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Article
Computer Science, Artificial Intelligence
Junchao Yang et al.
Summary: Real-time digital twin technology enhances traffic safety and provides scientific strategies for intelligent traffic management. A parallel intelligence-driven resource scheduling scheme is proposed to address the delay and load balance issues in intelligent vehicular systems with dual dependencies of timing and data. An adaptive particle swarm with genetic algorithm is used to optimize the offloading, resource allocation, and load balance.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Review
Energy & Fuels
Dimitrios Rimpas et al.
Summary: The modern era of green transportation is focused on electrifying all vehicles using industry 4.0 technologies. Electric vehicles (EVs) offer many advantages such as pollution-free operation, low maintenance costs, and economical operation, thanks to electric motors powered by lithium-ion batteries. However, due to limitations, a hybrid energy storage system (HESS) consisting of batteries and ultracapacitors is gaining attention. This paper reviews different motor technologies used in EVs, such as brushless motors, synchronous reluctance, and induction motors, and classifies them based on their ability to operate with a HESS and maximize efficiency and sizing.
Review
Energy & Fuels
Lorenzo Ricciardi Celsi et al.
Summary: The 2023 States General of Artificial Intelligence (AI) event was held in Italy from 28 February to 2 March 2023, sponsored by multinational companies. The purpose of the event was to provide a platform for international AI experts to discuss recent trends in AI. This paper reports on the current research on control engineering and AI methods for energy networks, focusing on EV charging, cyber-physical security, and predictive maintenance, which were identified as important topics during the event. The paper provides a critical discussion of methodologies and experimental setups, as well as an overview of future research directions.
Review
Energy & Fuels
Mehrdad Tarafdar-Hagh et al.
Summary: The transportation sector is a major contributor to greenhouse gas emissions globally, and electrifying this sector can significantly reduce pollutant emissions. The widespread connection of electric vehicles (EVs) to the power grid may bring challenges, such as increasing the network's peak load. Therefore, optimizing the use of EVs is necessary to improve the network's economic, security, and stability indicators. This review article examines different control models, EV models, and their comparison, communication standards for charging stations, the effects of EV integration with the power grid, and various charging methods. Additionally, it investigates battery technology and energy management systems in the electric vehicle industry, as well as government incentives and the combination of EVs with renewable energy sources.
Review
Energy & Fuels
Mubashir Rasool et al.
Summary: The global impact of HEVs is increasing rapidly as an emission-free and reliable alternative to fossil fuel-based vehicles. FCHEVs, in particular, have been widely studied as an energy-efficient and environmentally friendly transportation option with longer range. The optimal selection of EMSs plays a crucial role in the performance and reliability of FCHEVs.
Article
Energy & Fuels
Mehran Tahir et al.
Summary: Currently, the assessment of transformer winding condition using frequency response analysis (FRA) requires skilled personnel. However, there is still a lack of definitive methodology for interpreting and assessing the condition of transformer windings based on FRA results, which poses a challenge for industrial applications. To address this challenge, this paper proposes a transformer condition assessment (TCA) algorithm that utilizes numerical indices and supervised machine learning to interpret FRA results automatically. The algorithm includes random forest (RF) classifiers that can identify various faults in transformer windings and classify their condition. Through the analysis of 139 FRA measurements from over 80 power transformers with different ratings, sizes, designs, and manufacturers, the results demonstrate that the proposed TCA algorithm can effectively assess transformer winding condition with up to 93% accuracy without extensive human intervention.
Review
Energy & Fuels
Inese Mavlutova et al.
Summary: To create a sustainable future in smart cities, it is necessary to develop urban transportation concepts and reduce the use of traditional transportation, especially cars, to minimize environmental impact and move towards a sustainable urban future.
Review
Green & Sustainable Science & Technology
Dawei Qiu et al.
Summary: Electric vehicles (EVs) are important in power systems due to their mobility and flexibility. The increasing use of renewable energy sources brings challenges to power systems, which can be addressed by vehicle-to-grid (V2G) technology. Reinforcement learning (RL) algorithms have gained attention in solving EV dispatch problems due to their ability to adapt in real-time. This paper provides a comprehensive review of RL algorithms and their application to various EV dispatch problems.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Review
Chemistry, Multidisciplinary
Yunfei Cao et al.
Summary: With the increasing strain on global energy reserves and the necessity to comply with national carbon emission regulations, fuel efficiency and environmental friendliness in automobiles are gaining importance. Hybrid electric vehicles (HEVs) have been adopted as a reliable choice for improving fuel economy and reducing emissions due to their combination of long cruising range and energy efficiency. Research on energy management strategies for HEVs, focusing on controlling issues while ensuring battery life and meeting requirements for fuel consumption, emissions, and driving performance, has been conducted and various approaches have been proposed. This literature review provides a comprehensive assessment, highlighting contributions and serving as a complete reference for scholars interested in hybrid vehicle development, control, and optimization.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Cetengfei Zhang et al.
Summary: The digital twin is a promising technology that combines artificial intelligence and big data to provide a cyber-physical platform for rapid product development. In this study, a dedicated adaptive particle swarm optimization (DAPSO) algorithm is developed to optimize the energy management strategy (EMS) for a plug-in hybrid vehicle (PHEV) based on the digital twin. The DAPSO algorithm, which incorporates the widely used particle swarm optimization (PSO) algorithm and an adaptive swarm control strategy, improves the optimality and trustworthiness of the DT-based EMS optimization. Experimental evaluations show that the DAPSO algorithm outperforms conventional PSO algorithms, achieving significant improvements in cost function value and computing time for the PHEV application.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Review
Engineering, Civil
Jiongpeng Gan et al.
Summary: As an alternative to conventional fuel vehicles, hybrid electric vehicles (HEV) offer lower fuel consumption and fewer exhaust emissions. This paper reviews the research progress of energy management strategy (EMS) based on deep reinforcement learning (DRL), focusing on the algorithm, training environment, and its combination with intelligent transportation systems (ITS). The challenges and solutions of DRL-based EMSs are discussed, including the issue of automotive cyber security in the intelligent transportation environment.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Aditya Joshi et al.
Summary: In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Proceedings Paper
Engineering, Electrical & Electronic
Oussama Laayati et al.
Summary: By using the Tabu Search algorithm, this study proposes a novel approach to energy management in microgrids. It aims to reduce the total cost of energy consumption and maximize the utilization of renewable energy sources. The findings show that the Tabu Search algorithm performs better than alternative optimization strategies in terms of solution quality, convergence speed, and robustness.
2023 5TH GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE, GPECOM
(2023)
Proceedings Paper
Green & Sustainable Science & Technology
Disha L. Dinesha et al.
Summary: The application of blockchain technology in the energy industry is growing, enabling automation of local energy markets, energy trading, and market operations. However, scalability and low transaction rates present limitations. Moreover, the adoption of Blockchain Enabled Interconnected Smart Microgrids (BSMGs) may lead to heterogeneity in the system and possible monopolization by certain platforms. To overcome these drawbacks, interoperability between heterogeneous blockchains is proposed. In this paper, Ignite CLI is used to establish inter-chain communication among BSMGs, enabling inter-microgrid transactions, and exploring interoperability in the energy domain.
2023 IEEE GREEN TECHNOLOGIES CONFERENCE, GREENTECH
(2023)
Article
Computer Science, Information Systems
Vasileios Boglou et al.
Summary: The integration of photovoltaic panels and electric vehicles presents challenges for residential distribution grids due to unsynchronized new demand and time-limited production. This necessitates the use of energy storage systems (ESSs) despite their high costs. A distributed optimal small-scale PV energy system sizing strategy is proposed in this study, considering individual energy needs and EV charging. The optimization results show that this approach can significantly reduce energy costs by up to 40% while allowing distribution system operators to incorporate additional loads without network expansion.
