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Advanced Battery States Estimation and Charging Techniques for Electric Vehicles
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Advanced Battery States Estimation and Charging Techniques for Electric Vehicles

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 32959

Special Issue Editor

Prof. Dr. Weixiang Shen

E-Mail Website
Guest Editor
Department of Engineering Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
Interests: battery charging and battery capacity estimation in electric vehicles (EVs); integration of renewable energy into grid; vehicle-to-grid, and control of EVs, HEVs and PHEVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many countries have taken aggressive steps to promote electric vehicles (EVs) in order to achieve carbon neutrality, which has led to accelerated research and development of EV technologies. Among those EV technologies, battery management technologies (BMTs) are crucial in monitoring and controlling batteries in EVs for performance improvement and life extension. They mainly involve how to determine battery states and how to charge batteries. Novel battery states estimation and charging techniques have been developed recently. Furthermore, we have seen the increasing adoption of machine learning (e.g., deep learning) and big data (e.g., digital twin) in battery states estimation and charging, which is a promising development in these areas.   

For this Special Issue of Vehicles entitled “Advanced Battery States Estimation and Charging Techniques for Electric Vehicles”, we seek review articles and original contributions in battery states estimation and charging techniques, particularly those with hardware-in-the-loop validation or real-world implementation. Potential topics include, but are not limited to, battery charging; the application of machine learning and big data in battery states estimation and battery charging; EV battery indicator and EV battery charger; and the estimation of different battery states including state of charge, state of energy, state of power, and state of health.

Prof. Dr. Weixiang Shen
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 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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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 states estimation
  • co-estimation of battery states
  • battery charging
  • application of big data in battery states estimation and charging
  • application of machine learning in battery states estimation and charging
  • EV battery indicator
  • EV battery charger

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Published Papers (7 papers)

