Rui Xiong 1, Yongzhi Zhang 1, Hongwen He 1, Xuan Zhou 2 and Michael G. Pecht 3
1 Beijing Inst Technology, Beijing, Beijing China 100081
2 Kettering University, 3364 Flint, Michigan United States
3 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
In order for the battery management system in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This paper implements battery remaining available energy prediction and state of charge (SOC) estimation against testing temperature uncertainties as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2% even when the wrong temperature is indicated. In this case, the developed double-scale PF method is expected to be robust in practice.
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