Work

Machine learning-driven time series analysis for SOH prediction of lithium-ion batteries

Year: 2026

Type: article

Abstract: Energy storage batteries are essential for stabilizing renewable energy systems and ensuring power grid efficiency. However, challenges such as capacity degradation, inadequate data quality, and the need for real-time predictions highlight the importance of accurate State of Health (SOH) models. This study explores Random Forest(RF), Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) for SOH prediction across single lithium-ion battery, batterywith environmental factors and battery modules. Bi-LSTM demonstrated superior performance in evaluation indicators, achieving lower MAE, MSE and R² value closest to 1, with further accuracy gains through additional features like discharge time and median voltage. The results underscore Bi-LSTM’s effectiveness in capturing long-term dependencies and its potential to enhance battery health management, contributing to reliable and sustainable energy storage systems.

Source: Scientific Reports

DOI: 10.1038/s41598-025-33725-w

Authors: Yunlong Zhang, Xiaolei Bi, Shiqiang Wang, Bin Tao

Institutions: Sinopec (China), SINOPEC Research Institute of Safety Engineering Co., Ltd., Ministry of Emergency Management of the People's Republic of China

Language: English

Topic: Battery Modeling and Management

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,690