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Lithium-Ion Battery Diagnosis: Health and Safety
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Lithium-Ion Battery Diagnosis: Health and Safety

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 21428

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
Dr. Jinrui Nan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China
Interests: automotive bus; car electronics; new energy vehicle control technology; energy management technology

Special Issue Information

Dear Colleagues,

Environmental issues and energy crises have spawned a host of social and economic issues, which has led to attempting to use renewable clean energy. The reliance of the transportation sector on fossil fuels has made it one of the largest emitters of greenhouse gases and toxic pollution. Therefore, the electrification of transportation is seen as a promising way to reduce emissions of carbon and pollution, and to lower the dependence on limited, non-renewable natural resources. The mass marketing of battery-powered electric vehicles (EVs) requires that car buyers have high confidence in the performance, reliability, and safety of the battery in their vehicles. However, although steady progress has been made in developing technologies for battery diagnosis, there are still many challenges to be overcome to accurately predict battery state of health (SOH), cycle life, remaining useful life (RUL), and fault/failure, as well as abuse conditions in field applications. The safety, health, and reliability of lithium-ion batteries are more important now than ever because of their ubiquitous application scenarios. In this case, there is a pressing need to not only investigate physical mechanisms, but also to develop new techniques to model and predict the dynamics of multiphysics and multiscale battery systems. Data-driven approaches offer new opportunities in a more intelligent manner, which would accelerate the technology transfer from academic progress to engineering applications. We hope this Special Issue will be a useful contribution to the field of battery diagnosis in the automotive industry, and will generate maximum practical value.

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

Manuscript Submission Information

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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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • battery diagnosis and prognosis
  • battery safety
  • fault detection
  • thermal runaway
  • abuse conditions
  • state of health
  • cycle life
  • remaining useful lifetime
  • state of charge
  • data-driven
  • artificial intelligence
  • machine learning
  • deep learning
  • electric vehicles
  • battery management system
  • cloud computing and storage
  • edge computing
  • digital twin
  • cyber-physics
  • field application

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Published Papers (10 papers)

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Research

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18 pages, 2957 KiB  
Article
Improving State-of-Health Estimation for Lithium-Ion Batteries Based on a Generative Adversarial Network and Partial Discharge Profiles
by Hangyu Zhang and Yi-Horng Lai
World Electr. Veh. J. 2025, 16(5), 277; https://doi.org/10.3390/wevj16050277 - 16 May 2025
Viewed by 68
Abstract
The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge [...] Read more.
The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge data for SOH estimation to address the unstable output of traditional estimation models when using partial discharge data under low-voltage conditions. This study first used the DoppelGANger network to generate artificially synthesized data. After the data augmentation process, we trained the temporal convolutional network to construct a data-driven SOH model. Finally, the performance of the SOH model output was evaluated using three indicators: RMSE, MAPE, and delta. The proposed method improved five kinds of low-voltage operating conditions in seven testing scenarios compared with traditional SOH estimation models. The experimental results provide a practical solution for data-driven SOH estimation. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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14 pages, 5585 KiB  
Article
Experimental Study on Distributed Measurement of Internal Pressure in Lithium-Ion Batteries Using Thin-Film Sensors
by Qingyun Liu, Xiuwu Wang, Jiangong Zhu, Guiwen Jiang, Xuezhe Wei and Haifeng Dai
World Electr. Veh. J. 2025, 16(5), 270; https://doi.org/10.3390/wevj16050270 - 14 May 2025
Viewed by 209
Abstract
With the rapid development of electric vehicles, the safety and reliability of lithium-ion batteries (LIBs), as their core energy storage units, have become increasingly prominent. The variation in internal battery pressure is closely related to critical issues such as thermal runaway, mechanical deformation, [...] Read more.
With the rapid development of electric vehicles, the safety and reliability of lithium-ion batteries (LIBs), as their core energy storage units, have become increasingly prominent. The variation in internal battery pressure is closely related to critical issues such as thermal runaway, mechanical deformation, and lifespan degradation. The non-uniform distribution of internal pressure may trigger localized hot spots or even thermal runaway, posing significant threats to vehicle safety. However, traditional external monitoring methods struggle to accurately reflect internal pressure data, and single-point external pressure measurements fail to capture the true internal state of the battery, particularly within battery modules. This limitation hinders efficient battery management. Addressing the application needs of electric vehicle power batteries, this study integrates thin-film pressure sensors into LIBs through the integrated functional electrode (IFE), enabling distributed in situ monitoring of internal pressure during long-term cycling. Compared to non-implanted benchmark batteries, this design does not compromise electrochemical performance. By analyzing the pressure distribution and evolution data during long-term cycling, the study reveals the dynamic patterns of internal pressure changes in LIBs, offering new solutions for safety warnings and performance optimization of electric vehicle power batteries. This research provides an innovative approach for the internal state monitoring of power batteries, significantly enhancing the safety and reliability of electric vehicle battery systems. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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17 pages, 2722 KiB  
Article
Recognition of State of Health Based on Discharge Curve of Battery by Signal Temporal Logic
by Jing Ning, Bing Xiao and Wenhui Zhong
World Electr. Veh. J. 2025, 16(3), 127; https://doi.org/10.3390/wevj16030127 - 24 Feb 2025
Viewed by 417
Abstract
In order to study an algorithm that recognizes the state of health (SOH) of a battery rapidly and can be easily integrated into the micro-controller unit (MCU), it is proposed that signal temporal logic (STL) language is employed to describe the discharge curves, [...] Read more.
In order to study an algorithm that recognizes the state of health (SOH) of a battery rapidly and can be easily integrated into the micro-controller unit (MCU), it is proposed that signal temporal logic (STL) language is employed to describe the discharge curves, because the STL language is a formal language with strict mathematical definitions and the syntax is composed of simple logic, “and”, “or”, and “not”, under the constraints of time and parameter variation ranges, which is realizable and interpretable. Firstly, the drop voltage amplitude, drop time, voltage rebound amplitude, voltage rebound time, starting voltage, and ending voltage of the discharge curve are selected as the features of the STL formula, so the first-level and second-level primitive formulas are constructed to express the voltage of a battery in good health and poor health clearly. Secondly, the impurity measures of the information gain, misclassification gain, Gini gain, and robust extended gain are presented as the objective functions. Thirdly, the interpreter embedded in the MCU can interpret and execute each STL sentence. The voltage of a battery in good health rises slowly and falls slowly, while the voltage of a battery in poor health rises quickly and falls quickly. When the STL describes the discharge curve as “slow down slow up”, the battery is in good health. When the STL describes the discharge curve as “fast down, fast up”, the battery is in poor health. Among the different objective functions, the highest mean accuracy of the STL reaches 87.5%. In terms of the mean runtime, the extended misclassification gain and the extended Gini gain of the first-level primitives are 00851s and 0.0993, respectively. Under the same mean accuracy of 87%, the information gain and Gini gain of the second-level primitives are 0.2593 s and 0.2341 s. Compared with the existing machine learning algorithms, in terms of the mean runtime, the STL algorithm is superior to the CNN-BiLSTM-MHA model, RNN-LSTM-GRU model, and EC-MKRVM model. In terms of the mean accuracy, compared with the highest correct rate of the CNN-BiLSTM-MHA model, that is, 91.7%, the difference is 4%. As a means of quickly detecting whether the battery is in a healthy state, the accuracy difference is negligible, so the STL algorithm is apparently superior in terms of performance and realizability. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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18 pages, 3827 KiB  
Article
Adaptive Joint Sigma-Point Kalman Filtering for Lithium-Ion Battery Parameters and State-of-Charge Estimation
by Houda Bouchareb, Khadija Saqli, Nacer Kouider M’sirdi and Mohammed Oudghiri Bentaie
World Electr. Veh. J. 2024, 15(11), 532; https://doi.org/10.3390/wevj15110532 - 18 Nov 2024
Viewed by 1155
Abstract
Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different [...] Read more.
Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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20 pages, 6513 KiB  
Article
Experimental Investigation on Affecting Air Flow against the Maximum Temperature Difference of a Lithium-Ion Battery with Heat Pipe Cooling
by Chokchai Anamtawach, Soontorn Odngam and Chaiyut Sumpavakup
World Electr. Veh. J. 2023, 14(11), 306; https://doi.org/10.3390/wevj14110306 - 7 Nov 2023
Cited by 1 | Viewed by 2344
Abstract
Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high [...] Read more.
Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high heat transfer performance but fall short in maintaining uniform temperature distribution. This study presented forced air cooling by an axial fan as a method of improving the cooling performance of flat heat pipes coupled with aluminum fins (FHPAFs) and investigated the impact of air velocity on the battery pack’s maximum temperature differential (ΔTmax). All experiments were conducted on lithium nickel manganese cobalt oxide (NMC) pouch battery cells with a 20 Ah capacity in seven series connections at room temperature, under forced and natural convection, at various air velocity values (12.7 m/s, 9.5 m/s, and 6.3 m/s), and with 1C, 2C, 3C, and 4C discharge rates. The results indicated that at the same air velocity, increasing the discharge rate increases the ΔTmax significantly. Forced convection has a higher ΔTmax than natural convection. The ΔTmax was reduced when the air velocity was increased during forced convection. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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18 pages, 8565 KiB  
Article
Proposing a Hybrid Thermal Management System Based on Phase Change Material/Metal Foam for Lithium-Ion Batteries
by Soheil Saeedipour, Ayat Gharehghani, Jabraeil Ahbabi Saray, Amin Mahmoudzadeh Andwari and Maciej Mikulski
World Electr. Veh. J. 2023, 14(9), 240; https://doi.org/10.3390/wevj14090240 - 1 Sep 2023
Cited by 10 | Viewed by 2777
Abstract
The charging and discharging process of batteries generates a significant amount of heat, which can adversely affect their lifespan and safety. This study aims to enhance the performance of a lithium-ion battery (LIB) pack with a high discharge rate (5C) by proposing a [...] Read more.
The charging and discharging process of batteries generates a significant amount of heat, which can adversely affect their lifespan and safety. This study aims to enhance the performance of a lithium-ion battery (LIB) pack with a high discharge rate (5C) by proposing a combined battery thermal management system (BTMS) consisting of improved phase change materials (paraffin/aluminum composite) and forced-air convection. Battery thermal performance is simulated using computational fluid dynamics (CFD) to study the effects of heat transfer and flow parameters. To evaluate the impact of essential parameters on the thermal performance of the battery module, temperature uniformity and maximum temperature in the cells are evaluated. For the proposed cooling system, an ambient temperature of 24.5 °C and the application of a 3 mm thick paraffin/aluminum composite showed the best cooling effect. In addition, a 2 m/s inlet velocity with 25 mm cell spacing provided the best cooling performance, thus reducing the maximum temperature. The paraffin can effectively manage thermal parameters maintaining battery temperature stability and uniformity. Simulation results demonstrated that the proposed cooling system combined with forced-air convection, paraffin, and metal foam effectively reduced the maximum temperature and temperature difference in the battery by 308 K and 2.0 K, respectively. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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15 pages, 4526 KiB  
Article
Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine
by Baozhong Zhang and Guoqiang Ren
World Electr. Veh. J. 2023, 14(8), 202; https://doi.org/10.3390/wevj14080202 - 29 Jul 2023
Cited by 8 | Viewed by 2252
Abstract
Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due [...] Read more.
Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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Review