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Research

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30 pages, 9199 KiB  
Article
Intelligent Deep Learning Estimators of a Lithium-Ion Battery State of Charge Design and MATLAB Implementation—A Case Study
by Nicolae Tudoroiu, Mohammed Zaheeruddin, Roxana-Elena Tudoroiu, Mihai Sorin Radu and Hana Chammas
Vehicles 2023, 5(2), 535-564; https://doi.org/10.3390/vehicles5020030 - 2 May 2023
Cited by 3 | Viewed by 2446
Abstract
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive [...] Read more.
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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16 pages, 2715 KiB  
Article
Optimal Deployment of Wireless Charging Infrastructure for Electric Tram with Dual Operation Policy
by Young Kwan Ko, Yonghui Oh, Dae Young Ryu and Young Dae Ko
Vehicles 2022, 4(3), 681-696; https://doi.org/10.3390/vehicles4030039 - 16 Jul 2022
Cited by 4 | Viewed by 2539
Abstract
The wireless charging electric tram system is presently receiving attention as an eco-friendly means of transportation. The conventional electric tram system has a similar advantage in regards to environmental pollution, but it has several problems that are caused by the overhead power supply [...] Read more.
The wireless charging electric tram system is presently receiving attention as an eco-friendly means of transportation. The conventional electric tram system has a similar advantage in regards to environmental pollution, but it has several problems that are caused by the overhead power supply line. The battery-type electric tram system should be considered carefully, because the battery itself is an environmentally harmful material. Therefore, the wireless charging electric tram system is regarded as an alternative means of transportation. The adequate battery capacity and the location of the wireless charging infrastructure are investigated in this study, which consider the dual operation policy, and the objective is to minimize the total investment cost. The variation of the battery capacity and the location of the wireless charging infrastructure are examined that compare Case 1, which involves the electric trams operating only in normal operations, and Case 2, which includes the electric trams operating in normal and express operations. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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24 pages, 10165 KiB  
Article
Battery Management System for Unmanned Electric Vehicles with CAN BUS and Internet of Things
by Ngoc Nam Pham, Jan Leuchter, Khac Lam Pham and Quang Huy Dong
Vehicles 2022, 4(3), 639-662; https://doi.org/10.3390/vehicles4030037 - 25 Jun 2022
Cited by 10 | Viewed by 6082
Abstract
In recent decades, the trend of using zero-emission vehicles has been constantly evolving. This trend brings about not only the pressure to develop electric vehicles (EVs) or hybrid electric vehicles (HEVs) but also the demand for further developments in battery technologies and safe [...] Read more.
In recent decades, the trend of using zero-emission vehicles has been constantly evolving. This trend brings about not only the pressure to develop electric vehicles (EVs) or hybrid electric vehicles (HEVs) but also the demand for further developments in battery technologies and safe use of battery systems. Concerning the safe usage of battery systems, Battery Management Systems (BMS) play one of the most important roles. A BMS is used to monitor operating temperature and State of Charge (SoC), as well as protect the battery system against cell imbalance. The paper aims to present hardware and software designs of a BMS for unmanned EVs, which use Lithium multi-cell battery packs. For higher modularity, the designed BMS uses a distributed topology and contains a master module with more slave modules. Each slave module is in charge of monitoring and protecting a multi-cell battery pack. All information about the state of each battery pack is sent to the master module which saves and sends all data to the control station if required. Controlled Area Network (CAN) bus and Internet of Things technologies are designed for requirements from different applications for communications between slave modules and the master module, and between the master module and control station. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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19 pages, 3464 KiB  
Article
A Novel Model to Predict Electric Vehicle Rapid Charging Deployment on the UK Motorway Network
by Keith Chamberlain and Salah Al Majeed
Vehicles 2022, 4(2), 567-585; https://doi.org/10.3390/vehicles4020033 - 10 Jun 2022
Viewed by 2589
Abstract
Recent transformations from internal combustion engines (ICE) to electric vehicles (EVs) are challenged by limited the driving range per charge, thereby requiring the improvement or substantial deployment of rapid charging infrastructure to stimulate sufficient confidence in EV drivers. This study aims to establish [...] Read more.
Recent transformations from internal combustion engines (ICE) to electric vehicles (EVs) are challenged by limited the driving range per charge, thereby requiring the improvement or substantial deployment of rapid charging infrastructure to stimulate sufficient confidence in EV drivers. This study aims to establish the necessary level of EV motorway service station infrastructure for the United Kingdom (UK) based market. The investigation is founded on increasing the appropriate rapid charger availability and shorter charging times. EV charging patterns are determined, focusing on two Volkswagen iD3 EV models by measuring power curves across field-based rapid chargers at one-minute intervals. Datasets are analysed throughout rapid charging field tests. Additionally, variance synthesis is applied to establish variables within this study’s assessment for rapid charger capacity requirements in the UK. The operational performance for the utilised rapid chargers is correspondingly recorded, whilst the EV range is calculated at 3 miles per kWh, revealing a mean power delivery rate of just 27 kW per hour using a 50 kW rapid charger. Time-of-day charging sessions are used to generate data that is then amalgamated into our previous study data, confirming that rapid charging points on UK motorways are used primarily for EV journey range extension. If fully utilised for an entire 24h period, 434 chargers (with a variance consolidation number of 81) are required to service the UK-based motorway EV user base. Moreover, this study establishes that simply replacing current fuel pumps with individual rapid chargers on a like-for-like basis reduces availability and support for novel and existing users and may impact short-term grid availability. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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14 pages, 1329 KiB  
