Temperature Estimation of Lithium-Ion Battery Based on Dual Model Fusion

The proliferation of electric vehicles and energy storage systems has underscored the critical need for effective thermal management of lithium-ion batteries, given their pronounced temperature-dependent performance characteristics and operational lifespan. To address this challenge, a battery temperature estimation algorithm combining model-based and data-driven methodologies has been developed. The approach integrates fundamental thermal modeling principles with adaptive parameter optimization techniques, implemented and validated through systematic experimental testing. Verification results under diverse charging scenarios demonstrate the algorithm's capability to maintain estimation accuracy, with maximum temperature deviations consistently constrained within 0.15°C. This methodology provides a reliable framework for battery thermal monitoring while strictly adhering to experimentally validated performance boundaries.