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Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles,...
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Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 2336

Special Issue Editors

Dr. Dan Dan

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: electric vehicle; power battery; thermal management; heat pipe
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Xie

E-Mail Website
Guest Editor
College of Mechanical and Vehicular Engineering, Chongqing University, Chongqing 400044, China
Interests: battery thermal management system; vehicle thermal management system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the second edition of the successful Special Issue “Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles”.

With increasing pollutant emissions and the growing energy crisis, modern transportation is on the verge of a major paradigm shift. At present, electric vehicles (EVs) are seeing increasing popularity. In keeping with this trend, energy storage systems (ESSs) such as batteries have undergone significant development in the last decade. As the requirements for user experience and EV safety increase, so does the use of fast charging (FC) technologies, battery heating systems, thermal runaway suppression, and so on. These technologies require EVs to have a more advanced battery thermal management system (BTMS), which can cool or heat the battery quickly, estimate the battery SOT precisely, and extend the battery lifespan through the optimum management of battery temperature, all at a low energy cost. Innovations in battery thermal management technology are thus critical from a material and physical point of view. High-fidelity modeling, new cooling/heating structures, novel architectures of BTMS, and fault-tolerant management of ESS are also vital for the future of safe electric transportation. This vision can be facilitated by emerging technologies, such as new batteries (solid-state, lithium titanate oxide, and lithium–air sodium-based batteries, among others), advanced power electronics, intelligent management, environment-adaptive control, etc. This Special Issue seeks to highlight original research on recent innovations with unique applications in electric transportation. Topics of interest include, but are not limited to, the following:

  • Modeling, analysis, control, and management of batteries;
  • New structure for battery thermal management systems;
  • Advanced heating control methods for batteries;
  • Design methodology and control strategies for BTMSs;
  • Battery thermal runaway and methods for its suppression;
  • Application of batteries in extreme high/low temperatures.

Dr. Dan Dan
Dr. Yi Xie
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

  • battery
  • thermal management
  • modeling
  • state estimation
  • BTMS structure and control

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Related Special Issue

Published Papers (4 papers)

