JOINT FIRST ORDER SOC AND SOH ESTIMATION FRAMEWORK FOR LI-ION BATTERY BASED ON ECM-IGPR
State of charge and state of health estimation of Li-ion batteries are important elements of battery management systems,but existing studies usually ignore the association between them and estimating them separately.Therefore,this paper proposes a joint estimation framework of SOC-SOH for Li-ion battery based on Equivalent Circuit Model-Improved Gaussian Process Regression,which updates the SOC estimation with the single-cycle prediction of SOH.The contents are as follow:At first,HFs in the Incremental Capacity curve were extracted and Principal Component Analysis was performed to realize the optimization of HFs.And then the IGPR model for battery aging was developed for SOH prediction.On this basis,the state space model of Li-ion battery was established based on the parameter identification results and capacity estimates,which was combined with the Particle Filter algorithm to update the SOC estimation of the latter cycle.Thus,the joint estimation of SOH and SOC is achieved.Lastly,the eight cells in the Oxford dataset were used to verify the accuracy and adaptability of the framework.Eight batteries from the Oxford dataset are used to validate the accuracy and adaptability of the framework,which achieves good results.
Li-ion batteryincremental capacityjoint state estimateparticle filterGaussian process regression
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