An Improved Recursive Total Least Squares Estimation of Capacity for Electric Vehicle Lithium-Iron Phosphate Batteries

• Main Authors: Shaohua Wang, Yue Yang, Konghui Guo
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2020/9359076

A battery’s capacity is an important indicator of its state of health and determines the maximum cruising range of electric vehicles. It is also a crucial piece of information for helping improve state of charge (SOC) estimation, health prognosis, and other related tasks in the battery management system (BMS). In this paper, we propose an improved recursive total least squares approach to online capacity estimation, which is based on the constrained Rayleigh quotient in terms of battery capacity. This approach accounts for errors in both the SOC and accumulated current measurements not traditionally considered in the battery capacity model to give an unbiased estimation. Moreover, the forgetting factor, updated by minimizing the Rayleigh quotient of the capacity estimation model, is applied to track the changes in the model and get a more precise estimation of the capacity. Finally, the performance of the proposed algorithm is validated via simulation and experimental studies on lithium-iron phosphate batteries. The estimation results show that the proposed algorithm improves capacity estimation accuracy.