Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model

TLDR

Accurate lithium‑ion battery state‑of‑health evaluation is crucial for operating and managing battery‑based energy storage systems, yet experimental determination is problematic because standard functioning is required; machine learning techniques enable accurate data‑driven predictions in such situations. The paper proposes an optimized explainable AI model to predict battery discharge capacity. The authors developed three deep‑learning models (stacked LSTM, GRU, SRNN) using six input features, applied Ex‑AI to identify relevant features and optimize parameters, and employed the jellyfish meta‑heuristic for further optimization. The jellyfish‑Ex‑AI model yielded superior discharge capacity predictions, achieving a very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 with the stacked‑LSTM, demonstrating the method’s utility.

Abstract

Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility.