Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. batte...Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.展开更多
This paper proposes a new battery management system （BMS） based on a master-slave control mode for multi-cell li-ion battery packs. The proposed BMS can be applied in li-ion battery packs with any cell number. The w...This paper proposes a new battery management system （BMS） based on a master-slave control mode for multi-cell li-ion battery packs. The proposed BMS can be applied in li-ion battery packs with any cell number. The whole system is composed of a master processor and a string of slave manager cells （SMCs）. Each battery cell corresponds to an SMC. Unlike the conventional BMS, the proposed one has a novel method for communication, and it collects the battery status information in a direct and simple way. An SMC communicates with its adjacent counterparts to transfer the battery information as well as the commands from the master processor. The nethermost SMC communicates with the master processor directly. This method allows the battery management chips to be implemented in a standard CMOS （ complementary metal-oxide-semiconductor transistor） process. A testing chip is fabricated in the CSMC 0.5 μm 5 V N-well CMOS process. The testing results verify that the proposed method for data communication and the battery management system can protect and manage multi-cell li-ion battery packs.展开更多
Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of th...Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.展开更多
State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additio...State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.展开更多
The battery management system(BMS)is the main safeguard of a battery system for electric propulsion and machine electrifcation.It is tasked to ensure reliable and safe operation of battery cells connected to provide h...The battery management system(BMS)is the main safeguard of a battery system for electric propulsion and machine electrifcation.It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels.In addition to efectively monitoring all the electrical parameters of a battery pack system,such as the voltage,current,and temperature,the BMS is also used to improve the battery performance with proper safety measures within the system.With growing acceptance of lithium-ion batteries,major industry sectors such as the automotive,renewable energy,manufacturing,construction,and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefned the world of energy storage.Hence,the functional safety considerations,which are those relating to automatic protection,in battery management for battery pack technologies are particularly important to ensure that the overall electrical system,regardless of whether it is for electric transportation or stationary energy storage,is in accordance with high standards of safety,reliability,and quality.If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events,then the environment,people,and property could be endangered.This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems,particularly in areas concerned with hazardous environment.The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards.A comprehensive evaluation of the components,architecture,risk reduction techniques,and failure mode analysis applicable to BMS operation was also presented.The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.展开更多
When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside...When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.展开更多
The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensivel...The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.展开更多
This study proposed a battery management approach for the electric hydraulic pump system of a lifting trolley.The pump system was powered by two 12-V lead-acid batteries in series.Because direct measurement of the act...This study proposed a battery management approach for the electric hydraulic pump system of a lifting trolley.The pump system was powered by two 12-V lead-acid batteries in series.Because direct measurement of the actual battery state of charge is unlikely,it has mostly been determined through estimation based on the measured open-circuit voltage.A discharge current will result in a voltage drop and hence a lower voltage during discharge;however,the battery voltage will return to the original open-circuit voltage once the discharge stops.The operating current of the electric hydraulic pump system employed in this study was associated with three factors:the lifting height,lifting load,and battery state of charge.The operating current remained constant during the first half of the lifting phase and increased gradually with the lifting height in the second half.The operating current peaked when the lifting height reached the maximum.The power management approach for the electric hydraulic pump system featured the following basic functions:overcharge protection,overdischarge protection,short-circuit protection,overload protection,and an operating timer established in accordance with the system’s operating current variation.According to the manufacturer-defined maximum lifting load and lifting height of the lifting trolley,this study conducted experiments to obtain the maximum required operating time.An operating time greater than the maximum required operating time indicates the occurrence of an unexpected event,discharge should be stopped until the fault is resolved.展开更多
To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling...To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling.The primary innovation of the new hybrid BTMS lies in the use of an OHP with vertically arranged evaporator and condenser,enabling dual heat transfer pathways through liquid cooling plate and OHP.This study experimentally investigates the performance characteristics of the⊥-shaped OHP and hybrid BTMS.Results show that lower filling ratios significantly enhance the OHP’s startup performance but reduce operational stability,with optimal performance achieved at a 26.1%filling ratio.Acetone,as a single working fluid,exhibited superior heat transfer performance under low-load conditions compared to mixed fluids,while the acetone/ethanol mixture,forming a non-azeotropic solution,minimized temperature fluctuations.At 100 W,the⊥-shaped OHP with a horizontally arranged evaporator demonstrated better heat transfer performance than 2D-OHP designs.Compared to a liquid BTMS using water coolant at 280 W,the hybrid BTMS reduced the equivalent thermal resistance(RBTMS)and maximum temperature difference(ΔTmax)by 8.06%and 19.1%,respectively.When graphene nanofluid was used as the coolant in hybrid BTMS,the battery pack’s average temperature(Tb)dropped from 52.2℃ to 47.9℃,with RBTMS andΔTmax decreasing by 20.1%and 32.7%,respectively.These findings underscore the hybrid BTMS’s suitability for high heat load applications,offering a promising solution for electric vehicle thermal management.展开更多
