Badis LEKOUAGHET

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology.

Proton Exchange Membrane Fuel Cells (PEMFCs) represent a promising clean energy technology for electric vehicle (EV) applications due to their high efficiency and zero-emission operation. Precise parameter estimation is essential for effective simulation, optimal control, and performance evaluation of fuel cell systems. However, accurately determining the unknown parameters of PEMFC models from experimental voltage and current data presents a highly nonlinear and multimodal optimization challenge. Conventional deterministic methods often prove inadequate due to the problem's inherent complexity, while metaheuristic algorithms (MAs) offer superior solutions but require enhancements to avoid local optima trapping and accelerate convergence rates. Although advanced MAs have been recently developed to address these limitations, their application in fuel cell parameter identification remains relatively unexplored. Accordingly, this study aims to improve the accuracy and robustness of PEMFC parameter identification by evaluating two recently proposed MAs, namely the PID Search Algorithm (PSA) and Triangulation Topology Aggregation Optimizer (TTAO), for estimating the parameters of a semi-empirical electrochemical PEMFC model using experimental polarization curve data. These algorithms are assessed based on best fitness, average fitness, worst fitness, standard deviation (StD), average efficiency (Avg), and convergence characteristics. Results demonstrate that PSA achieves superior performance with significantly improved convergence stability and estimation accuracy. Specifically, PSA attains the lowest Sum of Squared Error (SSE) of 7.67426×10-3 with a standard deviation of 6.53764×10-4 for the Horizon 500W stack, and an SSE of 2.28813 with a standard deviation of 6.91510×10-4 for the NedStack PS6 stack, confirming its superior robustness and precision compared with competing optimizers.

Lithium-ion batteries (LiBs) have become the leading choice for energy storage systems (ESS) in electric vehicles (EVs) due to their superior performance characteristics. However, their sensitivity compared to other battery technologies necessitates the use of advanced battery management systems (BMS) for reliable and safe operation. Accurate parameter identification for LiBs is essential to assess the performance of energy storage systems within BMSs and EVs. This research explores the Fully Informed Search Algorithm (FISA), a novel optimization method, to determine the parameters of 2nd and 3rd order equivalent circuit models (ECM) for LiBs. As an enhanced version of the Rao algorithm, FISA is recognized for its efficiency in tackling real-world optimization problems while maintaining the simplicity and parameter-free nature of its predecessor. The study focuses on minimizing the error between the ECM-predicted voltage and the actual battery voltage measurements. To validate the proposed approach, a high dynamic profile (HDP) is employed, alongside a comparative analysis of FISA and the Rao algorithm. The simulation results demonstrate that FISA achieves highly accurate and stable parameter estimation, surpassing the precision of the Rao method. Moreover, findings indicate that the 3rd order ECM provides superior accuracy in capturing the parameters of the battery model.