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| Remaining Useful Life Prediction of Battery Using Metabolic Grey Particle Filter |
| Wei Haiyan1, Chen Jing1, Wang Huimin1, An Jingjing1, Chen Lin1,2 |
1. School of Mechanical Engineering Guangxi University Nanning 530004 China;
2. Guangxi Key Laboratory of Electrochemical Energy Materials Collaborative Innovation Center of Renewable Energy Materials Guangxi University Nanning 530004 China |
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Abstract Remaining useful life (RUL) prediction is one of the core technologies of battery management systems. In order to accurately predict the battery RUL with less data volume, a metabolic grey model particle filter (MGM-PF) algorithm is proposed. Firstly, the first-order RC model is used to estimate the battery capacity online. Then, based on the obtained capacity data, the grey development coefficient is dynamically updated by the metabolic grey model, and the coefficient is used as the model parameter to construct a dynamic state space model that characterizes the battery capacity degradation. Finally, the particle filter is used to track the battery capacity degradation to realize the battery RUL prediction and derive the uncertainty expression of the prediction results. The experimental results show that the proposed MGM-PF algorithm based on online capacity estimation can accurately predict the battery RUL.
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Received: 18 January 2019
Published: 27 March 2020
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2020, Vol. 35 
