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Powertrain Design and Control in Sustainable Electric Vehicles
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Powertrain Design and Control in Sustainable Electric Vehicles

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2950

Special Issue Editors

Prof. Dr. Jiageng Ruan

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Guest Editor
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Interests: hybrid powertrain configuration design; sustainable energy management strategy of hybrid powertrain and hybrid energy system; dedicated transmission design for electrified vehicles; sustainable application of machine learning technology in vehicle control
Special Issues, Collections and Topics in MDPI journals
Dr. Yahui Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066000, China
Interests: optimal control; control of new energy vehicles and hybrid electric vehicles; artificial intelligence
Dr. Yang Tian

E-Mail Website
Guest Editor
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066000, China
Interests: electric vehicle; hybrid electric vehicle; transmission design and control
Dr. Jing Zhao

E-Mail Website
Guest Editor
Department of Electromechanical Engineering, University of Macau, Macau, China
Interests: intelligent control; dynamics and control; mechanism and machine theory; autonomous system; fault tolerant control; artificial intelligence with engineering applications; machine learning methods; signal processing; intelligent transportation; system modeling and identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the transportation electrification trend began to sweep the automotive industry, powertrain configurations and control methods have changed greatly. The introduction of electric machines in powertrains not only offers various ways to improve vehicle performance and energy efficiency but also brings about challenges for academics and engineers to meet the emerging requirements. With the increase in system components, e.g., more energy sources and power sources, it is very difficult to balance the rapid iteration of system performance, advanced technology, and consumers’ needs. In consequence, it is essential to create, adapt, and apply theories, methods, and technologies for every segment of electric vehicles to achieve multi-object optimization in terms of dynamics, energy, lifetime, and cost.

Electric vehicles are no longer a simple mechanical system; the electrified and informationalized powertrain offers an opportunity to apply mass data, artificial intelligence, and other sustainable advanced technologies in the design and control of new energy vehicles. The aim of this Special Issue is to provide a platform to introduce state-of-the-art theories, methods, technologies, and any other methods to further improve electrified powertrain performance sustainability, including, but not limited to, configuration, efficiency, dynamic, weight, cost, and lifetime.

In this Special Issue, original research articles and reviews are welcome to be submitted. Research areas may include, but are not limited to, the following:

  • Design, modeling, control, and optimization of engine-based hybrid, fuel cell, and multi-motor electric powertrain; 
  • Multi-power, multi-speed, and distributed-drive powertrain design and control;
  • Sustainable energy management strategies for multi-power-based powertrain;
  • Dynamic, stability, NVH, sustainable energy efficiency, and lifetime optimization methods of electrified powertrain;
  • Dedicated transmission and power split system design;
  • The sustainable application of big data and machine learning technology in electric vehicles for safety and economic performance improvements;
  • Configuration, parameter matching, and control of sustainable hybrid energy system;
  • Market survey on acceptance and preference of different electrified powertrains;
  • The investigation of the whole life cycle cost of different electrified powertrains.

We look forward to receiving your contributions.

Prof. Dr. Jiageng Ruan
Dr. Yahui Zhang
Dr. Yang Tian
Dr. Jing Zhao
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. Sustainability 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 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 powertrain
  • electrified powertrain
  • fuel cell vehicle
  • distributed drive
  • sustainable energy management strategy
  • hybrid energy system
  • transmission
  • power split system

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Published Papers (4 papers)

