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
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
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
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
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 promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning...The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.展开更多
The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs,...The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.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
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
The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their correspon...The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.展开更多
This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery s...This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery.Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally,simulation and comparison results are given to illustrate the performance of the presented method.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
Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a m...Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.展开更多
A comparative numerical study has been conducted on the thermal performance of a heat pipe cooling system considering several influential factors such as the coolant flow rate,the coolant inlet temperature,and the inp...A comparative numerical study has been conducted on the thermal performance of a heat pipe cooling system considering several influential factors such as the coolant flow rate,the coolant inlet temperature,and the input power.A comparison between numerical data and results available in the literature has demonstrated that our numerical procedure could successfully predict the heat transfer performance of the considered heat pipe cooling system for a battery.Specific indicators such as temperature,heat flux,and pressure loss were extracted to describe the characteristics of such a system.On the basis of the distributions of the temperature ratio of the battery surface,together with the heat flux and the streamlines around the heat pipe condenser,we conclude that the low disturbance of the coolant is the cause of the temperature gradient along the fluid flow direction.展开更多
A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)ba...A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.展开更多
This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 A...This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.展开更多
Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configur...Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configuration of a China’s national renewable generation demonstration project combining a large-scale BESS with wind farm and photovoltaic(PV)power station,all coupled to a power transmission system,is introduced,and the key technologies including optimal control and management as well as operational status of this BESS are presented.Additionally,the technical benefits of such a large-scale BESS in dealing with power fluctuation and intermittence issues resulting from grid connection of large-scale renewable generation,and for improvement of operation characteristics of transmission grid,are discussed with relevant case studies.展开更多
Battery thermal management is very crucial for the safe and long-term operation of electric vehicles or hybrid electric vehicles.In this study,numerical simulation method is adopted to simulate the temperature field o...Battery thermal management is very crucial for the safe and long-term operation of electric vehicles or hybrid electric vehicles.In this study,numerical simulation method is adopted to simulate the temperature field of Li-ion battery cell and module.It is proved that the maximum temperature and maximum temperature difference of battery cell and module increase with the increase of charge/discharge rate(C-rate)of the battery.For battery module,it can reach a maximum temperature of 61.1℃at a C-rate of 2 under natural convection condition with the ambient temperature of 20.0℃.A battery thermal management system based on micro heat pipe array(BTMS-MHPA)is deeply investigated.Experiments are conducted to compare the cooling effect on the battery module with different cooling methods,which include natural cooling,only MHPA,MHPA with fan.The maximum temperature of battery module which is cooled by MHPA with a fan is 43.4℃at a C-rate of 2,which is lower than that in the condition of natural cooling.Meanwhile,the maximum temperature difference was also greatly reduced by the application of MHPA cooling.The experimental results confirm that the feasibility and superiority of the BTMS-MHPA for guaranteeing the working temperature range and temperature uniformity of the battery.展开更多
The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of ...The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.展开更多
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.
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 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.
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National Natural Science Foundation of China(Nos.61772130 and 62072096)Fundamental Research Funds for the Central Universities+2 种基金China(No.2232020A-12)International Cooperation Program of Shanghai Science and Technology Commission,China(No.20220713000)Young Top-Notch Talent Program in Shanghai,China。
文摘The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.
文摘The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.
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.
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the financial support from the China Scholarship Council(CSC)(No.202207550010)。
文摘The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.
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)
文摘This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery.Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally,simulation and comparison results are given to illustrate the performance of the presented method.
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 by the National Natural Science Foundation of China (Grant Nos. 91834301 and 22078088)the National Natural Science Foundation of China for Innovative Research Groups (Grant No. 51621002)the Shanghai Rising-Star Program (Grant No. 21QA1401900)。
文摘Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.
基金by the Natural Science Foundation of Jiangsu Province(Grants No.BK20170317).
文摘A comparative numerical study has been conducted on the thermal performance of a heat pipe cooling system considering several influential factors such as the coolant flow rate,the coolant inlet temperature,and the input power.A comparison between numerical data and results available in the literature has demonstrated that our numerical procedure could successfully predict the heat transfer performance of the considered heat pipe cooling system for a battery.Specific indicators such as temperature,heat flux,and pressure loss were extracted to describe the characteristics of such a system.On the basis of the distributions of the temperature ratio of the battery surface,together with the heat flux and the streamlines around the heat pipe condenser,we conclude that the low disturbance of the coolant is the cause of the temperature gradient along the fluid flow direction.
文摘A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.
文摘This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8 Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to ±25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing “Coulomb counting” as the state equation and Shepherd’s rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack’s full operating range without overcharging or undercharging concerns.
基金supported by National Natural Science Foundation of China(No.51107126 and No.512111046)the Key Projects in National Science and Technology Pillar Program(No.2011BAA07B07)+1 种基金the Beiing Nova Program(No.Z141101001814094)the Science and Technology Foundation of State Grid Corporation of China(No.DG71-14-032)
文摘Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configuration of a China’s national renewable generation demonstration project combining a large-scale BESS with wind farm and photovoltaic(PV)power station,all coupled to a power transmission system,is introduced,and the key technologies including optimal control and management as well as operational status of this BESS are presented.Additionally,the technical benefits of such a large-scale BESS in dealing with power fluctuation and intermittence issues resulting from grid connection of large-scale renewable generation,and for improvement of operation characteristics of transmission grid,are discussed with relevant case studies.
基金the financial support from National Key R&D Program of China(2018YFE0111200)。
文摘Battery thermal management is very crucial for the safe and long-term operation of electric vehicles or hybrid electric vehicles.In this study,numerical simulation method is adopted to simulate the temperature field of Li-ion battery cell and module.It is proved that the maximum temperature and maximum temperature difference of battery cell and module increase with the increase of charge/discharge rate(C-rate)of the battery.For battery module,it can reach a maximum temperature of 61.1℃at a C-rate of 2 under natural convection condition with the ambient temperature of 20.0℃.A battery thermal management system based on micro heat pipe array(BTMS-MHPA)is deeply investigated.Experiments are conducted to compare the cooling effect on the battery module with different cooling methods,which include natural cooling,only MHPA,MHPA with fan.The maximum temperature of battery module which is cooled by MHPA with a fan is 43.4℃at a C-rate of 2,which is lower than that in the condition of natural cooling.Meanwhile,the maximum temperature difference was also greatly reduced by the application of MHPA cooling.The experimental results confirm that the feasibility and superiority of the BTMS-MHPA for guaranteeing the working temperature range and temperature uniformity of the battery.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2022MS015)。
文摘The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.
