OPTIMISATION OF LITHIUM-ION BATTERY CHARGING PROFILES USING A THERMAL AND HEALTH-AWARE FUZZY LOGIC STRATEGY

This study addresses the critical challenge of optimizing charging performance in lithium-ion batteries (LIB) by designing and analysing an intelligent Battery Management System (BMS) with an adaptive Fuzzy Logic Controller (FLC). The research aims to resolve the trade-off between charging speed, thermal safety, and cycle life longevity by moving beyond rigid Constant Current–Constant Voltage (CC-CV) methods to a holistic, health-aware control strategy. The FLC was engineered to integrate three key active parameters: State of Charge (SOC), State of Health (SOH), and Temperature, as inputs to dynamically regulate the charging current (𝐼𝑐) and voltage (𝑉𝑐). The methodology adopted a quantitative simulation-based experimental design using the MATLAB/Simulink environment. A high fidelity Second-Order Equivalent Circuit Model (ECM) of the Molicel INR–21700–P45B cell was formulated to replicate realistic electro-thermal dynamics. A central component of the study involved a systematic comparative analysis of two different Membership Function (MF) types within the FLC: Triangular and Gaussian. This approach allowed for rigorous stress testing under diverse conditions, including varied SOC levels, degradation states (100% vs. 95% SOH), and a wide ambient temperature range (0°C to 55°C). The results demonstrated the definitive superiority of the Gaussian-type MF, which produced significantly smoother control transitions, minimized current ripple, and reduced electrical stress compared to the Triangular MF. The Gaussian-based FLC successfully maintained charging efficiency within the optimal thermal window (25°C to 45°C) and demonstrated a critical "Survival Mode" at 55°C, where it autonomously throttled the charging current by approximately 63% to prevent thermal runaway. Furthermore, the integration of SOH allowed the controller to intelligently derate current for aged cells, confirming its capability as a robust, safety-first solution for nextgeneration intelligent BMS architectures.