Article
Energy & Fuels
Vasileios Boglou et al.
Summary: The modernization of energy distribution grids due to the expected increase in global electricity demand and adoption of green energy sources has led to the development of microgrids, which improve electric power system reliability. The integration of electric vehicles is expected to have a negative impact on microgrid operation, but a decentralized energy management system based on multi-agent systems and fuzzy logic controller has been developed to efficiently charge EVs. This system reduces investment costs, increases chargeable EVs, and decreases peak load and load variances without delaying charging, offering operational and financial benefits for islanding distribution grids with a high penetration of electric vehicles.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Xiaolin Tang et al.
Summary: Practical vision-based technology is crucial for the autonomous driving of intelligent hybrid electric vehicles. This article proposes a hierarchical control structure that combines YOLO-based object detection and deep reinforcement learning-based intelligent control. Experimental results show that the hierarchical control structure achieves considerable computing efficiency on embedded devices while maintaining a safe following distance and achieving fuel economy.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Energy & Fuels
Guilherme Goncalves Pinheiro et al.
Summary: This study presents an application of series VSCs in medium voltage distribution grids for power flow management, which can increase the capacity of distribution grids without needing grid reconfigurations by interconnecting two feeders at the end of distribution lines.
Article
Energy & Fuels
Bashar Aldbaiat et al.
Summary: This paper presents a two-stage grid-connected PV system with reactive power management capability. The proposed algorithm does not disable the maximum power point tracking (MPPT) state and can send phase-shifted current to the grid during low-voltage periods to recover the voltage levels. The new method also offers overcurrent protection to the PV inverter for safety. The phase-locked loop based on the synchronous reference frame (SRF-PLL) is optimized using a genetic algorithm (GA), improving synchronization during low-voltage periods.
Article
Automation & Control Systems
Ji Li et al.
Summary: This article proposes a new algorithm environment for the multiobjective optimization of energy management systems in plug-in hybrid vehicles. By using physical and virtual data and introducing a confidence factor, the proposed algorithm achieves more accurate optimization. Experimental results show that the proposed algorithm requires lower R&D costs and can save more energy compared to other methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Engineering, Electrical & Electronic
Luona Xu et al.
Summary: This article presents a review of coordinated control strategies, stability analysis, and fault management for dc shipboard microgrids. It discusses load characteristics, energy management systems, voltage stability, shipboard protection system requirements and solutions, as well as the developing trends of existing commercial dc ships.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Wen Sun et al.
Summary: This article proposes a dynamic digital twin-based resource scheduling method for aerial-assisted Internet of Vehicles (IoV) and designs a two-stage incentive mechanism based on Stackelberg game and alternating direction method of multipliers (ADMMs) to improve the satisfaction of vehicles and the energy efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yuanjian Zhang et al.
Summary: This article introduces a virtual test controller based on machine learning that can effectively validate complex vehicle models and improve energy management performance of plug-in hybrid electric vehicles. The validation of the virtual test controller is achieved by utilizing the least-square support vector machine, random forest, and ReliefF algorithm to filter the internal data. The major innovation of this article lies in the development of an efficient virtual test controller, which provides convenience for the development of vehicle models and control strategy design.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Xiaolin Tang et al.
Summary: This article proposes an energy management strategy based on deep reinforcement learning to optimize the fuel economy of hybrid electric vehicles. By learning gear-shifting strategies and controlling engine throttle opening, the proposed strategy successfully reduces fuel consumption and improves computational efficiency.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Review
Engineering, Electrical & Electronic
Ahmed M. Ali et al.
