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Hybrid Energy Storage Systems for Electric Vehicles

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

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 20221

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Special Issue Editors

Prof. Dr. Rui Esteves Araújo

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Guest Editor

INESC TEC and Faculty of Engineering, University of Porto, Porto, Portugal
Interests: electric vehicles; energy management; hybrid energy storage systems; power electronics; motor drives; control systems; wind turbines; PV systems; fault detection and diagnosis; fault-tolerant control

Dr. Ricardo De Castro

E-Mail Website
Guest Editor

Institute of System Dynamics and Control, German Aerospace Center (DLR), Cologne (Köln), Germany
Interests: electric mobility; hybrid energy storage systems; energy management; optimal control; vehicle dynamics and control

Dr. Cláudio Pinto

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Guest Editor

INESC TEC and Faculty of Engineering, University of Porto, Porto, Portugal
Interests: hybrid energy storage systems; li-ion battery; supercapacitor; active battery balance systems; optimal control; battery thermal balance; electric vehicles; energy storage sizing

Special Issue Information

Dear Colleagues,

Over the last few years, electric vehicles (EVs) have been gaining traction and acceptance in the automobile market, as demonstrated by an increase in the number of electric mobility solutions being introduced by vehicle manufacturers. Part of this success is due to recent advances made in battery technology, which have led to the higher range (kilometers per charge) and better affordability of EVs. Despite this progress, battery-based EVs are still unable to match the performance and lower cost offered by vehicles with internal combustion engines. Further research is required to close the gap; one promising research line focuses on designing EVs with multiple energy storage units, such as batteries-supercapacitors, batteries with different chemistries (high-power and high-energy), batteries-fuel cells, etc. Because of their higher energy efficiency, reliability, and reduced degradation, these hybrid energy storage units (HESS) have shown the potential to lower the vehicle’s total costs of ownership. For instance, the controlled aging of batteries offered by HESS can increase their economic value in second-life applications (such as grid support).

This Special Issue encourages researchers working in this field to share their latest developments on HESS for electric vehicles. We welcome application-of-use cases, state-of-the-art reviews and benchmarking studies—e.g., the evaluation and comparison of different energy management methods. Our aim is to bring together innovative contributions covering (but not limited to):

- Design, system engineering, and field applications of HESS in road vehicles (cars, trucks, buses);

- Multiphysics modeling, simulation, and testing;

- Combined sizing and energy management of HESS;

- Power electronic architectures for HESS;

- Solid-state battery technology;

- Machine learning, big data, and cloud computing in HESS applications;

- Real-time energy management methodologies, including predictive strategies for optimal energy management;

- Monitoring and predictive maintenance of HESS;

- V2G and V2V functionalities and integration of HESS in smart grids;

- Life cycle analysis, including re-use of HESS in second life applications.

Prof. Dr. Rui Esteves Araújo
Dr. Ricardo de Castro
Dr. Cláudio Pinto
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.

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Keywords

electric vehicles
battery
supercapacitor
fuel cells
hybrid energy storage system
energy management
optimal control
optimal energy management
energy storage system sizing
real-time optimal power management
real-time optimization
rule-based and machine learning methods

Published Papers (5 papers)

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Research

26 pages, 6095 KiB

Open AccessArticle

Battery Model Identification Approach for Electric Forklift Application

by Cynthia Thamires da Silva, Bruno Martin de Alcântara Dias, Rui Esteves Araújo, Eduardo Lorenzetti Pellini and Armando Antônio Maria Laganá

Energies 2021, 14(19), 6221; https://doi.org/10.3390/en14196221 - 29 Sep 2021

Cited by 5 | Viewed by 2303

Abstract

Electric forklifts are extremely important for the world’s logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best model is intended for a state of charge estimation on a battery applied on an electric forklift. The nonlinear grey box model with the nonlinear least square method presented a better result for this purpose. This model was estimated with the best experiment design which was defined considering the fit to validation data, the parameter standard deviation and the output variance. With this approach, it was possible to reach more than 80% of fit in different validation data, a non-biased and little prediction error and a good one-step ahead result. Full article

(This article belongs to the Special Issue Hybrid Energy Storage Systems for Electric Vehicles)

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23 pages, 8183 KiB

Open AccessArticle

A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles

by Hoai-Linh T. Nguyen, Bảo-Huy Nguyễn, Thanh Vo-Duy and João Pedro F. Trovão

Energies 2021, 14(12), 3373; https://doi.org/10.3390/en14123373 - 08 Jun 2021

Cited by 14 | Viewed by 3051

Abstract

Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle. Full article

(This article belongs to the Special Issue Hybrid Energy Storage Systems for Electric Vehicles)

