Online model-based state-of-charge and state-of-health joint estimation approach for lithium-ion battery in electric vehicles

An accurate battery state-of-charge (SoC) and state-of-health (SoH) joint estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to put forward an advanced online model-based battery joint estimation approach for onboard battery management system application. First, through the recursive least square based system identification method and the adaptive extended Kalman filter algorithm based SoC estimation method, accurate SoC estimates can be obtained against different operating conditions. Second, the battery capacity can be calculated through the accurate SoC estimates and the accumulation of electricity, where the time scale separation technique is employed to avoid the capacity calculation fluctuation frequently from the variance SoC. Last, to ensure the reliable estimation for SoC and SoH, the convergence criterion is developed. The experiment and simulation results with the Lithium-ion polymer battery cell indicate that the proposed method has higher estimation accuracy.