Summary: With increasing demands on decarbonized transportation systems, meeting performance and environmental requirements for electromobility has become necessary. The major challenges of electrified transportation include rapid battery degradation, limited driving range and performance, and the need for charging scheduling within infrastructural capabilities. Intelligent power management systems (I-PMSs) have been recognized as a key solution to tackle these challenges, and this article provides a comparative evaluation of recent advances in predictive and cognitive PMS, discussing their problem formulation, solving techniques, and practical examples. It also examines the evolving role of I-PMS in intelligent transportation systems and connected vehicles technologies.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Atriya Biswas et al.
Summary: An online updating framework for an energy management system using deep reinforcement learning agent and Markov chain model is proposed. The framework can generate near-optimal policy for unknown drive cycles and achieve good fuel economy. However, it consumes more fuel for certain types of unknown drive cycles.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Xiaolin Tang et al.
Summary: This article proposes a battery health-aware and deep reinforcement learning energy management framework for power-split hybrid electric vehicles in a naturalistic driving scenario. The framework utilizes data-driven methods to establish driving scenarios and embeds expert knowledge into the deep deterministic policy gradient for faster convergence and guaranteed vehicle performance. The proposed strategy optimizes the tradeoff among fuel consumption, battery aging cost, and state of charge sustainability penalty, and outperforms existing strategies in terms of battery aging and iterative convergence.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Yuanjian Zhang et al.
Summary: In this study, a novel learning-based model predictive control strategy is developed for serial-parallel plug-in hybrid electric vehicles (PHEVs). This method addresses uncertainties in state estimation through a Gaussian process model, achieving optimized management of energy flow with strong real-time applicability.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Computer Science, Hardware & Architecture
Jingyi Wu et al.
Summary: This article presents the current research status and predictions of artificial intelligence in the intelligent development of transportation infrastructure in smart cities. It emphasizes the advantages of Digital Twins and AI in classification and information network management, providing reference for the intelligent development of transportation infrastructure in smart cities.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Review
Energy & Fuels
Nirvikar Singh
Summary: This paper reviews and evaluates India's energy policy in relation to its commitment to achieve the target of net-zero carbon emissions by 2070. It emphasizes the importance of green electrification, specifically through the expansion of solar power capacity. The paper also discusses policy options for advancing the target date to 2050 and examines the potential financial and growth implications.
Article
Energy & Fuels
Juan Sebastian Roncancio et al.
Summary: Access to electricity is crucial for human growth, yet a significant portion of the global population still lacks energy access. In rural communities in Colombia, the National Interconnected System's lack of quality and stability poses challenges to energy access. This research proposes a flexible energy market based on bi-level mixed-integer linear programming to improve the rural power grid's quality. The study focuses on utilizing energy from the rural grid to power a heating, ventilation, and air-conditioning system in a flower greenhouse, and evaluates the flexibility of the system under different pricing schemes.
Article
Energy & Fuels
Fatih Atalar et al.
Summary: This study tested three different dielectric liquids used in transformers and evaluated their insulation performance using various test methods. The results showed that although synthetic ester had higher dielectric strength, mineral oil exhibited the best durability under lightning impulse voltages. It was also found that mineral oil had good stability under AC and negative DC voltage.
Article
Energy & Fuels
Oussama Laayati et al.
Summary: This paper proposes a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms and health management approaches. It conducts a comparative study to select the best fit models, and connects to an online monitoring system to calculate important performance indicators for a smart energy management system.
Article
Engineering, Electrical & Electronic
Bo Hu et al.
Summary: With the development of artificial intelligence and machine learning, reinforcement learning has opened up new possibilities for hybrid electric vehicle energy management. However, current issues limit its application in industrial energy management strategy tasks. To overcome this, an adaptive hierarchical energy management strategy combining heuristic equivalent consumption minimization strategy knowledge and deep deterministic policy gradient algorithm is proposed. Experimental results show that the proposed strategy outperforms other benchmark strategies in terms of fuel consumption.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Bin Zhang et al.
