Work

Multi-Step Ahead Battery SOC Estimation Using Data-Driven Prognostics and Health Management

Year: 2024

Type: conference-paper

Abstract: This paper proposes a data-driven multi-step ahead battery state of charge (SOC) forecasting system that can be used for prognostics and health management (PHM) of a battery management system (BMS).Two long short-term memory (LSTM) recurrent neural networks (RNN) are implemented and tested, due to their unique capability to, at the same time, use a high number of past time steps and forecast a horizon of interest at real-time.This online inference is then capable to provide an advisory window in case the BMS needs to take any preventive action.The LSTM models, a stacked-LSTM and a Bidi-LSTM, are compared to a statistical-based algorithm which uses a combination of autoregressive integrated moving average (ARIMA) with a polynomial regressor that fits measured variables into the battery SOC.The three methods are tested and validated against a wealthy battery dataset and results demonstrate the feasibility of using the Bidi-LSTM RNN as multi-step ahead SOC forecast estimator.

Source: International Conference on Software and Information Engineering (ICSIE)

DOI: 10.1145/3708635.3708642

Authors: Juliano Pimentel, Alistair A. McEwan, Hong Qing Yu

Institution: University of Derby

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Funder: University of Derby