This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the...This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the nonlinear multiagent system,a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased(MVU)estimation of the unknown inputs and states.Compared with the conventional decentralized EKF-based unknown input filter,the proposed distributed filter has a more relaxed existence condition of the filter,which makes it more applicable in reality.This new type of filter is then successfully applied to the simultaneous estimation of state of charge(SOC)and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.展开更多
Solid-state batteries have garnered global attention due to their high energy density and safety,holding the potential to replace traditional lithium-ion batteries in the electric vehicle,alleviating"range anxiet...Solid-state batteries have garnered global attention due to their high energy density and safety,holding the potential to replace traditional lithium-ion batteries in the electric vehicle,alleviating"range anxiety".However,a comprehensive solid-state battery management system to complement these batteries has not yet been systematically proposed.We attempt to construct a management system for solid-state batteries based on various characteristics,considering both the demand-and supply-side.This review first introduces the advantages and disadvantages of polymer-based,oxide-based,and sulfide-based solid electrolytes,as well as anode and cathode materials with higher specific capacities and promising applications.It lists the cycling performance and safety demonstrated by assembled solid-state pouch cells.Then,we systematically analyzes the differences between all-solid-state batteries and traditional lithium-ion batteries,proposing the demands and development directions for all-solid-state battery management from multiple dimensions.Finally,we build an all-solid-state battery management system from aspects such as signal monitoring,model building,aging,and early warning of failure,which includes three parts:the electric management system,the thermal management system,and the pressure management system.This review aims to provide researchers with new insights and ideas,promoting the efficient development and industrial upgrading of battery management technology.展开更多
Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase chan...Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.展开更多
With the increasing requirements for fast charging and discharging,higher requirements have been put forward for the thermal management of power batteries.Therefore,there is an urgent need to develop efficient heat tr...With the increasing requirements for fast charging and discharging,higher requirements have been put forward for the thermal management of power batteries.Therefore,there is an urgent need to develop efficient heat transfer fluids.As a new type of heat transfer fluids,functional thermal fluids mainly includ-ing nanofluids(NFs)and phase change fluids(PCFs),have the advantages of high heat carrying density,high heat transfer rate,and broad operational temperature range.However,challenges that hinder their practical applications remain.In this paper,we firstly overview the classification,thermophysical prop-erties,drawbacks,and corresponding modifications of functional thermal fluids.For NFs,the high ther-mal conductivity and high convective heat transfer performance were mainly elaborated,while the stability and viscosity issues were also analyzed.And then for PCFs,the high heat carrying density was mainly elaborated,while the problems of supercooling,stability,and viscosity were also analyzed.On this basis,the composite fluids combined NFs and PCFs technology,has been summarized.Furthermore,the thermal properties of traditional fluids,NFs,PCFs,and composite fluids are compared,which proves that functional thermal fluids are a good choice to replace traditional fluids as coolants.Then,battery thermal management system(BTMS)based on functional thermal fluids is summarized in detail,and the thermal management effects and pump consumption are compared with that of water-based BTMS.Finally,the current technical challenges that parameters optimization of functional thermal fluids and structures optimization of BTMS systematically are presented.In the future,it is necessary to pay more attention to using machine learning to predict thermophysical properties of functional thermal fluids and their applications for BTMS under actual vehicle conditions.展开更多
With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic an...With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.展开更多
An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of ...An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved performance.In this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link described.Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS system.The battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control strategies.Currently,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the system.The utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.展开更多
The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical mod...The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.展开更多
Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce o...Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.展开更多
Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control par...Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.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
文摘Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.
基金The Key Science and Technology Project of Zhejiang Province(No.2007C21021)
文摘This paper proposes a new battery management system （BMS） based on a master-slave control mode for multi-cell li-ion battery packs. The proposed BMS can be applied in li-ion battery packs with any cell number. The whole system is composed of a master processor and a string of slave manager cells （SMCs）. Each battery cell corresponds to an SMC. Unlike the conventional BMS, the proposed one has a novel method for communication, and it collects the battery status information in a direct and simple way. An SMC communicates with its adjacent counterparts to transfer the battery information as well as the commands from the master processor. The nethermost SMC communicates with the master processor directly. This method allows the battery management chips to be implemented in a standard CMOS （ complementary metal-oxide-semiconductor transistor） process. A testing chip is fabricated in the CSMC 0.5 μm 5 V N-well CMOS process. The testing results verify that the proposed method for data communication and the battery management system can protect and manage multi-cell li-ion battery packs.
