Submit to Energies Review for Energies Propose a Special Issue

Journal Browser

► Journal Browser

Battery Management for Electric Vehicles

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 43730

Share This Special Issue

Special Issue Editors

Prof. Dr. Daniel Auger

E-Mail Website1 Website2
Guest Editor

School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK
Interests: the application of reduced-order physics-based models for fast model calibration and estimation; control of hybrid battery systems; electrical and module/pack-level thermal modelling and state estimation; and prognostic/diagnostic techniques for predicting and assessing battery health and remaining useful life
Special Issues, Collections and Topics in MDPI journals

Dr. Jorge Barreras

E-Mail Website
Guest Editor

Department of Mechanical Engineering, Imperial College London, London SW7 1AL, UK
Interests: energy storage; electric vehicles; batteries; battery management system; balancing system

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
Dr. Jorge Barreras
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. 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 2600 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 (13 papers)

Download All Papers

Research

15 pages, 1343 KiB

Open AccessArticle

Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter

by Ali Wadi, Mamoun Abdel-Hafez and Ala A. Hussein

Energies 2022, 15(10), 3717; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103717 - 19 May 2022

Cited by 4 | Viewed by 1557

Abstract

This paper proposes a state-of-charge estimation technique to meet highly dynamic power requirements in electric vehicles. When the power going in/out the battery is highly dynamic, the statistics of the measurement noise are expected to deviate and maybe change over time from the expected laboratory specified values. Therefore, we propose to integrate adaptive noise identification with the dual-Kalman filter to obtain a robust and computationally-efficient estimation. The proposed technique is verified at the pack and cell levels using a 3.6 V lithium-ion battery cell and a 12.8 V lithium-ion battery pack. Standardized electric vehicle tests are conducted and used to validate the proposed technique, such as dynamic stress test, urban dynamometer driving schedule, and constant-current discharge tests at different temperatures. Results demonstrate a sustained improvement in the estimation accuracy and a high robustness due to immunity to changes in the statistics of the process and measurement noise sequences using the proposed technique. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

25 pages, 13490 KiB

Open AccessArticle

Analysis of Heat Dissipation and Preheating Module for Vehicle Lithium Iron Phosphate Battery

by Shuwen Zhou, Yuemin Zhao and Shangyuan Gao

Energies 2021, 14(19), 6196; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196196 - 28 Sep 2021

Cited by 4 | Viewed by 1694

Abstract

The ambient temperature has a great influence on the discharge and charging performance of a lithium battery, which may cause thermal runaway of the battery pack in extreme cases. In terms of the poor cooling effect caused by only using the cooling bottom plate for liquid cooling and the fact that the battery pack needs to be preheated before it can be used normally, a new cooling structure design was carried out, and a variety of cooling schemes and preheating schemes were proposed for analysis and comparison. The Star ccm+ simulation software was used to analyze and study their liquid cooling performance and preheating performance under different conditions. The best cooling scheme and preheating scheme were obtained by comparing the results of the simulation analysis. The simulation results show that the cooling performance of the cooling scheme using two vertical cooling plates and one cooling bottom plate is the best, and the preheating performance is best when the preheating liquid is used with a certain temperature flow through the preheating pipe of the battery pack for a period of time, and then the battery pack is discharged until the battery pack temperature reaches the working temperature range. The research results have reference value for the control of the ambient temperature of a vehicle lithium iron phosphate battery. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

20 pages, 5000 KiB

Open AccessArticle

Holistic 1D Electro-Thermal Model Coupled to 3D Thermal Model for Hybrid Passive Cooling System Analysis in Electric Vehicles

by Danial Karimi, Hamidreza Behi, Mohsen Akbarzadeh, Joeri Van Mierlo and Maitane Berecibar

Energies 2021, 14(18), 5924; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185924 - 18 Sep 2021

