Special Issue "Battery Management for Electric Vehicles"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electric Vehicles".

Deadline for manuscript submissions: 15 March 2021.

Special Issue Editor

Dr. Daniel J. Auger
Website
Guest Editor
Advanced Vehicle Engineering Centre, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, United Kingdom
Interests: control applications; intelligent vehicles; battery management; state estimation; rapid prototyping; duty cycle testing; thermal management; lithium-sulfur batteries

Special Issue Information

Dear Colleagues,

Battery management plays a vital role in vehicle electrification, providing functions such as state estimation, thermal management, safe operation, fault detection and prognosis, and general housekeeping functions. Good battery management offers the potential to push cells built using the best available materials technologies as close as possible to their operational limits. Many disciplines are involved, including modeling, software and algorithms, mechanical engineering, estimation theory, computer science, and control.

This Special Issue is a dedicated outlet for up-to-date research on all aspects of battery management. Theoretical papers, practical studies and new methods are welcome, and we would particularly like to encourage papers that bridge the gap between theoretical research and practical deployment, and also those that bring in cross-disciplinary insights from outside the traditional battery domain. Review papers that bring particularly helpful insights and capture up-to-date technological landscapes are also welcome.

Topics of particular interest include (but are not limited to):

  • Algorithms for estimation of state of charge, health and function;
  • Advanced methods from control and estimation theory;
  • Advanced methods from computer science, including artificial intelligence and machine learning;
  • Dynamic temperature measurement and control;
  • Deployment of advanced sensing technologies;
  • Applications of novel power electronics and switching strategies;
  • Physics-driven and data-driven prognostics and diagnostics;
  • Battery management for batteries within hybrid energy storage systems;
  • Fault-tolerant architectures and fault management strategies;
  • Benchmark data sources describing cell performance at extreme limits;
  • Safety management and certification.

Dr. Daniel J. Auger
Guest Editor

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 papers will be 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. Energies is an international peer-reviewed open access semimonthly 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 1800 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
  • thermal management
  • state estimation
  • power electronics
  • prognostics
  • diagnostics
  • control theory
  • machine learning
  • artificial intelligence
  • fault-tolerant
  • safety
  • certification
  • sensors
  • hybrid energy storage

Published Papers (4 papers)

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Research

Open AccessArticle
Battery Models for Battery Powered Applications: A Comparative Study
Energies 2020, 13(16), 4085; https://doi.org/10.3390/en13164085 - 06 Aug 2020
Abstract
Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available [...] Read more.
Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed. Full article
(This article belongs to the Special Issue Battery Management for Electric Vehicles)
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Open AccessArticle
En-Route Battery Management and a Mixed Network Equilibrium Problem Based on Electric Vehicle Drivers’ En-Route Recharging Behaviors
Energies 2020, 13(16), 4061; https://doi.org/10.3390/en13164061 - 05 Aug 2020
Abstract
With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors [...] Read more.
With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers’ battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers’ en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal. Full article
(This article belongs to the Special Issue Battery Management for Electric Vehicles)
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Open AccessArticle
Effects of Overdischarge Rate on Thermal Runaway of NCM811 Li-Ion Batteries
Energies 2020, 13(15), 3885; https://doi.org/10.3390/en13153885 - 30 Jul 2020
Abstract
Overdischarge often occurs during the use of battery packs, owing to cell inconsistency in the pack. In this study, the overdischarge behavior of 2.9 Ah cylindrical NCM811 [Li(Ni0.8Co0.1Mn0.1)O2] batteries in an adiabatic environment was investigated. [...] Read more.
Overdischarge often occurs during the use of battery packs, owing to cell inconsistency in the pack. In this study, the overdischarge behavior of 2.9 Ah cylindrical NCM811 [Li(Ni0.8Co0.1Mn0.1)O2] batteries in an adiabatic environment was investigated. A higher overdischarge rate resulted in a faster temperature increase in the batteries. Moreover, the following temperatures increased: Tu, at which the voltage decreased to 0 V; Ti, at which the current decreased to 0 A; and the maximum temperature during the battery overdischarge (Tm). The following times decreased: tu, when the voltage decreased from 3 to 0 V, and ti, when the current decreased to 0 A. The discharge capacity of the batteries was 3.06–3.14 Ah, and the maximum discharge depth of the batteries was 105.51–108.27%. Additionally, the characteristic overdischarge behavior of the batteries in a high-temperature environment (55 °C) was investigated. At high temperatures, the safety during overdischarging decreased, and the amount of energy released during the overdischarge phase and short-circuiting decreased significantly. Shallow overdischarging did not significantly affect the battery capacity recovery. None of the overdischarging cases caused fires, explosions, or thermal runaway in the batteries. The NCM811 batteries achieved good safety performance under overdischarge conditions: hence, they are valuable references for battery safety research. Full article
(This article belongs to the Special Issue Battery Management for Electric Vehicles)
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Open AccessArticle
Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation
Energies 2020, 13(7), 1679; https://doi.org/10.3390/en13071679 - 03 Apr 2020
Abstract
A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of [...] Read more.
A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness. Full article
(This article belongs to the Special Issue Battery Management for Electric Vehicles)
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