基金Supported by National Natural Science Foundation of China(Grant No.51922006).
文摘Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.
文摘State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.
基金supported by Azure Mining Technology,CCTEG,and the University of Wollongong.
文摘The battery management system(BMS)is the main safeguard of a battery system for electric propulsion and machine electrifcation.It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels.In addition to efectively monitoring all the electrical parameters of a battery pack system,such as the voltage,current,and temperature,the BMS is also used to improve the battery performance with proper safety measures within the system.With growing acceptance of lithium-ion batteries,major industry sectors such as the automotive,renewable energy,manufacturing,construction,and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefned the world of energy storage.Hence,the functional safety considerations,which are those relating to automatic protection,in battery management for battery pack technologies are particularly important to ensure that the overall electrical system,regardless of whether it is for electric transportation or stationary energy storage,is in accordance with high standards of safety,reliability,and quality.If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events,then the environment,people,and property could be endangered.This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems,particularly in areas concerned with hazardous environment.The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards.A comprehensive evaluation of the components,architecture,risk reduction techniques,and failure mode analysis applicable to BMS operation was also presented.The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.
文摘When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.
基金supported by E-Werk Mittelbaden AG,Offenburg,Germany
文摘The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.
文摘This study proposed a battery management approach for the electric hydraulic pump system of a lifting trolley.The pump system was powered by two 12-V lead-acid batteries in series.Because direct measurement of the actual battery state of charge is unlikely,it has mostly been determined through estimation based on the measured open-circuit voltage.A discharge current will result in a voltage drop and hence a lower voltage during discharge;however,the battery voltage will return to the original open-circuit voltage once the discharge stops.The operating current of the electric hydraulic pump system employed in this study was associated with three factors:the lifting height,lifting load,and battery state of charge.The operating current remained constant during the first half of the lifting phase and increased gradually with the lifting height in the second half.The operating current peaked when the lifting height reached the maximum.The power management approach for the electric hydraulic pump system featured the following basic functions:overcharge protection,overdischarge protection,short-circuit protection,overload protection,and an operating timer established in accordance with the system’s operating current variation.According to the manufacturer-defined maximum lifting load and lifting height of the lifting trolley,this study conducted experiments to obtain the maximum required operating time.An operating time greater than the maximum required operating time indicates the occurrence of an unexpected event,discharge should be stopped until the fault is resolved.
基金funded by the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ2404911)the Ministry of Higher Education,Malaysia through the Fundamental Research Grant Scheme:FRGS/1/2024/TK10/UMP/02/15 and Universiti Malaysia Pahang Al-Sultan Abdullah(RDU240117).
文摘To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling.The primary innovation of the new hybrid BTMS lies in the use of an OHP with vertically arranged evaporator and condenser,enabling dual heat transfer pathways through liquid cooling plate and OHP.This study experimentally investigates the performance characteristics of the⊥-shaped OHP and hybrid BTMS.Results show that lower filling ratios significantly enhance the OHP’s startup performance but reduce operational stability,with optimal performance achieved at a 26.1%filling ratio.Acetone,as a single working fluid,exhibited superior heat transfer performance under low-load conditions compared to mixed fluids,while the acetone/ethanol mixture,forming a non-azeotropic solution,minimized temperature fluctuations.At 100 W,the⊥-shaped OHP with a horizontally arranged evaporator demonstrated better heat transfer performance than 2D-OHP designs.Compared to a liquid BTMS using water coolant at 280 W,the hybrid BTMS reduced the equivalent thermal resistance(RBTMS)and maximum temperature difference(ΔTmax)by 8.06%and 19.1%,respectively.When graphene nanofluid was used as the coolant in hybrid BTMS,the battery pack’s average temperature(Tb)dropped from 52.2℃ to 47.9℃,with RBTMS andΔTmax decreasing by 20.1%and 32.7%,respectively.These findings underscore the hybrid BTMS’s suitability for high heat load applications,offering a promising solution for electric vehicle thermal management.
基金supported by the National Natural Science Foundation of China(No.61822308 and No.61751307)the Natural Science Foundation of Shandong Province(No.JQ201812)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the nonlinear multiagent system,a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased(MVU)estimation of the unknown inputs and states.Compared with the conventional decentralized EKF-based unknown input filter,the proposed distributed filter has a more relaxed existence condition of the filter,which makes it more applicable in reality.This new type of filter is then successfully applied to the simultaneous estimation of state of charge(SOC)and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.
