Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles

Jinhao Meng, Mattia Ricco, Anirudh Budnar Acharya, Guangzhao Luo, Maciej Swierczynski, Daniel Ioan Stroe, Remus Teodorescu

科研成果: 期刊稿件文章同行评审

57 引用 (Scopus)

摘要

This paper proposes a low-complexity online state of charge estimation method for LiFePO4 battery in electrical vehicles. The proposed method is able to achieve accurate state of charge with less computational efforts in comparison with the nonlinear Kalman filters, and also can provide state of health information for battery management system. According to the error analysis of equivalent circuit model with two resistance and capacitance, two proportional-integral filters are designed to compensate the errors from inaccurate state of charge and current measurements, respectively. An error dividing process is proposed to tune the contribution of each filter to the finial estimation results, which enhances the validation and accuracy of the proposed method. Recursive least squares filter can provide the state of health information and updates the parameters of battery model online to eliminate the errors caused by parameters uncertainty. The proposed method is compared with extend Kalman filter in regards to accuracy and execution time. The execution time of the proposed method is measured on Zynq board platform to validate its suitability for online implementation. In this paper, the proposed method is able to obtain less than 1% error for state of charge estimation.

源语言英语
页(从-至)280-288
页数9
期刊Journal of Power Sources
395
DOI
出版状态已出版 - 15 8月 2018

指纹

探究 'Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles' 的科研主题。它们共同构成独一无二的指纹。

引用此