Evolution of parameters in the Doyle-Fuller-Newman model of cycling lithium ion batteries by multi-objective optimization. (15th May 2022) - Lin, Wei-Jen; Chen, Kuo-Ching
Highlights: Parameter identification of the DFN electrochemical model of the NMC battery is carried out. The fitting targets of the objective functions are discharge voltage and its first derivative. The genetic algorithm and the deep neural network are employed and compared. Evolutions of the 13 parameters are obtained, showing the dominated parameters in cycle aging. Abstract: An electrochemical model having high-fidelity parameters enables a correct description of the performance of lithium ion batteries. Hence, developing an accurate and high-efficiency parameter identification method is crucial to precisely predicting the battery's state of health. In the past, most studies on estimation of such parameters took the discharge voltage as the only target to be fitted. Here, the first-order derivative of the discharge curve, i.e., the d Q /d V curve, is proposed as another fitting target since this new curve is directly related to battery aging. Four different objective functions, associated with the discharge curve and its derivative curve, are used to perform the multi-objective optimization, where the two algorithms, namely the genetic algorithm and the deep neural network are employed. We show that, by using the genetic algorithms, the mean absolute errors of the discharge curve for each cycle are lower than 0.07 (V), while the errors of the ICA curve are below 0.32 (Ah/V), both of which show a good convergence. The deep neural network also leads to excellent result. We present the evolutions of 13 identified parameters and demonstrate that the initial lithium ion concentration in the negative electrode dominates the cycle age of the tested batteries.