Dream Optimized Explainable Bayesian Gated Recurrent Unit for Remaining Useful Life and State of Health Prediction in Electric Vehicles

Abstract

The Energy Storage System (ESS) is an important component of the Electric Vehicle (EV) system, wherein Lithium-ion (Li-ion) batteries are commonly deployed owing to high energy storage capacity and durability. But charging and discharging over time causes reduction in the efficiency of batteries. Further, degradation becomes very fast when batteries reach End of Life (EOL). This implies the need for efficient battery management systems in EVs. The Battery Management System (BMS) monitors various indicators of the battery including State of Charge (SOC), Remaining Useful Life (RUL), and State of Health (SOH). The monitoring of RUL and SOH is especially useful for forecasting the degradation of batteries and thus minimizing maintenance expenses. In this work, a deep learning framework-based approach for predicting the RUL and SOH of Li-ion batteries has been presented. In the initial stage, the acquired health data from the batteries is preprocessed using Min-Max normalization for consistency in data. After that, the prediction process is done using the developed Dream Optimized Explainable Bayesian Gated Recurrent Unit (DO EB_GRU). In this method, the Explainable Bayesian Gated Recurrent Unit (EB_GRU) is optimized using the Dream Optimization Algorithm (DOA). Experimental examination specified that DO EB_GRU accomplished an MSE of 0.0016, MAE of 0.015, R-Squared of 0.956.