A joint state-of-health and state-of-energy estimation method for lithium-ion batteries through combining the forgetting factor recursive least squares and unscented Kalman filter

The state of energy (SOE) estimation of lithium-ion batteries is the basis of the mileage prediction of electric vehicles. However, its accurate estimation is affected by various factors such as the state of health (SOH) and temperature. In this study, a joint SOH-SOE estimation method combining the forgetting factor recursive least squares (FFRLS) and the unscented Kalman filter algorithm is proposed to improve the SOE estimation accuracy under complex and dynamic working conditions. The FFRLS algorithm is used to identify and update the battery model parameters and the open circuit voltage curves online, and the ordinary least squares algorithm is used to update battery capacity. The experiments show that the proposed algorithm can achieve satisfactory estimation accuracy under different battery aging degrees and temperatures. SOH and SOE estimation errors are within 4 % and 3 %, respectively. Even at a low temperature of -10 degrees C, the SOE estimation error is less than 4 %.

Automated summary

This study proposes a joint estimation method of state of health (SOH) and state of energy (SOE) using forgetting factor recursive least squares (FFRLS) and unscented Kalman filter algorithm. The method improves the accuracy of SOE estimation under complex and dynamic working conditions.