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, 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 the battery; instead, it is generated on the complete data profile. The robustness of the model is tested by comparing with techniques such as Support vector regressor, Kalman Filter, 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 literature with high generalization to noise and other perturbations. The model is independent of the section of charging curve used for prediction of battery capacity. Various experimentation has been conducted on the model and the results have been validated.

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.