Fuzzy-Logic–Based Thermal Management and Performance Regulation of EV Battery Systems Using Integrated Modelling and MATLAB Simulation

One of the most significant issues in electric vehicles is the fear of battery overheating, which raises reliability and safety concerns, particularly during fast charging or heavy loading. Intense heat accelerates ageing, increasing internal resistance and increasing the risk of thermal runaway. The usual thermal-management systems based on fixed blockades or manually regulated control strategies often intervene too late, resulting in incident-free overcooling, and take unnecessary action to take power. In this work, the integration of lumped thermal modelling and an adaptive fuzzy-logic controller was unified to create a thermal management framework in MATLAB. Rust generation is modelled through the phenomenon of ohmic losses. Convective heat dissipation is worked out under natural, forced, and enhanced cooling regimes. With the help of the fuzzy controller, battery temperatures and the rate of temperature increase are treated as decision variables, so that it eventually adjusts the cooling intensity according to the flow-inference rules. The regulation is smooth and intelligent, without the need for accurate parameter tuning or access to training data. The simulation results indicated a significant temperature drop each time the fuzzy controller was applied to a temperature level above 50 to 55°C under control conditions. At the same time, stabilisation occurs faster, while maintaining good energy-efficient operation. The proposed alternative provides a practicable way to integrate intelligent, computationally simple, and robust thermal-management systems into electric vehicle battery systems, thereby enabling safer operation and longer life while achieving better performance under load.