to reduce the energy consumption of the compressor, which draws power from the vehicle’s battery pack. The design draws inspiration from the compact plate-fin heat exchanger, where the air passages are filled with PCM and the system is fully sealed. Due to the low thermal conductivity of the PCMs, aluminum fins are used as heat transfer technique. The main target of this article is to analyze the thermal behaviour of the system under different flow conditions, and to optimize the fin pitch and fin height in the system as a function of power requirements. The results showed that an efficient fin design enhances the heat transfer by 7.5 times between the PCM and the heat transfer fluid within the storage system.

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.

In this paper, feedback linearization and linear observer with a new analytical fractional-order proportional integral controller (FPIC) design method for controlling a double-integrating system is proposed. This method is based on Bode’s Ideal Transfer Function (BITF) and frequency domain specifications. For the design of the FPIC, the open loop transfer function associated the double integrating system and the FPIC is referenced to Bode’s ideal transfer function to have a constant phase margin. This property ensures the stability of the closed-loop system and robustness against gain variations. The proposed FPIC approach is successfully applied to the two-tank process. The effectiveness and tracking performance of the control law is demonstrated through simulation and experimental tests.

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.

In the last years have seen an increasing usage of Electrical Vehicle (EV). To guarantee safe and reliable operation, it's necessary to possess the capability to monitor, in real time, the state of health (SOH) of the battery. This paper presents a deep learning method which utilizes a Deep Neural network (DNN) for cell-level capacity estimation based on the voltage, current, and State Of Charge. First, a multi-physical models of the battery is done to extract input and output data for the different learning and testing phases. Second, two machine learning algorithms, including DNN and Convolution Neural Network (CNN), are used to predict SOH. Mean Absolute Error (MAE) and Mean Square Error (MSE) are selected as the evaluation index. The results show that the proposed algorithm DNN has the weakest error, which makes it possible to accurately predict the SOH and to have a better stability.

This paper presents an adaptive model-free control strategy designed for trajectory tracking of a 2-DOF Quanser Aero 2 helicopter. The approach employs a fuzzy logic system to approximate a model-based control law, effectively addressing system uncertainties and specific disturbances. The stability analysis of the closed-loop system is carried out using Lyapunov's theorem, guiding the adaptive law to adjust the parameters of the fuzzy system. It is proven that all signals within the closed-loop system remain bounded, and the tracking error converges to a small region near the origin. The proposed adaptive approach is successfully applied to control pitch and yaw angles. Through simulation and experimental tests, the effectiveness and tracking performance of the control law are demonstrated.

Currently, a major obstacle faced by multirotor unmanned aerial vehicles (UAVs) is the limitation of embedded energy. The research community has been exploring this issue through the analysis of design, control, and planning methods. Most of these strategies rely on comprehending the interrelated sub-dynamics of the DAV system, encompassing factors like propeller aerodynamics, electro-mechanical dynamics of the motor, and battery dynamic. This paper proposes an estimation algorithm based on a power consumption multiphysical model, which involves all these components and their coupling. Initially, we build sub-models for each component dynamics and then incorporate them to draw up a multiphysical model that can predict with some degree of precision the consumed power during the UAV flight operation.

The interest to electrify all modes of transportation has increased over these past years. Their source of power is primarily a systems of energy storage like Battery packs. The SOC of a battery indicates the amount of charge stored in the battery and is a critical parameter for its safe and efficient operation of the electric vehicles. However, it's not possible to be measured directly, but can only be estimated from related measurable variables. Data-driven methods have gained significant attention in recent years due to their ability to estimate SOC accurately, even under dynamic operating conditions. This paper provides a data-driven method for SOC estimation based on the Temporal Convolutional neural network (TCN) approach.

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.

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.

