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Energy Storage and Management for Electric Vehicles

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (10 June 2019) | Viewed by 62038

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Special Issue Editors

Prof. Dr. James Marco

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Guest Editor

WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: systems engineering; real-time control; systems modelling; design optimization; design of energy management control systems
Special Issues, Collections and Topics in MDPI journals

Dr. Dinh Quang Truong

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WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: mechatronic systems design, modelling, and control; energy saving and management technologies; control theories and applications; battery management systems; renewable energy

Dr. Stefano Longo

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Guest Editor

Advanced Vehicle Engineering Centre, Cranfield University, Cranfield MK43 0AL, UK
Interests: autonomous systems computing, simulation, and modelling; electric and hybrid vehicles; instrumentation and sensors; mechatronics and advanced controls; on-road vehicle dynamics; systems engineering

Special Issue Information

Dear Colleagues,

Within the automotive and road transport sector, one of the main drivers for technological development and innovation is the need to reduce the vehicle’s fuel consumption and exhaust emissions, while concurrently exceeding consumer expectations for quality, driveability, refinement, and range. To meet this challenge, vehicle manufacturers, subsystem suppliers, and academic institutions are working together to design, integrate, and validate the different technologies that will underpin future powertrain technologies for the next generation of hybridised and fully electric vehicles (e.g. HEV, EV). Within the context of many electrified vehicle applications, the energy storage system will be comprise of many hundreds of individual cells, safety devices, control electronics, and a thermal management subsystem. The aim of this Special Issue of Energies is to explore research innovation within the systems engineering challenge that incorporates mathematical modelling, control engineering, thermal management, mechanical design, packaging, and safety engineering—both at an energy storage system level and within the context of the complete vehicle and end-use application. Specific areas of interest include, but are not limited too:

New concepts in vehicle energy storage design, including the use of hybrid or mixed technology systems (e.g. battery and ultracapacitor) within both first-life and second-life applications.
New concepts in energy management optimisation and energy storage system design within electrified vehicles with greater levels of autonomy and connectivity.
The design, verification, and implementation of enhanced algorithms and models for battery control and monitoring, including new methods in state of charge estimation, state of health estimation, fast charge management, and active balancing.
Novel methods of thermal management, including the creation of new models, the use of new materials, and their integration within the broader thermal management requirements of the vehicle.
New techniques in battery system manufacture, including novel methods for cell interconnection, assembly, disassembly, and repurposing, both within the context of first-life and second-life applications.
Improved integration of the electrified vehicle within the energy system network including opportunities for optimised charging and vehicle-to-grid operation.
Telematics, big data mining, and machine learning for the performance analysis, diagnosis, and management of energy storage and integrated systems.

Dr. James Marco
Dr. Dinh Quang Truong
Dr. Stefano Longo
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

energy management optmisation
mechatronics and advanced controls
systems modelling
battery management systems
drive-cycle creation
thermal management
energy storage ageing and degrdation
system testing and verification
vehicle-to-grid
vehicle charging
life-cycle assessment
second-life energy storage applications

Published Papers (12 papers)

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Research

20 pages, 1847 KiB

Open AccessArticle

A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

by Woo-Yong Kim, Pyeong-Yeon Lee, Jonghoon Kim and Kyung-Soo Kim

Energies 2019, 12(17), 3383; https://doi.org/10.3390/en12173383 - 02 Sep 2019

Cited by 16 | Viewed by 3181

Abstract

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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11 pages, 3774 KiB