Cited by 21 | Viewed by 2253

Abstract

Thermal management is the most vital element of electric vehicles (EV) to control the maximum temperature of module/pack for safety reasons. This paper presents a novel passive thermal management system (TMS) composed of a heat sink (HS) and phase change materials (PCM) for lithium-ion capacitor (LiC) technology under the premise that the cell is cycled with a continuous 150 A fast charge/discharge current rate. The experiments are validated against numerical analysis through a computational fluid dynamics (CFD) model. For this purpose, a comprehensive electro-thermal model based on an equivalent circuit model (ECM) is designed. The designed electro-thermal model combines the ECM model with the thermal model since the performance of the LiC cell highly depends on the temperature. Then, the robustness of the model is evaluated using a precise second-order ECM. The extracted parameters of the electro-thermal model are verified by the experimental results in which the voltage and temperature errors are less than ±5% and ±4%, respectively. Finally, the thermal performance of the HS-assisted PCM TMS is studied under the fast charge/discharge current rate. The 3D CFD results exhibit that the temperature of the LiC when using the PCM-HS as the cooling system was reduced by 38.3% (34.1 °C) compared to the natural convection case study (55.3 °C). Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

30 pages, 883 KiB

Open AccessArticle

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

by Leandro do C. Martins, Rafael D. Tordecilla, Juliana Castaneda, Angel A. Juan and Javier Faulin

Energies 2021, 14(16), 5131; https://0-doi-org.brum.beds.ac.uk/10.3390/en14165131 - 19 Aug 2021

Cited by 15 | Viewed by 4144

Abstract

The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Graphical abstract

24 pages, 57848 KiB

Open AccessArticle

Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

by Matthieu Dubarry and David Beck

Energies 2021, 14(9), 2371; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092371 - 22 Apr 2021

Cited by 30 | Viewed by 4503

Abstract

The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO₄, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

19 pages, 1041 KiB

Open AccessArticle

Prolongation of Battery Lifetime for Electric Buses through Flywheel Integration

by Philipp Glücker, Klaus Kivekäs, Jari Vepsäläinen, Panagiotis Mouratidis, Maximilian Schneider, Stephan Rinderknecht and Kari Tammi

Energies 2021, 14(4), 899; https://0-doi-org.brum.beds.ac.uk/10.3390/en14040899 - 09 Feb 2021

Cited by 7 | Viewed by 3113

Abstract

Electrification of transportation is an effective way to tackle climate change. Public transportation, such as electric buses, operate on predetermined routes and offer quiet operation, zero local emissions and high energy efficiency. However, the batteries of these buses are expensive and wear out in use. The battery ageing is expedited by fast charging and power spikes during operation. The contribution of this paper is the reduction of the power spikes and thus a prolonged battery lifetime. A novel hybrid energy storage system for electric buses is proposed by introducing a flywheel in addition to the existing battery. A simulation model of the hybrid energy storage system is presented, including a battery ageing model to measure the battery lifetime. The bus was simulated during its daily driving operation on different routes with different energy management strategies and flywheel configurations. These different flywheels as well as the driving cycle had a significant impact on the battery life increase. The proposed hybrid battery/flywheel storage system resulted in a battery lifetime increase of 20% on average. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

19 pages, 7631 KiB

Open AccessArticle

An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery

by Wenxian Duan, Chuanxue Song, Silun Peng, Feng Xiao, Yulong Shao and Shixin Song

Energies 2020, 13(23), 6366; https://0-doi-org.brum.beds.ac.uk/10.3390/en13236366 - 03 Dec 2020

Cited by 25 | Viewed by 2234

Abstract

An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

21 pages, 8892 KiB

Open AccessArticle

Induction Heater Based Battery Thermal Management System for Electric Vehicles

by Waseem Raza, Gwang Soo Ko and Youn Cheol Park

Energies 2020, 13(21), 5711; https://0-doi-org.brum.beds.ac.uk/10.3390/en13215711 - 31 Oct 2020

Cited by 4 | Viewed by 4727

Abstract

The life and efficiency of electric vehicle batteries are susceptible to temperature. The impact of cold climate dramatically decreases battery life, while at the same time increasing internal impedance. Thus, a battery thermal management system (BTMS) is vital to heat and maintain temperature range if the electric vehicle’s batteries are operating in a cold climate. This paper presents an induction heater-based battery thermal management system that aims to ensure thermal safety and prolong the life cycle of Lithium-ion batteries (Li-Bs). This study used a standard simulation tool known as GT-Suite to simulate the behavior of the proposed BTMS. For the heat transfer, an indirect liquid heating method with variations in flow rate was considered between Lithium-ion batteries. The battery and cabin heating rate was analyzed using the induction heater powers of 2, 4, and 6 kW at ambient temperatures of −20, −10, and 0 °C. A water and ethylene glycol mixture with a ratio of 50:50 was considered as an operating fluid. The findings reveal that the thermal performance of the proposed system is generally increased by increasing the flow rate and affected by the induction heater capacity. It is evident that at −20 °C with 27 LPM and 6 kW heater capacity, the maximum heat transfer rate is 0.0661 °C/s, whereas the lowest is 0.0295 °C/s with 2 kW heater capacity. Furthermore, the proposed BTMS could be a practical approach and help to design the thermal system for electric vehicles in the future. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

