Authors: W Sung D S Hwang BJ Jeong J Lee T Kwon
Publish Date: 2016/04/30
Volume: 17, Issue: 3, Pages: 493-508
Abstract
This paper reports the development of a battery model and its parameter estimator that are readily applicable to automotive battery management systems BMSs Due to the parameter estimator the battery model can maintain reliability over the wider and longer use of the battery To this end the electrochemical model is used which can reflect the aginginduced physicochemical changes in the battery to the agingrelevant parameters within the model To update the effective kinetic and transport parameters using a computationally light BMS the parameter estimator is built based on a covariance matrix adaptation evolution strategy CMAES that can function without the need for complex Jacobian matrix calculations The existing CMAES implementation is modified primarily by regionbased memory management such that it satisfies the memory constraints of the BMS Among the several agingrelevant parameters only the liquidphase diffusivity of Liion is chosen to be estimated This also facilitates integrating the parameter estimator into the BMS because a smaller number of parameter estimates yields the fewer number of iterations thus the greater computational efficiency of the parameter estimator Consequently the BMSintegrated parameter estimator enables the voltage to be predicted and the capacity retention to be estimated within 1 error throughout the battery lifetime
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