Open AccessArticle

Analysis of the Effect of the Variable Charging Current Control Method on Cycle Life of Li-ion Batteries

by In-Ho Cho, Pyeong-Yeon Lee and Jong-Hoon Kim

Energies 2019, 12(15), 3023; https://doi.org/10.3390/en12153023 - 06 Aug 2019

Cited by 38 | Viewed by 7010

Abstract

Applications of rechargeable batteries have recently expanded from small information technology (IT) devices to a wide range of other industrial sectors, including vehicles, rolling stocks, and energy storage system (ESS), as a part of efforts to reduce greenhouse gas emissions and enhance convenience. The capacity of rechargeable batteries adopted in individual products is meanwhile increasing and the price of the batteries in such products has become an important factor in determining the product price. In the case of electric vehicles, the price of batteries has increased to more than 40% of the total product cost. In response, various battery management technologies are being studied to increase the service life of products with large-capacity batteries and reduce maintenance costs. In this paper, a charging algorithm to increase the service life of batteries is proposed. The proposed charging algorithm controls charging current in anticipation of heating inside the battery while the battery is being charged. The validity of the proposed charging algorithm is verified through an experiment to compare charging cycles using high-capacity type lithium-ion cells and high-power type lithium-ion cells. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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17 pages, 3206 KiB

Open AccessArticle

Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis

by Bizhong Xia, Yadi Yang, Jie Zhou, Guanghao Chen, Yifan Liu, Huawen Wang, Mingwang Wang and Yongzhi Lai

Energies 2019, 12(15), 2980; https://doi.org/10.3390/en12152980 - 01 Aug 2019

Cited by 18 | Viewed by 4842

Abstract

Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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19 pages, 1890 KiB

Open AccessArticle

The Effects of Lithium Sulfur Battery Ageing on Second-Life Possibilities and Environmental Life Cycle Assessment Studies

by Deidre Wolff, Lluc Canals Casals, Gabriela Benveniste, Cristina Corchero and Lluís Trilla

Energies 2019, 12(12), 2440; https://doi.org/10.3390/en12122440 - 25 Jun 2019

Cited by 13 | Viewed by 6055

Abstract

The development of Li-ion batteries has enabled the re-entry of electric vehicles into the market. As car manufacturers strive to reach higher practical specific energies (550 Wh/kg) than what is achievable for Li-ion batteries, new alternatives for battery chemistry are being considered. Li-Sulfur batteries are of interest due to their ability to achieve the desired practical specific energy. The research presented in this paper focuses on the development of the Li-Sulfur technology for use in electric vehicles. The paper presents the methodology and results for endurance tests conducted on in-house manufactured Li-S cells under various accelerated ageing conditions. The Li-S cells were found to reach 80% state of health after 300–500 cycles. The results of these tests were used as the basis for discussing the second life options for Li-S batteries, as well as environmental Life Cycle Assessment results of a 50 kWh Li-S battery. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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15 pages, 6334 KiB

Open AccessArticle

Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

by Xiangdong Sun, Jingrun Ji, Biying Ren, Chenxue Xie and Dan Yan

Energies 2019, 12(12), 2242; https://doi.org/10.3390/en12122242 - 12 Jun 2019

Cited by 97 | Viewed by 5901

Abstract

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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28 pages, 663 KiB

Open AccessArticle

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

by Hongjie Liu, Tao Tang, Jidong Lv and Ming Chai

Energies 2019, 12(10), 1876; https://doi.org/10.3390/en12101876 - 17 May 2019

Cited by 2 | Viewed by 2571

Abstract

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an

ϵ

-constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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20 pages, 9468 KiB

Open AccessArticle

A General Parameter Identification Procedure Used for the Comparative Study of Supercapacitors Models

by Henry Miniguano, Andrés Barrado, Cristina Fernández, Pablo Zumel and Antonio Lázaro

