A metaheuristic SBO-based thermal control strategy for heavy duty electric vehicle batteries

This study presents a high-fidelity simulation and control framework for a Battery Thermal Management System tailored to heavy-duty electric vehicles, with a focus on optimizing thermal regulation through the Satin Bowerbird Optimization algorithm. The primary objective is to maintain the battery pack temperature within the optimal range of 20–22 °C to ensure thermal stability, enhance battery performance, and improve overall energy efficiency. A dynamic driving cycle was integrated into a MATLAB Simulink environment to evaluate the BTMS against key performance indicators, including temperature tracking, coolant pump power, refrigerant energy usage, battery output power, and state-of-charge retention. The proposed SBO-based control strategy was benchmarked against traditional Proportional-Integral and Particle Swarm Optimization controllers. Results indicate that the SBO algorithm delivers superior thermal performance, reduces auxiliary energy consumption, and effectively manages real-time thermal fluctuations. The findings highlight the potential of SBO as a robust and energy-efficient control strategy for next-generation electric mobility platforms. • Developed a MATLAB Simulink-based BTMS for heavy-duty electric vehicles using the Satin Bowerbird Optimization (SBO) algorithm. • SBO strategy maintained battery temperature within the optimal 20–22 °C range under dynamic driving conditions. • Achieved significant reductions in coolant pump and refrigerant power consumption compared to PSO and PI controllers. • Enhanced battery State of Charge (SOC) retention and power output, supporting longer driving range and thermal safety. • Validated SBO’s superiority for real-time, intelligent thermal control in energy-intensive electric vehicle platforms.