Developing Lifetime Prediction Model of Lithium-ion Battery Based on Minimizing Prediction Errors of Cycling and Operational Time Degradation Using Genetic Algorithm

Accurate prediction of the useful life of lithium-ion batteries is a great challenge for the researchers and engineers who are involved in battery applications such as electric vehicle and satellite. In this work, a semi-empirical model is introduced to predict the capacity loss of lithium-ion batteries as a function of charge and discharge cycles, operational time and temperature. The model parameters are obtained via minimizing prediction errors of the experimental capacity loss for each charge/discharge cycles at 25oC, 35oC, and 45oC. The optimum values of the model parameters are obtained using a genetic algorithm as one of the optimization tools of Matlab software. The model accurately predicts the capacity loss of lithium-ion battery for more charge and discharge cycles at 25 °C with an average error of 4%. The mentioned cycles are used only to validate the prediction.

Developing Lifetime Prediction Model of Lithium-ion Battery Based on Minimizing Prediction Errors of Cycling and Operational Time Degradation Using Genetic Algorithm Keywords:

Lithium-ion battery , capacity loss , charge and discharge , cycle-life , operational time