Multi-Objective Performance Optimization Design of Batteries Using Response Surface Methodology

The increasing adoption of electric vehicles (EVs) has created a growing need for battery systems that can deliver high efficiency, reliable operation, and effective thermal management. This study develops an EV battery model using MATLAB/Simulink and Simscape, with the BYD ATTO 3 specifications used as a reference, to investigate the influence of battery voltage, coolant flow, and battery capacity on four key performance indicators: battery temperature, power loss, state of charge (SOC), and state of health (SOH). Response surface methodology (RSM), combined with a Box–Behnken design (BBD) was employed to develop a quadratic regression model, evaluate the significance of the selected factors through analysis of variance (ANOVA), and identify the optimal operating conditions using the desirability function. The simulation results show that battery voltage and battery capacity have a greater influence on electrical performance, whereas coolant flow was more influential in thermal regulation. Under the optimal operating conditions, the battery achieved a temperature of 21.63 °C, a power loss of 3966.30 W, an SOC of 91.37%, and an SOH of 91.34%. The developed regression models demonstrated excellent predictive capability for all response variables, confirming the suitability of the proposed optimization approach. These findings demonstrate that the proposed optimization framework can balance thermal behavior, energy efficiency, and battery health. The developed methodology also provides a practical foundation for battery management and lookup-table-based control strategies in future EV applications.