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27 pages, 3525 KiB

Open AccessArticle

Energy, Exergy, Economic, and Exergoenvironmental Analyses of a Novel Hybrid System to Produce Electricity, Cooling, and Syngas

by Saeed Esfandi, Simin Baloochzadeh, Mohammad Asayesh, Mehdi Ali Ehyaei, Abolfazl Ahmadi, Amir Arsalan Rabanian, Biplab Das, Vitor A. F. Costa and Afshin Davarpanah

Energies 2020, 13(23), 6453; https://doi.org/10.3390/en13236453 - 06 Dec 2020

Cited by 38 | Viewed by 3454

Abstract

Efficient solar and wind energy to electricity conversion technologies are the best alternatives to reduce the use of fossil fuels and to evolve towards a green and decarbonized world. As the conventional photovoltaic systems use only the 600–1100 nm wavelength range of the solar radiation spectrum for electricity production, hybrid systems taking advantage of the overall solar radiation spectrum are gaining increasing interest. Moreover, such hybrid systems can produce, in an integrated and combined way, electricity, heating, cooling, and syngas through thermochemical processes. They have thus the huge potential for use in residential applications. The present work proposes a novel combined and integrated system for residential applications including wind turbines and a solar dish collector for renewables energy harvesting, an organic Rankine cycle for power production, an absorption chiller for cold production, and a methanation plant for CH₄ production from captured CO₂. This study deals with the energy, exergy, economic, and exergoenvironmental analyses of the proposed hybrid combined system, to assess its performance, viability, and environmental impact when operating in Tehran. Additionally, it gives a clear picture of how the production pattern of each useful product depends on the patterns of the collection of available renewable energies. Results show that the rate of methane production of this hybrid system changes from 42 up to 140 Nm³/month, due to CO₂ consumption from 44 to 144 Nm³/month during a year. Moreover, the energy and exergy efficiencies of this hybrid system vary from 24.7% and 23% to 9.1% and 8%, respectively. The simple payback period of this hybrid system is 15.6 and the payback period of the system is 21.4 years. Full article

(This article belongs to the Special Issue Hybrid Energy Storage Systems for Electric Vehicles)

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24 pages, 6565 KiB

Open AccessArticle

A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

by Sadam Hussain, Muhammad Umair Ali, Gwan-Soo Park, Sarvar Hussain Nengroo, Muhammad Adil Khan and Hee-Je Kim

Energies 2019, 12(24), 4662; https://doi.org/10.3390/en12244662 - 08 Dec 2019

Cited by 49 | Viewed by 5690

Abstract

The energy storage system (ESS) is the main issue in traction applications, such as battery electric vehicles (BEVs). To alleviate the shortage of power density in BEVs, a hybrid energy storage system (HESS) can be used as an alternative ESS. HESS has the dynamic features of the battery and a supercapacitor (SC), and it requires an intelligent energy management system (EMS) to operate it effectively. In this study, a real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller. The proposed EMS intelligently distributes the required power from the battery and SC during acceleration. It allocates the braking energy to the SC on the basis of the state of charge. A simulation study was conducted for three standard drive cycles (New York City cycle, Artemis urban cycle, and New York composite cycle) using MATLAB Simulink. Comparative analysis of conventional and proposed EMSs was carried out. The results reveal that the proposed EMS reduced the stress, temperature, and power losses of the battery. The steady-state charging performance of the SC was 98%, 95%, and 96% for the mentioned drive cycles. Full article

(This article belongs to the Special Issue Hybrid Energy Storage Systems for Electric Vehicles)

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21 pages, 10950 KiB

Open AccessArticle

Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles

by Alessandro Serpi and Mario Porru

Energies 2019, 12(22), 4260; https://doi.org/10.3390/en12224260 - 08 Nov 2019

Cited by 14 | Viewed by 3456

Abstract

Modelling and design of real-time energy management systems for optimising the operating costs of a fuel cell/battery electric vehicle are presented in this paper. The proposed energy management system consists of optimally sharing the propulsion power demand between the fuel cell and battery by enabling them to support each other for operating cost minimisation. The optimisation is achieved through real-time minimisation of a cost function, which accounts for fuel cell and battery degradation, hydrogen consumption and charge sustaining costs. A detailed analysis of each term of the overall cost function is performed and presented, which enables the development of a real-time, advanced energy management system for improving a previously presented simplified version using more accurate modelling and by considering cost function minimisation over a given time horizon. The performance of the proposed advanced energy management system are verified through numerical simulations over different driving cycles; particularly, simulations were performed in MATLAB-Simulink by considering a hysteresis-based energy management system and both simplified and advanced versions of the proposed energy management system for comparison. Full article

(This article belongs to the Special Issue Hybrid Energy Storage Systems for Electric Vehicles)

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