The study of control techniques for exoskeleton robots used in upper-extremity rehabilitation is gaining popularity. These robots are connected to the human upper limb at multiple points, allowing for smooth and independent joint movements. Therefore, providing a robust, precise, and safe control system is necessary. Sliding control-based approaches are well reputed for their robustness to parameter uncertainties, modeling errors, and external disturbances. Nevertheless, their major disadvantage is chattering. This paper describes two controllers based on sliding mode theory to reduce this undesirable effect: the generalized variable structure control and the higher-order finite-time sliding mode control. The former incorporates the derivative of the torque input in the model, while the latter is based on homogeneity and higher-order sliding modes to diminish chattering. A comparison between the two controllers is achieved by simulating passive rehabilitation mode.

The interest of research and automotive industries is concentrating progressively on the Electric Vehicles (EV) which are a global transportation development currently in order to achieve considerable carbon emission reductions. Electric batteries are the essential component of the EV and precise remaining useful life prediction is the main to ensure its reliability. As a result, the inside workings of these battery systems must be fully included. There is presently no precise model for predicting an EV battery's aging. This paper presents an intelligent method for estimating the State Of Charge (SOC) of the battery.

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.

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.

This paper proposes a novel hybrid intelligent backstepping controller (HIBC) for control of uncertain nonlinear chaotic systems. The novel HIBC uses a hybrid Fuzzy System-Wavelet Neural Network identifier (FS-WNN) to identify the system unknown dynamics. The hybrid FS-WNN identifier is a combination of a Fuzzy System approximator (FS) and a Wavelet Neural Network approximator (WNN). Each approximator approximates the unknown functions of the system separately. Then, the different approximations of the same function are combined using modulation technique. Since the hybrid FS-WNN uses wavelet neural networks and fuzzy systems, its approximation accuracy and generalization capability are superior to those of conventional individual wavelet neural network and fuzzy system for system identification. The adaptation laws of the control system are obtained using Lyapunov's method to guarantee the asymptotic stability of the closed loop system. The proposed HIBC is applied to control a chaotic uncertain nonlinear system. Simulation results prove that the proposed HIBC can achieve a superior tracking performance by incorporation of WNN identifier, FS identifier, adaptive backstepping control technique, and Lyapunov's theory of stability. A comparison of the HIBC performance with further models in literature is given to highlight the efficiency of the proposed control scheme.

The development of hybrid and electric vehicles in recent years has increased the use of batteries. However The failure of one cell of the battery involves the nonavailability of the embedded battery. To resolve this problem, one can use modular battery associated with specific converter. This method allows the system functioning in the case of cells failure. In this article, a strategy to control a five-level diode inverter associated with a storage system (four batteries modules) is presented. The equation formulation for this control method is based on the combination of different conversion functions. This control makes it possible to determine the modulation for each voltage reference vector of this strategy and its position in the voltage modulation plane in an easy way because this spatial vector modulation does not use the park transform. In order to ensure the state of charge (SOC) balancing between the four battery modules we use the different redundant vectors with closed-loop control. Simulation results demonstrate the good performance of the proposed system.

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.

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study, we introduce an optimization algorithm to minimize the consumed energy in the flying vehicle mission under windy conditions. In order to calculate the energy consumed by the quadrotor, we present a power loss model, where the energy is formulated as a function of rotor speed and acceleration. Then, we formulate the energy minimization problem as an optimal control problem and solve this problem in order to calculate minimum energy for a point-to-point quadrotor mission under windy conditions. In order to highlight the proposed optimization approach, we compare energy consumption obtained by optimization algorithm with an adaptive control approach in simulation experiment.

Nowadays, electric vehicles represent one of the most significant chances to reduce the pollution production rate. Unfortunately, in electric motors, the efficiency decreases by the relationship between speed proposed by the driver and the torque required by the vehicle. Those parameters can be estimated in order to make an efficiency optimization based on present and future road/weather conditions. Regrettably, this kind of control (optimal control) requires a model with low compilation time. Since bicycle motorcycle has nonlinearities, in this article, a state reduced linear dynamic model able to reproduce the behavior of a TWEV by more than one minute will be proposed. The model is oriented to an optimal controller in energetic field, for this reason, the most significant states are longitudinal speed to be used with the input torque to calculate the efficiency of the electric motor and the Yaw angle to creates constraints over the trajectory that has to be covered. After the model is proposed, its accuracy is tested by a comparison with a numerical iteration software. Finally, a sensitivity test is made in order to determine the behavior of the error according to the friction coefficients of the rear and front pneumatics.

