Special Issue "Recent Advances in Intelligent Energy Management and Battery Management for Hybrid/Electric Vehicles"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 30 June 2023 | Viewed by 847

Special Issue Editors

School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: electric vehicle; electrochemical energy storage system; battery system; battery management system; lithium-ion battery
Special Issues, Collections and Topics in MDPI journals
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250023, China
Interests: unmanned vehicles; electric vehicles; power system simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoling Fu
Guest Editor
Department of Physics, Changji College, Changji 831100, China
Interests: hybrid/electric vehicles; energy management
Dr. Zhongkai Zhou
E-Mail Website
Guest Editor
School of Automation, Qingdao University, Qingdao 266071, China
Interests: lithium ion batteries; battery management system

Special Issue Information

Dear Colleagues,

Due to the energy crisis and environmental pollution, new energy/electric vehicles have been paid more and more attention and become the main direction of future automobile development. According to research conducted by Favigham in the United States, by 2035, the total number of vehicles in the world will be at least 2 billion, but electric vehicles will only account for one-tenth. Compared with traditional vehicles (internal combustion engine vehicles), electric vehicles (EVs) are energy-saving and environmentally friendly. Hybrid electric vehicles (HEVs) are a form between traditional vehicles and EVs, which can reduce fuel consumption and reduce emissions. Among them, plug-in HEVs are classified as new energy vehicles, while non-plug-in HEVs still belong to traditional vehicles.

During the operation of the vehicle, the estimation error of the internal state of the battery such as SOC, SOH, and SOP is large, and as the battery ages, the estimation error becomes larger and larger, so that the battery management system (BMS) cannot monitor the actual state of the battery, which can easily lead to the battery overcharge and over-discharge, accelerated aging, reduced usable capacity, and shortened remaining life. On the other hand, the energy management system cannot accurately allocate power requirements according to the actual state of the battery, resulting in the unreasonable implementation of energy management strategies, increased battery loss, and halfway breakdown.

Therefore, there is an urgent need to investigate new strategies and promising approaches for intelligent energy management and battery management for hybrid/electric vehicles. With this Special Issue, we aim to provide an overview of recent advances in artificial intelligence/machine learning/deep learning for energy management and battery management and their applications in different fields. A further aim of this Special Issue is to provide a contribution to advances in modeling, estimation, optimal control, optimal charging, energy management of electric vehicles and applications of related devices and components.

Potential topics include, but are not limited to:

  • Electric vehicles
  • Fuel cell vehicles
  • Hybrid vehicles
  • Plug-in vehicles
  • Electric vehicle batteries
  • Electric vehicle battery management systems
  • Electric vehicle charging systems
  • Autonomous driving
  • Autonomous vehicles
  • Performance of electric vehicles
  • Artificial intelligence applications for vehicles and traffic
  • Machine learning/deep learning algorithms for vehicles and transportation

Dr. Qi Zhang
Dr. Wenhui Pei
Prof. Dr. Xiaoling Fu
Dr. Zhongkai Zhou
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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.


  • artificial intelligence
  • hybrid/electric vehicles
  • batteries and management systems
  • autonomous driving/vehicles
  • machine learning

Published Papers (1 paper)

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Participation of Electric Vehicles in a Delay-Dependent Stability Analysis of LFC Considering Demand Response Control
Electronics 2022, 11(22), 3682; https://doi.org/10.3390/electronics11223682 - 10 Nov 2022
Viewed by 568
Today, time-varying delays may result from a communication network’s engagement in frequency control services. These delays have an impact on the effectiveness of the load frequency control (LFC) system, which can occasionally lead to power system instability. The electric vehicle (EV) can be [...] Read more.
Today, time-varying delays may result from a communication network’s engagement in frequency control services. These delays have an impact on the effectiveness of the load frequency control (LFC) system, which can occasionally lead to power system instability. The electric vehicle (EV) can be used as a beneficial source for the LFC system with the development of demand-side response due to its vehicle-to-grid capacity. Although demand response control has certain advantages for the power system, communication networks used in LFC systems result in time delays that reduce the stability of the LFC schemes. A stability study of an LFC system, comprising an EV aggregator with two additive time-varying delays, is demonstrated in this work. An enhanced Lyapunov–Krasovskii functional (LKF), which incorporates time-varying delays using the linear matrix inequality approach, is used to perform a delay-dependent stability analysis of the LFC to determine the stability zone and criterion. In conclusion, the efficiency of the proposed stability criterion is validated by making use of the thorough simulation findings. Full article
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