The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain....The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.展开更多
Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply c...Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.展开更多
With the growing adoption of Electrical Vehicles(EVs),it is expected that a large number of on-board Li-ion batteries will be retired from EVs in the near future.Retired batteries will typically retain 80%of their ini...With the growing adoption of Electrical Vehicles(EVs),it is expected that a large number of on-board Li-ion batteries will be retired from EVs in the near future.Retired batteries will typically retain 80%of their initial capacities and can be recycled as second life batteries(SLBs).Although the capital costs of SLBs are much cheaper,their operational reliability is an important concern since used batteries may suffer from a higher failure rate.This paper aggregates brand new batteries and SLBs together to improve power system’s operating performance with renewable energy resources.In the context of a day-ahead and intra-day dispatch framework,a two-stage coordinated optimal scheduling method is proposed.Specifically,the energy cost of brand-new batteries and SLBs is calculated based on detailed battery degradation model,and the reliability of batteries is modeled based on the Weibull distribution.Moreover,Conditional value at risk(CVaR)criterion is applied to evaluate the risk induced by intermittent renewable power output,load demand variation and SLBs failure probability.Simulation tests demonstrate the effectiveness of the proposed method.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
Prolonging the lifetime of batteries is a long-term pursuit,and it is also one of the prerequisites for the practical application of batteries.However,this endeavor is challenging for high-energy Li–O_(2)batteries du...Prolonging the lifetime of batteries is a long-term pursuit,and it is also one of the prerequisites for the practical application of batteries.However,this endeavor is challenging for high-energy Li–O_(2)batteries due to their poor charge efficiency and cathode passivation-induced by-products accumulation.Here,we demonstrated that overcharging Li–O_(2)batteries could facilitate the decomposition of accumulated residue products and revive the cathode;thus,the battery lifespan could be significantly extended.This long battery lifetime not only made full use of the Li anode but also enabled the battery to recycle in a safer way without the risk of firing and explosion.Furthermore,overcharge could be used in Li–O_(2)batteries with high mass loading,high rate,and large capacity.This overcharge strategy simplified the cathode regenerating procedures and realized system-level efficient use of battery components,thereby prolonging the life of Li–O_(2)batteries to meet the requirements of practical applications.展开更多
Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of ...Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.展开更多
基金The authors are grateful to the Raytheon Chair for Systems Engineering for funding.
文摘The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.
基金This work was supported by the UK HVM Catapult project(8248 CORE)the National Natural Science Foundation of China(52072038,62122041).
文摘Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.
基金supported in part by the National Natural Science Foundation of China (NO.52278003 and NO.72171026)in part by the National Natural Science Foundation of Hunan province (NO.21A0217)。
文摘With the growing adoption of Electrical Vehicles(EVs),it is expected that a large number of on-board Li-ion batteries will be retired from EVs in the near future.Retired batteries will typically retain 80%of their initial capacities and can be recycled as second life batteries(SLBs).Although the capital costs of SLBs are much cheaper,their operational reliability is an important concern since used batteries may suffer from a higher failure rate.This paper aggregates brand new batteries and SLBs together to improve power system’s operating performance with renewable energy resources.In the context of a day-ahead and intra-day dispatch framework,a two-stage coordinated optimal scheduling method is proposed.Specifically,the energy cost of brand-new batteries and SLBs is calculated based on detailed battery degradation model,and the reliability of batteries is modeled based on the Weibull distribution.Moreover,Conditional value at risk(CVaR)criterion is applied to evaluate the risk induced by intermittent renewable power output,load demand variation and SLBs failure probability.Simulation tests demonstrate the effectiveness of the proposed method.
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
基金This work was financially supported by the National Natural Science Foundation of China(21725103)National Key R&D Program of China(2020YFE0204500)+2 种基金Key Research Program of the Chinese Academy of Sciences(ZDRW-CN-2021-3)Changchun Science and Technology Development Plan Funding Project(21ZY06)K.C.Wong Education Foundation(GJTD-2018-09).
文摘Prolonging the lifetime of batteries is a long-term pursuit,and it is also one of the prerequisites for the practical application of batteries.However,this endeavor is challenging for high-energy Li–O_(2)batteries due to their poor charge efficiency and cathode passivation-induced by-products accumulation.Here,we demonstrated that overcharging Li–O_(2)batteries could facilitate the decomposition of accumulated residue products and revive the cathode;thus,the battery lifespan could be significantly extended.This long battery lifetime not only made full use of the Li anode but also enabled the battery to recycle in a safer way without the risk of firing and explosion.Furthermore,overcharge could be used in Li–O_(2)batteries with high mass loading,high rate,and large capacity.This overcharge strategy simplified the cathode regenerating procedures and realized system-level efficient use of battery components,thereby prolonging the life of Li–O_(2)batteries to meet the requirements of practical applications.
基金the National Natural Science Foundation of China(Grant Nos.51975048,U1764257 and 51705480)the Beijing Institute of Technology Research Fund Program for Young Scholars。
文摘Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.