Article
Thermodynamics
Fei Ren, Chenlu Tian, Guiqing Zhang, Chengdong Li, Yuan Zhai
Summary: This paper proposes a hybrid method based on SARIMA and deep learning for power demand prediction of electrical vehicles. The method extracts the linear trend and non-linear components of power demand, and combines periodic features for prediction. Experimental results demonstrate that the proposed method achieves higher prediction accuracy compared to other data-driven models.
Article
Green & Sustainable Science & Technology
Pawan Kumar Pathak, Anil Kumar Yadav, Sanjeevi Kumar, Bhekisipho Twala, Innocent Kamwa
Summary: This paper proposes an intelligent battery charging scheme for hybrid electric vehicles (HEVs) using a fuel cell as the main power source and solar photovoltaic (PV) and battery as auxiliary power sources. The scheme includes a minimized oscillation-based improved perturb and observe (I-P&O) maximum power point tracking (MPPT) scheme for the PV, as well as cascaded DC-DC boost and buck power converters. The proposed MPPT scheme achieves a tracking efficiency of 99.80% for the considered PV array.
IET RENEWABLE POWER GENERATION
(2023)
Article
Computer Science, Information Systems
Chigozie Uzochukwu Udeogu, Wansu Lim
Summary: This paper proposes an improved and adaptive deep learning-based velocity prediction control EMS for battery-supercapacitor HEVs, which prolongs battery lifetime and increases energy utilization efficiency through feature engineering and optimized power allocation.
Article
Thermodynamics
Xinyou Lin, Yutian Xia, Wei Huang, Hailin Li
Summary: A trip distance SOC adaptive power prediction control strategy is developed for a plug-in fuel cell electric vehicle (PFCEV) based on equivalent consumption minimization strategy (ECMS), aiming to optimize the energy ratio provided by the fuel cell and battery to minimize hydrogen consumption. The proposed method significantly decreases the HC for variable trip distances according to validation results, showcasing its potential in reducing the fuel consumption of PFCEVs.
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
Thermodynamics
Fengqi Zhang, Xiaosong Hu, Reza Langari, Lihua Wang, Yahui Cui, Hui Pang
Summary: An adaptive energy management strategy based on the equivalent consumption minimization strategy (ECMS) framework is developed to optimize gearshift commands and torque distribution for automated parallel hybrid electric vehicles. The methodology utilizes flexible torque requests to simultaneously consider drivability and fuel economy, resulting in improved powertrain optimization and promising fuel efficiency.
Article
Thermodynamics
Tai-Yu Ma, Sebastien Faye
Summary: Public charging station occupancy prediction is important for developing a smart charging strategy. Existing studies have limited accuracy, so we propose a new mixed long short-term memory neural network model that incorporates historical charging state sequences and time-related features. The proposed method outperforms benchmark approaches and achieves high prediction accuracy.
Article
Thermodynamics
Chao Yang, Kaijia Liu, Xiaohong Jiao, Weida Wang, Ruihu Chen, Sixiong You
Summary: This paper proposes an intelligent EMS for PHEVs using a novel adaptive firework algorithm to optimize control parameters, aiming to improve fuel economy. The EMS includes rule-based gear shift strategy and torque distribution strategy, with a modified AFWA used to efficiently optimize control parameters of these strategies.
Article
Automation & Control Systems
Yong Wang, Huachun Tan, Yuankai Wu, Jiankun Peng
Summary: This study combines computer vision and deep reinforcement learning to improve the fuel economy of hybrid electric vehicles, achieving significant reduction in fuel consumption and 96.5% fuel economy of the global optimum-dynamic programming in real driving scenarios.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Mechanical
Shi DeHua, Rong XiangWei, Wang ShaoHua, Cai YingFeng, Shen HuaPing, Yang Tao
Summary: This study proposes an adaptive ECMS strategy based on average predicted power, which is predicted using a polynomial function. The coefficients of the prediction model are updated using the recursive least squares algorithm, and the parameters of the explicit expression model are derived using particle swarm optimization. Simulation results demonstrate that the proposed model performs well in terms of prediction accuracy and battery charging sustainability.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Xizheng Zhang, Zhangyu Lu, Chongzhuo Tan, Zeyu Wang
Summary: The study proposes a fuzzy adaptive filtering-based energy management strategy for a hybrid energy storage system, which effectively reduces battery current amplitude and optimizes battery and super-capacitor SOC to varying degrees. The energy consumption is significantly lower compared to other energy management strategies, validating the effectiveness of the proposed strategy.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Chemical
Dapai Shi, Shipeng Li, Kangjie Liu, Yun Wang, Ruijun Liu, Junjie Guo
Summary: Under the dual-carbon goal, research on energy conservation and emission reduction of new energy vehicles has once again become a hot topic. This study proposes an adaptive energy management strategy for plug-in hybrid electric vehicles (PHEVs) to improve fuel economy based on intelligent prediction of driving cycles. Simulation results show that the proposed strategy achieves a 9.85% higher fuel saving rate compared to the rule-based strategy and a 5.30% higher rate compared to the ECMS strategy without prediction, further enhancing the fuel saving potential of PHEVs.
Article
Engineering, Mechanical
Changyin Wei, Xiaodong Wang, Yunxing Chen, Huawei Wu, Yong Chen
Summary: This study proposes a fuzzy energy management strategy based on driving pattern recognition using a neural network. By optimizing energy distribution, the strategy achieves optimal fuel economy and shows significant advantages in fuel consumption compared to traditional methods.
Article
Computer Science, Theory & Methods
Zhong Yuan, Hongmei Chen, Tianrui Li, Jia Liu, Shu Wang
Summary: This study proposes a hybrid feature outlier detection method based on fuzzy information entropy, which calculates the outlier degree of objects using fuzzy approximate space and fuzzy similarity relation. Experimental results demonstrate that this method is effective and adaptable compared to main outlier detection algorithms.
FUZZY SETS AND SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Haitao Zhao, Jiawen Tang, Bamidele Adebisi, Tomoaki Ohtsuki, Guan Gui, Hongbo Zhu
Summary: An adaptive vehicle clustering algorithm based on fuzzy C-means algorithm is proposed in this paper, aiming to minimize the power consumption of vehicles. By dynamically allocating computing resources and selecting clustering heads, the algorithm can decrease power consumption while satisfying vehicle delay requirements, as confirmed by simulation results.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
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.