Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.0
Journals
Energies
Special Issues
Hybrid Electric Powertrain System Modelling and Control
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Hybrid Electric Powertrain System Modelling and Control

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

Deadline for manuscript submissions: 10 September 2025 | Viewed by 1990

Special Issue Editors

Prof. Dr. Jiaqiang E

grade E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: combustion & micro-combustion; biodiesel combustion; micro-thermophotovoltaic power generators; after-treatment system of automotive systems; battery thermal management system
Special Issues, Collections and Topics in MDPI journals
Dr. Bolan Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: HEV modelling and control; powertrain control and PHM

Special Issue Information

Dear Colleagues,

The hybrid electric powertrain (HEP) is undoubtedly one of the main power systems used for vehicular application to achieve carbon peaking and carbon neutrality goals. Particularly, in recent years, HEPs have presented a situation where various types of HEPs have coexisted for different applications.

Although the systems are different, the composition and operation mode of HEPs are becoming more complex, as a common technology trend. In order to achieve more efficient HEPs, modelling and control are considering to be the main measures. The objective of this Special Issue is to promote the academic achievements in this field.

Modelling and control are important aspects of system research. Modelling can be tailored to different application scenarios, and modelling for control is usually a one-dimensional model that includes modules such as engines, motors, batteries, and controllers. After the model is established and verified, it can serve as the basic object for subsequent control. Control has always been the core of hybrid power system research, including energy management, fault diagnosis, health management, and other dimensions. The algorithms used have evolved from ordinary classical control to modern control and intelligent control, which is an important aspect of the development of hybrid power technology.

Based on the above, this Special Issue will collect reviews and research articles on various aspects of hybrid power modelling and control. We welcome submissions from research institutions to promote the development of hybrid power technology.

Prof. Dr. Jiaqiang E
Dr. Bolan Liu
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. 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 powertrain
  • modelling
  • control
  • vehicle system dynamics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 9650 KiB  
Article
Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning
by Tao Zheng, Hui Xie and Boqiang Liang
Energies 2025, 18(11), 2847; https://doi.org/10.3390/en18112847 - 29 May 2025
Viewed by 191
Abstract
The series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To accurately identify and analyze the torsional [...] Read more.
The series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To accurately identify and analyze the torsional vibration characteristics induced by shaft resonance in this process, a torsional vibration feature identification algorithm based on disturbance observation and parameter partition learning is proposed. A simplified model of the drivetrain shaft system is first established, and an extended state Kalman filter (ESKF) is designed to accurately estimate the torque of the torsional damper. The inclusion of extended disturbance states enhances the model’s robustness against system uncertainties. Subsequently, continuous wavelet transform (CWT) is employed to identify the resonance characteristics in the torsional vibration process from the torque signal. Combined with the parameter partition learning strategy, resonance frequencies are utilized to infer key system parameters. The results demonstrate that, under a 20% perturbation of structural parameters, the observer model with fixed parameters yields a root mean square error (RMSE) of 10.16 N·m for the torsional damper torque. In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. Using the Morlet wavelet with a frequency resolution parameter (VPO) of 15 at a 50 Hz sampling rate, the identified resonance frequency was 14.698 Hz, showing a 1.1% deviation from the actual natural frequency of 14.53 Hz. Full article
(This article belongs to the Special Issue Hybrid Electric Powertrain System Modelling and Control)
►▼ Show Figures

Figure 1

26 pages, 5455 KiB  
Article
Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health
by Jingxian Tang, Bolan Liu, Wenhao Fan, Dawei Zhong and Liang Liu
Energies 2024, 17(21), 5413; https://doi.org/10.3390/en17215413 - 30 Oct 2024
Viewed by 1319
Abstract
Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system [...] Read more.
Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations. Full article
(This article belongs to the Special Issue Hybrid Electric Powertrain System Modelling and Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop