Electric vehicles (EVs) play an important role in reducing carbon emissions. As EV adoption accelerates, safety issues caused by EV batteries have become an important research topic. In order to benchmark and develop data-driven methods for this task, we introduce a large and comprehensive dataset of EV batteries. Our dataset includes charging records collected from hundreds of EVs from three manufacturers over several years. Our dataset is the first large-scale public dataset on real-world battery data, as existing data either include only several vehicles or is collected in the lab environment. Meanwhile, our dataset features two types of labels, corresponding to two key tasks - battery health estimation and battery capacity estimation. In addition to demonstrating how existing deep learning algorithms can be applied to this task, we further develop an algorithm that exploits the data structure of battery systems. Our algorithm achieves better results and shows that a customized method can improve model performances. We hope that this public dataset provides valuable resources for researchers, policymakers, and industry professionals to better understand the dynamics of EV battery aging and support the transition toward a sustainable transportation system.
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
Clemson University（克莱姆森大学）；University of California, Berkeley（加州大学伯克利分校）；Hunan University（湖南大学）；University of Michigan（密歇根大学）
Propulsion system electrification revolution has been undergoing in the automotive industry. The electrified propulsion system improves energy efficiency and reduces the dependence on fossil fuel. However, the batteries of electric vehicles experience degradation process during vehicle operation. Research considering both battery degradation and energy consumption in battery/ supercapacitor electric vehicles is still lacking. This study proposes a Q-learning-based strategy to minimize battery degradation and energy consumption. Besides Q-learning, two heuristic energy management methods are al…
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）
User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model
Delft Center for Systems and Control (DCSC), Department of Mechanical Engineering, Delft University of Technology (TU Delft)（代尔夫特系统与控制中心（DCSC）、机械工程系、代尔夫特理工大学）
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）
School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）
Recently, there has been an increasing interest in the roll-out of electric vehicles (EVs) in the global automotive market. Compared to conventional internal combustion engine vehicles (ICEVs), EVs can not only help users reduce monetary costs in their daily commuting, but also can effectively help mitigate the increasing level of traffic emissions produced in cities. Among many others, battery electric vehicles (BEVs) exclusively use chemical energy stored in their battery packs for propulsion. Hence, it becomes important to understand how much energy can be consumed by such vehicles in vario…
Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven mod…
Electric vehicles (EVs) have been growing rapidly in popularity in recent years and have become a future trend. It is an important aspect of user experience to know the Remaining Charging Time (RCT) of an EV with confidence. However, it is difficult to find an algorithm that accurately estimates the RCT for vehicles in the current EV market. The maximum RCT estimation error of the Tesla Model X can be as high as 60 minutes from a 10 % to 99 % state-of-charge (SOC) while charging at direct current (DC). A highly accurate RCT estimation algorithm for electric vehicles is in high demand and will …
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…
Heriot-Watt University（赫瑞-瓦特大学）；University of Maryland（马里兰大学）
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 pa…
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 …
Institute of Transportation Studies（交通研究学院）；University of California, Davis（加州大学戴维斯分校）
Convolutional Neural Networks (CNN) have been a good solution for understanding a vast image dataset. As the increased number of battery-equipped electric vehicles is flourishing globally, there has been much research on understanding which charge levels electric vehicle drivers would choose to charge their vehicles to get to their destination without any prevention. We implemented deep learning approaches to analyze the tabular datasets to understand their state of charge and which charge levels they would choose. In addition, we implemented the Image Generator for Tabular Dataset algorithm t…
M. S. Ramaiah Institute of Technology（M.S.拉迈亚理工学院）；Indian Institute of Science（印度科学研究所）
The Electric Vehicle (EV) Industry has seen extraordinary growth in the last few years. This is primarily due to an ever increasing awareness of the detrimental environmental effects of fossil fuel powered vehicles and availability of inexpensive Lithium-ion batteries (LIBs). In order to safely deploy these LIBs in Electric Vehicles, certain battery states need to be constantly monitored to ensure safe and healthy operation. The use of Machine Learning to estimate battery states such as State-of-Charge and State-of-Health have become an extremely active area of research. However, limited avail…
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）
There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov Decision Process mode…
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）
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…
Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, strengthening opposition to electric vehicles. In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs). One of the constraints in building models to suggest interventions for shifting preferences is that each interventi…
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 …
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。
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）
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…
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…