Summary: An online updating energy management strategy based on deep reinforcement learning is proposed to reduce fuel consumption and improve the adaptability of the algorithm. The simulation results show that the strategy achieves good fuel economy performance.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Energy & Fuels
Xin Wang et al.
Summary: This paper proposes a chance-constrained stochastic model predictive control (CCSMPC) method to improve system operation in terms of cost and reconfiguration activities, as well as the ability to deal with uncertainties due to fluctuating load demands. The effectiveness of the strategy is verified through comprehensive comparison studies, showing that CCSMPC outperforms deterministic MPC (DMPC) in both normal and faulty operating conditions.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Electrical & Electronic
Yuechuan Tao et al.
IEEE Transactions on Transportation Electrification
(2022)
Article
Engineering, Electrical & Electronic
Jiankun Peng et al.
IEEE Transactions on Transportation Electrification
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Leo Raju et al.
Summary: The concept of P2P energy transfer, when combined with blockchain technology, offers a cost reduction solution in microgrids. Blockchain has applications beyond cryptocurrencies, spanning various fields such as financial services, IoT, and voting systems. By providing a new perspective, it reshapes the security, efficiency, and stability of digital systems.
2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON
(2022)
Proceedings Paper
Energy & Fuels
Hamed Jafari Kaleybar et al.
Summary: This paper proposes a model of future 9 kV MVDC electric railway systems (ERSs) using MATLAB software. The model integrates distributed energy sources and EV charging infrastructures, and is capable of simulating a digital twin (DT) based on real data from a physical railway system. The Rome-Florence high-speed railway line is used as a real case study, and an example modeling of wind turbines (WT), photovoltaic (PV), and EV charging infrastructures as auxiliary power supply to the MVDC railway microgrid is presented, considering the regenerative braking energy of trains.
2022 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC)
(2022)
Proceedings Paper
Energy & Fuels
Xu Cheng et al.
Summary: This paper provides a review of the federated learning paradigm, comparing types, network structures, and global model aggregation methods. It also conducts a comprehensive review of FL applications in the energy domain, including demand response, identification, prediction, and federated optimizations. The discussion highlights challenges, opportunities, and limitations in the energy informatics applications.
2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022)
(2022)
Article
Computer Science, Information Systems
Sadiqa Jafari et al.
Summary: Electric vehicles depend on batteries with lower energy and power densities compared to liquid fuels, limiting their mainstream adoption. Effective management of lithium-ion batteries is crucial for a low-carbon future. This research proposes a battery digital twin-based solution to enhance battery management systems and optimize battery storage units. The solution utilizes digitalization and artificial intelligence to predict battery state and extend battery life.
Proceedings Paper
Energy & Fuels
Oussama Laayati et al.
Summary: Smart energy management systems provide real-time key performance indicators to help consumers and producers optimize energy consumption; grid self-diagnosis technology enables decentralized energy production, creating a large energy market.
2022 IEEE 4TH GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE (IEEE GPECOM2022)
(2022)
Article
Computer Science, Information Systems
Zeyi Xi et al.
Summary: This study uses reinforcement learning to train a neural network controller for optimizing energy management and wind gradient energy acquisition of high-altitude solar aircraft, and the simulation experiments verify its advantages.
Article
Computer Science, Information Systems
Junaid Khalid et al.
Summary: This paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning, which integrates renewable energy sources to cope with the dynamic evolution and complexity of power systems. The method demonstrated superior performance compared to other techniques in testing, effectively handling nonlinearities caused by load-generation variations.
Article
Urban Studies
Ahm Shamsuzzoha et al.
Summary: The study critically reviewed the smart city research paradigm, identifying pitfalls, conflicting results, and areas for further study. It conducted a qualitative comparison of smart city initiatives in selected countries and cities and used grounded theory to generate arguments and conclusions. The research found that current smart city research does not fully address the complex nature, conflicts, and interdependencies of smart city objectives, and that smart city initiatives require holistic evaluation due to varying evaluation methods and rankings.
