A Study on Lithium-Ion Battery Health Estimation and Remaining Life Prediction Based on Real-World Operational Data
- 1School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
- 2School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- 3Department of Automation, Tsinghua University, Beijing 100084, China
Abstract
Lithium-ion batteries are the primary power source for new energy equipment, such as electrochemical energy storage systems and electric vehicles. Accurate state of health (SOH) estimation and remaining useful life (RUL) prediction are essential for ensuring safe operation and reducing lifecycle operation and maintenance costs. Since existing studies are largely based on ideal laboratory data, the proposed algorithms often exhibit limited generalizability and engineering implementation difficulties in real-world scenarios. To address this gap, this paper provides a comprehensive review of the latest research progress with field data as the core focus. First, it defines the scope, acquisition characteristics, and preprocessing strategies of in-service battery data and quantitatively evaluates the applicability of representative domestic and international open-source and industrial proprietary datasets. Second, it clarifies the quantitative standards, intrinsic relationships, and application-specific requirements for SOH and RUL. The performance, applicability, and limitations of model-based, data-driven, and hybrid approaches are comprehensively analyzed based on real-world datasets, with emphasis on their underlying principles, recent technical advances, and engineering potential. Finally, it summarizes challenges, including multi-factor coupled degradation, battery pack consistency assessment, and small-sample transfer learning, and it outlines future directions, including multimodal data fusion, digital twin modeling, and edge intelligence collaboration. Unlike previous reviews that primarily focus on laboratory data, this review establishes an engineering-oriented analytical framework centered on real-world operational data. The findings indicate that hybrid approaches present a highly promising qualitative trend for balancing prediction accuracy and model interpretability, offering valuable guidance for bridging the gap between laboratory research and practical industrial applications.
Related
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- Critical Evaluation of Electrochemical Impedance Spectroscopy for State-of-Health Estimation of Lithium-Ion Cells Against Battery Management System Baselines
- Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Artificial Neural Network-Supported Wiener Process with Random Effects
- Machine Learning-Based Electric Vehicle Remaining Range Prediction: Assessing the Relative Impact of Battery Status, Driving Context, and Environmental Conditions
- Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter
BibTeX
@article{wang2026study,
title = {{A Study on Lithium-Ion Battery Health Estimation and Remaining Life Prediction Based on Real-World Operational Data}},
author = {Jiwei Wang and Wenpeng Si and Masrafe Alam Munna and Zhongwei Deng and Linxuan Zhang},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100387},
url = {https://www.mdpi.com/2313-0105/12/10/387},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Study on Lithium-Ion Battery Health Estimation and Remaining Life Prediction Based on Real-World Operational Data
AU - Jiwei Wang
AU - Wenpeng Si
AU - Masrafe Alam Munna
AU - Zhongwei Deng
AU - Linxuan Zhang
PY - 2026
DA - 2026/10/01
JO - Batteries
DO - 10.3390/batteries12100387
UR - https://www.mdpi.com/2313-0105/12/10/387
AB - Lithium-ion batteries are the primary power source for new energy equipment, such as electrochemical energy storage systems and electric vehicles. Accurate state of health (SOH) estimation and remaining useful life (RUL) prediction are essential for ensuring safe operation and reducing lifecycle operation and maintenance costs. Since existing studies are largely based on ideal laboratory data, the proposed algorithms often exhibit limited generalizability and engineering implementation difficulties in real-world scenarios. To address this gap, this paper provides a comprehensive review of the latest research progress with field data as the core focus. First, it defines the scope, acquisition characteristics, and preprocessing strategies of in-service battery data and quantitatively evaluates the applicability of representative domestic and international open-source and industrial proprietary datasets. Second, it clarifies the quantitative standards, intrinsic relationships, and application-specific requirements for SOH and RUL. The performance, applicability, and limitations of model-based, data-driven, and hybrid approaches are comprehensively analyzed based on real-world datasets, with emphasis on their underlying principles, recent technical advances, and engineering potential. Finally, it summarizes challenges, including multi-factor coupled degradation, battery pack consistency assessment, and small-sample transfer learning, and it outlines future directions, including multimodal data fusion, digital twin modeling, and edge intelligence collaboration. Unlike previous reviews that primarily focus on laboratory data, this review establishes an engineering-oriented analytical framework centered on real-world operational data. The findings indicate that hybrid approaches present a highly promising qualitative trend for balancing prediction accuracy and model interpretability, offering valuable guidance for bridging the gap between laboratory research and practical industrial applications.
SN - 2313-0105
ER -