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WEVJ
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Electrochemical and Thermal Modeling of Batteries for Electric Vehicle
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Electrochemical and Thermal Modeling of Batteries for Electric Vehicle

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4784

Special Issue Editors

Dr. Rongheng Li

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48126, USA
Interests: renewable energy; battery modeling; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Kettering University, 1700 University Ave., Flint, MI 48504, USA
Interests: materials and systems for energy storage; battery materials R&D and manufacturing; biomedical composite coating for implant applications; contact materials (CuCr, AgSnO2); battery modeling and battery management for electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of WEVJ is open for submission on the topic of numerical modeling of batteries utilized for electric vehicles (EV), including battery cell simulation, battery thermal management, battery state of health estimation, battery pack modeling, and optimization of charging strategies.

The development of batteries is crucial to the performance of electric vehicles. Compared to the equivalent circuit models, the electrochemical model, i.e., the single particle model (SPM), and the pseudo-two-dimensional (P2D) model, provides a more accurate estimation of battery characteristics, which is beneficial to the design of batteries used for EV applications.

We invite scientists and engineers to submit articles related to the topics in one or more of the following areas:

  1. Modeling of the electrochemical and thermal process for distinct types of batteries, such as Lithium-Ion batteries, solid-state batteries, and second-life batteries;
  2. Optimization of the parameters of batteries (i.e., choice of materials, dimensions, and cell arrangements) for specific EV applications such as heavy-duty pickup trucks;
  3. Modeling of the battery degradation to optimize the charging and discharging strategies;
  4. Design of the cooling strategies of EV batteries through thermal-fluid modeling;
  5. Incorporate the electrochemical and thermal models of batteries into the system modeling of electric vehicles.

Dr. Rongheng Li
Dr. Xuan Zhou
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. 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

  • electric vehicles
  • battery modeling
  • state of charge and health prediction
  • battery degradation
  • second-life batteries
  • solid-state batteries
  • cooling strategies

Published Papers (3 papers)

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Research

11 pages, 3519 KiB  
Article
Identification Method and Quantification Analysis of the Critical Aging Speed Interval for Battery Knee Points
by Xinyu Jia, Caiping Zhang, Linjing Zhang, Weige Zhang and Zhongling Xu
World Electr. Veh. J. 2023, 14(12), 346; https://doi.org/10.3390/wevj14120346 - 12 Dec 2023
Viewed by 1524
Abstract
The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can [...] Read more.
The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can be identified using a wealth of aging data and various regression-based methods. However, KP identification relies on a large amount of aging data, which is exceedingly time-consuming and resource-intensive. To overcome this issue, we propose a novel method based on KP characteristics to identify the KPs and critical aging speed. Firstly, we extract the main aging trajectory using curve-fitting techniques. Secondly, we calculate the aging speed at each cycle to identify the KPs. We then explore the relationship between the KPs and cycle life and develop a knee point identification algorithm. The correlation coefficient between the KPs and cycle life provides a valuable indicator of the critical aging speed, enabling accurate identification of KPs. To validate our approach, we apply it to the Li(NiCoMn)O2, LiFePO4, and LiCoO2 cell datasets. Our results demonstrate a strong correlation between the KPs and cycle life for these battery types. By employing our proposed method, KPs can be identified for battery life prediction, product design, and health management. Moreover, we summarize a critical degradation speed of −0.03%/cycle can serve as an empirical threshold for warning against capacity diving and KPs. The statistical transition speed threshold can eliminate the dependence on extensive aging data throughout the entire battery’s lifecycle for identifying capacity knee points. Full article
(This article belongs to the Special Issue Electrochemical and Thermal Modeling of Batteries for Electric Vehicle)
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17 pages, 17797 KiB  
Article
Research on Temperature Inconsistency of Large-Format Lithium-Ion Batteries Based on the Electrothermal Model
by Chao Yu, Jiangong Zhu, Xuezhe Wei and Haifeng Dai
World Electr. Veh. J. 2023, 14(10), 271; https://doi.org/10.3390/wevj14100271 - 01 Oct 2023
Viewed by 1506
Abstract
Large-format lithium-ion (Li-ion) batteries are increasingly applied in energy storage systems for electric vehicles, owing to their flexible shape design, lighter weight, higher specific energy, and compact layouts. Nevertheless, the large thermal gradient of Li-ion batteries leads to performance degradation and irreversible safety [...] Read more.
Large-format lithium-ion (Li-ion) batteries are increasingly applied in energy storage systems for electric vehicles, owing to their flexible shape design, lighter weight, higher specific energy, and compact layouts. Nevertheless, the large thermal gradient of Li-ion batteries leads to performance degradation and irreversible safety issues. The difference in the highest temperature position at various operational modes makes accurate temperature monitoring complicated. Accordingly, a full understanding of the temperature inconsistency of large-format Li-ion batteries is crucial. In this study, these inconsistent characteristics are analyzed by establishing an electrothermal model and conducting experiments based on an 8-Ah pouch-type ternary Li-ion battery with contraposition tabs. Regarding the characteristic of inhomogeneous temperature distribution, the analysis results demonstrate that it is primarily attributable to the uneven heat generation within the battery system and the effects of the two tabs. For the evolution of the highest temperature position, this study compares the maximum temperature rise of the positive tab and main battery body. The results illustrate that the operating temperature has a greater impact on the maximum temperature rise of the main battery body since its resistance strongly depends on the operating temperature compared to the positive and negative tabs. In addition, the electrothermal model is expected to be employed for the battery thermal management system (BTMS) to mitigate the battery temperature inconsistency. Full article
(This article belongs to the Special Issue Electrochemical and Thermal Modeling of Batteries for Electric Vehicle)
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18 pages, 6635 KiB  
Article
Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications
by Hegazy Rezk and Rania M. Ghoniem
World Electr. Veh. J. 2023, 14(8), 201; https://doi.org/10.3390/wevj14080201 - 27 Jul 2023
Viewed by 979
Abstract
There has been a suggestion for the best energy management method for an electric vehicle with a hybrid power system. The objective is to supply the electric vehicle with high-quality electricity. The hybrid power system comprises a supercapacitor (SC) bank and a lithium-ion [...] Read more.
There has been a suggestion for the best energy management method for an electric vehicle with a hybrid power system. The objective is to supply the electric vehicle with high-quality electricity. The hybrid power system comprises a supercapacitor (SC) bank and a lithium-ion battery. The recommended energy management plan attempts to maintain the bus voltage while providing the load demand with high-quality power under various circumstances. The management controller is built on a metaheuristic optimization technique that enhances the flatness theory-based controller’s trajectory generation parameters. The SC units control the DC bus while the battery balances the power on the common line. This study demonstrates the expected contribution using particle swarm optimization and performance are assessed under various optimization parameters, including population size and maximum iterations. Their effects on controller performance are examined in the study. The outcomes demonstrate that the number of iterations significantly influences the algorithm’s ability to determine the best controller parameters. The results imply that combining metaheuristic optimization techniques with flatness theory can enhance power quality. The suggested management algorithm ensures power is shared efficiently, protecting power sources and providing good power quality. Full article
(This article belongs to the Special Issue Electrochemical and Thermal Modeling of Batteries for Electric Vehicle)
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