Battery Health Monitoring using Guided Wave Signal Features

Abstract

Abstract The high-speed development of the new energy industry and the renewable energy facilities have made battery energy storage critical for Electric Vehicles (EVs) and large-scale Energy Storage Systems (ESSs). However, the complexity of the internal chemical reactions and the uncertainty of the external service environment seriously affect the reliability of batteries. Therefore, Battery Management Systems (BMS) have become essential to monitor battery health and performance, detect potential issues at an early stage, improve safety, and reduce costs associated with battery maintenance and replacement. This paper proposes a Battery Health Monitoring (BHM) system based on guided wave signal features to monitor and predict State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL) of batteries. A data collection system is developed to realize the integration of wave generation and reception, with subsequent feature extraction of the recorded ultrasound signals and characterization in different forms, enabling the prediction of battery health status and early alert of rapid battery capacity deterioration. The proposed method is based on time-frequency analysis of ultrasonic responses and weighted averaging algorithms for signal feature extraction, which facilitates the acquisition of indicative trends corresponding to battery SOC, SOH, and RUL variations. Experimental results indicate that the system can achieve real-time monitoring of battery SOC cycles, obtain battery SOH trends and health warnings of severe battery aging, and accurately predict the decay rate of the standard battery capacity to achieve RUL estimation, thus demonstrating the excellent evaluating capability of the active sensing method in battery health monitoring.