基金funded by the National Natural Science Foundation of China(NSFC,Nos.52377211,52107230,U20A20310,and 52176199)the Fundamental Research Funds for the Central Universities.Xiaodong Wu gratefully acknowledge the financial support from the National Key R&D Program of China(No.2022YFE0207300)support from National Engineering Research Center of New Energy Vehicles and Power Systems and Shanghai Key Lab of Vehicle Aerodynamics and Vehicle Thermal Management Systems.
文摘Solid-state batteries have garnered global attention due to their high energy density and safety,holding the potential to replace traditional lithium-ion batteries in the electric vehicle,alleviating"range anxiety".However,a comprehensive solid-state battery management system to complement these batteries has not yet been systematically proposed.We attempt to construct a management system for solid-state batteries based on various characteristics,considering both the demand-and supply-side.This review first introduces the advantages and disadvantages of polymer-based,oxide-based,and sulfide-based solid electrolytes,as well as anode and cathode materials with higher specific capacities and promising applications.It lists the cycling performance and safety demonstrated by assembled solid-state pouch cells.Then,we systematically analyzes the differences between all-solid-state batteries and traditional lithium-ion batteries,proposing the demands and development directions for all-solid-state battery management from multiple dimensions.Finally,we build an all-solid-state battery management system from aspects such as signal monitoring,model building,aging,and early warning of failure,which includes three parts:the electric management system,the thermal management system,and the pressure management system.This review aims to provide researchers with new insights and ideas,promoting the efficient development and industrial upgrading of battery management technology.
基金financially supported by the National Key Research and Development Program(Grant No.2022YFE0207400)the National Natural Science Foundation of China(Grant No.U22A20168 and 52174225)。
文摘Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.
基金supported by the National Natural Science Foundation of China(Grant No.52271320)"Mechanics+"interdisciplinary innovation youth fund project of Ningbo University(LJ2023005).
文摘With the increasing requirements for fast charging and discharging,higher requirements have been put forward for the thermal management of power batteries.Therefore,there is an urgent need to develop efficient heat transfer fluids.As a new type of heat transfer fluids,functional thermal fluids mainly includ-ing nanofluids(NFs)and phase change fluids(PCFs),have the advantages of high heat carrying density,high heat transfer rate,and broad operational temperature range.However,challenges that hinder their practical applications remain.In this paper,we firstly overview the classification,thermophysical prop-erties,drawbacks,and corresponding modifications of functional thermal fluids.For NFs,the high ther-mal conductivity and high convective heat transfer performance were mainly elaborated,while the stability and viscosity issues were also analyzed.And then for PCFs,the high heat carrying density was mainly elaborated,while the problems of supercooling,stability,and viscosity were also analyzed.On this basis,the composite fluids combined NFs and PCFs technology,has been summarized.Furthermore,the thermal properties of traditional fluids,NFs,PCFs,and composite fluids are compared,which proves that functional thermal fluids are a good choice to replace traditional fluids as coolants.Then,battery thermal management system(BTMS)based on functional thermal fluids is summarized in detail,and the thermal management effects and pump consumption are compared with that of water-based BTMS.Finally,the current technical challenges that parameters optimization of functional thermal fluids and structures optimization of BTMS systematically are presented.In the future,it is necessary to pay more attention to using machine learning to predict thermophysical properties of functional thermal fluids and their applications for BTMS under actual vehicle conditions.
基金supported by the National Natural Science Foundation of China (No.62373224,62333013,and U23A20327)。
文摘With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.
基金supported in part by National Natural Science Foundation of China(61533017,61273140,61304079,61374105,61379099,61233001)Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3)the Open Research Project from SKLMCCS(20150104)
基金This work was supported by National Key R&D Program of China(2016YFB0100300)the EPSRC Faraday Institution’s Multi-Scale Mod-elling Project(EP/S003053/1,grant number FIRG003).
文摘An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long lifetimes.With recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved performance.In this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link described.Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS system.The battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control strategies.Currently,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the system.The utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.
基金supported by the Open Project of Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle(No.ZDSYS202304)the National Natural Science Foundation of China(No.62303007)the Anhui Provincial Natural Science Foundation(No.2308085ME142)。
文摘The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.
文摘Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.
基金supported by the National Natural Science Foundation of China (62373224,62333013,U23A20327)。
文摘Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.
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.
