Lithium-Ion Battery Diagnosis: Health and Safety

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 7106

Special Issue Editors


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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

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
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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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

Published Papers (4 papers)

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Research

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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 - 07 Nov 2023
Viewed by 1470
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 - 01 Sep 2023
Cited by 3 | Viewed by 1500
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 5 | Viewed by 1260
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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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 - 05 Sep 2023
Cited by 1 | Viewed by 2312
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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