Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.6
Journals
WEVJ
Special Issues
Smart Lithium-Ion Battery Systems: Advanced Modeling, State Estimation, and Control
share announcement

Smart Lithium-Ion Battery Systems: Advanced Modeling, State Estimation, and Control

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 10517

Special Issue Editors

Dr. Haijun Ruan

E-Mail Website
Guest Editor
Dyson School of Design Engineering, Imperial College London, London SW7 2BX, UK
Interests: lithium-ion batteries; battery management system; machine learning; optimization and control; energy storage system
Special Issues, Collections and Topics in MDPI journals
Dr. Alastair Hales

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Bristol, Bristol BS8 1TH, UK
Interests: lithium-ion batteries; battery thermal management; battery thermal dependency; equivalent circuit models; electric vehicles

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are deployed in a wide variety of applications, such as portable electronics, electric vehicles, and stationary power storage systems, by virtue of their high energy and power densities and long lifetime. With emerging techniques such as artificial intelligence and blockchain, smart battery systems, incorporating state-of-the-art battery hardware with advanced battery management processes, are moving rapidly from a research field towards a requirement for technology functionality. Advanced modeling, state estimation, and control compose the key technologies of smart battery systems, which contribute to extending battery lifetime and enhancing battery safety. This Special Issue is a dedicated outlet for up-to-date research on all aspects of advanced modeling, state estimation, and control for smart lithium-ion battery systems. Manuscripts from cross-disciplinary fields, such as artificial intelligence, blockchain, electrochemistry, power electronics, and thermal and mechanical technologies are strongly encouraged. We would particularly like to welcome papers that bridge the gap between theoretical research and practical deployment for lithium-ion batteries.

We invite the submission of original research, review, perspective articles and the topics of particular interest include (but are not limited to):

  • Advanced battery modeling: electrical, electrochemical, thermal, mechanical and aging models;
  • Next generation battery model parameterization techniques;
  • Battery state estimation: state of charge, state of power, temperature and state of health;
  • Advanced thermal management: cooling and heating of lithium-ion batteries;
  • Battery charging methods;
  • Battery degradation, faults and safety management;
  • Battery diagnostics and prognostics;
  • Machine learning, big data, and battery fusion methods.

Dr. Haijun Ruan
Dr. Alastair Hales
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

  • lithium-ion battery
  • modeling
  • state estimation
  • control
  • machine learning
  • big data
  • diagnostics
  • prognostics
  • charging
  • thermal management
  • heating
  • cooling
  • safety management
  • health management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 858 KiB  
Article
A Numerical Study of the Suitability of Phase-Change Materials for Battery Thermal Management in Flight Applications
by Daeyeun Kim, Saber Abdallahh, Gloria Bosi and Alastair Hales
World Electr. Veh. J. 2023, 14(1), 15; https://doi.org/10.3390/wevj14010015 - 5 Jan 2023
Cited by 4 | Viewed by 2903
Abstract
Battery pack specific energy, which can be enhanced by minimising the mass of the battery thermal management system (BTMS), is a limit on electric fixed-wing flight applications. In this paper, the use of phase-change materials (PCMs) for BTMSs is numerically explored in the [...] Read more.
Battery pack specific energy, which can be enhanced by minimising the mass of the battery thermal management system (BTMS), is a limit on electric fixed-wing flight applications. In this paper, the use of phase-change materials (PCMs) for BTMSs is numerically explored in the 3D domain, including an equivalent circuit battery model. A parametric study of PCM properties for effective thermal management is conducted for a typical one-hour flight. PCMs maintain an ideal operating temperature (288.15 K–308.15 K) throughout the entire battery pack. The PCM absorbs heat generated during takeoff, which is subsequently used to maintain cell temperature during the cruise phase of flight. In the control case (no BTMS), battery pack temperatures fall below the ideal operating range. We conduct a parametric study highlighting the insignificance of PCM thermal conductivity on BTMS performance, with negligible enhancement observed across the tested window (0.1–10 W m−1 K−1). However, the PCM’s latent heat of fusion is critical. Developers of PCMs for battery-powered flight must focus on enhanced latent heat of fusion, regardless of the adverse effect on thermal conductivity. In long-haul flight, an elongated cruise phase and higher altitude exasperate this problem. The unique characteristics of PCM offer a passive low-mass solution that merits further investigation for flight applications. Full article
(This article belongs to the Special Issue Smart Lithium-Ion Battery Systems: Advanced Modeling, State Estimation, and Control)
►▼ Show Figures

Figure 1

15 pages, 3669 KiB  
Article
Online Estimation of Internal Short Circuit Resistance for Large-Format Lithium-Ion Batteries Combining a Reconstruction Method of Model-Predicted Voltage
by Anci Chen, Weige Zhang, Bingxiang Sun, Hao Li and Xinyuan Fan
World Electr. Veh. J. 2022, 13(9), 170; https://doi.org/10.3390/wevj13090170 - 13 Sep 2022
Cited by 3 | Viewed by 2387
Abstract
The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery [...] Read more.
The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery mainly presents abnormal stage of charge (SOC) depletion behaviors, the SOC estimation processes based on state observers and battery models will act an important basis of the ISC resistance estimation problem. However, as it will be exhibited in this paper, when directly using the measured voltage of the ISC battery as the output variable of the state observer, the battery model error will limit the SOC estimation accuracy and further lead to very inaccurate or even divergent ISC resistance estimation results for large-format batteries, which present quite slight SOC depletion behaviors at the ISC state. To this end, this paper proposes a novel SOC and ISC resistance co-estimation method which combines a reconstruction method of the model-predicted voltage of the ISC battery. Experimental validations are carried out with a 37 Ah battery, results show that the proposed method which uses the reconstructed model-predicted voltage (RMPV) as the output variable of the state observer only present maximum estimation errors of 39.96 Ω and 2.00 Ω for the ISC resistances of 100 Ω and 10 Ω, respectively. Full article
(This article belongs to the Special Issue Smart Lithium-Ion Battery Systems: Advanced Modeling, State Estimation, and Control)
►▼ Show Figures

Figure 1

18 pages, 3657 KiB  
Article
Modification of Cycle Life Model for Normal Aging Trajectory Prediction of Lithium-Ion Batteries at Different Temperatures and Discharge Current Rates
by Xinyu Jia, Caiping Zhang, Leyi Wang, Weige Zhang and Linjing Zhang
World Electr. Veh. J. 2022, 13(4), 59; https://doi.org/10.3390/wevj13040059 - 28 Mar 2022
Cited by 4 | Viewed by 3989
Abstract
Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health [...] Read more.
Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%. Full article
(This article belongs to the Special Issue Smart Lithium-Ion Battery Systems: Advanced Modeling, State Estimation, and Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop