System Identification for Battery State Prediction and Lifespan Estimation

Chenyi Li, Long Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

In this paper, a nonlinear system identification method, wavelet-network-based
Nonlinear Auto-Regressive Exogenous (NLARX) approach, is employed for battery state
estimation and lifespan estimation. More specifically, three key battery parameters and health metrics, including temperature, voltage and State of Charge (SOC), are estimated and these parameters are essential for condition or state monitoring. Further, State of Health (SOH), crucial for forecasting the battery remaining useful life, is also predicted. Two open datasets are used to train and validated the performance of the proposed method. For temperature and voltage forecasting, the NLARX model outperforms the existing Thermal Single Particle Model with electrolyte (TSPMe) for prediction horizons under 600 seconds. In SOC estimations, the NLARX method produces consistent 15-second ahead prediction results even only using a small percentage of training data, while the SOH estimation, the proposed metho provides precise SOH variation prediction for 400 post cycles with less than 10% of the batterys life for training. Extensive results demonstrates that the NLARX model’s promise for the precise prediction of key battery parameters and health metrics and it can be used as a useful tool for battery fault detection and remaining useful life prediction.
Original languageEnglish
Title of host publication12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes
Publication statusPublished - Apr 2024

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