Lithium battery model parameter identification based on the GA-LM algorithm

ABSTRACT

The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy.

KEYWORDS:

• Lithium batteries
• second-order RC model
• parameter identification
• genetic algorithm (GA)
• Levenberg-Marquardt (LM) algorithm

Acknowledgements

This study is supported by the Chinese Academy of Engineering’s Promoting Energy Production and Consumption Revolution (2035, Phase III) Strategic Research Subtopic Six Energy Revolution Promoting the Rise of the Central Region Project (2018-ZD-06), China. J.Z., X.Q., B.J., and B.W. would like to thank the CALCE battery group that provided the battery test data (https://web.calce.umd.edu/batteries/data.htm).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Research data are not shared.