Article
Chemistry, Multidisciplinary
Bernardo Tormos, Benjamin Pla, Pau Bares, Douglas Pinto
Summary: Due to air quality concerns and rising fuel prices, urban bus fleets are increasingly adopting hybrid electric vehicles (HEVs) for their higher efficiency and lower emissions. This paper proposes an algorithm to adapt the energy management strategy (EMS) of HEVs to specific driving conditions on a particular bus route. The algorithm estimates the driving cycle based on a previous trip and applies dynamic programming and one-step look-ahead to optimize energy consumption. Simulation results show that the proposed method can keep the battery charge within the required range and achieve near-optimal performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
M. Abul Masrur
Summary: While HEV/EV technology is mature and widely used, there is a lack of literature on its application for off-road vehicles and nonautomotive purposes. This article introduces the current status of the technology and discusses the decision-making process required before its implementation in these areas.
PROCEEDINGS OF THE IEEE
(2021)
Article
Metallurgy & Metallurgical Engineering
Shuai Bin, Li Yan-fei, Zhou Quan, Xu Hong-ming, Shuai Shi-jin
Summary: This paper studied a supervisory control system for a hybrid off-highway electric vehicle and proposed a new predictive double Q-learning with backup models scheme (PDQL) to improve energy efficiency. Experimental evaluations showed that PDQL achieved better energy efficiency in fewer learning iterations compared to the standard double Q-learning scheme.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2022)
Article
Energy & Fuels
Weiwei Xin, Enyong Xu, Weiguang Zheng, Haibo Feng, Jirong Qin
Summary: This paper proposes an energy management framework based on model predictive control for fuel cell commercial vehicles. By introducing vehicle mass as a variable parameter, a vehicle mass identification model is established and evaluated the influence of variable algorithm parameters. The study also discusses the effects of mass varying on vehicle performance and evaluates the performance for different drive cycles on different loaded situations.
Article
Energy & Fuels
Kevin Mallon, Francis Assadian
Summary: This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. It proposes an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging, resulting in increased battery lifespan with minimal decrease in fuel economy.
Article
Engineering, Civil
Bin Xu, Xiaolin Tang, Xiaosong Hu, Xianke Lin, Huayi Li, Dhruvang Rathod, Zhe Wang
Summary: The study investigates the adaptability of Q-learning based supervisory control for HEVs, comparing it with other control strategies and finding that the Q-learning control shows strong adaptability under different conditions, leading the fuel economy among all supervisory controls in all three varying conditions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Thermodynamics
Desiree Alcazar-Garcia, Jose Luis Romeral Martinez
Summary: This paper presents an adaptive and high-accuracy methodology that utilizes genetic algorithms to accelerate the design and implementation of ecological vehicles in smart cities. The methodology maximizes vehicle range with minimal computational effort and provides predictive information on cost, volume, and weight. The reliability and precision of the model have been verified using commercially available vehicles.
Article
Energy & Fuels
R. Saravanan, O. Sobhana, M. Lakshmanan, P. Arulkumar
Summary: This manuscript proposes a hybrid technique, called JSO-RSA method, for the energy management of a battery-based FC electric vehicle system. The method aims to minimize the equivalent consumption by controlling the operational mode, state machine, and dynamic power factor. The proposed method demonstrates high efficiency (90.2%) and low operating cost ($568) compared to existing methods.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Piyush Girade, Harsh Shah, Karan Kaushik, Akil Patheria, Bin Xu
Summary: This paper introduces two new energy management strategies, with the Adaptive-ECMS being suitable for urban driving conditions and the Cost Optimization for Finite Horizon strategy being suitable for highway driving conditions. The new strategies show an average fuel economy improvement of 5% compared to the baseline strategy.
Article
Engineering, Electrical & Electronic
Yue Hu, Hui Xu, Zhonglin Jiang, Xinyu Zheng, Jianfeng Zhang, Wenhui Fan, Kun Deng, Kun Xu
Summary: This paper proposes a supplementary learning controller (SLC) based on deep reinforcement learning (DRL) to compensate for an existing rule-based energy management system (EMS) for hybrid electric vehicles (HEVs). The SLC works alongside the rule-based EMS and reduces the uncertainty of the algorithm to the system. A distributed architecture is designed for the DRL-based SLC, where each vehicle's SLC interacts with its own driving cycle, shares a neural network, and sends experience data to the cloud for learning updates.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Jin Woo Bae, Kwang-Ki K. Kim
Summary: An energy-efficient supervisory control method for parallel hybrid electric vehicles is proposed to improve fuel economy and reduce emissions, utilizing dynamic programming and Gaussian process regression to optimize power management. The method achieved significant fuel efficiency improvements in real-world driving conditions, showcasing the potential for practical application in the automotive industry.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Chemistry, Physical
Jie Li, Yonggang Liu, Yuanjian Zhang, Zhenzhen Lei, Zheng Chen, Guang Li
Summary: This paper proposes a data-driven eco-driving control strategy for plug-in hybrid electric vehicles, which can improve fuel economy and computational efficiency through neural network models.
JOURNAL OF POWER SOURCES
(2021)
Article
Energy & Fuels
Pedro Maroto Estrada, Daniela de Lima, Peter H. Bauer, Marco Mammetti, Joan Carles Bruno
Summary: This paper proposes a methodology for developing hybrid models to evaluate fuel consumption, CO2 and pollutant emissions in hybrid electric vehicles (HEVs). The use of Convolutional Neural Networks (CNNs) enhances the accuracy of pollutant emissions prediction. The proposed models have advantages in terms of real-time performance and computational complexity, and are applicable to real-time simulations and hardware/software-in-the-loop applications.