Article
Design of Fast Charging Station with Energy Management for eBuses
by Hossam A. Gabbar, Yasser Elsayed, Abu Bakar Siddique, Abdalrahman Elshora and Ajibola Adeleke
Vehicles 2021, 3(4), 807-820; https://doi.org/10.3390/vehicles3040048 - 23 Nov 2021
Cited by 4 | Viewed by 3422
Abstract
The popularity of the eBus has been increasing rapidly in recent years due to its low greenhouse gases (GHG) emissions and its low dependence on fossil fuels. This incremental use of the eBus increases the burden to the power grid for its charging. [...] Read more.
The popularity of the eBus has been increasing rapidly in recent years due to its low greenhouse gases (GHG) emissions and its low dependence on fossil fuels. This incremental use of the eBus increases the burden to the power grid for its charging. Charging eBus requires a high amount of power for a feasible amount of time. Therefore, developing a fast-charging station (FCS) integrated with Micro Energy Grid (MEG) and hybrid energy storage is crucial for charging eBuses. This paper presents a design of FCS for eBus that integrates MEG with hybrid energy storage with the energy management system. To reduce the dependency on the main utility grid, a hybrid micro energy grid based on a renewable source (i.e., PV) have been included. In addition, hybrid energy storage of batteries and flywheels has also been developed to mitigate the power demand of the fast-charging station during peak time. Furthermore, a multiple-input DC-DC converter has been developed for managing the DC power transfer between the common DC bus and the multiple energy sources. Finally, an energy management system and the controller has been designed to achieve an extensive performance from the fast charging station. MATLAB Simulink has been used for the simulation work of the overall design. Different test case scenarios are tested for evaluating the performance parameters of the proposed FCS and also for evaluating its performance. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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13 pages, 1637 KiB  
Article
Influence of Charging Losses on Energy Consumption and CO2 Emissions of Battery-Electric Vehicles
by Benedikt Reick, Anja Konzept, André Kaufmann, Ralf Stetter and Danilo Engelmann
Vehicles 2021, 3(4), 736-748; https://doi.org/10.3390/vehicles3040043 - 4 Nov 2021
Cited by 15 | Viewed by 6726
Abstract
Due to increasing sales figures, the energy consumption of battery-electric vehicles is moving further into focus. In addition to efficient driving, it is also important that the energy losses during AC charging are as low as possible for a sustainable operation. In many [...] Read more.
Due to increasing sales figures, the energy consumption of battery-electric vehicles is moving further into focus. In addition to efficient driving, it is also important that the energy losses during AC charging are as low as possible for a sustainable operation. In many situations it is not possible or necessary to charge the vehicle with the maximum charging power e.g., in apartment buildings. The influence of the charging mode (number of phases used, in-cable-control-box or used wallbox, charging current) on the charging efficiency is often unknown. In this work, the energy consumption of two electric vehicles in the Worldwide Harmonized Light-Duty Vehicles Test Cycle is presented. In-house developed measurement technology and vehicle CAN data are used. A detailed breakdown of charging losses, drivetrain efficiency, and overall energy consumption for one of the vehicles is provided. Finally, the results are discussed with reference to avoidable CO2 emissions. The charging losses of the tested vehicles range from 12.79 to 20.42%. Maximum charging power with three phases and 16 A charging current delivers the best efficiencies. Single-phase charging was considered down to 10 A, where the losses are greatest. The drivetrain efficiency while driving is 63.88% on average for the WLTC, 77.12% in the “extra high” section and 23.12% in the “low” section. The resulting energy consumption for both vehicles is higher than the OEM data given (21.6 to 44.9%). Possible origins for the surplus on energy consumption are detailed. Over 100,000 km, unfavorable charging results in additional CO2 emissions of 1.24 t. The emissions for an assumed annual mileage of 20,000 km are three times larger than for a class A+ refrigerator. A classification of charging modes and chargers thus appears to make sense. In the following work, efficiency improvements in the charger as well as DC charging will be proposed. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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Review

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29 pages, 3885 KiB  
Review
A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles
by Ruohan Guo and Weixiang Shen
Vehicles 2022, 4(1), 1-29; https://doi.org/10.3390/vehicles4010001 - 21 Dec 2021
Cited by 58 | Viewed by 7589
Abstract
With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent [...] Read more.
With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future. Full article
(This article belongs to the Special Issue Advanced Battery States Estimation and Charging Techniques for Electric Vehicles)
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