关键词: 锂离子电池, 能量状态, 等效电路模型, 扩展卡尔曼滤波, Sage-Husa自适应扩展卡尔曼滤波

Abstract:

It is necessary to accurately estimate the state of energy (SOE) of a battery to make full use of the energy stored in the battery and prevent over-discharge and over-charge. Generally, SOE is defined as the ratio of the remaining energy to the standard rated energy. The current SOE estimation algorithms have not fully considered the influence of temperature, operating conditions, etc., leading to low accuracy of the estimation results. This study proposes an adaptive extended Kalman filter (S-H EKF) based on Sage-Husa to accurately estimate the energy state of a battery. The algorithm's energy state estimation approach for lithium-ion batteries is compared with the traditional SOE estimation algorithm based on the EKF. First, the influence of temperature on the battery's energy characteristics was examined, and the standard energy of the battery at various temperatures was obtained. Then, a second-order RC equivalent circuit model considering the variation of parameter values with temperature and SOE value is established. Combined with an experiment of mixed pulse power characteristics, the least squares approach is employed to identify the model parameters, and the model accuracy is verified. With a good simulation of the battery's terminal voltage having high accuracy, the verification findings reveal that the model can be compared better. Finally, by employing S-H EKF and EKF to estimate the SOE under dynamic conditions and intermittent high-rate charging conditions, the comparison results demonstrate that: taking the mean absolute error as the comparison standard, the estimation accuracy of S-H EKF is 20.72% higher than that of EKF, and the maximum SOE estimation is the absolute error is less than 3%, which is more appropriate for energy estimation of lithium-ion batteries.

Key words: lithium ion battery, energy state, equivalent circuit model, extended Kalman filter, adaptive extended Kalman filter