Article
Engineering, Electrical & Electronic
Bin Xu, Jun Hou, Junzhe Shi, Huayi Li, Dhruvang Rathod, Zhe Wang, Zoran Filipi
Summary: This study aims to reduce the learning iterations of Q-learning in HEV application utilizing warm-start methods, resulting in significant improvements compared to traditional cold-start methods.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Computer Science, Information Systems
Gonzalo Curiel-Olivares, Scott Johnson, Gerardo Escobar, Ricardo Schacht-Rodriguez
Summary: In this study, a model predictive control-based energy management system is designed and implemented to control the power flow of a series hybrid electric agricultural tractor. The system successfully achieves the objectives of battery state of charge regulation and fuel consumption minimization, while also positively impacting battery state of health and temperature compared to a conventional rule-based system.
Article
Engineering, Electrical & Electronic
Boli Chen, Xuefang Li, Simos A. Evangelou, Roberto Lot
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2020)
Article
Automation & Control Systems
Min Yu, Simos Andreas Evangelou, Daniele Dini
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2020)
Article
Automation & Control Systems
Simos A. Evangelou, M. A. Rehman-Shaikh
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Automation & Control Systems
Jawad Arif, Muhammad Asim Rehman-Shaikh, Simos A. Evangelou
Summary: Subsea production systems technologies are steadily developing to address deep-sea environmental issues, with a comprehensive technology qualification program proposed to ensure reliable operation of electrical/electronic systems on the seabed. By conducting environmental stress tests compliant with international standards, such as API, ISO, IEC, and IEEE, technology readiness level of subsea electrical/electronics assembly can be achieved.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Energy & Fuels
Jawad Arif, Muhammad Asim Rehman-Shaikh, Simos A. Evangelou
Summary: To ensure the reliability of products in subsea production systems, a comprehensive technology qualification program is proposed with a focus on a novel technology readiness level 4 process. This process includes analytical assessment of product design, operating conditions, compliance stress tests, modified environmental tests, and novel product safety procedures to meet acceptance criteria set in the qualification process.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Min Yu, Cheng Cheng, Simos Evangelou, Daniele Dini
Summary: This article presents the development of a full-car prototype of a mechatronic suspension system, aiming to be an alternative solution to fully active suspensions for suspension original equipment manufacturers. Through mechanical modifications and real-time embedded system development, the suspension performance improvement and ride comfort enhancement have been achieved.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Min Yu, Simos A. Evangelou, Daniele Dini
Summary: In this article, the parallel active link suspension (PALS) for road vehicles is investigated in the scenario of a sport utility vehicle (SUV). By utilizing PID and $H_{infinity}$ control schemes, the study focuses on chassis attitude stabilization and ride comfort enhancement.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Engineering, Civil
Xiao Pan, Boli Chen, Stelios Timotheou, Simos A. Evangelou
Summary: This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. It is shown that the underlying optimization problem, subject to safety constraints, can be formulated as two second-order cone programs with convexification and relaxation. The investigation of Pareto optimal solutions highlights the importance of optimizing the trade-off between travel time and energy consumption.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Zilin Feng, Min Yu, Simos A. Evangelou, Imad M. Jaimoukha, Daniele Dini
Summary: This article presents a combined mu-synthesis PID control scheme, employing a frequency separation paradigm, for a recently proposed novel active suspension, the Parallel Active Link Suspension (PALS). The developed mu-synthesis control scheme is superior to the conventional H-infinity control, previously designed for the PALS, in terms of ride comfort and road holding (higher frequency dynamics), with important realistic uncertainties, such as in vehicle payload, taken into account. The developed PID control method is applied to guarantee good chassis attitude control capabilities and minimization of pitch and roll motions (low frequency dynamics).
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Anastasis Georgiou, Furqan Tahir, Imad M. Jaimoukha, Simos A. Evangelou
Summary: This article investigates the problem of robust model predictive control (RMPC) of linear-time-invariant discrete-time systems subject to structured uncertainty and bounded disturbances. A novel approach is proposed to linearize the nonlinear and nonconvex constrained RMPC problem, with reduced computational burden, through the use of semidefinite relaxation techniques. The effectiveness of the proposed scheme is demonstrated through numerical examples.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Boli Chen, Xiao Pan, Simos A. Evangelou
Summary: This article presents an energy management (EM) control strategy for series hybrid electric vehicles (HEVs) with an engine start-stop system (SSS), aiming to optimize energy distribution and minimize fuel consumption. The control strategy incorporates a fuel penalty mechanism and proposes the hysteresis power threshold strategy () to realistically model engine restarts. The article demonstrates the effectiveness and robustness of the control strategy, suggesting its potential as a benchmark strategy for high-fidelity HEV models.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Xiao Pan, Boli Chen, Li Dai, Stelios Timotheou, Simos A. Evangelou
Summary: This paper focuses on cooperative vehicle management at a signal-free intersection, considering vehicle modeling uncertainties and sensor measurement disturbances. A hierarchical robust control strategy is proposed, with optimal control and robust model predictive control methods designed to solve the crossing order and velocity trajectories of connected and automated vehicles. The optimization problems are formulated as convex second-order cone programs, providing computationally efficient solutions. Simulation results demonstrate the effectiveness and robustness of the proposed strategy, highlighting the trade-off between energy consumption and journey time.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Proceedings Paper
Automation & Control Systems
Anastasis Georgiou, Furqan Tahir, Simos A. Evangelou, Imad M. Jaimoukha
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
(2020)
Proceedings Paper
Automation & Control Systems
Xiao Pan, Boli Chen, Simos A. Evangelou, Stelios Timotheou
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
(2020)
Proceedings Paper
Automation & Control Systems
Xiao Pan, Boli Chen, Simos A. Evangelou
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