Energies 2019, 12(9), 1776; https://doi.org/10.3390/en12091776 - 10 May 2019

Cited by 51 | Viewed by 4729

Abstract

Supercapacitors with characteristics such as high power density, long cycling life, fast charge, and discharge response are used in different applications like hybrid and electric vehicles, grid integration of renewable energies, or medical equipment. The parametric identification and the supercapacitor model selection are two complex processes, which have a critical impact on the system design process. This paper shows a comparison of the six commonly used supercapacitor models, as well as a general and straightforward identification parameter procedure based on Simulink or Simscape and the Optimization Toolbox of Matlab^®. The proposed procedure allows for estimating the different parameters of every model using a different identification current profile. Once the parameters have been obtained, the performance of each supercapacitor model is evaluated through two current profiles applied to hybrid electric vehicles, the urban driving cycle (ECE-15 or UDC) and the hybrid pulse power characterization (HPPC). The experimental results show that the model accuracy depends on the identification profile, as well as the robustness of each supercapacitor model. Finally, some model and identification current profile recommendations are detailed. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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17 pages, 1521 KiB

Open AccessEditor’s ChoiceArticle

A Techno-Economic Analysis of Vehicle-to-Building: Battery Degradation and Efficiency Analysis in the Context of Coordinated Electric Vehicle Charging

by Stefan Englberger, Holger Hesse, Daniel Kucevic and Andreas Jossen

Energies 2019, 12(5), 955; https://doi.org/10.3390/en12050955 - 12 Mar 2019

Cited by 24 | Viewed by 10129

Abstract

In the context of the increased acceptance and usage of electric vehicles (EVs), vehicle-to-building (V2B) has proven to be a new and promising use case. Although this topic is already being discussed in literature, there is still a lack of experience on how such a system, of allowing bidirectional power flows between an EV and building, will work in a residential environment. The challenge is to optimize the interplay of electrical load, photovoltaic (PV) generation, EV, and optionally a home energy storage system (HES). In total, fourteen different scenarios are explored for a German household. A two-step approach is used, which combines a computationally efficient linear optimizer with a detailed modelling of the non-linear effects on the battery. The change in battery degradation, storage system efficiency, and operating expenses (OPEX) as a result of different, unidirectional and bidirectional, EV charging schemes is examined for both an EV battery and a HES. The simulations show that optimizing unidirectional charging can improve the OPEX by 15%. The addition of V2B leads to a further 11% cost reduction, however, this corresponds with a 12% decrease in EV battery lifetime. Techno-economic analysis reveals that the V2B charging solution with no HES leads to strong self-consumption improvements (EUR 1381 savings over ten years), whereas, this charging scheme would not be justified for a residential prosumer with a HES (only EUR 160 savings). Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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27 pages, 498 KiB

Open AccessArticle

Optimal Sizing of Storage Elements for a Vehicle Based on Fuel Cells, Supercapacitors, and Batteries

by José Luis Sampietro, Vicenç Puig and Ramon Costa-Castelló

Energies 2019, 12(5), 925; https://doi.org/10.3390/en12050925 - 10 Mar 2019

Cited by 25 | Viewed by 3772

Abstract

To achieve a vehicle-efficient energy management system, an architecture composed of a PEM fuel cell as the main energy source and a hybrid storage system based on battery banks and supercapacitors is proposed. This paper introduces a methodology for the optimal component sizing aiming at minimizing the total cost, achieving a cheaper system that can achieve the requirements of the speed profiles. The chosen vehicle is an urban transport bus, which must meet the Buenos Aires Driving Cycle, and the Manhattan Driving Cycle. The combination of batteries and supercapacitors allows a better response to the vehicle’s power demand, since it combines the high energy density of the batteries with the high power density of the supercapacitors, allowing the best absorption of energy coming from braking. In this way, we address the rapid changes in power without reducing the global efficiency of the system. Optimum use of storage systems and fuel cell is analyzed through dynamic programming. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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15 pages, 5565 KiB

Open AccessFeature PaperArticle

Simulation of Thermal Behaviour of a Lithium Titanate Oxide Battery

by Seyed Saeed Madani, Erik Schaltz and Søren Knudsen Kær

Energies 2019, 12(4), 679; https://doi.org/10.3390/en12040679 - 20 Feb 2019

Cited by 14 | Viewed by 3763

Abstract

One of the reasonable possibilities to investigate the battery behaviour under various temperature and current conditions is the development of a model of the lithium-ion batteries and then by employing the simulation technique to anticipate their behaviour. This method not only can save time but also they can predict the behaviour of the batteries through simulation. In this investigation, a three-dimensional model is developed to simulate thermal and electrochemical behaviour of a 13Ah lithium-ion battery. In addition, the temperature dependency of the battery cell parameters was considered in the model in order to investigate the influence of temperature on various parameters such as heat generation during battery cell operation. Maccor automated test system and isothermal battery calorimeter were used as experimental setup to validate the thermal model, which was able to predict the heat generation rate and temperature at different positions of the battery. The three-dimensional temperature distributions which were achieved from the modelling and experiment were in well agreement with each other throughout the entire of discharge cycling at different environmental temperatures and discharge rates. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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19 pages, 4986 KiB

Open AccessArticle

Optimal Control for Hybrid Energy Storage Electric Vehicle to Achieve Energy Saving Using Dynamic Programming Approach

by Chaofeng Pan, Yanyan Liang, Long Chen and Liao Chen

Energies 2019, 12(4), 588; https://doi.org/10.3390/en12040588 - 13 Feb 2019

Cited by 24 | Viewed by 5777

Abstract

In this paper, the efficiency characteristics of battery, super capacitor (SC), direct current (DC)-DC converter and electric motor in a hybrid power system of an electric vehicle (EV) are analyzed. In addition, the optimal efficiency model of the hybrid power system is proposed based on the hybrid power system component’s models. A rule-based strategy is then proposed based on the projection partition of composite power system efficiency, so it has strong adaptive adjustment ability. Additionally. the simulation results under the New European Driving Cycle (NEDC) condition show that the efficiency of rule-based strategy is higher than that of single power system. Furthermore, in order to explore the maximum energy-saving potential of hybrid power electric vehicles, a dynamic programming (DP) optimization method is proposed on the basis of the establishment of the whole hybrid power system, which takes into account various energy consumption factors of the whole system. Compared to the battery-only EV based on simulation results, the hybrid power system controlled by rule-based strategy can decrease energy consumption by 13.4% in line with the NEDC condition, while the power-split strategy derived from the DP approach can reduce energy consumption by 17.6%. The results show that compared with rule-based strategy, the optimized DP strategy has higher system efficiency and lower energy consumption. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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17 pages, 5803 KiB

Open AccessFeature PaperArticle

Equivalent Circuit Model Construction and Dynamic Flow Optimization Based on Zinc–Nickel Single-Flow Battery

by Shouguang Yao, Xiaofei Sun, Min Xiao, Jie Cheng and Yaju Shen

Energies 2019, 12(4), 582; https://doi.org/10.3390/en12040582 - 13 Feb 2019

Cited by 5 | Viewed by 2760

Abstract

Based on the zinc–nickel single-flow battery, a generalized electrical simulation model considering the effects of flow rate, self-discharge, and pump power loss is proposed. The results compared with the experiment show that the simulation results considering the effect of self-discharge are closer to the experimental values, and the error range of voltage estimation during charging and discharging is between 0% and 3.85%. In addition, under the rated electrolyte flow rate and different charge–discharge currents, the estimation of Coulomb efficiency by the simulation model is in good agreement with the experimental values. Electrolyte flow rate is one of the parameters that have a great influence on system performance. Designing a suitable flow controller is an effective means to improve system performance. In this paper, the genetic algorithm and the theoretical minimum flow multiplied by different flow factors are used to optimize the variable electrolyte flow rate under dynamic SOC (state of charge). The comparative analysis results show that the flow factor optimization method is a simple means under constant charge–discharge power, while genetic algorithm has better performance in optimizing flow rate under varying (dis-)charge power and state of charge condition in practical engineering. Full article

(This article belongs to the Special Issue Energy Storage and Management for Electric Vehicles)

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