A novel data-driven method for mining battery open-circuit voltage characterization  

作　　者：Cheng Chen Rui Xiong Ruixin Yang Hailong Li 

机构地区：[1]Advanced Energy Storage and Application(AESA)Group,School of Mechanical Engineering,Beijing Institute of Technology,Beijing,100081,China [2]School of Business,Society&Engineering,M€alardalen University,V€asterås,SE-72123,Sweden

出　　处：《Green Energy and Intelligent Transportation》2022年第1期133-140,共8页新能源与智能载运（英文）

摘　　要：Lithium-ion batteries(LiB)are widely used in electric vehicles(EVs)and battery energy storage systems,and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage(OCV)and State-of-Charge(SOC)is the basis for their safe and efficient applications.To avoid the time-consuming lab test needed for obtaining OCV-SOC curves,this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour(Ah).To guarantee high reliability,a series of constraints have been implemented.To verify the effectiveness of this method,the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health(SOH),which are compared with data from both lab tests and EV manufacturers.Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH,for which the maximum deviations are less than 3.0%and 2.9%respectively.

关 键 词：Li-ion battery OCV-SOC STATE-OF-CHARGE STATE-OF-HEALTH Operation data 

分 类 号：TM912[电气工程—电力电子与电力传动]