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Research

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24 pages, 17098 KiB  
Article
A Combined Energy Management Strategy for Heavy-Duty Trucks Based on Global Traffic Information Optimization
by Haishan Wu, Liang Li and Xiangyu Wang
Sustainability 2025, 17(14), 6361; https://doi.org/10.3390/su17146361 - 11 Jul 2025
Viewed by 161
Abstract
As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global [...] Read more.
As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle. Full article
(This article belongs to the Special Issue Powertrain Design and Control in Sustainable Electric Vehicles)
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20 pages, 19366 KiB  
Article
Active Collision-Avoidance Control Based on Emergency Decisions and Planning for Vehicle–Pedestrian Interaction Scenarios
by Zexuan Han, Jiageng Ruan, Ying Li, He Wan, Zhenpeng Xue and Jinming Zhang
Sustainability 2025, 17(5), 2016; https://doi.org/10.3390/su17052016 - 26 Feb 2025
Cited by 1 | Viewed by 581
Abstract
Safe driving and effective collision avoidance are critical challenges in the development of autonomous driving technology. As the dynamic interactions between vehicles and pedestrians become increasingly complex, making rational decisions and accurately executing planning and control in emergency situations has become a core [...] Read more.
Safe driving and effective collision avoidance are critical challenges in the development of autonomous driving technology. As the dynamic interactions between vehicles and pedestrians become increasingly complex, making rational decisions and accurately executing planning and control in emergency situations has become a core issue for sustainable development relating to traffic mobility and safety. This paper proposes an active collision-avoidance control strategy based on emergency decisions and planning in the context of vehicle–pedestrian interactions. A safety-distance model is developed with consideration given to the dynamic interactions between these two entities, and an emergency-decision mechanism is designed using the integration of priority rules. To generate smooth collision-avoidance trajectories, a quintic polynomial method is employed to construct trajectory clusters that meet the desired specifications. Moreover, a multi-objective optimization value function which considers multiple factors comprehensively is used to select the optimal path. To enhance collision-avoidance control accuracy, an RBF (radial basis function)–optimized SMC (sliding mode control) algorithm is introduced. Additionally, an FD-SF (force demand–based speed feedback) algorithm is designed to accurately track the longitudinal braking path. The results indicate that the proposed strategy can generate efficient, comfortable, and smooth optimal collision-avoidance paths, significantly improving vehicle response speed and control accuracy. Full article
(This article belongs to the Special Issue Powertrain Design and Control in Sustainable Electric Vehicles)
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23 pages, 16564 KiB  
Article
Cooperative Control of Distributed Drive Electric Vehicles for Handling, Stability, and Energy Efficiency, via ARS and DYC
by Ningyuan Guo, Jie Ye and Zihao Huang
Sustainability 2024, 16(24), 11301; https://doi.org/10.3390/su162411301 - 23 Dec 2024
Cited by 1 | Viewed by 1130
Abstract
Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists [...] Read more.
Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists in designing a control strategy that effectively coordinates the objectives of handling, stability, and energy efficiency under both lateral and longitudinal driving conditions. To this end, this paper proposes a cooperative control strategy for DDEVs, incorporating active rear steering (ARS) and direct yaw moment control (DYC) to enhance handling capabilities, stability, and energy efficiency. A stability boundary is delineated using an analytical expression that correlates with the front wheel steering angle, and an adjustment factor is introduced to quantify vehicle stability based on this input parameter. This factor aids in establishing a coordinated control reference for handling and stability. At the upper-level motion control layer, a model predictive control method is developed to track this reference and implement ARS and DYC for superior performance. Specifically, the rear lateral force serves as the control command for ARS, which is converted into a rear wheel steering angle using a tire inverse model. Meanwhile, the front lateral force is modeled as linear-time-varying to simplify calculations. At the lower-level torque allocation layer, the adjustment factor is utilized to balance tire workload rate and in-wheel motors’ (IWM) energy consumption, enabling efficient switching between energy consumption and driving stability targets, and the torque allocation is conducted to acquire the expected IWMs’ command. Both the upper and lower-level optimization problems are formulated as convex problems, ensuring efficient and effective solutions. Simulations verify the effectiveness of this strategy in improving handling, stability, and energy economy under DLC cases, while maintaining high computational efficiency. Full article
(This article belongs to the Special Issue Powertrain Design and Control in Sustainable Electric Vehicles)
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Review

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30 pages, 3374 KiB  
Review
Review and Outlook of Fuel Cell Power Systems for Commercial Vehicles, Buses, and Heavy Trucks
by Xingxing Wang, Jiaying Ji, Junyi Li, Zhou Zhao, Hongjun Ni and Yu Zhu
Sustainability 2025, 17(13), 6170; https://doi.org/10.3390/su17136170 - 4 Jul 2025
Viewed by 471
Abstract
The power system, which is also one of the most crucial parts of fuel cell cars, marks the biggest distinction between them and conventional automobiles. Fuel cell hybrid power systems are reviewed in this paper along with their current state of research. Three [...] Read more.
The power system, which is also one of the most crucial parts of fuel cell cars, marks the biggest distinction between them and conventional automobiles. Fuel cell hybrid power systems are reviewed in this paper along with their current state of research. Three different kinds of fuel cell hybrid power systems—fuel cell–battery, fuel cell–supercapacitor, and fuel cell–battery–supercapacitor—are thoroughly compared and analyzed, and they are systematically explained in the three areas of passenger cars, buses, and heavy duty trucks. Existing fuel cell hybrid systems and energy strategies are systematically reviewed and summarized, including predictive control strategies based on game theory, power allocation strategies, fuzzy control strategies, and adaptive super twisted sliding mode control (ASTSMC) energy management techniques. This study offers recommendations and direction for the future direction of fuel cell hybrid power system research and development. Full article
(This article belongs to the Special Issue Powertrain Design and Control in Sustainable Electric Vehicles)
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