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Batteries
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Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition
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Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2562

Special Issue Editors

Dr. Fei Feng

E-Mail Website
Guest Editor
College of Automation, Chongqing University, Shapingba District, Chongqing 400044, China
Interests: battery health management; battery storage energy management; virtual power plant
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rui Ling

E-Mail Website
Guest Editor
School of Automation, Chongqing University, Chongqing 40044, China
Interests: energy management and control technology of energy storage system; robot control technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Xie

E-Mail Website
Guest Editor
College of Mechanical and Vehicular Engineering, Chongqing University, Chongqing 400044, China
Interests: battery thermal management system; vehicle thermal management system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shunli Wang

E-Mail Website
Guest Editor
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
Interests: lithium battery modeling; state estimation; battery balancing; battery management system; new energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhao Meng

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: battery management systems; battery state estimation; fast charging control; battery impedance measurement
Special Issues, Collections and Topics in MDPI journals
Dr. Jiale Xie

E-Mail Website
Guest Editor
Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: battery characteristic modeling; fault diagnosis; states estimation; thermal management; energy equilibrium
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With increasing attention paid to environmental issues, "carbon neutrality" has become one of the main policy goals in many countries and regions. The development of new energy vehicles and battery energy storage is of great significance for the control of carbon emissions. As a key component of new energy vehicles and energy storage systems, battery life and cost directly affect the life and economy of the whole system. Therefore, improving the reliability, durability, and economy of the whole life cycle of the battery system has become an urgent scientific and major engineering problem.

This Special Issue will focus on battery energy storage and its health management system. Papers are invited in all different areas of battery health management, as battery energy storage is a multidisciplinary topic that involves research areas, such as electrochemistry, materials, control, electrical and mechanical issues, as well as economic and environmental aspects. Both theoretical and experimental studies, or a combination of both, are welcome. In recent years, the diagnosis of battery thermal runaway has attracted attention and encouraged scientists to explore new methods for the diagnosis of new physical quantities of battery thermal runaway.

Topics of interest for publication include, but are not limited to, the following:

  • Multi-physical field battery modeling (electrical, thermal, force, gas, etc.);
  • Battery degradation mechanism and modeling;
  • Battery thermal runaway mechanism and modeling;
  • Battery numerical calculation and simulation technology;
  • Battery states estimation algorithm (SOC, SOH, SOP, SOE, etc.);
  • Battery thermal runaway and fault diagnosis;
  • Battery life prediction and health management;
  • Battery reliability evaluation methods;
  • Battery charging control strategy;
  • Battery balancing control strategy;
  • Battery thermal management control strategy.

Dr. Fei Feng
Prof. Dr. Rui Ling
Dr. Yi Xie
Prof. Dr. Shunli Wang
Dr. Jinhao Meng
Dr. Jiale Xie
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. Batteries 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 2700 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 modeling
  • battery reliability
  • battery fault diagnosis
  • battery control strategy
  • battery health management

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Related Special Issue

Published Papers (4 papers)

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Research

17 pages, 3113 KiB  
Article
A Physics–Guided Machine Learning Approach for Capacity Fading Mechanism Detection and Fading Rate Prediction Using Early Cycle Data
by Jiwei Yao, Qiang Gao, Tao Gao, Benben Jiang and Kody M. Powell
Batteries 2024, 10(8), 283; https://doi.org/10.3390/batteries10080283 - 8 Aug 2024
Viewed by 347
Abstract
Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accurately determining the dominant fading mechanism and predicting the average capacity fading rate. [...] Read more.
Lithium–ion battery development necessitates predicting capacity fading using early cycle data to minimize testing time and costs. This study introduces a hybrid physics–guided data–driven approach to address this challenge by accurately determining the dominant fading mechanism and predicting the average capacity fading rate. Physics–guided features, derived from the electrochemical properties and behaviors within the battery, are extracted from the first five cycles to provide meaningful, interpretable, and predictive data. Unlike previous models that rely on a single regression approach, our method utilizes two separate regression models tailored to the identified dominant fading mechanisms. Our model achieves 95.6% accuracy in determining the dominant fading mechanism using data from the second cycle and a mean absolute percentage error of 17.09% in predicting lifetime capacity fade from the first five cycles. This represents a substantial improvement over state–of–the–art models, which have an error rate approximately three times higher. This study underscores the significance of physics–guided data characterization and the necessity of identifying the primary fading mechanism prior to predicting the capacity fading rate in lithium–ion batteries. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)
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17 pages, 1503 KiB  
Article
An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements
by Alexis Kalk, Lea Leuthner, Christian Kupper and Marc Hiller
Batteries 2024, 10(8), 267; https://doi.org/10.3390/batteries10080267 - 26 Jul 2024
Viewed by 430
Abstract
This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and [...] Read more.
This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)
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Graphical abstract

16 pages, 7267 KiB  
Article
Diffusion-Equation-Based Electrical Modeling for High-Power Lithium Titanium Oxide Batteries
by Haoze Chen, Weige Zhang, Caiping Zhang, Bingxiang Sun, Sijia Yang and Dinghong Chen
Batteries 2024, 10(7), 238; https://doi.org/10.3390/batteries10070238 - 3 Jul 2024
Viewed by 781
Abstract
Lithium titanium oxide (LTO) batteries offer superior performance compared to graphite-based anodes in terms of rapid charge/discharge capability and chemical stability, making them promising candidates for fast-charging and power-assist vehicle applications. However, commonly used battery models often struggle to accurately describe the current–voltage [...] Read more.
Lithium titanium oxide (LTO) batteries offer superior performance compared to graphite-based anodes in terms of rapid charge/discharge capability and chemical stability, making them promising candidates for fast-charging and power-assist vehicle applications. However, commonly used battery models often struggle to accurately describe the current–voltage characteristics of LTO batteries, particularly before the charge/discharge cutoff conditions. In this work, a novel electrical model based on the solid-phase diffusion equation is proposed to capture the unique electrochemical phenomena arising from the diffusion mismatch between the positive and negative electrodes in high-power LTO batteries. The robustness of the proposed model is evaluated under various loading conditions, including constant current and dynamic current tests, and the results are compared against experimental data. The experimental results for LTO batteries exhibit remarkable alignment with the model estimation, demonstrating a maximum voltage error below 3%. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)
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16 pages, 2549 KiB  
Article
Fault Diagnosis for Lithium-Ion Battery Pack Based on Relative Entropy and State of Charge Estimation
by Tian-E Fan, Fan Chen, Hao-Ran Lei, Xin Tang and Fei Feng
Batteries 2024, 10(7), 217; https://doi.org/10.3390/batteries10070217 - 21 Jun 2024
Viewed by 712
Abstract
Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault [...] Read more.
Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault diagnosis method based on relative entropy and state of charge (SOC) estimation is proposed to detect fault in lithium-ion batteries. First, the relative entropies of the voltage, temperature and SOC of battery cells are calculated by using a sliding window, and the cumulative sum (CUSUM) test is adopted to achieve fault diagnosis and isolation. Second, the SOC estimation of the short-circuit cell is obtained, and the short-circuit resistance is estimated for a quantitative analysis of the short-circuit fault. Furthermore, the effectiveness of our method is validated by multiple fault tests in a thermally coupled electrochemical battery model. The results show that the proposed method can accurately detect different types of faults and evaluate the short-circuit fault degree by resistance estimation. The voltage/temperature sensor fault is detected at 71 s/58 s after faults have occurred, and a short-circuit fault is diagnosed at 111 s after the fault. In addition, the standard error deviation of short-circuit resistance estimation is less than 0.12 Ω/0.33 Ω for a 5 Ω/10 Ω short-circuit resistor. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)
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