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.

Electric vehicles (EVs) are a step toward ecologic transportation and carmakers should improve their attractiveness. The energy storage system represents a huge piece of the total EV price and can reach 45%. Moreover, the battery has a limited lifespan that could be considered as insufficient (around five years) against an equivalent thermal vehicle. The aim of this paper is to study battery ageing to find a way to improve battery life and then make the EV more economically attractive. The study is here applied to the category of light commercial vehicles (LCV) used in a professional context. This category are limited to 3.5t. The electric energy storage system ages depending on the use but also when the component is not used. Customers' usages have been recorded with a specific tool and a model of the entire vehicle including a multi-physical battery model with ageing were done in order to predict customers' needs. Calendar and cycling degradations of battery cell are modeled based on literature and identified from experimental results. Finally, charging strategies are developed and its impacts are studied in order to improve battery lifespan.

The energy required by a two wheels electric vehicle (TWEV) to complete a trip is lower than common electric cars or internal combustion vehicles. However, there are considerable losses along the electric driving chain. Those losses added to a limited energy storage cause an impact over the TWEV autonomy. This appears to be the main factor, which limits the large-scale market penetration of TWEV. This paper aims to analyze the multiphysic behavior of the complete power-chain in order to study its effect on its energetic losses. Even when many dynamics model oriented to hardware design approach can represent the come multiphysic behavior of one or two elements of the power train, the approach proposed in this paper presents a balanced representation of all power chain able to be used in real-time optimization. This study will help to improve the capabilities of an onboard TWEV efficiency estimator system which uses a longitudinal force model. As a conclusion, the error of autonomy estimation is compared with thermal considerations and without them according to different operating points.

This paper deals with the body-rate stabilization of quadrotors. The main idea is the design of a robust intelligent self-tuning PID controller for the quadrotor system. The key to successfully control a quadrotor with a PID controller is modeling the quadrotor dynamics accurately, then calculate the PID parameters based on the quadrotor identified model. However, the quadrotor is an uncertain underactuated system. Finding an accurate quadrotor model is a difficult task. Therefore, unlike the classical PID controller that depends on the system model, we propose an intelligent PID controller that employs an adaptive Wavelet Neural Network (WNN) to online estimate the optimal proportional, integral and derivative parameters. The adaptation laws of the PID parameters are derived using Lyapunov's stability method to guarantee the stability of the closed-loop system. The proposed intelligent controller does not require prior knowledge of the quadrotor dynamics. The proposed controller has a simple architecture that generates the body-rate commands to stabilize the quadrotor. The simulation results of the proposed intelligent self-tuning PID controller compared to the classical PID controller in stabilization of the quadrotor system prove the efficiency of the proposed scheme.

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.

In this paper, a disturbance observer based robust adaptive fuzzy tracking control algorithm is developed. The problem of trajectory tracking and wind disturbance rejection for a quadrotor unmanned aerial vehicle are investigated. An adaptive fuzzy controller is used to provide good tracking performances for the quadrotor vehicle. While, a state and disturbance observer is employed in order to estimate the immeasurable states and the unknown wind disturbances. The stability analysis of the global controller/observer system is proved using Lyapunov theory. It is shown that all signals in the closed-loop system are uniformly ultimately bounded (UUB). The proposed design can guarantees the desired tracking performances and external wind disturbance rejection. Simulation studies are presented to highlight the efficiency of the proposed control scheme.

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.

In this paper we deals with the limitation of embedded energy for quadrotor unmanned aerial vehicles. Quadrotor UAVs are flying machines that use lift generated by several rotors, and because of this, a large proportion of their available energy is consumed by rotors in order to maintain the vehicle in the air. In this concept, two optimal control problems are formulated and solved. For the first problem, minimum-energy control effort is computed for desired initial and final configurations with respect to the angular velocity of rotors. Where in the second, minimum-time control effort is computed for a desired energy. The proposed method is illustrated by simulation experiment for a quadrotor UAV.

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.