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WEVJ
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Power System and Energy Management of Hybrid Electric Vehicles
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Power System and Energy Management of Hybrid Electric Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 34461

Special Issue Editors

Dr. Daniela Chrenko

E-Mail Website
Guest Editor
FEMTO-ST Institute, University Bourgogne Franche-Comté, CNRS, 90000 Belfort, France
Interests: battery systems; energy demand in real use; battery aging and second use
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alan Keromnes

E-Mail Website
Guest Editor
DRIVE EA 1859, Univ. Bourgogne Franche Comté, F58027 Nevers, France
Interests: internal combustion engines; hcci; auto-ignition, rapid compression machine; shock tube
Prof. Dr. El Hassane Aglzim

E-Mail Website
Guest Editor
DRIVE EA 1859, Univ. Bourgogne Franche Comté, F58027 Nevers, France
Interests: electric vehicles; lithium-ion battery charge

Special Issue Information

Dear Colleagues,

Exhaust gases from personal transport are among the key contributors to global warming. As personal transport is of high importance today, considerable effort is being made to make vehicles more environmentally friendly. Numerous power systems for hybrid electric vehicles have been studied, and several solutions, including not only internal combustion engines and fuel cell-based solutions, will likely coexist in the future. Energy management is a topic that is crucial for all hybrid electric vehicles, and considerable work still needs to be done to consolidate an energy management strategy that unites aspects of optimization with real-time application. In this context, questions of route planning and prediction are interesting not only with regard to recharge planning. Furthermore, the link between energy management of hybrid electric vehicles and autonomous driving might also be of interest. In conclusion, the topic of power system and energy management of hybrid electric vehicles is key in the development of sustainable personal transport in the future.

Prof. Dr. Daniela Chrenko
Prof. Dr. Alan Keromnes
Prof. Dr. El Hassane Aglzim
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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.

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Published Papers (5 papers)

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Research

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23 pages, 5279 KiB  
Article
Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler
by Supriya Kalyankar-Narwade, Ramesh Kumar Chidambaram and Sanjay Patil
World Electr. Veh. J. 2021, 12(1), 35; https://doi.org/10.3390/wevj12010035 - 1 Mar 2021
Cited by 6 | Viewed by 3265
Abstract
Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, [...] Read more.
Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, and energy optimization. Two power sources need to be synchronized skillfully for optimum energy utilization. A prominent variant of HEV is that it easily converts conventional scooters into parallel hybrids by “Through-the-Road (TTR)” architecture. This paper focuses on three switching control strategies of HEVs based on the state of charge, fuzzy logic, and neural network. Further, to optimize energy usage, all these control strategies are compared. Energy management control for the TTR model is developed with vehicle parameters in the Simulink environment and simulated using the “World Harmonized Motorcycle Test Cycle” (WMTC) drive cycle. The multivariable input model is presented with a fuzzy rule-based hybrid switching control. A similar system is also modeled with a neural network-based decision control and the observations are tabulated for the fuel economy and energy management. Simulation results show that the neural network-based optimization results in minimal energy consumption among all three hybrid operations. Full article
(This article belongs to the Special Issue Power System and Energy Management of Hybrid Electric Vehicles)
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25 pages, 4468 KiB  
Article
Computationally Efficient Energy Management in Hybrid Electric Vehicles Based on Approximate Pontryagin’s Minimum Principle
by Fengqi Zhang, Lihua Wang, Serdar Coskun, Yahui Cui and Hui Pang
World Electr. Veh. J. 2020, 11(4), 65; https://doi.org/10.3390/wevj11040065 - 9 Oct 2020
Cited by 8 | Viewed by 3787
Abstract
This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology [...] Read more.
This article presents an energy management method for a parallel hybrid electric vehicle (HEV) based on approximate Pontryagin’s Minimum Principle (A-PMP). The A-PMP optimizes gearshift commands and torque distribution for overall energy efficiency. As a practical numerical solution in PMP, the proposed methodology utilizes a piecewise linear approximation of the engine fuel rate and state of charge (SOC) derivative by considering drivability and fuel economy simultaneously. Moreover, battery aging is explicitly studied by introducing a control-oriented model, which aims to investigate the effect of battery aging on the optimization performance in the development of the HEVs. An approximate energy management strategy with piecewise linear models is then formulated by the A-PMP, which targets a better performance for the Hamiltonian optimization. The gearshift map is extracted from the optimal results in the standard PMP to hinder frequent gearshift by considering both drivability and fuel economy. Utilizing an approximated Hamilton function, the torque distribution, gearshift command, and the battery aging degradation are jointly optimized under a unified framework. Simulations are performed for dynamic programming (DP), PMP, and A-PMP to validate the effectiveness of the proposed approach. The results indicate that the proposed methodology achieves a close fuel economy compared with the DP-based optimal solution. Moreover, it improves the computation efficiency by 50% and energy saving by 3.5%, compared with the PMP, while ensuring good drivability and fuel efficiency. Full article
(This article belongs to the Special Issue Power System and Energy Management of Hybrid Electric Vehicles)
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29 pages, 6520 KiB  
Article
Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics
by Zachary Bosire Omariba, Lijun Zhang, Hanwen Kang and Dongbai Sun
World Electr. Veh. J. 2020, 11(3), 50; https://doi.org/10.3390/wevj11030050 - 23 Jul 2020
Cited by 20 | Viewed by 5879
Abstract
There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges [...] Read more.
There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation. Full article
(This article belongs to the Special Issue Power System and Energy Management of Hybrid Electric Vehicles)
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13 pages, 581 KiB  
Article
Primary Frequency Response Improvement in Interconnected Power Systems Using Electric Vehicle Virtual Power Plants
by Hassan Haes Alhelou, Pierluigi Siano, Massimo Tipaldi, Raffaele Iervolino and Feras Mahfoud
World Electr. Veh. J. 2020, 11(2), 40; https://doi.org/10.3390/wevj11020040 - 16 May 2020
Cited by 33 | Viewed by 4169
Abstract
The smart grid concept enables demand-side management, including electric vehicles (EVs). Thus way, some ancillary services can be provided in order to improve the power system stability, reliability, and security. The high penetration level of renewable energy resources causes some problems to independent [...] Read more.
The smart grid concept enables demand-side management, including electric vehicles (EVs). Thus way, some ancillary services can be provided in order to improve the power system stability, reliability, and security. The high penetration level of renewable energy resources causes some problems to independent system operators, such as lack of primary reserve and active power balance problems. Nowadays, many countries are encouraging the use of EVs which provide a good chance to utilize them as a virtual power plant (VPP) in order to contribute to frequency event. This paper proposes a new control method to use EV as VPP for providing primary reserve in smart grids. The primary frequency reserve helps the power system operator to intercept the frequency decline and to improve the frequency response of the whole system. The proposed method calculates the electric vehicles’ primary reserve based on EVs’ information, such as the state of charge (SOC), the arriving time and the vehicle’s departure time. The effectiveness of the proposed scheme is verified by several simulation scenarios on a real-world modern power system with different generating units, such as conventional power plants, renewable energy resources, and electric vehicles. Full article
(This article belongs to the Special Issue Power System and Energy Management of Hybrid Electric Vehicles)
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Review

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20 pages, 2639 KiB  
Review
A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges
by Nassim Noura, Loïc Boulon and Samir Jemeï
World Electr. Veh. J. 2020, 11(4), 66; https://doi.org/10.3390/wevj11040066 - 16 Oct 2020
Cited by 156 | Viewed by 16267
Abstract
To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, [...] Read more.
To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy. Full article
(This article belongs to the Special Issue Power System and Energy Management of Hybrid Electric Vehicles)
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