Article
Engineering, Electrical & Electronic
Pierre Major et al.
Summary: One objective of smart cities is to make smart decisions using big data and citizen engagement to address sustainability and climate change. However, local governments need to address the barriers of data privacy concerns and the complexity of big data for nonexperts in order to achieve this goal.
IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE
(2021)
Review
Energy & Fuels
Amela Ajanovic et al.
Summary: Electric mobility is a potential solution to environmental problems caused by transport modes, and efforts are being made globally to increase the use of various types of electric vehicles. While electricity has been successfully used in some public transport modes for over a hundred years, policies implemented will have a significant impact on the future development of electric mobility.
Article
Thermodynamics
Yangyang Li et al.
Summary: This study optimized the performance of the Atkinson cycle engine on series hybrid electric vehicles using digital twins technology and evolutionary non-dominated sorting genetic algorithm, successfully reducing fuel consumption and nitric oxide emissions. It can provide theoretical support and digital model for the development of new energy vehicles.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Engineering, Electrical & Electronic
Shuai Zhao et al.
Summary: This article provides an overview of the applications of artificial intelligence in power electronic systems, correlating the design, control, and maintenance phases with tasks addressed by AI. It discusses the applications of four categories of AI and reviews more than 500 publications to identify common understandings, practical challenges, and research opportunities in the field. The article includes an Excel file listing relevant publications for statistical analytics.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Guodong Du et al.
Summary: A heuristic deep reinforcement learning control strategy is proposed for energy management of series hybrid electric vehicles, utilizing methods such as adaptive moment estimation and experience replay for improved efficiency. The strategy shows faster training and better fuel economy compared to existing methods, demonstrating adaptability and stability across different driving cycles.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Green & Sustainable Science & Technology
Ghanishtha Bhatti et al.
Summary: This study aims to comprehensively review the application of digital twin technology in smart electric vehicles, systematically building the conceptual groundwork for the reader to explore the potential and challenges within the smart vehicle system, evaluating its holistic technical and socio-economic impact.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Automation & Control Systems
Jingda Wu et al.
Summary: This article presents a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, focusing on thermal safety and degradation of onboard lithium-ion battery system. The strategy utilizes a multiconstrained least costly formulation and soft actor-critic deep reinforcement learning to optimize power allocation. Tests show its superiority in terms of converging effort, enforcement of battery safety, and reduction of driving cost.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Hongwen He et al.
Summary: This study proposes a cyber-physical system (CPS)-based energy management strategy (EMS) using deep reinforcement learning (DRL) in hybrid electric vehicles (HEVs). The results show significant fuel economy improvement and verify the effectiveness of the proposed EMS in different types of vehicles.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Engineering, Electrical & Electronic
Hailong Zhang et al.
Summary: This study proposes an RL framework named CADC for energy management optimization in hybrid electric vehicles. The framework combines coach-actor-double-critic algorithm design, which effectively considers constrained conditions for online energy management to improve energy-saving rate.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Review
Engineering, Electrical & Electronic
Xiaosong Hu et al.
Summary: Electrified vehicles are seen as a promising technology for energy savings and emission reductions. This article provides a comprehensive review of powertrain design and energy management in electrified vehicles. The current challenges and future trends of electrified powertrain design and control are discussed, offering valuable insights for researchers in this field.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Proceedings Paper
Energy & Fuels
Sabrina Nguyen et al.
Summary: Digital twins are introduced as a solution for various power distribution system applications and security, providing real-time calculations and analyses to enhance understanding of current system functions and improve decision-making efficiency in power grids.
2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Dalia Casanova Mombiela et al.
Summary: This paper introduces a new algorithm for designing ship power systems, integrating embedded control for optimization purposes. By considering various alternatives and key indicators, the algorithm helps in designing power plants that meet requirements and conducting initial evaluations through simplified simulations.
