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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 6754

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Special Issue Editors

Dr. Nan Xu

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Guest Editor

College of Automotive Engineering, Jilin University, Changchun 130025, China
Interests: vehicle energy management; vehicle energy flow analysis; vehicle energy consumption testing and evaluation
Special Issues, Collections and Topics in MDPI journals

Dr. Yifan Jia

E-Mail Website
Guest Editor

College of Mechanical Engineering, Hebei University of Technology, Tianjin 300400, China
Interests: hybrid electric vehicle energy management; drive system control of electric vehicles; vehicle dynamics control

Special Issue Information

Dear Colleagues,

Against the global backdrop of the energy transition and carbon neutrality, the energy management system (EMS) has increasingly emerged as the central decision-making hub for vehicle energy optimization in new energy vehicles. With diverse technological pathways, such as range-extended electric vehicles (REEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs), achieving intelligent coordinated control of multi-energy systems, improving energy utilization efficiency, and extending the lifespan of power batteries have become cutting-edge topics of shared interest in academia and industry. Significant technical challenges remain, particularly in global energy management for intelligent connected vehicles, adaptability to complex operating conditions, dynamic demand response, and utilization of information.

This Special Issue aims to establish an international academic exchange platform by focusing on the latest research advances and innovative practices in energy management for new energy vehicles. It will cover the entire technical chain, from fundamental algorithm development to system integration and validation, such as powertrain modeling, condition analysis, control, ecological driving, testing, and evaluation.

We cordially invite scholars and experts worldwide to share theoretical research achievements and engineering application experiences, collectively driving the innovation and development of intelligent energy management technologies and supporting the transformation and upgrading of the new energy vehicle industry.

Topics of interest include, but are not limited to, the following:

Optimal control strategies for multi-energy powertrain systems;
Intelligent energy management algorithms;
Information utilization in energy management;
Vehicle energy flow analysis;
Design of novel topology-based energy management systems;
Dynamic allocation and real-time optimization of vehicle energy flow;
Coordinated control of thermal-electric coupling management systems;
Intelligent state-of-health (SOH) assessment and lifespan extension strategies for batteries;
Vehicle thermal management;
Digital twin-based simulation and optimization of energy systems;
Hardware-in-the-Loop (HIL) testing and real-vehicle validation methodologies.

Through this Special Issue, we anticipate fostering academic exchanges that provide theoretical foundations and practical insights for technological innovation in new energy vehicle energy management. Together, we aim to advance the sustainable development of clean energy transportation systems.

Dr. Nan Xu
Dr. Yifan Jia
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. Energies 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 2600 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

new energy vehicles
energy management
multi-energy powertrain systems
battery health management
thermal-electric coupling
global energy optimization
sustainable transportation

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Published Papers (6 papers)

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Research

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17 pages, 19279 KB

Open AccessArticle

Performance of Microscale Maple Seed Wind Turbine Blade

by Kunio Shimada

Energies 2026, 19(10), 2268; https://doi.org/10.3390/en19102268 - 8 May 2026

Viewed by 264

Abstract

Strategies to reduce global warming by limiting the consumption of fossil fuels are expected in part to include the integration of various renewable energies in residential buildings, which emit a considerable volume of greenhouse gases. Wind turbines are an important technology for moving toward this goal, as they are especially effective in isolated or rural areas for small-grid power systems. However, the approach requires that they be miniaturized, and that the production of wind blades be more convenient. The present study proposes a maple seed-shaped blade mimicking the practical shape and performance of a toy, in which the blade is so simple it can just be bent, eliminating the cumbersome production process of current propeller-type blades. We demonstrate with visualization, experiments of the flow characteristics, and numerical analysis that the power coefficient C_p of the wind turbine with micrometer-scale maple seed blades (7.5 cm long) was superior to that of the one using a propeller-type blade. A better C_p was observed with maple seed-shaped blades than with rectangular plates. This was found to be due to the behavior of the vortex near the blade. The performance of the maple seed wind turbine provides perspective on the development of microscale wind turbine-inspired dual-mode possessing characteristics of both lift and drag wind turbines. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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28 pages, 2201 KB

