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Applied Sciences
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Lithium-Ion Battery Health and Safety Estimation
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Lithium-Ion Battery Health and Safety Estimation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 2473

Special Issue Editors

Prof. Dr. Andrew F. Burke

E-Mail Website
Guest Editor
Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA
Interests: electric and hybrid vehicle design analysis and testing; applications of batteries and ultracapacitors for electric vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Jingyuan Zhao

E-Mail Website
Guest Editor
Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA
Interests: electric vehicles; cyber-BMS; battery diagnosis; machine learning; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The environmental issues and energy crises we face have resulted in a wide range of social and economic problems. To address these issues, we must prioritize the use of renewable and clean energy sources. The transportation sector, which heavily relies on fossil fuels, is a significant contributor to greenhouse gas emissions and toxic pollution. As a result, the electrification of transportation has emerged as a promising solution with which to reduce carbon emissions, pollution, and the dependence on limited non-renewable resources. The mass adoption of battery-powered electric vehicles (EVs) requires car buyers to have high confidence in a battery's performance, reliability, and safety. Although progress has been made in developing technologies for battery diagnosis, challenges remain in accurately predicting a battery's state of charge (SOC), state of health (SOH), cycle life, calendar life, remaining useful life (RUL), state of safety (SOS), and fault/failure. Given the ubiquitous application of lithium-ion batteries, the safety, health, and reliability of these batteries are more important than ever. Therefore, there is a pressing need to not only investigate physical mechanisms but also develop new techniques with which to model and predict the dynamics of multiphysics and multiscale battery systems. We believe that this Special Issue will provide valuable contributions to battery diagnoses in the automotive industry and generate maximum practical value. By prioritizing the development of safe and reliable lithium-ion batteries, we can ensure a cleaner and more sustainable future.

Prof. Dr. Andrew F. Burke
Dr. Jingyuan Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • state of charge
  • state of health
  • remaining useful lifetime
  • cycle life
  • calendar life
  • capacity estimation
  • state of safety
  • fault detection
  • failure prediction
  • thermal runaway
  • abuse conditions
  • electrochemical model
  • physical model
  • data-driven
  • machine learning
  • deep learning
  • electric vehicles

Published Papers (3 papers)

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Research

18 pages, 2731 KiB  
Article
State of Health Estimation and Remaining Useful Life Prediction of Lithium-Ion Batteries by Charging Feature Extraction and Ridge Regression
by Minghu Wu, Chengpeng Yue, Fan Zhang, Rui Sun, Jing Tang, Sheng Hu, Nan Zhao and Juan Wang
Appl. Sci. 2024, 14(8), 3153; https://doi.org/10.3390/app14083153 - 09 Apr 2024
Viewed by 351
Abstract
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This [...] Read more.
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Health and Safety Estimation)
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19 pages, 2864 KiB  
Article
Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles
by Saadin Oyucu, Ferdi Doğan, Ahmet Aksöz and Emre Biçer
Appl. Sci. 2024, 14(6), 2306; https://doi.org/10.3390/app14062306 - 09 Mar 2024
Cited by 2 | Viewed by 723
Abstract
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the [...] Read more.
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Health and Safety Estimation)
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22 pages, 4651 KiB  
Article
Data-Driven Semi-Empirical Model Approximation Method for Capacity Degradation of Retired Lithium-Ion Battery Considering SOC Range
by Wanwan Xu, Huiying Cao, Xingyu Lin, Fuchun Shu, Jialu Du, Junzhou Wang and Junjie Tang
Appl. Sci. 2023, 13(21), 11943; https://doi.org/10.3390/app132111943 - 31 Oct 2023
Cited by 1 | Viewed by 914
Abstract
The rapid development of the electric vehicle industry produces large amounts of retired power lithium-ion batteries, thus resulting in the echelon utilization technology of such retired batteries becoming a research hotspot in the field of renewable energy. The relationship between the cycle times [...] Read more.
The rapid development of the electric vehicle industry produces large amounts of retired power lithium-ion batteries, thus resulting in the echelon utilization technology of such retired batteries becoming a research hotspot in the field of renewable energy. The relationship between the cycle times and capacity decline of retired batteries performs as a fundamental guideline to determine the echelon utilization. The cycle conditions can influence the characteristics of the degradation of battery capacity; especially neglection of the SOC ranges of batteries leads to a large error in estimating the capacity degradation. Practically, the limited cycle test data of the SOC ranges of the retired battery cannot support a model to comprehensively describe the characteristics of the capacity decline. In this background, based on the limited cycle test data of SOC ranges, this paper studies and establishes a capacity degradation model of retired batteries that considers the factors affecting the battery cycle more comprehensively. In detail, based on the data-driven method and combined with the empirical model of retired battery capacity degradation, three semi-empirical modeling methods of retired battery capacity degradation based on limited test data of SOC ranges are proposed. The feasibility and accuracy of these methods are verified through the experimental data of retired battery cycling, and the conclusions are drawn to illustrate their respective scenarios of applicability. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Health and Safety Estimation)
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