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Recent Advances in Lithium-Ion Battery Safety and Aging Issues for...
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Recent Advances in Lithium-Ion Battery Safety and Aging Issues for Electric Vehicles

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6004

Special Issue Editors

Dr. Xueyuan Wang

E-Mail Website
Guest Editor
School of Automotive Studies, Tongji University, Shanghai, China
Interests: electric vehicles; lithium-ion battery; battery management system; eletrochemical impedance spectroscopy; safety; state of health; diagnosis; fast charge; lithium plating; smart battery
Dr. Jinhao Meng

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: energy storage system; battery modeling and states estimation; battery management system for EV
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangdong Kong

E-Mail Website
Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing, China
Interests: battery safety; defect battery; physics-based reduced-order battery model; AI for battery; intelligent battery manufacturing

Special Issue Information

Dear Colleagues,

The continuous development of automobile electrification is of great significance to achieve carbon neutrality in the transportation field. Recently, the development of high-energy-density, large-scale, and fast charging of automotive power batteries has increased people's concerns about battery safety and aging. These two issues have been crucial to the normal use of electric vehicles under complex working conditions for several years. Additionally, retired batteries have become the focus of academic and industrial attention, and it is becoming an increasingly important topic to evaluate their safety and aging state to determine their residual values.

This Special Issue is a dedicated outlet for novel and original research on all aspects of recent advances in lithium-ion battery safety and aging issues for electric vehicles, including experiment, characterization, mechanism, modeling, algorithm, system, etc. In particular, we invite papers focusing on the gap between basic research and practical application to accelerate EV development.

Dr. Xueyuan Wang
Dr. Jinhao Meng
Dr. Xiangdong Kong
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

  • degradation mechanism
  • battery abuse
  • internal/external short-circuit
  • thermal runaway
  • advanced sensing, monitoring, and analysis
  • multiphysics modeling
  • digital twin
  • machine learning
  • SOX estimation
  • safety diagnosis, early warning, and protection
  • aging state
  • thermal management, including cooling and heating
  • fast charging
  • battery swap
  • battery management system
  • residual value evaluation
  • vehicle-to-grid
  • battery industry policy
  • smart battery

Published Papers (3 papers)

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Research

21 pages, 7957 KiB  
Article
Online Broadband Impedance Identification for Lithium-Ion Batteries Based on a Nonlinear Equivalent Circuit Model
by Hongyu Pan, Xueyuan Wang, Luning Zhang, Rong Wang, Haifeng Dai and Xuezhe Wei
World Electr. Veh. J. 2023, 14(7), 168; https://doi.org/10.3390/wevj14070168 - 26 Jun 2023
Viewed by 1015
Abstract
Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery’s mechanism; however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) [...] Read more.
Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery’s mechanism; however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage–current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursive least squares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance. Full article
(This article belongs to the Special Issue Recent Advances in Lithium-Ion Battery Safety and Aging Issues for Electric Vehicles)
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18 pages, 6128 KiB  
Article
Neural Network PID-Based Preheating Control and Optimization for a Li-Ion Battery Module at Low Temperatures
by Song Pan, Yuejiu Zheng, Languang Lu, Kai Shen and Siqi Chen
World Electr. Veh. J. 2023, 14(4), 83; https://doi.org/10.3390/wevj14040083 - 25 Mar 2023
Cited by 1 | Viewed by 1449
Abstract
Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase [...] Read more.
Low temperatures induce limited charging ability and lifespan in lithium-ion batteries, and may even cause accidents. Therefore, a reliable preheating strategy is needed to address this issue. This study proposes a low-temperature preheating strategy based on neural network PID control, considering temperature increase rate and consistency. In this strategy, electrothermal films are placed between cells for preheating; battery module areas are differentiated according to the convective heat transfer rate; a controller regulates heating power to control the maximum temperature difference during the preheating process; and a co-simulation model is established to verify the proposed warm-up strategy. The numerical calculation results indicate that the battery module can be preheated to the target temperature under different ambient temperatures and control targets. The coupling relationship between the preheating time and the maximum temperature difference during the preheating process is studied and multi-objective optimization is carried out based on the temperature increase rate and thermal uniformity. The optimal preheating strategy is proven to ensure the temperature increase rate and effectively suppress temperature inconsistency of the module during the preheating process. Although preheating time is extended by 17%, the temperature difference remains within the safety threshold, and the maximum temperature difference is reduced by 49.6%. Full article
(This article belongs to the Special Issue Recent Advances in Lithium-Ion Battery Safety and Aging Issues for Electric Vehicles)
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12 pages, 1878 KiB  
Article
State of Health Estimation Method for Lithium-Ion Batteries via Generalized Additivity Model and Transfer Component Analysis
by Mingqiang Lin, Chenhao Yan and Xianping Zeng
World Electr. Veh. J. 2023, 14(1), 14; https://doi.org/10.3390/wevj14010014 - 05 Jan 2023
Cited by 1 | Viewed by 2326
Abstract
Battery state of health (SOH) is a momentous indicator for aging severity recognition of lithium-ion batteries and is also an indispensable parameter of the battery management system. In this paper, an innovative SOH estimation algorithm based on feature transfer is proposed for lithium-ion [...] Read more.
Battery state of health (SOH) is a momentous indicator for aging severity recognition of lithium-ion batteries and is also an indispensable parameter of the battery management system. In this paper, an innovative SOH estimation algorithm based on feature transfer is proposed for lithium-ion batteries. Firstly, sequence features with battery aging information are sufficiently extracted based on the capacity increment curve. Secondly, transfer component analysis is employed to obtain the mapping that minimizes the data distribution difference between the training set and the test set in the shared feature space. Finally, the generalized additive model is investigated to estimate the battery health status. The experimental results demonstrate that the proposed algorithm is capable of forecasting the SOH for lithium-ion batteries, and the results are more outstanding than those of several comparison algorithms. The predictive error evaluation indicators for each battery are both less than 2.5%. In addition, satisfactory SOH estimation results can also be obtained by only relying on a small amount of data as the training set. The comparative experiments using traditional features and different machine learning methods also testify to the superiority of the proposed algorithm. Full article
(This article belongs to the Special Issue Recent Advances in Lithium-Ion Battery Safety and Aging Issues for Electric Vehicles)
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