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On-board Capacity Estimation of Lithium-ion Batteries Based on Charge Phase
Yapeng Zhou* and Miaohua Huang
Abstract Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, its found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.
Keyword Lithium-ion battery, On-board, Capacity estimation, RVM
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