Open AccessArticle

Addressing Mixed-Integer Nonlinear Energy Management in Hybrid Vehicles: Comparing Genetic Algorithm and Sequential Quadratic Programming Within Model Predictive Control

by Ferris Herkenrath, Silas Koßler, Marco Günther and Stefan Pischinger

Energies 2026, 19(6), 1535; https://doi.org/10.3390/en19061535 - 20 Mar 2026

Viewed by 435

Abstract

Model Predictive Control (MPC) has emerged as a promising approach for energy management in hybrid electric vehicles, enabling predictive optimization of powertrain operation. The energy management problem in parallel hybrid powertrains constitutes a Mixed-Integer Nonlinear Programming (MINLP) problem, combining continuous decision variables such as torque distribution with discrete decisions including engine on/off states and clutch engagement. This problem structure presents distinct challenges for different optimization approaches. Gradient-based methods such as Sequential Quadratic Programming (SQP) solve continuous, differentiable optimization problems and require auxiliary methods to handle integer variables, while metaheuristic approaches such as Genetic Algorithms (GA) can handle the mixed-integer structure directly at the cost of increased computational effort. This study presents a systematic comparison between GA and SQP as optimization solvers within an MPC framework for a P1P3 parallel hybrid powertrain. A multi-objective cost function is formulated to simultaneously optimize system efficiency, battery state of charge management, and noise emissions. Both approaches are evaluated across the WLTC as well as a real-world RDE scenario. On the WLTC, both MPC approaches reduce fuel consumption by 0.5–1.0% and improve system efficiency by 3.7–4.6% compared to a state-of-the-art deterministic reference strategy optimized for fuel consumption. At the same time, both approaches additionally achieve substantial reductions in noise emissions compared to the deterministic reference, which was not optimized for acoustic behavior. On both cycles, the GA-based MPC achieves favorable performance compared to SQP, with the performance gap widening from the WLTC to the RDE cycle. Both methods achieve real-time capability, yet SQP reduces computational time by a factor of four compared to GA. As long as computational resources in automotive ECUs remain constrained, this efficiency advantage positions gradient-based optimization for series production applications, whereas metaheuristic methods offer greater flexibility for concept development stages with relaxed real-time requirements. The findings contribute to the understanding of optimization algorithm selection for MINLP energy management problems in hybrid electric vehicles. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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24 pages, 6216 KB

Open AccessArticle

Three-Dimensional Surface High-Precision Modeling and Loss Mechanism Analysis of Motor Efficiency Map Based on Driving Cycles

by Jiayue He, Yan Sui, Qiao Liu, Zehui Cai and Nan Xu

Energies 2026, 19(2), 302; https://doi.org/10.3390/en19020302 - 7 Jan 2026

Viewed by 524

Abstract

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power

P_{e}

as a function of motor speed

n

and mechanical power

P_{m}

. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using

R^{2}

, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4

(+ P_{m}^{2})

offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4

(+ P_{m}^{2})

polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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33 pages, 11067 KB

Open AccessArticle

CFD-Driven Design Optimization of Corrugated-Flange Diffuser-Integrated Wind Turbines for Enhanced Performance

