Fast estimation of liquid-cooled module temperature based on electro-thermal- liquid coupling model

Battery thermal management is vital for electrified transportation and energy storage, playing a crucial role in preventing thermal runaway and extending battery lifespan. Effective thermal management hinges on real-time and accurate battery temperature feedback. However, relying solely on surface-mounted sensors in battery systems fails to capture internal temperatures adequately. Moreover, limited sensor quantity due to cost and space constraints impedes comprehensive temperature characterization. Fundamentally, this problem addresses heat generation in batteries under current excitation and heat transfer within battery packs under liquid cooling. The key is establishing an electro-thermal-fluid coupling physical model. This paper presents a rapid and accurate temperature distribution estimation model tailored for battery systems. The model considers the impact of time-varying parameters in battery models on heat generation and internal heat transfer processes within battery packs under bottom liquid cooling. Additionally, it incorporates Kalman filtering correction based on limited surface sensors. Validation against experimental results demonstrates that under constant and dynamic conditions, the error between measured and simulated temperatures of individual cells is within 2.1 °C, and module temperature estimation error is within 1.6 °C. This approach is precise, efficient, and eliminates the need for additional sensors, providing real-time and accurate feedback for thermal management control. • Real-time internal battery temperature monitoring for thermal management is limited by sparse sensors. • An electro-thermal-liquid coupling model is developed for bottom-cooled battery packs thermal analysis. • Kalman filtering is employed to correct the model with sparse surface sensor data for closed-loop temperature estimation. • Module temperature estimation errors are validated to be within 1.6 °C under experimental conditions. • Enables accurate, real-time full temperature distribution for battery thermal control without additional sensors.