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Vehicles
Special Issues
Advanced Vehicle Powertrain Control and Energy Management Strategies
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Advanced Vehicle Powertrain Control and Energy Management Strategies

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 798

Editors

Prof. Dr. Yong Chen

E-Mail Website
Guest Editor
1. School of Mechanical Engineering, Guangxi University, Nanning, China
2. New Energy Vehicle Research Center, Guangxi University, Nanning, China
Interests: research and development of new energy vehicle transmission system; composite modification and strengthening technology of gear bearing surface; NVH control of new energy vehicle electric drive system; energy management of hybrid electric vehicles and extended-range electric vehicles; performance of new energy vehicle wheel hub motor
Special Issues, Collections and Topics in MDPI journals
Dr. Qin Li

E-Mail Website
Guest Editor
1. School of Mechanical Engineering, Guangxi University, Nanning, China
2. New Energy Vehicle Research Center, Guangxi University, Nanning, China
Interests: transportation systems and new energy vehicles; data analysis; vehicle dynamics modeling; trajectory tracking and stability control for autonomous electric vehicles
Dr. Jie Li

E-Mail Website
Guest Editor
1. School of Mechanical Engineering, Guangxi University, Nanning, China
2. New Energy Vehicle Research Center, Guangxi University, Nanning, China
Interests: eco-driving control and powertrain control for connected electric vehicles

Special Issue Information

Dear Colleagues,

Amidst the growing pressures of global energy restructuring and environmental protection, the transformation and upgrading of the automotive industry has emerged as a critical direction for global industrial development. Vehicles featuring innovative powertrain architectures—such as pure electric, hybrid electric, and range-extended electric vehicles—have undergone rapid advancement and continue to capture an increasing market share. Their distinct energy transfer mechanisms impose higher demands on powertrain control strategies. Advanced powertrain control technologies enable efficient energy distribution among different sources, optimized energy management, and intelligent power coordination, thereby ensuring enhanced driving performance, ride comfort, and energy efficiency. By integrating powertrain control strategies with vehicle-level energy management approaches, it is possible to improve system reliability while maximizing energy efficiency and extending driving range.

This Special Issue of Vehicles focuses on recent research advances, emerging concepts, and practical solutions related to vehicle powertrain control and energy management for new energy vehicles. Topics of interest include, but are not limited to, distributed-drive electric vehicle control, energy management strategies, traffic and vehicle big data analysis, vehicle–road cooperative control for energy-efficient driving, electric motor control, and noise and vibration suppression technologies.

We welcome and encourage researchers in related fields to contribute original research employing methodologies such as information fusion, intelligent algorithms, and predictive control, and to submit their latest theoretical developments and practical findings to this Special Issue.

Prof. Dr. Yong Chen
Dr. Qin Li
Dr. Jie Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles 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 1800 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

  • vehicle powertrain
  • intelligent control technology
  • energy management strategy
  • distributed-drive electric vehicle
  • vehicle–road–cloud collaboration
  • autonomous vehicles
  • multi-objective optimization

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Published Papers (2 papers)

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Research

33 pages, 3270 KB  
Article
Topology Design, Multi-Objective Optimization, and Dynamic Performance Evaluation of a PCM-Buffered SOFC-MGT Hybrid Powertrain for Heavy-Duty Trucks
by Saeed Shirazi, Majid Ghassemi and Mahmoud Chizari
Vehicles 2026, 8(7), 144; https://doi.org/10.3390/vehicles8070144 (registering DOI) - 27 Jun 2026
Viewed by 57
Abstract
Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid [...] Read more.
Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport. Full article
(This article belongs to the Special Issue Advanced Vehicle Powertrain Control and Energy Management Strategies)
22 pages, 19410 KB  
Article
Model Predictive Control Optimization Energy Management Strategy with Fused Temporal Features Speed Prediction
by Yong Chen, Yuhai Li, Yuguo Xu, Baitan Ma and Qing Zhou
Vehicles 2026, 8(5), 105; https://doi.org/10.3390/vehicles8050105 - 8 May 2026
Viewed by 422
Abstract
To address the stochasticity of real-world driving conditions and the optimality of energy allocation in a hybrid electric vehicle (HEV), this paper proposes a model predictive control (MPC) energy management strategy based on the Stacked–CNN–BiLSTM–Attention (SCBA) network. First, an SCBA-based vehicle speed prediction [...] Read more.
To address the stochasticity of real-world driving conditions and the optimality of energy allocation in a hybrid electric vehicle (HEV), this paper proposes a model predictive control (MPC) energy management strategy based on the Stacked–CNN–BiLSTM–Attention (SCBA) network. First, an SCBA-based vehicle speed prediction model is constructed by enhancing the bidirectional long short-term memory (BiLSTM) network with a double-layer convolutional structure and an attention mechanism, enabling the model to extract and fuse temporal features of the speed sequence, thereby overcoming the insufficient characterization of local abrupt speed variations and improving the accuracy of speed prediction. Secondly, a novel global optimization algorithm, the Rüppell’s Fox Optimizer (RFO), which possesses strong global search capability, is embedded as the solver for the multi-objective optimization problem in a rolling-horizon MPC framework, delivering superior energy-saving performance. Simulation results show that, compared with the conventional BiLSTM model, the proposed speed prediction model reduces the maximum root-mean-square error (RMSE) by 46.12% and the end-point prediction RMSE by 62.6%. The proposed RFO-MPC energy management strategy reaches 97.04% of the fuel-saving performance of dynamic programming (DP), representing a 5.6% improvement over the DP-MPC strategy. Finally, the effectiveness of the energy management strategy (EMS) is verified by hardware-in-the-loop (HIL) testing. Full article
(This article belongs to the Special Issue Advanced Vehicle Powertrain Control and Energy Management Strategies)
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