An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack

作者：Wang Junping^*; Guo Jingang; Ding Lei

来源：Energy Conversion and Management, 2009, 50(12): 3182-3186.

DOI：10.1016/j.enconman.2009.08.015

• Electric vehicle
• Battery management system (BMS)
• State of Charge (SOC)
• Adaptive extended Kalman filter (AEKF)

Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as "AEKFAh", is proposed. This method uses the adaptive Kalman flitering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.

• 出版日期2009-12
• 单位西安交通大学