A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.展开更多
With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage de...With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage devices due to their advantages.The aqueous zinc ion battery is generally composed of zinc metal as the anode,active material as the cathode,and aqueous electrolyte.However,there are still many problems with the cathode/anode material and voltage window of the battery,which limit its use.This review introduces the recent research progress of zinc-ion batteries,including the advantages and disadvantages,energy storage mechanisms,and common cathode/anode materials,electrolytes,etc.It also gives a summary of the current research status of each material and provides solutions to the problems they face.Finally,it looks at the future direction and methods to optimize the performance of zinc-ion full batteries.展开更多
Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-...Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy （SNOPS） algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS n manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.展开更多
Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the stora...Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.展开更多
Wearable exoskeleton is a wearable device to enhance human ability,however,it is too heavy because of some constraints,such as material,structure and energy storage battery. Thus it brings fatigue to people after a lo...Wearable exoskeleton is a wearable device to enhance human ability,however,it is too heavy because of some constraints,such as material,structure and energy storage battery. Thus it brings fatigue to people after a long period of wearing. The research aims at optimizing shoulder fatigue and improving wearing comfort by means of changing the device-body contact material. After analyzing the current wearable exoskeletons ' weight, a standard load was set and a wearable exoskeleton was designed that could switch the weight. The experiment chose movement stability and change of cumulative pressure upon shoulder as the indexes of fatigue. The indexes were measured and analyzed before and after changing the contact material to memory foam with the standard load. The results showed promotion in action stability and obvious decrease in cumulative pressure upon shoulder.The experiment proves that the using of memory foam in wearable exoskeleton has evident effects on optimizing shoulder fatigue with load,promoting movement stability and wearing comfort.展开更多
Deep Reinforcement Learning(DRL)presents a promising avenue for optimizing Energy Storage Systems(ESSs)dispatch in distribution networks.This paper introduces RL-ADN,an innovative open-source library specifically desi...Deep Reinforcement Learning(DRL)presents a promising avenue for optimizing Energy Storage Systems(ESSs)dispatch in distribution networks.This paper introduces RL-ADN,an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks.RL-ADN offers unparalleled flexibility in modeling distribution networks,and ESSs,accommodating a wide range of research goals.A standout feature of RL-ADN is its data augmentation module,based on Gaussian Mixture Model and Copula(GMC)functions,which elevates the performance ceiling of DRL agents,achieving an average performance improvement of 21.43%,1.08%,2.76%,by augmenting five-year,one-year and three-month data,respectively.Additionally,RL-ADN incorporates the Tensor Power Flow solver,significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy,maintaining voltage magnitude with an average error not exceeding 0.0001%.The effectiveness of RL-ADN is demonstrated using distribution networks with size varying,showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks.Furthermore,RL-ADN achieves a tenfold increase in computational efficiency during training,making it highly suitable for large-scale network applications.The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly.展开更多
Energy conservation is a critical problem in recently-emerging wireless sensor networks （WSNs）. Pulse position modulation （PPM）, as an exploring-worthy modulation format for energy efficiency, is tailored for WSNs...Energy conservation is a critical problem in recently-emerging wireless sensor networks （WSNs）. Pulse position modulation （PPM）, as an exploring-worthy modulation format for energy efficiency, is tailored for WSNs into two schemes, mono-mode PPM and multi-mode PPM, in this paper. Resorting to an idealized system model and a practical system model, which combine the power consumptions in transmission and reception modules of nodes with the idealized and real- istic battery characteristics, the battery energy efficiencies of mono-mode PPM and multi-mode PPM are evaluated and compared. To minimize the battery energy consumption （BEC）, these schemes are further optimized in terms of constellation size M for a link in path-loss channels. Our analytical and numerical results show that considerable energy can be saved by multi-mode PPM; and the optimization performances of these schemes are noticeable at various communication distances though their optimization properties are different.展开更多
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.
