Article
Thermodynamics
Wei Zhou, Yaoqi Chen, Haoran Zhai, Weigang Zhang
Summary: This paper presents a new approach to generating reference SoC trajectories for predictive energy management control of plug-in hybrid electric vehicles, which outperforms traditional methods in terms of fuel economy and global optimality.
Article
Energy & Fuels
Yujie Liu, Qun Sun, Congzhi Liu, Qiang Han, Hongqiang Guo, Wenxiao Han
Summary: In this paper, a robust SOC trajectory planning method is proposed for a PHEB in the vehicle-following scenario. The trajectory areas of SOC are planned based on TRD considering the noises of the vehicle mass and driving cycles. The driving control of PHEB is transformed into a multi-objective optimal problem that considers driving safety, fuel economy, and ride comfort.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Wei Zhou, Xuan Cai, Yaoqi Chen, Junqiu Li, Xiaoyan Peng
Summary: This paper addresses the critical issue of optimal global charge trajectory planning in Plug-in Hybrid Electric Vehicles and provides insights on the optimal charge depletion behaviors in different driving conditions. The theoretical analysis is validated through model-in-the-loop tests using a high-fidelity vehicle simulator.
Article
Thermodynamics
Wei Cui, Naxin Cui, Tao Li, Zhongrui Cui, Yi Du, Chenghui Zhang
Summary: This study proposes a multi-objective hierarchical energy management strategy to improve the performance of connected plug-in hybrid electric vehicles (PHEVs). The strategy incorporates resistance network-triggered motion planning and convex torque optimization based on the alternating direction method of multipliers (ADMM), aiming to comprehensively optimize energy saving, safety, traffic efficiency, and computational efficiency.
Article
Thermodynamics
Yonggang Liu, Bin Huang, Yang Yang, Zhenzhen Lei, Yuanjian Zhang, Zheng Chen
Summary: This paper investigates a hierarchical energy management control strategy for autonomous plug-in hybrid electric vehicles in vehicle-following environment. The strategy includes an upper layer controller with grey neural network and fuzzy adaptive control algorithm, and a lower layer controller with genetic algorithm and fuzzy logic algorithm, to predict speed, plan target speed, and optimize energy consumption, achieving 95.43% optimality compared to dynamic programming results.
Article
Engineering, Electrical & Electronic
Donghai Hu, Shan Cheng, Jiaming Zhou, Leli Hu
Summary: An adaptive rolling planning method was proposed for the state of charge (SOC) trajectory of the power battery in the energy management system of plug-in hybrid-electric buses (PHEBs). A mathematical model was established for simulation research. The driving cycles of PHEBs were collected and the optimal SOC trajectories were obtained through dynamic programming. Incremental learning was applied to further reduce fuel consumption.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Green & Sustainable Science & Technology
Zhenzhen Lei, Jianjun Cai, Jie Li, Dekun Gao, Yuanjian Zhang, Zheng Chen, Yonggang Liu
Summary: This study proposes a dynamic inverse hierarchical optimization method to plan economic velocity for PHEVs, aiming to improve energy efficiency. By incorporating traffic signal and road data, as well as utilizing V2X and autonomous driving technologies, this method enhances computational efficiency and energy consumption economy in PHEVs.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Energy & Fuels
Ahmet Mandev, Patrick Plotz, Frances Sprei, Gil Tal
Summary: This study investigated the charging behavior of plug-in hybrid electric vehicles (PHEV) and found that most users do not charge their vehicles overnight and engage in additional charging on 20-26% of driving days. The study also indicated that the utility factor should not be the sole measure of PHEV environmental performance.
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
Energy & Fuels
Hongwen He, Ruchen Huang, Xiangfei Meng, Xuyang Zhao, Yong Wang, Menglin Li
Summary: This article proposes a novel hierarchy-based predictive energy management strategy combined with the DDPG algorithm to improve the energy economy of plug-in hybrid electric buses (PHEB). The simulation results show that this strategy can improve the energy economy by 4.32% compared to the DDPG algorithm and achieve 98.61% of the global optimal algorithm.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Thermodynamics
Lingxiong Guo, Xudong Zhang, Yuan Zou, Ningyuan Guo, Jianwei Li, Guodong Du
Summary: This paper proposes an EMS based on MPC to minimize fuel cost, electricity usage and battery ageing through a speed predictor and SOC reference generator, achieving desirable performance as shown in simulation results.
Article
Energy & Fuels
Ahmet Mandev, Patrick Ploetz, Frances Sprei, Gil Tal
Summary: The study investigated the daily charging behavior of over 10,000 Chevrolet Volt PHEVs and found that a percentage of users do not charge overnight, while additional charging occurs on 20-26% of driving days. It also concluded that the utility factor should not be the sole measure of environmental performance for PHEVs.
Article
Engineering, Civil
Peihong Shen, Zhiguo Zhao, Qiuyi Guo, Peidong Zhou
Summary: This study proposed an economic velocity planning algorithm based on road information for PHEV, which can improve the energy economy of the vehicle and make the output torques of the engine and driving motor more stable, contributing to the energy economy of the vehicle.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(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
Thermodynamics
Chao Yang, Muyao Wang, Weida Wang, Zesong Pu, Mingyue Ma
Summary: The paper proposes an efficient vehicle-following energy management strategy based on model predictive control to achieve optimal fuel economy while maintaining a safe following distance, utilizing an improved sequential quadratic programming algorithm to solve the receding horizon optimization problem and estimate the real-time efficiency of the engine and electric motor. The strategy is validated on a real-world cargo truck and demonstrates enhanced fuel economy without compromising driving safety.
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.