Method for Rapid Prediction of Electric Vehicle Battery Temperature for the Early Design Phase

Thermal management of electric vehicle (EV) battery systems is critical for ensuring optimal performance, user safety, and battery longevity. Existing high-fidelity simulation methods provide detailed thermal profiles, but their computational intensity makes them inefficient for early design iterations or real-time assessments. This paper introduces a streamlined, physics-based one-dimensional transient thermal model coded in MATLAB for efficiently predicting battery temperature behavior under various driving cycles. The model integrates vehicle dynamics to estimate power demands, calculates battery current output and heat generation from electrochemical principles, and determines the battery temperature profile through a 1D conduction model connected to a thermal resistance network boundary condition that incorporates the effect of coolant heat capacity. The model achieved prediction errors below 1% when compared to analytical solutions for conditions of no heat generation and steady-state operation. Additionally, simulated results from the LA92 and UDDS drive cycles reveal the model’s ability to predict battery temperature profiles, coolant temperature, and vehicle range. The proposed approach improves simulation time (<10 seconds) over traditional computational thermal-fluid dynamic simulations while preserving sufficient accuracy for iterative thermal design of EV battery packs and cooling systems.