A core temperature estimation method of lithium-ion battery based on electrothermal model

With the wide application of lithium-ion batteries (LIBs), its safety has attracted much attention. Due to the limitations of existing sensors and measurement technologies, the internal temperature of LIBs cannot be measured directly, which makes it impossible to monitor the core temperature of LIBs in real time. This will cause hidden hazards to the safe use of LIBs. In this paper, the accurate battery heat generation model is established by combining the heat generation model proposed by Bernardi with the heat transfer and heat dissipation model. Particle swarm optimization (PSO) algorithm is used to identify the parameters of the lumped parameter two-state thermal model of LIBs proposed in this paper. In the experiment, after drilling the battery, the built-in temperature sensor is utilized to measure the real core temperature, and then the unscented Kalman filter (UKF) algorithm is employed to estimate the core temperature. The experimental results show that the estimation errors by the method used in this paper are small and have good adaptability and robustness, which provides a reliable method for core temperature monitoring of LIBs.