2021 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC)
(2021)
Article
Computer Science, Information Systems
Ahmad Aziz Al Alahmadi et al.
Summary: With global environmental changes, nuclear power risks, and rising energy costs, there is a growing desire for more renewable energy in electricity generation. Smart places like smart cities and smart universities are becoming popular, but integrating smart grid systems presents challenges.
Article
Computer Science, Information Systems
Woong Lee et al.
Summary: A practical method for estimating an appropriate costate based on Deep Q-Networks is proposed in this study to address the issue of difficulty in evaluating the performance of the control parameter before driving is complete. The proposed control concept outperforms an existing ECMS using an adaptive technique for determining the costate in simulation results, showcasing the feasibility and effectiveness of the approach.
Article
Computer Science, Information Systems
Heeyun Lee et al.
Summary: This study introduces a reinforcement learning-based approach to determine the equivalent factor in hybrid electric vehicles, indirectly extracted from reinforcement learning results. By combining reinforcement learning with the equivalent consumption minimization strategy, the proposed method achieves near-optimal performance compared to dynamic programming and improves performance compared to existing strategies.
Article
Computer Science, Information Systems
Heeyun Lee et al.
Summary: This study utilizes reinforcement learning for energy management of fuel cell electric vehicles, continuously optimizing control strategies through learned models, showing reduced fuel consumption in vehicle simulation compared to rule-based strategies.
Article
Engineering, Civil
Ruilong Deng et al.
Summary: In recent years, electrification of transportation, particularly electric buses (EBs), has gained significant attention due to its potential to reduce metropolitan air pollution caused by fossil fuel-powered vehicles. EBs, powered by decarbonized electricity, offer benefits such as reduced air pollution and noise levels, as well as the ability to recover electricity from regenerative braking. Ongoing research focuses on energy storage, power management, and charging scheduling for EBs, with future opportunities including extending research from electric vehicles (EVs) to EBs, modeling EB charging demand, and studying EB impacts on power systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Aerospace
Jose Pedro Soares Pinto Leite et al.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2020)
Article
Energy & Fuels
Vasileios Boglou et al.
Review
Energy & Fuels
Shahriar Rahman Fahim et al.
Article
Engineering, Electrical & Electronic
Bin Xu et al.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2020)
Review
Energy & Fuels
Qicheng Xue et al.
Article
Energy & Fuels
Hyang-A Park et al.
Review
Engineering, Multidisciplinary
Fei Teng et al.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Muhammed Y. Worku et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Ganghong Zhang et al.
2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020)
(2020)
Article
Computer Science, Information Systems
Luming Chen et al.
Article
Computer Science, Information Systems
Xiaowei Guo et al.
Article
Computer Science, Information Systems
Heeyun Lee et al.
Article
Computer Science, Information Systems
Taha Alfakih et al.
Article
Engineering, Electrical & Electronic
Jichao Liu et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Review
Automation & Control Systems
Giampaolo Buticchi et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2019)
Article
Engineering, Electrical & Electronic
Pier Giuseppe Anselma et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Review
Engineering, Electrical & Electronic
Atriya Biswas et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Yuecheng Li et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Xuning Feng et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Engineering, Electrical & Electronic
Guoqiang Li et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Computer Science, Information Systems
Chengrun Qiu et al.
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Engineering, Electrical & Electronic
Teng Liu et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Computer Science, Information Systems
Ahmed Fathy et al.
Article
Computer Science, Information Systems
Huifang Kong et al.
Proceedings Paper
Engineering, Electrical & Electronic
Eivind Bohn et al.
2019 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS' 19)
(2019)
Article
Computer Science, Information Systems
Jinlong Wu et al.
Article
Computer Science, Information Systems
Yuandou Wang et al.
Article
Computer Science, Information Systems
Jiang Zhu et al.
IEEE INTERNET OF THINGS JOURNAL
(2018)
Article
Computer Science, Artificial Intelligence
Bahare Kiumarsi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
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