21 pages, 28478 KiB

Open AccessArticle

Design of an Optimized Thermal Management System for Li-Ion Batteries under Different Discharging Conditions

by Ankur Bhattacharjee, Rakesh K. Mohanty and Aritra Ghosh

Energies 2020, 13(21), 5695; https://0-doi-org.brum.beds.ac.uk/10.3390/en13215695 - 30 Oct 2020

Cited by 57 | Viewed by 5039

Abstract

The design of an optimized thermal management system for Li-ion batteries has challenges because of their stringent operating temperature limit and thermal runaway, which may lead to an explosion. In this paper, an optimized cooling system is proposed for kW scale Li-ion battery stack. A comparative study of the existing cooling systems; air cooling and liquid cooling respectively, has been carried out on three cell stack 70Ah LiFePO₄ battery at a high discharging rate of 2C. It has been found that the liquid cooling is more efficient than air cooling as the peak temperature of the battery stack gets reduced by 30.62% using air cooling whereas using the liquid cooling method it gets reduced by 38.40%. The performance of the liquid cooling system can further be improved if the contact area between the coolant and battery stack is increased. Therefore, in this work, an immersion-based liquid cooling system has been designed to ensure the maximum heat dissipation. The battery stack having a peak temperature of 49.76 °C at 2C discharging rate is reduced by 44.87% to 27.43 °C after using the immersion-based cooling technique. The proposed thermal management scheme is generalized and thus can be very useful for scalable Li-ion battery storage applications also. Full article

(This article belongs to the Special Issue Battery Management for Electric Vehicles)

► Show Figures

Figure 1

26 pages, 7076 KiB

Open AccessArticle

Battery Models for Battery Powered Applications: A Comparative Study

by Nicola Campagna, Vincenzo Castiglia, Rosario Miceli, Rosa Anna Mastromauro, Ciro Spataro, Marco Trapanese and Fabio Viola

Energies 2020, 13(16), 4085; https://0-doi-org.brum.beds.ac.uk/10.3390/en13164085 - 06 Aug 2020

Cited by 36 | Viewed by 5291

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 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)

► Show Figures

Graphical abstract

14 pages, 1479 KiB

Open AccessArticle

En-Route Battery Management and a Mixed Network Equilibrium Problem Based on Electric Vehicle Drivers’ En-Route Recharging Behaviors

by Kai Liu, Sijia Luo and Jing Zhou

Energies 2020, 13(16), 4061; https://0-doi-org.brum.beds.ac.uk/10.3390/en13164061 - 05 Aug 2020

Cited by 3 | Viewed by 1969

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 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)

► Show Figures

Graphical abstract

14 pages, 5411 KiB

Open AccessArticle

Effects of Overdischarge Rate on Thermal Runaway of NCM811 Li-Ion Batteries

by Dong Wang, Lili Zheng, Xichao Li, Guangchao Du, Zhichao Zhang, Yan Feng, Longzhou Jia and Zuoqiang Dai

Energies 2020, 13(15), 3885; https://0-doi-org.brum.beds.ac.uk/10.3390/en13153885 - 30 Jul 2020

Cited by 16 | Viewed by 3508

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(Ni_0.8Co_0.1Mn_0.1)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: T_u, at which the voltage decreased to 0 V; T_i, at which the current decreased to 0 A; and the maximum temperature during the battery overdischarge (T_m). The following times decreased: t_u, when the voltage decreased from 3 to 0 V, and t_i, 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)

► Show Figures

Figure 1

19 pages, 4775 KiB

Open AccessArticle

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

by Fang Liu, Jie Ma, Weixing Su, Hanning Chen and Maowei He

Energies 2020, 13(7), 1679; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071679 - 03 Apr 2020

Cited by 5 | Viewed by 1941

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 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)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Battery Management for Electric Vehicles

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI