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Energy Management Control of Hybrid Electric Vehicles
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Energy Management Control of 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: 15 June 2024 | Viewed by 1976

Special Issue Editor

Prof. Dr. Liang Chu

E-Mail Website
Guest Editor
College of Automotive Engineering, Jilin University, Changchun 130025, China
Interests: braking energy recovery technology; energy management theory

Special Issue Information

Dear Colleagues,

In the last decade, due to stricter emission regulations, hybrid electric vehicles with multiple power sources have emerged as a viable option for eco-friendly transportation. With the development of powertrains in hybrid electric vehicles, the variety of configurations lays the groundwork for enhanced energy-saving possibilities. Yet, the complex nonlinear and multi-degree-of-freedom nature of these powertrains complicates the task of energy-saving control in hybrid electric vehicles, especially with various driving scenarios. To improve the overall performance of multi-power hybrid electric vehicles, a range of technologies are being progressively implemented, such as vehicle–environment cooperation, control parameter optimization, and accurate future driving information prediction for hybrid electric vehicles. This Special Issue welcomes submissions from global researchers, engineers, and students, calling for original research papers that delve into theory development, system applications, and algorithmic demonstrations. Topics of interest for this Special Issue encompass, but are not limited to, energy management strategies, sensing and navigation technologies, and energy optimization.

Prof. Dr. Liang Chu
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

  • hybrid electric vehicle
  • energy management strategy
  • vehicle–environment cooperation

Published Papers (3 papers)

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Research

23 pages, 16447 KiB  
Article
A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon
by Benxiang Lin, Chao Wei, Fuyong Feng and Tao Liu
Energies 2024, 17(10), 2288; https://doi.org/10.3390/en17102288 - 9 May 2024
Viewed by 341
Abstract
Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of [...] Read more.
Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%. Full article
(This article belongs to the Special Issue Energy Management Control of Hybrid Electric Vehicles)
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31 pages, 1531 KiB  
Article
A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy
by Yinhang Wang, Liqing Zhou, Liang Chu, Di Zhao, Zhiqi Guo and Zewei Jiang
Energies 2024, 17(9), 2032; https://doi.org/10.3390/en17092032 - 25 Apr 2024
Viewed by 366
Abstract
Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking [...] Read more.
Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. Full article
(This article belongs to the Special Issue Energy Management Control of Hybrid Electric Vehicles)
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39 pages, 16376 KiB  
Article
Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles
by Yavuz Eray Altun and Osman Akın Kutlar
Energies 2024, 17(7), 1696; https://doi.org/10.3390/en17071696 - 2 Apr 2024
Viewed by 871
Abstract
Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, [...] Read more.
Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated. Full article
(This article belongs to the Special Issue Energy Management Control of Hybrid Electric Vehicles)
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