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Energies
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Smart Energy Management for Electric and Hybrid Electric Vehicles
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Smart Energy Management for Electric and Hybrid Electric Vehicles

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

Deadline for manuscript submissions: closed (6 September 2023) | Viewed by 18237

Special Issue Editor

Dr. Daniela Chrenko

E-Mail Website
Guest Editor
FEMTO-ST Institute, FCLAB, University Bourgogne Franche-Comté, CNRS, Belfort, France
Interests: energy storage; lithium-ion batteries; energy management; driving cycles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Personal transport is among the key contributors for global warming, and suffers from limited fuel resources. As personal transport is of high importance for society, considerable effort is being made in order to make vehicles more sustainable. Numerous power systems for electric and hybrid electric vehicles are under evaluation. It is likely that several solutions, including batteries, internal combustion engines, super capacitors, and fuel cell-based solutions, to name a few, will likely be co-existing in the future depending on the application.

A topic that is crucial for all vehicles is energy management, and considerable work still has to be done in order to consolidate energy management that unites aspects of optimization with real-time application. In this context, questions of real vehicle use, route planning, and prediction are interesting, not only with regard to energy management, but also to recharge planning. Furthermore, sustainability has to be applied over system life imposing economical questions including business models. Finally, the link between energy need and energy management in link with autonomous driving might also of interest.

In conclusion, the topic of smart energy management of electric and hybrid electric vehicles is highly multidisciplinary and a key topic in order to develop sustainable personal transport for the future.

Dr. Daniela Chrenko
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. 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

  • Electric vehicle
  • Hybrid electric vehicle
  • Energy management
  • Energy storage (batteries, super capacitors, fuel cells, and internal combustion engine)
  • Recharge
  • Sustainable business models
  • Autonomous driving
  • Driving cycles
  • Energy need prediction

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Published Papers (6 papers)

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22 pages, 5823 KiB  
Article
Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control
by Matteo Acquarone, Claudio Maino, Daniela Misul, Ezio Spessa, Antonio Mastropietro, Luca Sorrentino and Enrico Busto
Energies 2023, 16(6), 2749; https://doi.org/10.3390/en16062749 - 15 Mar 2023
Cited by 3 | Viewed by 1731
Abstract
The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel [...] Read more.
The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel economy of hybrid electric vehicles. In the recent years, reinforcement-learning-based controllers have been shown to outperform well-established real-time strategies for specific applications. Nevertheless, the effects produced by variation in the reward function have not been thoroughly analyzed and the potential of the adoption of a given RL agent under different testing conditions is still to be assessed. In the present paper, the performance of different agents, i.e., Q-learning, deep Q-Network and double deep Q-Network, are investigated considering a full hybrid electric vehicle throughout multiple driving missions and introducing two distinct reward functions. The first function aims at guaranteeing a charge-sustaining policy whilst reducing the fuel consumption (FC) as much as possible; the second function in turn aims at minimizing the fuel consumption whilst ensuring an acceptable battery state of charge (SOC) by the end of the mission. The novelty brought by the results of this paper lies in the demonstration of a non-trivial incapability of DQN and DDQN to outperform traditional Q-learning when a SOC-oriented reward is considered. On the contrary, optimal fuel consumption reductions are attained by DQN and DDQN when more complex FC-oriented minimization is deployed. Such an important outcome is particularly evident when the RL agents are trained on regulatory driving cycles and tested on unknown real-world driving missions. Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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21 pages, 7429 KiB  
Article
Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems
by Matthieu Matignon, Toufik Azib, Mehdi Mcharek, Ahmed Chaibet and Adriano Ceschia
Energies 2023, 16(6), 2645; https://doi.org/10.3390/en16062645 - 10 Mar 2023
Cited by 5 | Viewed by 1714
Abstract
Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) [...] Read more.
Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) concept. It uses a nested approach combining the best of the three EMS categories (optimization-based (OBS), rules-based (RBS), and learning-based (LBS) strategies) to overcome the real-time operating condition limitations of the fuel cell hybrid electric vehicle (FCHEV). Through a fuel cell/battery hybrid architecture, the purpose is to improve hydrogen consumption and manage the battery state of charge (SOC) under real-time driving conditions. The proposed iEMS approach is based on an OBS with optimal control to make the energy-optimal decision. However, it requires the adaptations of real-time operating conditions and a dynamic SOC horizon management. These requirements are supported by combining an RBS based on expert and fuzzy rules to compute the SOC target on each sliding window and an LBS based on fuzzy C-mean clustering to enhance the cooperative environment data processing and adapt it to the FHCEV topology. Our approach obtained simple and realistic system behaviors while having an acceptable computing time suitable for real time constraint. It was then designed and validated using a 27-h real-time measured database. The results show the effectiveness of the proposed iEMS concept with an excellent performance close to the optimal offline strategy (an under 2% consumption gap). Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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19 pages, 965 KiB  
Article
Power Management of a Plug-in Hybrid Electric Vehicle Using Neural Networks with Comparison to Other Approaches
by Da Huo and Peter Meckl
Energies 2022, 15(15), 5735; https://doi.org/10.3390/en15155735 - 7 Aug 2022
Cited by 12 | Viewed by 2020
Abstract
Many researchers spent much effort on the online power management strategies for plug-in hybrid vehicles (PHEVs) and hybrid electric vehicles (HEVs). Nowadays, artificial neural networks (ANNs), one of the machine learning techniques, have also been applied to this problem due to their good [...] Read more.
Many researchers spent much effort on the online power management strategies for plug-in hybrid vehicles (PHEVs) and hybrid electric vehicles (HEVs). Nowadays, artificial neural networks (ANNs), one of the machine learning techniques, have also been applied to this problem due to their good performance in learning non-linear and complicated multi-inputs multi-outputs (MIMO) dynamic systems. In this paper, an ANN is applied to the online power management for a plug-in hybrid electric vehicle (PHEV) by predicting the torque split between an internal combustion engine (ICE) and an electric motor (e-Motor) to optimize the greenhouse gas (GHG) emissions by using dynamic programming (DP) results as training data. Dynamic programming can achieve a global minimum solution while it is computationally intensive and requires prior knowledge of the entire drive cycle. As such, this method cannot be implemented in real-time. The DP-based ANN controller can get the benefit of using an ANN to fit the DP solution so that it can be implemented in real-time for an arbitrary drive cycle. We studied the hyper-parameters’ effects on the ANN model and different structures of ANN models are compared. The minimum training mean square error (MSE) models in each comparison set are selected for comparison with DP and equivalent consumption minimization strategy (ECMS). The total GHG emissions and state of charge (SOC) are the metrics used for the analysis and comparison. All the selected ANNs provide results that are comparable to the optimal DP solution, which indicates that ANNs are almost as good as the DP solution. It is found that the multiple hidden-layer ANN shows more efficiency in the training process than the single hidden-layer ANN. By comparing the results with ECMS, the ANN shows great potential in real-time application with the smallest deviation from the results of DP. In addition, our approach does not require any additional trip information, and its output (torque split) is more directly implementable on real vehicles. Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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15 pages, 2015 KiB  
Article
Car Engines Comparative Analysis: Sustainable Approach
by Sebastian Grzesiak and Adam Sulich
Energies 2022, 15(14), 5170; https://doi.org/10.3390/en15145170 - 16 Jul 2022
Cited by 9 | Viewed by 5642
Abstract
The European Union takes significant steps to support the development of the electric sector of the automotive market. This is confirmed by the signed declaration in Glasgow, which leads to a ban on the sale of cars with combustion engines from 2035. This [...] Read more.
The European Union takes significant steps to support the development of the electric sector of the automotive market. This is confirmed by the signed declaration in Glasgow, which leads to a ban on the sale of cars with combustion engines from 2035. This document changes the car industry and makes it dependent on electricity production. The problem identified in this article is the actual impact of implemented solutions concerning the type of engine in cars offered for sale in Czechia, Germany, and Poland. Therefore, the aim of this scientific paper is car engines’ multilevel comparative analysis. The aim of the article is accompanied by a research question: are electric vehicles less harmful to the natural environment? The paper compares cars of the same producer, class, and type with petrol, diesel, hybrid (petrol-electric), and electric engines in terms of the environmental impact. The research method is a comparative SUV analysis supported by the comparison of selected countries’ conditions for electromobility development. The results of this study indicate that vehicles with electric engines emit the least amount of carbon dioxide and are the most environmentally friendly solution in the given comparison criteria. Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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19 pages, 1697 KiB  
Article
Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles
by Yaqian Wang and Xiaohong Jiao
Energies 2022, 15(9), 3235; https://doi.org/10.3390/en15093235 - 28 Apr 2022
Cited by 27 | Viewed by 2737
Abstract
This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) [...] Read more.
This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions. Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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21 pages, 13161 KiB  
Article
Intelligent Multi-Vehicle DC/DC Charging Station Powered by a Trolley Bus Catenary Grid
by Michéle Weisbach, Tobias Schneider, Dominik Maune, Heiko Fechtner, Utz Spaeth, Ralf Wegener, Stefan Soter and Benedikt Schmuelling
Energies 2021, 14(24), 8399; https://doi.org/10.3390/en14248399 - 13 Dec 2021
Cited by 10 | Viewed by 2952
Abstract
This article deals with the major challenge of electric vehicle charging infrastructure in urban areas—installing as many fast charging stations as necessary and using them as efficiently as possible, while considering grid level power limitations. A smart fast charging station with four vehicle [...] Read more.
This article deals with the major challenge of electric vehicle charging infrastructure in urban areas—installing as many fast charging stations as necessary and using them as efficiently as possible, while considering grid level power limitations. A smart fast charging station with four vehicle access points and an intelligent load management algorithm based on the combined charging system interface is presented. The shortcomings of present implementations of the combined charging system communication protocol are identified and discussed. Practical experiments and simulations of different charging scenarios validate the concept and show that the concept can increase the utilization time and the supplied energy by a factor of 2.4 compared to typical charging station installations. Full article
(This article belongs to the Special Issue Smart Energy Management for Electric and Hybrid Electric Vehicles)
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