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Machines
Special Issues
Energy Management and ECO-Driving Strategies of Hybrid Electric Vehicles
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Energy Management and ECO-Driving Strategies of Hybrid Electric Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4089

Special Issue Editor

Dr. Xiaodong Wu

E-Mail Website
Guest Editor
Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Interests: intelligent vehicle control; vehicle energy management

Special Issue Information

Dear Colleagues,

In recent years, as a large amount of fossil fuel is consumed and carbon dioxide is produced from road transport, energy conservation and emission reduction have received increasing attention from the automotive industry. Vehicle electrification represented by hybrid electric vehicles is a lucrative solution to improve vehicle fuel economy. An energy management strategy of the multiple power sources in the hybrid powertrain is one of the crucial technologies for hybrid electric vehicles. However, it is challenging to achieve real-time optimal control owing to the complex nature caused by powertrain configurations, human behaviors, travel demand, and so forth. In addition, the emerging technologies such as vehicle to everything (V2X) may provide the potential to further excavate the energy-saving potential of the hybrid electric vehicles.

This Special Issue aims to encourage academics and engineers to discuss the recent advances and emerging technologies posed by energy management control and ECO-driving strategy. Interest fields of the Special Issue include, but are not limited to, the following topics:

  • Energy-oriented design and ECO-driving control methods for hybrid powertrains.
  • Multi-objective optimization energy management strategy such as fuel consumption, electric consumption, and battery degradation.
  • Driver-in-the loop energy management strategy, incorporating the human driver behavior into energy management strategy.
  • ECO-driving control and real-time optimization of the electric vehicles on the road.
  • Learning-based energy management strategy, combining machine learning and optimal control.
  • Advanced technologies for vehicle thermal management, such as air conditioning control, and battery thermal management.
  • Energy management strategies interactions with intelligent transportation systems
  • Experimental results describing real-life applications of novel technologies.

Dr. Xiaodong Wu
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. Machines 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 2400 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

  • hybrid electric vehicles
  • energy management
  • vehicle energy management
  • machine learning
  • optimal control

Published Papers (3 papers)

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Research

23 pages, 4099 KiB  
Article
Effective Energy Management Strategy with Model-Free DC-Bus Voltage Control for Fuel Cell/Battery/Supercapacitor Hybrid Electric Vehicle System
by Omer Abbaker Ahmed Mohammed, Lingxi Peng, Gomaa Haroun Ali Hamid, Ahmed Mohamed Ishag and Modawy Adam Ali Abdalla
Machines 2023, 11(10), 944; https://doi.org/10.3390/machines11100944 - 07 Oct 2023
Cited by 1 | Viewed by 1021
Abstract
This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an [...] Read more.
This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an efficient frequency-separating based-energy management strategy (EMS) is designed using Harr wavelet transform (HWT), adaptive low-pass filter, and interval type–2 fuzzy controller (IT2FC) to determine the appropriate power distribution for different power sources. Second, the ultra-local model (ULM) is introduced to re-formulate the FCHEV system by the knowledge of the input and output signals. Then, a novel adaptive model-free integral terminal sliding mode control (AMFITSMC) based on nonlinear disturbance observer (NDO) is proposed to force the actual values of the DC-link bus voltage and the power source’s currents track their obtained reference trajectories, wherein the NDO is used to approximate the unknown dynamics of the ULM. Moreover, the Lyapunov theorem is used to verify the stability of AMFITSMC via a closed-loop system. Finally, the FCHEV system with the presented method is modeled on a Matlab/Simulink environment, and different driving schedules like WLTP, UDDS, and HWFET driving cycles are utilized for investigation. The corresponding simulation results show that the proposed technique provides better results than the other methods, such as operational mode strategy and fuzzy logic control, in terms of the reduction of fuel consumption and fuel cell power fluctuations. Full article
(This article belongs to the Special Issue Energy Management and ECO-Driving Strategies of Hybrid Electric Vehicles)
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20 pages, 6629 KiB  
Article
Equivalent Consumption Minimization Strategy of Hybrid Electric Vehicle Integrated with Driving Cycle Prediction Method
by Dacheng Ni, Chao Yao, Xin Zheng, Qing Huang, Derong Luo and Farong Sun
Machines 2023, 11(6), 576; https://doi.org/10.3390/machines11060576 - 23 May 2023
Cited by 1 | Viewed by 1484
Abstract
Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic [...] Read more.
Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved. Full article
(This article belongs to the Special Issue Energy Management and ECO-Driving Strategies of Hybrid Electric Vehicles)
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19 pages, 7410 KiB  
Article
Real-Time NMPC for Speed Planning of Connected Hybrid Electric Vehicles
by Fei Ju, Yuhua Zong, Weichao Zhuang, Qun Wang and Liangmo Wang
Machines 2022, 10(12), 1129; https://doi.org/10.3390/machines10121129 - 28 Nov 2022
Cited by 3 | Viewed by 1127
Abstract
Eco-cruising is considered an effective approach for reducing energy consumption of connected vehicles. Most eco-cruising controllers (ECs) do not comply with real-time implementation requirements when a short sampling interval is required. This paper presents a solution to this problem. Model predictive control (MPC) [...] Read more.
Eco-cruising is considered an effective approach for reducing energy consumption of connected vehicles. Most eco-cruising controllers (ECs) do not comply with real-time implementation requirements when a short sampling interval is required. This paper presents a solution to this problem. Model predictive control (MPC) framework was applied to the speed-planning problem for a power-split hybrid electric vehicle (HEV). To overcome the limitations of time-domain MPC (TMPC), a nonlinear space-domain MPC (SMPC) was proposed in the space domain. A real-time iteration (RTI) algorithm was developed to accelerate nonlinear SMPC computations via generating warm initializations and subsequently forming the SMPC-RTI. Proposed speed controllers were evaluated in a hierarchical EC, where a heuristic energy management strategy was selected for powertrain control. Simulation results indicated that the proposed SMPC yields comparable fuel savings to the TMPC and the globally optimal solution. Meanwhile, SMPC reduced MPC computation time by 41% compared to TMPC, and SMPC-RTI further reduced MPC computation time without compromising optimization. During the hardware-in-loop (HIL) test, the mean computation time was 9.86 ms, demonstrating potential for real-time applications. Full article
(This article belongs to the Special Issue Energy Management and ECO-Driving Strategies of Hybrid Electric Vehicles)
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