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Research

21 pages, 4794 KB  
Article
Heat Transfer and Mechanical Performance Analysis and Optimization of Lattice Structure for Electric Vehicle Thermal Management
by Xiaokang Ye, Xiaoxia Sun, Zhixuan Liang, Ran Tian, Mingshan Wei, Panpan Song and Lili Shen
Electronics 2026, 15(2), 347; https://doi.org/10.3390/electronics15020347 - 13 Jan 2026
Viewed by 319
Abstract
With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, [...] Read more.
With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, which compromises cooling efficiency. Concurrently, these compact components must possess sufficient mechanical strength to withstand operational loads such as vibration. Therefore, designing structures that simultaneously suppress heat transfer and ensure mechanical intensity presents a critical challenge. This study introduces Triply Periodic Minimal Surface (TPMS) and Body-Centered Cubic (BCC) lattice structures as multifunctional solutions to address the undesired heat transfer and mechanical support requirements. Their thermal and mechanical performances are analyzed, and a feedforward neural network model is developed based on CFD simulations to map key structural parameters to thermal and mechanical outputs. A dual-objective optimization approach is then applied to identify optimal structural parameters that balance thermal and mechanical requirements. Validation via CFD confirms that the neural network-based optimization effectively achieves a trade-off between heat transfer suppression and structural strength, providing a reliable design methodology for integrated thermal management systems. Full article
(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)
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30 pages, 4654 KB  
Article
A Non-Cooperative Game-Based Retail Pricing Model for Electricity Retailers Considering Low-Carbon Incentives and Multi-Player Competition
by Zhiyu Zhao, Bo Bo, Xuemei Li, Po Yang, Dafei Jiang, Ge Wang and Fei Wang
Electronics 2025, 14(23), 4713; https://doi.org/10.3390/electronics14234713 - 29 Nov 2025
Viewed by 357
Abstract
This paper addresses the retail pricing problem for electricity retailers who also act as virtual power plant (VPP) operators, aggregating distributed energy resources (DERs). In future power markets where multiple such retailers compete for customers, a key challenge is to design pricing strategies [...] Read more.
This paper addresses the retail pricing problem for electricity retailers who also act as virtual power plant (VPP) operators, aggregating distributed energy resources (DERs). In future power markets where multiple such retailers compete for customers, a key challenge is to design pricing strategies that balance economic profitability with low-carbon objectives. Existing research often overlooks the impact of retailers’ heterogeneous resource portfolios, particularly the share of low-carbon resources like photovoltaics (PVs), on their competitive advantage and pricing decisions. To bridge this gap, we propose a novel retail pricing model that integrates a non-cooperative game framework with Markov Decision Processes (MDPs). The model enables each retailer to formulate optimal real-time pricing strategies by anticipating competitors’ actions and customer responses, ultimately reaching a Nash equilibrium. A distinctive feature of our approach is the incorporation of spatially differentiated carbon emission factors, which are adjusted based on each retailer’s share of PV generation. This creates a tangible low-carbon incentive, allowing retailers with greener resource mixes to leverage their environmental advantage. The proposed framework is validated on a modified IEEE 30-bus system with six competing retailers. Simulation results demonstrate that our method effectively incentivizes optimal load distribution, alleviates network congestion, and improves branch loading indices. Critically, retailers with a higher share of PV resources achieved significantly higher profits, directly translating their low-carbon advantage into economic value. Notably, the Branch Load Index (BLI) was reduced by 12% and node voltage deviations were improved by 1.32% at Bus 12, demonstrating the model’s effectiveness in integrating economic and low-carbon objectives. Full article
(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)
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19 pages, 2253 KB  
Article
A Domain-Adversarial Mechanism and Invariant Spatiotemporal Feature Extraction Based Distributed PV Forecasting Method for EV Cluster Baseline Load Estimation
by Zhiyu Zhao, Qiran Li, Bo Bo, Po Yang, Xuemei Li, Zhenghao Wu, Ge Wang and Hui Ren
Electronics 2025, 14(23), 4709; https://doi.org/10.3390/electronics14234709 - 29 Nov 2025
Cited by 2 | Viewed by 337
Abstract
Against the backdrop of high-penetration distributed photovoltaic (DPV) integration into distribution networks, the limited measurability of small-scale DPV systems poses significant challenges to accurately estimating the baseline load of electric vehicle (EV) clusters. To address this issue, effective forecasting of DPV power output [...] Read more.
Against the backdrop of high-penetration distributed photovoltaic (DPV) integration into distribution networks, the limited measurability of small-scale DPV systems poses significant challenges to accurately estimating the baseline load of electric vehicle (EV) clusters. To address this issue, effective forecasting of DPV power output becomes essential. This paper proposes a domain-adversarial architecture for ultra-short-term DPV power prediction, designed to support baseline load estimation for EV clusters. The power output of DPV systems is influenced by scattered geographical distribution and abrupt weather changes, leading to complex spatiotemporal distribution shifts. These shifts result in a notable decline in the generalization capability of traditional models that rely on historical statistical patterns. To enhance the robustness of models in complex and dynamic environments, this paper proposes a domain-adversarial architecture for ultra-short-term DPV power forecasting, explicitly designed to address spatiotemporal distribution shifts by extracting spatiotemporal invariant features robust to distribution shifts. First, a Graph Attention Network (GAT) is utilized to capture spatial dependencies among PV stations, characterizing asynchronous power fluctuations caused by factors such as cloud movement. Next, the spatiotemporally fused features generated by the GAT are adaptively partitioned into multiple distribution domains using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), providing pseudo-supervised signals for subsequent adversarial learning. Finally, a Temporal Convolutional Network (TCN)-based domain-adversarial mechanism is introduced, where gradient reversal training forces the feature extractor to discard domain-specific characteristics, thereby effectively extracting spatiotemporal invariant features across domains. Experimental results on real-world distributed PV datasets validate the effectiveness of the proposed method in improving prediction accuracy and generalization capability under transitional weather conditions. Full article
(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)
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22 pages, 5988 KB  
Article
Research on Battery Aging and User Revenue of Electric Vehicles in Vehicle-to-Grid (V2G) Scenarios
by Zhiyu Zhao, Shuaihao Kong, Bo Bo, Xuemei Li, Ling Hao, Fei Xu and Lei Chen
Electronics 2025, 14(23), 4567; https://doi.org/10.3390/electronics14234567 - 21 Nov 2025
Cited by 1 | Viewed by 1075
Abstract
With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this [...] Read more.
With the development of vehicle-to-grid (V2G) technology, electric vehicles (EVs) are increasingly participating in grid interactions. However, V2G-induced energy consumption and battery aging intensify range anxiety among users, reduce participation willingness, and decrease discharge capacity and revenue due to capacity loss. In this study, aging models for power batteries in electric passenger vehicles and electric trucks are established. A time-of-use electricity price model and an economic model considering battery aging costs are constructed. Two scenarios were established for daily use and V2G operation. The impacts of different scenarios and charging/discharging patterns on battery life and user profit are analyzed. The results indicate that the additional V2G discharging process increases the cyclic aging rate of EV batteries. Within the studied parameter ranges, the cyclic aging rate increased by 5.89% for electric passenger vehicles and 3.72% for electric trucks, respectively. Additionally, the initial V2G revenue may struggle to cover early-stage battery aging costs, but the subsequent slowdown in degradation may eventually offset these costs. With appropriate charging and discharging strategies, the maximum revenue per year reaches 18,200 CNY for electric trucks and 5600 CNY for electric passenger vehicles. This study may provide theoretical support for optimizing EV charging/discharging strategies and formulating policies in V2G scenarios. Full article
(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)
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