Average Temperature Estimation for Lithium-Ion Batteries at Variable Environment Temperature

The performance of lithium-ion batteries is greatly affected by the temperature change. Therefore, it is of great significance to get the temperature information to develop the thermal management system. In order to solve the problem that the local temperature caused by the traditional temperature sensor arrangement cannot replace the overall average temperature, it is necessary to conduct studies of average temperature estimation for 10 A·h ternary nicke clobalt manganese lithium-ion batteries at variable environment temperature. First, a lithium-ion battery is modeled and the experimental platform is built to obtain the parameters which are necessary for modeling. Then, an average temperature estimation method for lithium-ion batteries based on recursive least squares and the extended Kalman filter algorithm is proposed. Finally, to verify the effectiveness of the proposed method, the experimental data of the battery at variable environment temperature is obtained under improved hybrid pulse power characteristic (IHPPC). The results show that the proposed method can estimate the average temperature of lithium-ion batteries in real time. The maximum error of the temperature estimation model after the algorithm convergence is 1.8 ℃, and the average error is only 1 ℃.