A Genetic Algorithm and RNN-LSTM Model for Remaining Battery Capacity Prediction

Li-ion batteries have diversified applications in everyday life. The temperature change, overcharging and over-discharging, is playing critical roles in affecting battery life in a significant manner. In this paper, the deep learning-based method is applied for the prognostics of a single Li-ion battery. The proposed design uses a recurrent neural network variant, long–short-term memory. The model’s parameters are optimized through a genetic algorithm-based parameter selector. The method applies to a sequence of data values comprising of the voltage, the charge capacity, the current, and the temperature. The estimation of battery capacity is not only based on the current or defined state of battery, instead, it is generated on the complete data profile. The robustness of the model is tested by comparing it with techniques such as support vector regressor, Kalman filter, and neural networks on normal and noisy test sets. The paper also proposes a feature selection and engineering scheme for battery capacity prediction. The proposed model outperforms the techniques available in the literature with high generalization to noise and other perturbations. The model is independent of the section of the charging curve used for the prediction of battery capacity. Various experimentation has been conducted on the model and the results have been validated.