Battery State of Charge Estimation using Adaptive Subspace Identification Method
Estimation of battery state of charge (SOC) is essential for many emerging battery powered applications such as smart phones, electric and hybrid electric vehicles. In this paper, we propose a new battery SOC estimation method using adaptive subspace identification method. The subspace identification method is a numerically robust approach and is used to build the dynamic linear model based on batterys terminal voltages and current. To deal with the non-linearity of the battery, the transient battery terminal voltages and current are partitioned into piecewise linear regions and subspace identification is performed on each linear region. As a result, the battery SOC can be accurately calculated for each region. Our experiments show that the new method has an error margin of 1.4％ from ideal SOC values as given by Dualfoil, a powerful battery simulator. This outperforms the least square estimation algorithm, which is found to have a higher error margin of 4.5％ for some load profiles, while not converging at all for some other load profiles.
Sahana Swarup Sheldon X. -D. Tan Zao Liu Hai Wang Zhigang Hao Guoyong Shi
Department of Electrical Engineering, University of California, Riverside Shanghai Jiao Tong University, Shanghai, 200240, China
国际会议
2011 IEEE 9th International Conference on ASIC(2011年第九届IEEE国际专用集成电路大会)
厦门
英文
99-102
2011-10-25（万方平台首次上网日期，不代表论文的发表时间）