<![CDATA[Battery temperature plays a vital role in determining the performance, safety, and lifespan of lithium-ion batteries in electric vehicle (EV) applications. This study presents a simulation-based approach for monitoring surface temperature using Kalman filter estimation, which integrates air temperature, current load, and battery characteristics. A mathematical model of thermal dynamics is developed and used for real-time temperature prediction. The results demonstrate that the Kalman filter is effective in estimating the surface temperature accurately, even with uncertain measurements. This work also discusses the integration of an actuator (fan/cooler) and PID control to maintain the temperature around the ideal level of 25°C, showcasing the potential of this system for smart thermal battery management in cost-constrained embedded systems. Keywords - Temperature Monitoring; Kalman Filter; Thermal Modeling; Estimation Algorithm; State Estimation; Simulation;]]>
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