Advanced Electric Vehicle Technology, 3rd Edition

Topic Information

Dear Colleagues,

This Topic is a continuation of the successful previous Topic “Advanced Electric Vehicle Technology, 2nd Volume” (https://www.mdpi.com/topics/DTVV1QW14T). Electric vehicles are a highly exciting topic of research and industrial development. In the last 10 years, rapid development in both electric vehicle technology and commercial activities has occurred. The number of research papers, webinars, tutorial courses, and PhD graduates in this field has rapidly increased. Commercial electric vehicles have increased in terms of both their sales and models. Electric vehicles are also a hot topic in the news and online media. However, their batteries, energy storage, packaging, and chargers still require significant research efforts. Other associated technologies, such as Vehicle-to-X and new motors and actuators, are now replacing the conventional mechanical and hydraulic systems in vehicles. Because of the demand for smart cities and robotic activities, new control methods and autonomous driving are now being applied to and developed in most vehicles. All automotive enterprises have gradually changed their models into electric versions. The associated infrastructure and government policies and standards are evolving, further adding to the significance of this area of research. Today, electric vehicle technology has been extended to vessels, underwater vehicles, air transport, and space vehicles. This technology is not restricted to electrical, electronic, and computer engineering but also extends to multi-disciplinary research.

We have initialized this Topic because we can see that the market development for electric vehicles is expanding rapidly. The next 50 years will be a transition period from fossil fuel vehicles to electric vehicles. The next 20 years are critical to electric vehicle development, and we now invite you to submit a paper to report, discuss and predict the research and development in this exciting field of science.

Prof. Dr. Ka Wai Eric Cheng
Prof. Dr. Junfeng Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.8	6.6	2015	19.2 Days	CHF 2700	Submit
Designs designs	-	4.8	2017	18.5 Days	CHF 1600	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit

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Published Papers (3 papers)

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All Batteries Designs Energies Sustainability Electronics

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58 pages, 9073 KB  

Open AccessArticle

Hybrid CryStAl and Random Decision Forest Algorithm Control for Ripple Reduction and Efficiency Optimization in Vienna Rectifier-Based EV Charging Systems

by Mohammed Abdullah Ravindran, Kalaiarasi Nallathambi, Mohammed Alruwaili, Ahmed Emara and Narayanamoorthi Rajamanickam

Energies 2026, 19(3), 830; https://doi.org/10.3390/en19030830 - 4 Feb 2026

Viewed by 125

Abstract

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is [...] Read more.

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 3rd Edition)

28 pages, 5655 KB  

Open AccessArticle

Crayfish-Optimized Adaptive Equivalent Consumption Minimization Strategy for Medium-Duty Commercial Vehicles

by Jiading Bao, Haibo Wang, Weiguang Zheng and Jirong Qin

Sustainability 2026, 18(3), 1534; https://doi.org/10.3390/su18031534 - 3 Feb 2026

Viewed by 85

Abstract

Under China’s dual-carbon policy, medium-duty commercial vehicles (MDCVs)—widely used in urban distribution with high load fluctuation and long operating hours—are key to transportation energy conservation and emission reduction. Optimizing powertrain parameters and energy management is essential for fuel-cell MDCVs. However, traditional powertrain parameter [...] Read more.

Under China’s dual-carbon policy, medium-duty commercial vehicles (MDCVs)—widely used in urban distribution with high load fluctuation and long operating hours—are key to transportation energy conservation and emission reduction. Optimizing powertrain parameters and energy management is essential for fuel-cell MDCVs. However, traditional powertrain parameter selection relies on fixed thresholds and lacks optimization, while the equivalent consumption minimization strategy (ECMS) suffers from poor driving cycle adaptability despite addressing hydrogen consumption and online application challenges. To overcome these issues, this study proposes an innovative approach for fuel cell-powered MDCVs: a driving cycle model was constructed based on hydrogen consumption and fuel cell degradation rates. Subsequently, the powertrain system parameters were optimized, culminating in the development of an adaptive ECMS (A-ECMS). Specifically, the method includes: (1) a driving cycle construction approach analyzing driving cycle clustering’s impact on adaptive control parameters; (2) a powertrain parameter optimization method considering vehicle performance under synthetic driving cycles; and (3) an A-ECMS enhanced by a crayfish optimization algorithm (COA) to improve driving cycle adaptability. Simulations show that A-ECMS achieves hydrogen consumption close to the dynamic programming algorithm (DP) optimum, reducing consumption by 2.12% and 1.45% compared to traditional ECMS under synthetic and World Transient Vehicle Cycle (WTVC) cycles, significantly improving MDCV economy. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 3rd Edition)

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21 pages, 2537 KB  

Open AccessArticle

State of Health Prediction of Lithium-Ion Batteries Based on Dual-Time-Scale Self-Supervised Learning

by Yuqi Li, Longyun Kang, Xuemei Wang, Di Xie and Shoumo Wang

Batteries 2025, 11(8), 302; https://doi.org/10.3390/batteries11080302 - 8 Aug 2025

Cited by 1 | Viewed by 3046

Abstract

Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging–discharging conditions) and shallow charging–discharging conditions prevalent in real-world [...] Read more.

Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging–discharging conditions) and shallow charging–discharging conditions prevalent in real-world scenarios. To address these challenges, this study proposes a self-supervised learning framework for SOH estimation. The framework employs a dual-time-scale collaborative pre-training approach via masked voltage sequence reconstruction and interval capacity prediction tasks, enabling automatic extraction of cross-time-scale aging features from unlabeled data. Innovatively, it integrates domain knowledge into the attention mechanism and incorporates time-varying factors into positional encoding, significantly enhancing the capability to extract battery aging features. The proposed method is validated on two datasets. For the standard dataset, using only 10% labeled data, it achieves an average RMSE of 0.491% for NCA battery estimation and 0.804% for transfer estimation between NCA and NCM. For the shallow-cycle dataset, it achieves an average RMSE of 1.300% with only 2% labeled data. By synergistically leveraging massive unlabeled data and extremely sparse labeled samples (2–10% labeling rate), this framework reduces the labeling burden for battery health monitoring by 90–98%, offering an industrial-grade solution with near-zero labeling dependency. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 3rd Edition)

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