by Debela Alema Teklemariyem, Nasir Hussain Razvi Syed and Phong Ba Dao

Energies 2025, 18(17), 4601; https://doi.org/10.3390/en18174601 - 29 Aug 2025

Cited by 3 | Viewed by 1836

Abstract

In the global shift toward sustainable energy, enhancing the efficiency of renewable energy systems plays a pivotal role in advancing the Sustainable Development Goals. This study focuses on optimizing the design of a corrugated-flange diffuser integrated with a wind turbine to enhance its performance, particularly in low-wind conditions. While most previous research has examined wind farm performance at high wind speeds, the challenge of effective power extraction at low wind speeds remains largely unresolved. The potential of diffusers to enhance wind turbine efficiency under low-wind conditions has received limited investigation, with most prior studies focusing solely on empty diffuser configurations without turbine integration. In addition, the influence of flange geometry on diffuser performance remains largely unexplored. In this study, parametric analyses were conducted to identify the optimal diffuser design, followed by comparative performance evaluations of configurations with and without turbine integration, using computational fluid dynamics (CFD) simulations. The results show that integrating a turbine with the optimized corrugated-flange diffuser increased flow velocity by 67.85%, achieving an average of approximately 14 m/s around the blade region. In comparison, the optimized corrugated-flange diffuser alone increased flow velocity by 44%, from 4.5 m/s to 8.036 m/s. These findings highlight the potential of optimized diffuser designs to enhance small-scale wind turbine performance in low-wind conditions. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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19 pages, 8482 KB

Open AccessFeature PaperArticle

Waste Heat Recovery in the Energy-Saving Technology of Stretch Film Production

by Krzysztof Górnicki, Paweł Obstawski and Krzysztof Tomczuk

Energies 2025, 18(15), 3957; https://doi.org/10.3390/en18153957 - 24 Jul 2025

Viewed by 1335

Abstract

The stretch film production is highly energy intensive. The components of the technological line are powered by electrical energy, and the heat is used to change the physical state of the raw material (granules). The raw material is poured into FCR (the first calender roller). To solidify the liquid raw material, the calendar must be cooled. The low-temperature heat, treated as waste heat, has dissipated in the atmosphere. Technological innovations were proposed: (a) the raw material comprises raw material (primary) and up to 80% recyclate (waste originating mainly from agriculture), (b) the use of low-temperature waste heat (the cooling of FCR in the process of foil stretch production). A heat recovery line based on two compressor heat pumps (HP, hydraulically coupled) was designed. The waste heat (by low-temperature HP) was transformed into high-temperature heat (by high-temperature HP) and used to prepare the raw material. The proposed technological line enables the management of difficult-to-manage post-production waste (i.e., agriculture and other economic sectors). It reduces energy consumption and raw materials from non-renewable sources (CO₂ and other greenhouse gas emissions are reducing). It implements a closed-loop economy based on renewable energy sources (according to the European Green Deal). Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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Review

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41 pages, 2244 KB

Open AccessReview

Cutting-Edge Research: Artificial Intelligence Applications and Control Optimization in Advanced CO₂ Cycles

by Jiaqi Dong, Yufu Zheng, Jianguang Zhao, Jun Luo and Yijian He

Energies 2025, 18(19), 5114; https://doi.org/10.3390/en18195114 - 25 Sep 2025

Cited by 2 | Viewed by 1561

Abstract

In recent years, advanced CO₂ cycles, including supercritical CO₂ power cycles, transcritical CO₂ power cycles and refrigeration cycles, have demonstrated significant potential for application across a broad spectrum of energy conversion processes, owing to their high efficiency and compact components that are environmentally benign and non-polluting. This study presents a comprehensive review of the dynamic performance and control strategies of these advanced CO₂ cycles. It details the selection of system configurations and various control strategies, detailing the principles behind different control strategies, their applicable scopes, and their respective advantages. Furthermore, this study conducts a comparison between the joint control strategy and single control strategies for CO₂ cycles, demonstrating the superiority of the joint control strategy in CO₂ cycles. It then delves into the potential of novel control technologies for CO₂ cycles, using model-based control technology powered by artificial intelligence as a case study. This study also offers an extensive overview of control theory, methodology, scope of application, and the pros and cons of various control strategies, with examples including extreme value-seeking control, model predictive control (MPC) based on an artificial neural network model, and MPC based on particle swarm optimization. Finally, it explores the application of AI-controlled CO₂ cycles in new energy vehicles, solar power generation, aerospace, and other fields. It also provides an outlook on the development direction of CO₂ cycle control strategies in light of the evolving trends in the energy sector and advancements in AI methodologies. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Energy Management)

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