Lithium-ion batteries are ubiquitous in modern day applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade - a metric of battery health - on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45\%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasising the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and can be generalized to other critical components that require real-time estimation of SOH.
The complex nature of lithium-ion battery degradation has led to many machine learning based approaches to health forecasting being proposed in literature. However, machine learning can be computationally intensive. Linear approaches are faster but have previously been too inflexible for successful prognosis. For both techniques, the choice and quality of the inputs is a limiting factor of performance. Piecewise-linear models, combined with automated feature selection, offer a fast and flexible alternative without being as computationally intensive as machine learning. Here, a piecewise-linear…
State-of-Health Prediction for EV Lithium-Ion Batteries via DLinear and Robust Explainable Feature Selection
Department of Chemical Engineering（化学工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Chemical and Biomolecular Engineering（化学与生物分子工程系）；Yonsei University（延世大学）；Department of Battery Engineering（电池工程系）
Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation
It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize …
SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring
Robotics and Internet of Things Laboratory, College of Computer and Information Sciences（机器人与物联网实验室，计算机与信息科学学院）；RIADI Laboratory, National School of Computer Science, University of Manouba（RIADI实验室，国家计算机科学学院，曼努巴大学）；College of Electrical and Computer Engineering, King Abdulaziz University（电气与计算机工程学院，国王阿卜杜勒阿齐兹大学）
针对传统方法无法有效表征锂电池非线性与时序退化特征的问题，提出 SOH-KLSTM 框架，通过在 LSTM 中集成 KAN 候选单元状态，结合两者的长期依赖学习与非线性近似能力，实现了更准确的电池健康状态预测。
A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory…
MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
Dept.\ of Computer Science \& Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham, India ORCID: 0009-0006-0153-5674 , 0000-0001-6103-259X
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Technical University of Darmstadt（达姆施塔特工业大学）
Knowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics
Information and Communications Technology Cluster, Singapore Institute of Technology（信息与通信技术集群，新加坡理工学院）；Department of EEE, Chaitanya Bharathi Institute of Technology（电子工程系，查蒂兰·巴哈瓦蒂理工学院）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；School of Materials Science and Engineering, Nanyang Technological University（材料科学与工程学院，南洋理工大学）
Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network
Singapore Institute of Technology（新加坡科技学院）；Nanyang Technological University（南洋理工大学）；Agency for Science, Technology and Research（新加坡科技研究局）；National University of Singapore（新加坡国立大学）
Battery State of Health Estimation Using LLM Framework
Purdue University（普渡大学）；Drexel University（德雷塞尔大学）；Stanford University（斯坦福大学）；Indian Institute of Science（印度科学学院）；Temsa Research & Development Center（Temsa研发中心）
The Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）
The state of health (SOH) estimation plays an essential role in battery-powered applications to avoid unexpected breakdowns due to battery capacity fading. However, few studies have paid attention to the problem of uneven length of degrading cycles, simply employing manual operation or leaving to the automatic processing mechanism of advanced machine learning models, like long short-term memory (LSTM). As a result, this causes information loss and caps the full capability of the data-driven SOH estimation models. To address this challenge, this paper proposes an innovative cycle synchronizatio…
The Singapore University of Technology and Design（新加坡科技设计大学）
Accurately estimating a battery's state of health (SOH) helps prevent battery-powered applications from failing unexpectedly. With the superiority of reducing the data requirement of model training for new batteries, transfer learning (TL) emerges as a promising machine learning approach that applies knowledge learned from a source battery, which has a large amount of data. However, the determination of whether the source battery model is reasonable and which part of information can be transferred for SOH estimation are rarely discussed, despite these being critical components of a successful …
Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting
Singapore Institute of Technology（新加坡理工学院）；Singapore University of Social Sciences（新加坡社科大学）；Shanghai Jiao Tong University（上海交通大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高级智能与计算研究所）；Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局材料研究与工程研究所）
The State of Lithium-Ion Battery Health Prognostics in the CPS Era
San Jose State University（圣何塞州立大学）；Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）；Manipal Institute of Technology（马尼帕尔理工学院）；University of Denver（丹佛大学）
PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
Singapore Institute of Technology（新加坡理工学院）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Nanyang Technological University (NTU)（南洋理工大学）
Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；University of Alberta（阿尔伯塔大学）
As a significant ingredient regarding health status, data-driven state-of-health (SOH) estimation has become dominant for lithium-ion batteries (LiBs). To handle data discrepancy across batteries, current SOH estimation models engage in transfer learning (TL), which reserves apriori knowledge gained through reusing partial structures of the offline trained model. However, multiple degradation patterns of a complete life cycle of a battery make it challenging to pursue TL. The concept of the stage is introduced to describe the collection of continuous cycles that present a similar degradation p…
Hundreds of millions of people lack access to electricity. Decentralised solar-battery systems are key for addressing this whilst avoiding carbon emissions and air pollution, but are hindered by relatively high costs and rural locations that inhibit timely preventative maintenance. Accurate diagnosis of battery health and prediction of end of life from operational data improves user experience and reduces costs. But lack of controlled validation tests and variable data quality mean existing lab-based techniques fail to work. We apply a scaleable probabilistic machine learning approach to diagn…
Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）
By informing accurate performance (e.g., capacity), health state management plays a significant role in safeguarding battery and its powered system. While most current approaches are primarily based on data-driven methods, lacking in-depth analysis of battery performance degradation mechanism may discount their performances. To fill in the research gap about data-driven battery performance degradation analysis, an invariant learning based method is proposed to investigate whether the battery performance degradation follows a fixed behavior. First, to unfold the hidden dynamics of cycling batte…
The Singapore University of Technology and Design（新加坡科技设计大学）
To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics. The conventional SOH estimation approaches with digital twin are end-of-cycle estimation that require the completion of a full charge/discharge cycle to observe the maximum available capacity. However, under dynamic operating conditions with partially discharged data, it is impossible to sense accurate re…
A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life
School of Electronics and Information Engineering, Hangzhou Dianzi University（杭州电子科技大学电子与信息工程学院）；School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics（浙江财经大学信息科技与人工智能学院）；Engineering Training Center, China Jiliang University（中国浙江科技大学工程训练中心）
When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
Singapore Institute of Technology（新加坡科技学院）；Institute of Materials Research and Engineering（材料研究与工程研究所）；Agency for Science, Technology and Research（科技研究局）；Institute of High Performance Computing（高性能计算研究所）
Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling
Singapore Institute of Technology (SIT)（新加坡理工大学）；Singapore University of Social Sciences (SUSS)（新加坡社会科学大学）；Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR)（材料研究与工程研究所，新加坡科技研究局）
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction
AIWARE Limited Company（AIWARE有限公司）；AIT Austrian Institute of Technology GmbH（奥地利技术研究院有限公司）；FPT University（FPT大学）；Ton Duc Thang University（东德大学）；Van Lang University（范朗大学）；School of Technology, Van Lang University（范朗大学技术学院）
Early Cycle Charge Trajectory Generative Prediction and Full Life Cycle Health Management of Iron-Chromium Flow Batteries Based on FlowBD-E1
State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing)（中国石油大学（北京）重质油国家重点实验室）；Zhonghai Energy Storage Technology (Beijing) Co., Ltd.（中海储能科技（北京）有限公司）
Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling
Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR)（材料研究与工程研究院，新加坡科技研究局）；Institute for Infocomm Research (I²R), Agency for Science, Technology and Research (A*STAR)（资讯通信研究院，新加坡科技研究局）；Posh Robotics
