Sequent extended Kalman filter capacity estimation method for lithium-ion batteries based on discrete battery aging model and support vector machine

Precise battery capacity estimation plays an important role in the future intelligent battery management system. In this paper, a fusion estimation method based on support vector machine and discrete battery aging model is put forward to enhance the online capacity estimation accuracy of lithium-ion batteries under variable temperature conditions. During the constant current charging process, the support vector machine is developed to estimate the battery capacity, which first trains a single 18650 battery offline and then tests the accuracy of the model using two other batteries of the same type intermittently. Subsequently, the discrete aging model of the battery is proposed to continuously estimate the capacity of the battery. However, unmodelled dynamics between battery aging model and real physical battery is easily occur in the process of modeling, which affects the accuracy and robustness of the model. Therefore, a sequent extended Kalman filter algorithm is deployed for solving the problem. The first Kalman filter takes the identified value of support vector machine as observation value to update the model parameters of discrete battery aging model. The second Kalman filter fuses the identified value of support vector machine and the discrete battery aging model after updating model parameters to improve the precision of online battery capacity estimation. The experimental results indicate that the proposed discrete battery aging model and support vector machine have good applicability, and the algorithm used can online modify the parameters of the model. When the model parameters are modified four times, the fusion estimation error is less than 2%.