Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction. (6th May 2019)

• Record Type: Journal Article
• Authors: He, Jiangtao; Feng, Daiwei; Hu, Chuan; Wei, Zhongbao; Yan, Fengjun
• Is Part Of: International journal of energy research. Volume 43:Number 8(2019)
• Page Start: 3837
• Page End: 3852
• Publication Date: 2019-05-06
• Subjects: adaptive extended Kalman filter -- current bias -- model uncertainties -- offset‐free equivalent circuit model -- state of charge; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.4557
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Because of the harsh working condition in electrified vehicles, the measured current and voltage signals typically contain non‐ignorable noises and bias, which potentially decline the accuracy of state‐of‐charge estimation. In this regard, the noise and bias corruption should be well addressed to maintain sufficient accuracy and robustness. This paper improves the existing methods in the literature from two aspects: (a) A novel offset‐free equivalent circuit model is developed to remove the current bias; and (b) based on the offset‐free equivalent circuit model, a two‐layer estimator is proposed to estimate the state of charge using real‐time identified model parameters. The robustness of the two‐layer estimator against model uncertainties and the aging effect is further evaluated. Simulation and experimental results show that the proposed two‐layer estimator can effectively attenuate the current bias and estimate the state of charge accurately with the error confined to ±4% under different levels of current bias and model uncertainties. Abstract : A novel two‐layer state of charge (SOC) estimator is proposed, which can accurately estimate the SOC online using the bias corrupted current data. In the layer I, the bias added current data is filtered by the offset‐free equivalent circuit model (OECM) based adaptive extended Kalman filter (AEKF). In the layer II, the SOC can be estimated online using the bias‐reduced current.