基金supported by the National Natural Science Foundation of China(No.U22A20140)the Jinan City-School Integration Development Strategy Project(No.JNSX2023015)+3 种基金Independent Cultivation Program of Innovation Team of Ji’nan City(No.202333042)the University of Jinan Disciplinary Cross-Convergence Construction Project 2023(No.XKJC-202309)the Youth Innovation Group Plan of Shandong Province(No.2022KJ095)Project supported by State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China。
文摘With the development of science and technology,there is an increasing demand for energy storage batteries.Aqueous zinc-ion batteries(AZIBs)are expected to become the next generation of commercialized energy storage devices due to their advantages.The aqueous zinc ion battery is generally composed of zinc metal as the anode,active material as the cathode,and aqueous electrolyte.However,there are still many problems with the cathode/anode material and voltage window of the battery,which limit its use.This review introduces the recent research progress of zinc-ion batteries,including the advantages and disadvantages,energy storage mechanisms,and common cathode/anode materials,electrolytes,etc.It also gives a summary of the current research status of each material and provides solutions to the problems they face.Finally,it looks at the future direction and methods to optimize the performance of zinc-ion full batteries.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2002AA1Z1490)the Spe-cialized Research Fund for the Doctoral Program of Higher Education of China (20040486049)
文摘Dynamic voltage scaling （DVS） is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy （OPS II） balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy （SNOPS） algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS n manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.
基金supported by the China Scholarship Council and Donghua University Graduate Student Degree Thesis Innovation Fund Project (Grant No. CUSF-DH-D-2013059)
文摘Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.
基金the Fundamental Research Funds for the Central Universities,China(No.16D110301)Research Innovation Project of Shanghai Municipal Education Commission,China(No.201506000008)Science and Technology Guidance Project of Chinese Textile Industry Association(No.2015109)
文摘Wearable exoskeleton is a wearable device to enhance human ability,however,it is too heavy because of some constraints,such as material,structure and energy storage battery. Thus it brings fatigue to people after a long period of wearing. The research aims at optimizing shoulder fatigue and improving wearing comfort by means of changing the device-body contact material. After analyzing the current wearable exoskeletons ' weight, a standard load was set and a wearable exoskeleton was designed that could switch the weight. The experiment chose movement stability and change of cumulative pressure upon shoulder as the indexes of fatigue. The indexes were measured and analyzed before and after changing the contact material to memory foam with the standard load. The results showed promotion in action stability and obvious decrease in cumulative pressure upon shoulder.The experiment proves that the using of memory foam in wearable exoskeleton has evident effects on optimizing shoulder fatigue with load,promoting movement stability and wearing comfort.
基金the project ALIGN4Energy(with project number NWA.1389.20.251)of the research programme NWA ORC 2020 which is(partly)financed by the Dutch Research Council(NWO)The Netherlands This work is part of the DATALESs project(with project number 482.20.602)jointly financed by The Netherlands Organization for Scientific Research(NWO)the National Natural Science Foundation of China(NSFC).
文摘Deep Reinforcement Learning(DRL)presents a promising avenue for optimizing Energy Storage Systems(ESSs)dispatch in distribution networks.This paper introduces RL-ADN,an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks.RL-ADN offers unparalleled flexibility in modeling distribution networks,and ESSs,accommodating a wide range of research goals.A standout feature of RL-ADN is its data augmentation module,based on Gaussian Mixture Model and Copula(GMC)functions,which elevates the performance ceiling of DRL agents,achieving an average performance improvement of 21.43%,1.08%,2.76%,by augmenting five-year,one-year and three-month data,respectively.Additionally,RL-ADN incorporates the Tensor Power Flow solver,significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy,maintaining voltage magnitude with an average error not exceeding 0.0001%.The effectiveness of RL-ADN is demonstrated using distribution networks with size varying,showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks.Furthermore,RL-ADN achieves a tenfold increase in computational efficiency during training,making it highly suitable for large-scale network applications.The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly.
基金the National Natural Science Foundation of China (Grant Nos. 60642007 and 10374051)the Scientific Fundation of Guangxi Education Department of China (Grant No. [2002]316)
文摘Energy conservation is a critical problem in recently-emerging wireless sensor networks （WSNs）. Pulse position modulation （PPM）, as an exploring-worthy modulation format for energy efficiency, is tailored for WSNs into two schemes, mono-mode PPM and multi-mode PPM, in this paper. Resorting to an idealized system model and a practical system model, which combine the power consumptions in transmission and reception modules of nodes with the idealized and real- istic battery characteristics, the battery energy efficiencies of mono-mode PPM and multi-mode PPM are evaluated and compared. To minimize the battery energy consumption （BEC）, these schemes are further optimized in terms of constellation size M for a link in path-loss channels. Our analytical and numerical results show that considerable energy can be saved by multi-mode PPM; and the optimization performances of these schemes are noticeable at various communication distances though their optimization properties are different.
