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Lithium-Ion Batteries for Electric Vehicle
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Special Issue "Lithium-Ion Batteries for Electric Vehicle"

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

Deadline for manuscript submissions: 30 September 2023 | Viewed by 3260

Special Issue Editors

Dr. Yujie Wang

E-Mail Website
Guest Editor
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: new energy vehicle technology; complex system modeling; simulation and control; fuel cell system management and optimization control
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaopeng Tang

E-Mail Website
Guest Editor
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
Interests: lithium batteries; energy storage; battery; kalman filtering; electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Featuring high energy and power density, a long lifespan, and a continuously decreasing cost, Li-ion batteries are regarded as the key energy storage components for electric vehicles. As is the case with most electrochemical systems, Li-ion batteries are highly nonlinear systems with complicated physical and chemical reactions. They are fragile to external factors, such as voltage, current, temperature, vibration, and humidity. The internal states of the batteries are mostly unmeasurable with the existing commercial sensors. Issues such as ultrafast charging, lifespan, second-life utilization, and reliability under extreme temperatures remain unsolved. Therefore, the intelligent control and management of these batteries are critical to the safe, fluent, and efficient use of these batteries.

This Special Issue will highlight recent studies related to Li-ion batteries that could potentially advance their use in electric vehicles. Topics of interest include but are not limited to:

  • Battery materials, design, and manufacturing;
  • Battery analysis, testing, and modeling;
  • Battery sorting, grouping, and grading;
  • Battery control, monitoring, charging, and maintenance;
  • Battery state estimation and life cycle assessment;
  • Battery thermal management and safety control;
  • Battery control in hybrid systems and V2X systems;
  • Battery infrastructure for electric vehicles;
  • Energy policy for batteries in electric vehicles.

Dr. Yujie Wang
Dr. Xiaopeng Tang
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 1000 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 management system
  • lithium-ion batteries
  • electric vehicles

Published Papers (4 papers)

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Article
Research on Calendar Aging for Lithium-Ion Batteries Used in Uninterruptible Power Supply System Based on Particle Filtering
by
Wei Xu
and
Hongzhi Tan
World Electr. Veh. J. 2023, 14(8), 209; https://doi.org/10.3390/wevj14080209 - 08 Aug 2023
Viewed by 206
Abstract
The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible [...] Read more.
The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Article
State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory
by
Yanmei Li
,
Laijin Luo
,
Chaolong Zhang
and
Huihan Liu
World Electr. Veh. J. 2023, 14(7), 188; https://doi.org/10.3390/wevj14070188 - 14 Jul 2023
Viewed by 351
Abstract
The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this [...] Read more.
The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Article
An Improved Gaussian Process Regression Based Aging Prediction Method for Lithium-Ion Battery
by
Weiwei Qu
,
Hu Deng
,
Yi Pang
and
Zhanfeng Li
World Electr. Veh. J. 2023, 14(6), 153; https://doi.org/10.3390/wevj14060153 - 09 Jun 2023
Viewed by 703
Abstract
A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to [...] Read more.
A reliable aging-prediction method is significant for lithium-ion batteries (LIBs) to prolong the service life and increase the efficiency of operation. In this paper, an improved Gaussian-process regression (GPR) is proposed to predict the degradation rate of LIBs under coupled aging stress to simulate working conditions. The complicated degradation processes at different ranges of the state of charge (SOC) under different discharge rates were analyzed. A composed kernel function was conducted to optimize the hyperparameter. The inputs for the kernel function of GPR were improved by coupling the constant and variant characteristics. Moreover, previous aging information was employed as a characteristic to improve the reliability of the prediction. Experiments were conducted on a lithium–cobalt battery at three different SOC ranges under three discharge rates to verify the performance of the proposed method. Some tips to slow the aging process based on the coupled stress were discovered. Results show that the proposed method accurately estimated the degradation rate with a maximum estimation root-mean-square error of 0.14% and regression coefficient of 0.9851. Because of the proposed method’s superiority to the exponential equation and GPR by fitting all cells under a different operating mode, it is better for reflecting the true degradation in actual EV. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Article
An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning
by
Yujie Wang
,
Wenhuan Li
,
Zeyan Liu
and
Ling Li
World Electr. Veh. J. 2023, 14(3), 57; https://doi.org/10.3390/wevj14030057 - 24 Feb 2023
Cited by 1 | Viewed by 1425
Abstract
Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and [...] Read more.
Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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