A Novel Quick Temperature Prediction Algorithm for Battery Thermal Management Systems Based on a Flat Heat Pipe

Abstract

Predicting the core temperature of a Li-ion battery is crucial for precise state estimation, but it is difficult to directly measure. Existing quick temperature-predicting approaches can hardly consider the thermal mass of complex structure that may cause time delays, particularly under high C-rate dynamic conditions. In this paper, we developed a quick temperature prediction algorithm based on a thermal convolution method (TCM) to calculate the core temperature of a flat heat pipe-based battery thermal management system (FHP-BTMS) under dynamic conditions. The model could predict the core temperature rapidly through convolution of the thermal response map which contains full physical information. Firstly, in order to obtain a high fidelity spatio-temporal temperature distribution, the thermal capacitance-resistance network (TCRN) of the FHP-BTMS is established and validated by constant and dynamic discharging experiments. Then, the response map of the core temperature motivated by various impulse heat sources and heat sinks is obtained. Specifically, the dynamic thermal characteristics of an FHP are discussed to correct the boundary conditions of the TCM. Afterwards, the temperature prediction performances of the TCM and a lumped model under different step operating conditions are compared. The TCM results show a 70–80% accuracy improvement and better dynamic adaptivity than the lumped model. Lastly, a vertical take-off and landing (VTOL) profile is employed. The temperature prediction accuracy results show that the TCM can maintain a relative error below 5% throughout the entire prediction period.