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29 pages, 7168 KiB  
Review
Research Progress on Thermal Runaway Warning Methods and Fire Extinguishing Technologies for Lithium-Ion Batteries
by Peicheng Shi, Hailong Zhu, Xinlong Dong and Bin Hai
World Electr. Veh. J. 2025, 16(2), 81; https://doi.org/10.3390/wevj16020081 - 6 Feb 2025
Cited by 3 | Viewed by 2662
Abstract
Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the [...] Read more.
Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the mechanisms of TR and strategies for early warning and prevention to enhance battery safety. It begins by detailing TR mechanisms and their triggers, then reviews various TR early warning technologies, fire prevention methods, and the effectiveness and mechanisms of novel extinguishing agents such as hydrogels, perfluorohexanone, liquid nitrogen (LN), dry powder, and aqueous vermiculite dispersion (AVD). The study also explores advancements in new fire-retardant coatings for batteries. Finally, it summarizes current challenges and forecasts future research directions in battery technology. This review offers readers a clear, systematic overview of TR mechanisms, warning systems, and prevention technologies, providing comprehensive insights into TR management. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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40 pages, 5615 KiB  
Review
A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications
by Radhika Swarnkar, Harikrishnan Ramachandran, Sawal Hamid Md Ali and Rani Jabbar
World Electr. Veh. J. 2023, 14(9), 247; https://doi.org/10.3390/wevj14090247 - 5 Sep 2023
Cited by 11 | Viewed by 5504
Abstract
In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for [...] Read more.
In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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Other

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21 pages, 4532 KiB  
Perspective
Battery Prognostics and Health Management: AI and Big Data
by Di Li, Jinrui Nan, Andrew F. Burke and Jingyuan Zhao
World Electr. Veh. J. 2025, 16(1), 10; https://doi.org/10.3390/wevj16010010 - 28 Dec 2024
Viewed by 2670
Abstract
In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities [...] Read more.
In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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