Advanced SOH and SOC Prediction Models for Lithium-Ion Batteries: Integrating Machine Learning with Battery Management Systems Tianjin University, State Key Laboratory of Engines

Format:

Book

Conference/Event

Author/Creator:

Yuan, Chuo, author.

Contributor:

Bao, Zhiming

Jiao, Kui

Li, Weizhuo

Liu, Zezheng

Zhao, Xu

Conference Name:

SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

This paper focuses on the development and validation of predictive models for battery management systems, specifically targeting State of Health (SOH) and State of Charge (SOC) estimation, as well as the design of a comprehensive Battery Management System (BMS). The study begins by establishing and evaluating SOH prediction models, employing both linear regression and Long Short-Term Memory (LSTM) algorithms. Comparative analysis is conducted to assess the prediction accuracy between Recurrent Neural Networks (RNN) and LSTM, highlighting the superior performance of the LSTM algorithm in forecasting battery health. The second part of the paper addresses SOC estimation, outlining common methods and introducing an Extended Kalman Filter (EKF) algorithm for real-time SOC prediction. The EKF model is constructed through three primary stages: the establishment of the observed signal section, the ECU section, and the algorithmic structure itself. Rigorous validation confirms the EKF model's effectiveness in providing accurate SOC predictions. Lastly, the paper delves into the design and validation of a robust BMS model. Key components such as the high- and low-limit trigger signal module and SOC subscription module are integrated into the BMS framework. Validation results demonstrate that the proposed BMS model can efficiently monitor and manage battery performance, ensuring reliability and safety. The paper concludes with a discussion of future work aimed at enhancing the predictive capabilities and real-time applications of these models in battery management systems

Notes:

Vendor supplied data

Publisher Number:

2025-01-7015

Access Restriction:

Restricted for use by site license