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Electronics
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AI-Driven Energy Optimization, Diagnosis, and Control for Next-Generation Electric Vehicles
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AI-Driven Energy Optimization, Diagnosis, and Control for Next-Generation 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: closed (15 April 2026) | Viewed by 2020

Special Issue Editors

Dr. Yiming Ye

E-Mail Website
Guest Editor
1. Department of Intelligent Vehicle, Chang’an University, Xi’an 710018, China
2. Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Interests: hybrid energy storage systems; energy management strategies; battery management systems
Dr. Qiao Wang

E-Mail Website
Guest Editor
School of Automobile, Chang'an University, Xi'an 710018, Shaanxi, China
Interests: electric vehicles; energy storage; battery management; artificial Intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuan Zhao

E-Mail Website
Guest Editor
Department of Intelligent Vehicle, Chang’an University, Xi’an 710018, China
Interests: dynamic control and energy management of electric vehicles; autonomous vehicle control and evaluation technology; fault diagnosis and intelligent detection of electric vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid transition towards transportation electrification has positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as cornerstones of a sustainable future. However, realizing their full potential hinges on overcoming critical challenges related to energy efficiency, battery lifespan, operational reliability, and sophisticated vehicle control. Artificial intelligence (AI) has emerged as a transformative technology capable of addressing these complexities, enabling predictive, adaptive, and highly optimized vehicle systems.

This Special Issue aims to gather the latest research and innovations in the application of AI and machine learning techniques to the core challenges in modern EVs. We invite contributions that explore novel AI-driven strategies for energy optimization, intelligent diagnosis and prognosis, and advanced vehicle control. We seek research that will define the state of the art and illuminate the path for the next generation of intelligent, efficient, and reliable electric vehicles. Original and high-quality research, reviews, and perspectives are invited for publication. Potential topics include, but are not limited to, the following:

  • Machine learning and deep learning for energy management strategies in HEVs and EVs.
  • Intelligent optimization and control of hybrid energy storage systems.
  • Predictive and adaptive energy management based on traffic flow, route, and driving behavior.
  • Reinforcement learning for real-time powertrain energy optimization.
  • AI-based state of charge, state of health, and remaining useful life estimation.
  • Machine learning models for battery degradation diagnosis and prognosis.
  • Data-driven battery modeling and parameter identification.
  • Intelligent fault diagnosis and anomaly detection in battery management systems.
  • Smart control strategies for EV and HEV dynamics.
  • AI applications in motion planning, stability, and trajectory control.
  • Intelligent control and evaluation technologies for autonomous vehicles.
  • Sensor fusion and perception algorithms for intelligent driving.
  • Data-driven fault detection and resilient control for vehicle systems.

Dr. Yiming Ye
Dr. Qiao Wang
Prof. Dr. Xuan 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 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. Electronics 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

  • electric vehicles
  • energy management strategies
  • battery management systems
  • fault diagnosis and prognosis
  • artificial intelligence

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

Published Papers (2 papers)

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Research

37 pages, 8964 KB  
Article
A Novel ANFIS-Dynamic Programming Fusion Strategy for Real-Time Energy Management Optimization in Fuel Cell Electric Commercial Vehicles
by Juan Du, Xuening Zhang, Shanglin Wang, Xiaodong Liu and Manxi Xing
Electronics 2025, 14(23), 4601; https://doi.org/10.3390/electronics14234601 - 24 Nov 2025
Viewed by 604
Abstract
The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework [...] Read more.
The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs. Full article
(This article belongs to the Special Issue AI-Driven Energy Optimization, Diagnosis, and Control for Next-Generation Electric Vehicles)
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31 pages, 5169 KB  
Article
Harmonic Mitigation in Unbalanced Grids Using Hybrid PSO-GA Tuned PR Controller for Two-Level SPWM Inverter
by Pema Dorji, Taimoor Muzaffar Gondal, Stefan Lachowicz and Octavian Bass
Electronics 2025, 14(21), 4351; https://doi.org/10.3390/electronics14214351 - 6 Nov 2025
Viewed by 1070
Abstract
This study proposes an integrated control–optimization framework for harmonic mitigation in two-level, grid-connected inverters with battery energy storage operating under unbalanced grid conditions. A proportional–resonant controller in the stationary αβ frame and a proportional–integral controller in the synchronous dq frame are [...] Read more.
This study proposes an integrated control–optimization framework for harmonic mitigation in two-level, grid-connected inverters with battery energy storage operating under unbalanced grid conditions. A proportional–resonant controller in the stationary αβ frame and a proportional–integral controller in the synchronous dq frame are compared, with controller gains optimized using PSO, GA, and a hybrid PSO–GA approach. The hybrid method achieves superior trade-offs among THD, convergence speed, and computational effort. For the PR controller, hybrid PSO–GA reduces THD to 1.07%, satisfying IEEE 1547 and IEC 61727 standards, while for the PI controller it achieves 2.70%, outperforming standalone PSO (4.12%) and GA (3.38%). The hybrid-optimized gains further minimize tracking error indices (IAE, ISE, ITAE, ITSE), ensuring precise steady-state current regulation. Convergence analysis shows that hybrid PSO–GA attains optimal solutions within three iterations for both controllers, faster than GA and comparable to PSO for the PR case. Simulation studies on the IEEE 13-bus unbalanced feeder in DIgSILENT PowerFactory validate the proposed framework. Results confirm that the PR controller delivers a 60.36% THD reduction and tenfold ISE improvement over the optimized PI design, establishing a robust and scalable solution for harmonic suppression in unbalanced grid-tied energy systems. Full article
(This article belongs to the Special Issue AI-Driven Energy Optimization, Diagnosis, and Control for Next-Generation Electric Vehicles)
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