Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee...Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.展开更多
As the core component of the energy storage system, the capacity and management effect of lead-acid battery directly affect the application performance and economy of the whole energy storage system. Therefore, it is ...As the core component of the energy storage system, the capacity and management effect of lead-acid battery directly affect the application performance and economy of the whole energy storage system. Therefore, it is necessary to strengthen the balanced management of the capacity of lead-acid battery in the energy storage system. This paper will try to design an automatic and information-based lead-acid battery capacity balance management system for energy storage system, so as to realize accurate estimation and dynamic monitoring of lead-acid battery capacity, and lay a good foundation for lead-acid battery balance management of energy storage system.展开更多
Fe（3–x）O4 raspberry shaped nanostructures/graphene nanocomposites were synthesized by a one-step polyol-solvothermal method to be tested as electrode materials for Li-ion battery（LIB）. Indeed, Fe（3–x）O4 raspbe...Fe（3–x）O4 raspberry shaped nanostructures/graphene nanocomposites were synthesized by a one-step polyol-solvothermal method to be tested as electrode materials for Li-ion battery（LIB）. Indeed, Fe（3–x）O4 raspberry shaped nanostructures consist of original oriented aggregates of Fe（3–x）O4 magnetite nanocrystals, ensuring a low oxidation state of magnetite and a hollow and porous structure, which has been easily combined with graphene sheets. The resulting nanocomposite powder displays a very homogeneous spatial distribution of Fe（3–x）O4 nanostructures at the surface of the graphene sheets. These original nanostructures and their strong interaction with the graphene sheets resulted in very small capacity fading upon Li＋ion intercalation. Reversible capacity, as high as 660 m Ah/g, makes this material promising for anode in Li-ion batteries application.展开更多
As university campuses look to decrease their greenhouse gas emissions, plug-in electric buses may provide a low carbon alternative to conventionally fossil-powered buses. This study investigates the viability for Uni...As university campuses look to decrease their greenhouse gas emissions, plug-in electric buses may provide a low carbon alternative to conventionally fossil-powered buses. This study investigates the viability for Unitrans, the bus service for the greater Davis area and the university campus, to replace current compressed natural gas buses with plug-in electric versions. This study presents an inventory of market available electric buses, their associated costs, incentives, and infrastructure concerns, and compares projected energy use, net present cost, and greenhouse gas emissions with their CNG counterparts. ADVISOR vehicle simulation software is used to estimate the energy use of a typical electric bus (New Flyer Xcelsior XE40 300 kW) and compare to the current CNG model (Orion V) along an actual Unitrans route. The model estimates that the selected bus can travel 146 miles on a single charge, with a fuel economy of 1.75 kWh per mile, which meets the service requirements. Results for bus replacement schedules between 5 and 49 in the 12-year analysis period indicate that between 1600 and 22,000 MT of carbon can be avoided. The net present cost analysis indicates that the potential savings from the replacement of a single CNG bus with an electric bus (with available incentives) ranges from $146,000 - $211,000 per bus over its lifetime, depending on infrastructure costs.展开更多
As one of the next-generation energy-storage devices,Li-O_2 battery has become the main research direction for the academic researchers due to its characteristics of environmental friendship,relatively simple structur...As one of the next-generation energy-storage devices,Li-O_2 battery has become the main research direction for the academic researchers due to its characteristics of environmental friendship,relatively simple structures,high energy density of 3500Wh/kg and low cost.However,Li-O_2 battery cannot be commercialized on a large scale because of the challenging issues including high-efficient electrocatalysts,membranes,Li-based anode and so on.In this review,we focused on the recent development of electrocatalyst materials as cathodes for the non-aqueous Li-O_2 batteries which are relatively simpler than other Li-O_2 batteries＇ structures.Electrocatalysts were summarized including noble metals,nanocarbon materials,transition metals and their hybrids.We points out that the challenges of preparation high-efficient catalysts not only require high catalytic activity and conductivity,but also have novel nanoarchitectures with large interface and porous volume for LiO_x storage.Furthermore,the further investigation of reaction mechanism and advanced in situ analysis technologies are welcome in the coming work.展开更多
Boron-doped Ketjenblack is attempted as cathode catalyst for non-aqueous rechargeable Li–O2 batteries. The boron-doped Ketjenblack delivers an extremely high discharge capacity of 7193 m Ah/g at a current density of ...Boron-doped Ketjenblack is attempted as cathode catalyst for non-aqueous rechargeable Li–O2 batteries. The boron-doped Ketjenblack delivers an extremely high discharge capacity of 7193 m Ah/g at a current density of 0.1 m A/cm2, and the capacity is about 2.3 times as that of the pristine KB. When the batteries are cycled with different restricted capacity, the boron-doped Ketjenblack based cathodes exhibits higher discharge platform and longer cycle life than Ketjenblack based cathodes. Additionally, the boron-doped Ketjenblack also shows a superior electrocatalytic activity for oxygen reduction in 0.1 mol/L KOH aqueous solution. The improvement in catalytic activity results from the defects and activation sites introduced by boron doping.展开更多
The growing number of distributed energy resources(DERs)in distribution networks brings new opportunities for local energy sharing.This paper proposes a multi-timescale en-ergy sharing approach among DER aggregators a...The growing number of distributed energy resources(DERs)in distribution networks brings new opportunities for local energy sharing.This paper proposes a multi-timescale en-ergy sharing approach among DER aggregators and distribution system operators(DSOs)considering grid-battery energy storage system(BESS)capacity rental and network operations.An energy sharing coordinator is created to manage the energy sharing with price determination.In an hour-ahead stage,the buying/selling energy and required grid-BESS rental capacity are optimally determined by the aggregators while the network operation is robustly considered by the DSO.In addition to renewable generation and loads,the power exchanges of the aggregators are treated as uncertainties.Then during each hour,15-min-ahead energy transaction and controllable DERs are optimized to track uncertainty realization.The uncertainties in the aggregators and the DSO are addressed by stochastic and robust optimization methods,respectively.To efficiently solve the proposed energy sharing problem,a distributed solution algorithm with step length control and step reduction techniques is developed.The simulation results verify the high efficiency of the proposed energy sharing approach.Index Terms-Battery capacity rental,distributed solution algorithm,energy sharing,optimization,uncertainty.展开更多
Future battery advances and economies of scale will help scrub CO2emissions from transportation and the grid.Economical energy storage lets battery-powered electric vehicles replace internal combustion engines in the ...Future battery advances and economies of scale will help scrub CO2emissions from transportation and the grid.Economical energy storage lets battery-powered electric vehicles replace internal combustion engines in the transportation sector,which now accounts for the plurality of CO2emissions.For grid-scale applications,the benefits of adding storage are many and well documented[1–2].Beyond increased penetration of intermittent renewable energy generated from such as solar panels展开更多
文摘Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
文摘As the core component of the energy storage system, the capacity and management effect of lead-acid battery directly affect the application performance and economy of the whole energy storage system. Therefore, it is necessary to strengthen the balanced management of the capacity of lead-acid battery in the energy storage system. This paper will try to design an automatic and information-based lead-acid battery capacity balance management system for energy storage system, so as to realize accurate estimation and dynamic monitoring of lead-acid battery capacity, and lay a good foundation for lead-acid battery balance management of energy storage system.
基金supported by the funding from the European Research Council(ERCAdvanced Grant,ERC-2011-AdG,Project 291543-IONACES)+2 种基金the Materials Institute Carnot Alsace(MICA)from the Direction Générale de l’Armement(DGA)French-German Research Institute of Saint-Louis(ISL)
文摘Fe（3–x）O4 raspberry shaped nanostructures/graphene nanocomposites were synthesized by a one-step polyol-solvothermal method to be tested as electrode materials for Li-ion battery（LIB）. Indeed, Fe（3–x）O4 raspberry shaped nanostructures consist of original oriented aggregates of Fe（3–x）O4 magnetite nanocrystals, ensuring a low oxidation state of magnetite and a hollow and porous structure, which has been easily combined with graphene sheets. The resulting nanocomposite powder displays a very homogeneous spatial distribution of Fe（3–x）O4 nanostructures at the surface of the graphene sheets. These original nanostructures and their strong interaction with the graphene sheets resulted in very small capacity fading upon Li＋ion intercalation. Reversible capacity, as high as 660 m Ah/g, makes this material promising for anode in Li-ion batteries application.
文摘As university campuses look to decrease their greenhouse gas emissions, plug-in electric buses may provide a low carbon alternative to conventionally fossil-powered buses. This study investigates the viability for Unitrans, the bus service for the greater Davis area and the university campus, to replace current compressed natural gas buses with plug-in electric versions. This study presents an inventory of market available electric buses, their associated costs, incentives, and infrastructure concerns, and compares projected energy use, net present cost, and greenhouse gas emissions with their CNG counterparts. ADVISOR vehicle simulation software is used to estimate the energy use of a typical electric bus (New Flyer Xcelsior XE40 300 kW) and compare to the current CNG model (Orion V) along an actual Unitrans route. The model estimates that the selected bus can travel 146 miles on a single charge, with a fuel economy of 1.75 kWh per mile, which meets the service requirements. Results for bus replacement schedules between 5 and 49 in the 12-year analysis period indicate that between 1600 and 22,000 MT of carbon can be avoided. The net present cost analysis indicates that the potential savings from the replacement of a single CNG bus with an electric bus (with available incentives) ranges from $146,000 - $211,000 per bus over its lifetime, depending on infrastructure costs.
基金supported by the Natural Science Foundation of China(No.21303038)Scientific Research Foundation for the Returned Overseas Chinese Scholars+1 种基金State Education Ministry One Hundred,Talents Program of Anhui ProvinceOpen Funds of the State Key Laboratory of Rare Earth Resource Utilization(No.RERU2016004)
文摘As one of the next-generation energy-storage devices,Li-O_2 battery has become the main research direction for the academic researchers due to its characteristics of environmental friendship,relatively simple structures,high energy density of 3500Wh/kg and low cost.However,Li-O_2 battery cannot be commercialized on a large scale because of the challenging issues including high-efficient electrocatalysts,membranes,Li-based anode and so on.In this review,we focused on the recent development of electrocatalyst materials as cathodes for the non-aqueous Li-O_2 batteries which are relatively simpler than other Li-O_2 batteries＇ structures.Electrocatalysts were summarized including noble metals,nanocarbon materials,transition metals and their hybrids.We points out that the challenges of preparation high-efficient catalysts not only require high catalytic activity and conductivity,but also have novel nanoarchitectures with large interface and porous volume for LiO_x storage.Furthermore,the further investigation of reaction mechanism and advanced in situ analysis technologies are welcome in the coming work.
基金supported by the MOST(Grant nos.2013CB934000and 2014DFG71590)Beijing Municipal Program(Grant no.YETP0157)
文摘Boron-doped Ketjenblack is attempted as cathode catalyst for non-aqueous rechargeable Li–O2 batteries. The boron-doped Ketjenblack delivers an extremely high discharge capacity of 7193 m Ah/g at a current density of 0.1 m A/cm2, and the capacity is about 2.3 times as that of the pristine KB. When the batteries are cycled with different restricted capacity, the boron-doped Ketjenblack based cathodes exhibits higher discharge platform and longer cycle life than Ketjenblack based cathodes. Additionally, the boron-doped Ketjenblack also shows a superior electrocatalytic activity for oxygen reduction in 0.1 mol/L KOH aqueous solution. The improvement in catalytic activity results from the defects and activation sites introduced by boron doping.
基金supported by the Australian Research Council(ARC)Research Hub for Integrated Energy Storage Solutions(IH180100020)the Fundamental Research Funds for the Central Universities(423165)and NTU Start-Up Grant(021542-00001).
文摘The growing number of distributed energy resources(DERs)in distribution networks brings new opportunities for local energy sharing.This paper proposes a multi-timescale en-ergy sharing approach among DER aggregators and distribution system operators(DSOs)considering grid-battery energy storage system(BESS)capacity rental and network operations.An energy sharing coordinator is created to manage the energy sharing with price determination.In an hour-ahead stage,the buying/selling energy and required grid-BESS rental capacity are optimally determined by the aggregators while the network operation is robustly considered by the DSO.In addition to renewable generation and loads,the power exchanges of the aggregators are treated as uncertainties.Then during each hour,15-min-ahead energy transaction and controllable DERs are optimized to track uncertainty realization.The uncertainties in the aggregators and the DSO are addressed by stochastic and robust optimization methods,respectively.To efficiently solve the proposed energy sharing problem,a distributed solution algorithm with step length control and step reduction techniques is developed.The simulation results verify the high efficiency of the proposed energy sharing approach.Index Terms-Battery capacity rental,distributed solution algorithm,energy sharing,optimization,uncertainty.
文摘Future battery advances and economies of scale will help scrub CO2emissions from transportation and the grid.Economical energy storage lets battery-powered electric vehicles replace internal combustion engines in the transportation sector,which now accounts for the plurality of CO2emissions.For grid-scale applications,the benefits of adding storage are many and well documented[1–2].Beyond increased penetration of intermittent renewable energy generated from such as solar panels
