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SOC/SOH estimation method for AGM VRLA battery by combining ARX model for online parameters estimation and DEKF considering hysteresis and diffusion effects

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of battery, since SOC/SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. In addition, Dual Extended Kalman Filter (DEKF) can provide an elegant and powerful solution in SOC/ SOH estimation based on the battery impedance model. However, battery parameters strongly depend on the operating condition such as SOC, current rate, temperature and the battery life cycle. In this research, a novel method for SOC/SOH estimation which combines DEKF algorithm considering hysteresis and diffusion effect and Auto Regressive with external input (ARX) method used for online parameters estimation is proposed. The DEKF provides the precise information of the battery open circuit voltage to the ARX model and the ARX model keeps monitoring the parameter variation and supply it to the DEKF. The validation of the demonstrated algorithm is proved by the experiments.