Efficient Prediction of Electric Vehicle Battery Temperature

Maintaining the temperature of lithium-ion batteries within electric vehicles across a wide range of driving scenarios and ambient temperatures is critical for ensuring both safety and long battery life. However, accurately predicting the temperature of batteries that thermally interact with dynamic systems—such as drive cycles, cooling systems, and vehicle components—can be computationally expensive and too slow for the iterative nature of design processes. To address this, our research employs a combination of 1D thermal modeling, a modified lumped capacitance approach, and experimental testing to develop a fast and reliable framework for analyzing battery thermal performance. The model captures the interaction between the drive cycle, cooling plate, coolant flow, thermal interface material, and battery geometry and properties. This hybrid approach balances accuracy and computational efficiency, enabling rapid exploration of thermal design strategies during early-stage vehicle development. The study provides insights into how system-level parameters—such as coolant flow rate, material thermal conductivities, and component dimensions—affect heat generation, dissipation, and worst-case battery temperatures. The model’s predictions are validated against experimental data, confirming its capability to guide design decisions under realistic operating conditions. This efficient and adaptable method serves as a valuable tool for thermal engineers working on electric vehicle platforms and other battery-powered applications.