State Of Charge(SOC) Estimation Of Lithium Ion Battery Based On Unscented Kalman Filter

In recent years, automotive industry develops electric vehicles(EV) as the environmentally friendly transport, which is clean, efficient and non-polluting. The performance of battery pack equipped on electric vehicles will directly affect EV`s range, acceleration, and efficiency of Braking Energy Recovery.In terms of EV, battery management system plays a significant role in the development, which is also the current research focus and key to the reliability and safety of the battery.In this paper, the main contents are divided into the following aspects:First of all, this article describes the structure and working principle of lithium battery, definition of state of charge, and several estimation methods of state of charge. Afterwards, this paper chooses Kalman Filter as the estimation method.After that, in order to improve the accuracy of the estimation, and simulate the characteristics and the working process of the battery. We propose a second order RC model, then identify the parameters using the HPPC experimental data, and verify the model`s validity through simulation. And we choose forgetting factor recursive least square(FFRLS) as an adaptive parameter on-line identification method and program the algorithm. After comparing the results with the off-line identification values, this method can effectively improve the accuracy of the model parameters.In the end, we apply the unscented kalman filter to the estimation of the state of charge(So C) of the battery, then we compare the estimation results with the experimental results to demonstrate the efficiency of the algorithm.