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Failure Prediction Modeling of Lithium Ion Battery toward Distributed Parameter Estimation
Liang Huang,Chang Yao*
Author NameAffiliationE-mail
Liang Huang School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China  
Chang Yao* Information Center, National Natural Science Foundation of China, Beijing 100085, China cyao@bjtu.edu.cn 
Abstract:
Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.
Key words:  Lithium ion battery  Failure prediction  Battery model  Distributed parameter
FundProject:This work was supported by the Fundamental Research Funds for the Central Universities (No.2017JBM003), the National Natural Science Foundation of China (No.61575053, No.61504008), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20130009120042).
基于分布参数估计的锂离子电池故障预测建模
黄亮,姚畅*
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DOI:10.1063/1674-0068/30/cjcp1705088
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