Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
Advanced Battery Management Technology in Electric Vehicles: Present Status and...
share announcement

Advanced Battery Management Technology in Electric Vehicles: Present Status and Future Trends

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electromechanical Energy Conversion Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5939

Special Issue Editors

Dr. Xinghua Liu

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: electrical and electronics engineering; control theory; sliding mode control; security; sensors; markov chains; autonomous vehicles; power systems; energy; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaqiang Tian

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: electric vehicles; battery management technology; energy storage system; fault diagnosis; system modeling
Prof. Dr. Beibei Li

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Interests: industrial control system security; data security and privacy; AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing global attention to environmental protection and sustainable development, electric vehicles have become a key solution for reducing greenhouse gas emissions. The battery system is one of the core components of electric vehicles. Battery management technology directly impacts battery life, charging speed, range, and user experience. The advancement of this technology will also promote the integration of electric vehicles and renewable energy. An efficient battery system can better integrate with renewable energy sources such as solar and wind power, achieving efficient energy utilization and storage. This helps achieve a greener energy system and promotes the energy independence and stability of electric vehicles.

This Special Issue gathers the latest research achievements and innovative perspectives in the field, which will promote the development and application of battery management technology. Research topics that are of interest for this Special Issue include but are not limited to:

  • Battery materials, design technology;
  • Battery modeling, state estimation, energy management, fault diagnosis;
  • Artificial intelligence in battery manufacturing, use, and management;
  • Hybrid energy storage technology;
  • Wireless charging technology for batteries;
  • Fuel cell;
  • Electric vehicles regulate grid load.

Dr. Xinghua Liu
Dr. Jiaqiang Tian
Prof. Dr. Beibei 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-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

  • modeling
  • estimation
  • diagnosis
  • management
  • control
  • optimization

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 (3 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

24 pages, 8857 KB  
Article
Cooperative Control and Energy Management for Autonomous Hybrid Electric Vehicles Using Machine Learning
by Jewaliddin Shaik, Sri Phani Krishna Karri, Anugula Rajamallaiah, Kishore Bingi and Ramani Kannan
Machines 2026, 14(1), 73; https://doi.org/10.3390/machines14010073 - 7 Jan 2026
Viewed by 525
Abstract
The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the [...] Read more.
The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article
(This article belongs to the Special Issue Advanced Battery Management Technology in Electric Vehicles: Present Status and Future Trends)
►▼ Show Figures

Figure 1

16 pages, 1378 KB  
Article
Power Control and Voltage Regulation for Grid-Forming Inverters in Distribution Networks
by Xichao Zhou, Zhenlan Dou, Chunyan Zhang, Guangyu Song and Xinghua Liu
Machines 2025, 13(7), 551; https://doi.org/10.3390/machines13070551 - 25 Jun 2025
Viewed by 2521
Abstract
This paper proposes a robust voltage control strategy for grid-forming (GFM) inverters in distribution networks to achieve power support and voltage optimization. Specifically, the GFM control approach primarily consists of a power synchronization loop, a voltage feedforward loop, and a current control loop. [...] Read more.
This paper proposes a robust voltage control strategy for grid-forming (GFM) inverters in distribution networks to achieve power support and voltage optimization. Specifically, the GFM control approach primarily consists of a power synchronization loop, a voltage feedforward loop, and a current control loop. A voltage feedforward control circuit is presented to achieve error-free tracking of voltage amplitude and phase. In particular, the current gain is designed to replace voltage feedback for improving the current response and simplifying the control structure. Additionally, in order to optimize voltage and improve the power quality at the terminal of the distribution network, an optimization model for distribution transformers is established with the goal of the maximum qualified rate of the load-side voltage and minimum switching times of transformer tap changers. An enhanced whale optimization algorithm (EWOA) is designed to complete the algorithm solution, thereby achieving the optimal system configuration, where an improved attenuation factor and position updating mechanism is proposed to enhance the EWOA’s global optimization capability. The simulation results demonstrate the validity and feasibility of the proposed strategy. Full article
(This article belongs to the Special Issue Advanced Battery Management Technology in Electric Vehicles: Present Status and Future Trends)
►▼ Show Figures

Figure 1

17 pages, 926 KB  
Article
State of Change-Related Hybrid Energy Storage System Integration in Fuzzy Sliding Mode Load Frequency Control Power System with Electric Vehicles
by Yuzhe Xie, Peng Liao, Zhihao Liang and Dan Zhou
Machines 2025, 13(1), 57; https://doi.org/10.3390/machines13010057 - 16 Jan 2025
Cited by 3 | Viewed by 1297
Abstract
In the context of the integration of hybrid energy storage systems (HESSs) and electric vehicles (EVs), this paper investigates the load frequency control (LFC) issue of the power system. Weighting coefficients are set for the generators, HESSs and EVs, respectively, to show their [...] Read more.
In the context of the integration of hybrid energy storage systems (HESSs) and electric vehicles (EVs), this paper investigates the load frequency control (LFC) issue of the power system. Weighting coefficients are set for the generators, HESSs and EVs, respectively, to show their different abilities to regulate the power system. A fuzzy logic-based sliding mode control approach is designed to ensure the stable performance of the LFC power system integrated with HESSs and EVs. The improvement of the proposed method is the application of the linear matrix inequality (LMI) toolbox in fuzzy controller design, which solves the limitations and uncertainties caused by trial-error or experience in common fuzzy controllers. There is no general form for the membership function of the fuzzy control. This paper presents a design approach for the membership function based on the calculation results of LMI. Simulations are tested on an IEEE 39-bus system integrated with HESSs and EVs. The simulation results prove that the proposed method reduces the time required for the power system frequency to reach stability by approximately 8.8%, demonstrating the superiority and usability of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Battery Management Technology in Electric Vehicles: Present Status and Future Trends)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop