An H-infinity Filter Based Approach For Lithium-ion Battery SOC And SOH Estimation

Lithium-ion batteries are widely used as an energy source for electric vehicles. A well-designed battery management system is necessary to keep batteries safe and guarantee a stable drive cycle for the vehicle. A real-time battery inner-state value estimation is key in battery management, and among those, the SOC and SOH estimation is the most complex part, which had attracted worldwide consideration. A battery’s state of charge(SOC) is the percentage of the electric power left in the battery, which is important to estimate the energy left for the electric vehicle; and the state of health(SOH) is a reflection of the battery life, making a reference when the vehicle is under repair.Extended Kalman filter algorithm is widely used for SOC and SOH estimation, due to its simplicity and efficiency. This method is based on the state space model of the battery, and guarantees an optimal estimation under white noise environment. However,extended Kalman filter is not designed for colored noise, and often becomes unstable when there’s biased current noise in the outer enviroment, leading to a serious deviation in the estimation results. In real world applications, it is often suggested to combine Kalman filter method with other algorithms, trying to reach the required system performance reluctantly.As a robust filter algorithm, H_∞ algorithm is an improvement for Kalman filter,which in theory, can keep estimation error stable in different noise environments.In this paper, at first we build the equivalent circuit model of the battery, and calculate the corresponding parameters by experiments. After that, we present a basic process for H_∞ filter algorithm, and verify its correctness by observing the algorithm output using simulated and actual current/voltage data. The performance is largely improved when compared with the traditional extended Kalman filter method, especially under biased current noises.In SOH estimation research, we present a new strategy for biased noise inhibition using H_∞ filter algorithm, afterwhich we designed a joint estimation algorithm using a combination of H_∞ filter and Kalman filter, tracking the real-time change of battery’s inner parameters, while maintaining an accurate estimation of battery’s SOC.Finally, we successfully immigrate the algorithm to an XMC-4500 microcontroller,and verify its correctness. Performance test shows that this algorithm is fast and accurate enough to be used in real world battery management systems.In short, there’s still not enugh academic research on the H_∞ filter’s application on SOC and SOH estimation, and this paper has discussed a complete H_∞ realization method in detail, and specifically designed a new algorithm structure for SOH estimation using an original noise inhibition method. We want to make this paper as an important reference for related researchers.