This paper throws light on the State-Of-Charge (SOC) and the detection technology of vehicle battery based on the Kalman filter algorithm. To fill the gaps of the Ampere-hour integration estimation algorithm and the extended Kalman filter estimation algorithm based on Thevenin model, a dual Kalman filter algorithm is proposed based on the two algorithms to estimate the battery SOC. At last, the battery is tested on a special platform under constant-current and custom discharge conditions. A comparison runs for the estimated SOCs from these three algorithms against the value actually measured on vehicle battery. In this way, we bear out that the dual Kalman filter algorithm has a faster convergence speed and higher estimation accuracy than the ampere-hour integration and the extended Kalman filter algorithms alone.
