his 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 aging-induced physicochemical changes in the battery to the
aging-relevant 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 (CMA-ES) that
can function without the need for complex Jacobian matrix calculations. The
existing CMA-ES implementation is modified primarily by region-based memory
management such that it satisfies the memory constraints of the BMS. Among the
several aging-relevant parameters, only the liquid-phase diffusivity of Li-ion 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 BMS-integrated parameter estimator enables the
voltage to be predicted and the capacity retention to be estimated within 1% error
throughout the battery life-time.
