This paper introduces a kind of substitute bench testing method for vehicle application development and testing method of the test requirements,including battery fast conversion cycle test equipment,enter type incubat...This paper introduces a kind of substitute bench testing method for vehicle application development and testing method of the test requirements,including battery fast conversion cycle test equipment,enter type incubator,liquid-cooled machine and ancillary equipment composed of a set of test system,through the walk-in constant temperature box to simulate the new energy vehicles under different environmental conditions of the test requirements,Liquid-cooled machine and auxiliary parts to complete the battery thermal management system need cooling fluid conditions,the battery conversion cycle test equipment to simulate the dc fast charging way of filling pile,complete battery thermal management system test,shorten the filling fast charging time and improve battery fast charge security,for troubleshooting and data collection and analysis,Improve work efficiency,save costs,and eliminate customer anxiety about battery life and charging time.展开更多
Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination...Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.展开更多
The increasing gross weight of electric Unmanned Aerial Vehicle(UAV) poses a challenge in practical applications. The range and endurance of the electric UAV are limited by the fixed mass of the battery package. In th...The increasing gross weight of electric Unmanned Aerial Vehicle(UAV) poses a challenge in practical applications. The range and endurance of the electric UAV are limited by the fixed mass of the battery package. In this work, a design optimization method for the battery package topology of small electric UAV is proposed to enhance the performance. To improve the accuracy of the method, the dynamic battery model and simplified electric component models are presented.These models are utilized by the trajectory optimization method, which takes the dynamic characteristic into consideration to calculate the aircraft performance. The direct optimal control method is used for solving the trajectory optimization problem, and this method is tested on a small blended-wing-body electric aircraft. The test result shows that the range and energy-consumption are mainly influenced by the parallel topology of the battery package, while the flight time in climb phase is more sensitive to the series topology. It is deduced that the range-and energy-optimal design points can be considered concurrently in design optimization. The work proves the feasibility of integrating the trajectory optimization and battery package design.展开更多
Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using...Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.展开更多
The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above...The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.展开更多
The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs....The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs. Firstly, in this paper we make use of an accelerated life test and a statistical analysis method to establish the capacity accelerated degradation model under three constant stress parameters according to the degradation data, which are charge rate, discharge rate, and operating temperature, and then we propose a capacity degradation model according to the current residual capacity of a Li-ion cell under dynamic stress parameters. Secondly, we analyze the charge and discharge process of a series power battery pack and interpret the correlation between the capacity degradations of the battery pack and its charge/discharge rate. According to this cycling condition, we establish a capacity degradation model of a series power battery pack under inconsistent capacity of cells, and analyze the degradation mechanism with capacity variance and operating temperature difference. The comparative analysis of test results shows that the inconsistent operating temperatures of cells in the series power battery pack are the main cause of its degradation; when the difference between inconsistent temperatures is narrowed by 5 ℃, the cycle life can be improved by more than 50%. Therefore, it effectively improves the cycle life of the series battery pack to reasonably assemble the batteries according to their capacities and to narrow the differences in operating temperature among cells.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
In order to solve the problems of high temperature and inconsistency in the operation of electric vehicle（ EV） battery pack,computational fluid dynamics（ CFD） simulation method is used to simulate and optimize the...In order to solve the problems of high temperature and inconsistency in the operation of electric vehicle（ EV） battery pack,computational fluid dynamics（ CFD） simulation method is used to simulate and optimize the heat dissipation of battery pack. The heat generation rate at different discharge magnifications is identified by establishing the heat generation model of the battery. In the forced air cooling mode,the Fluent software is used to compare the effects of different inlet and outlet directions,inlet angles,outlet angles,outlet sizes and inlet air speeds on heat dissipation. The simulation results show that the heat dissipation effect of the structure with the inlet and outlet on the same side is better than that on the different sides; the appropriate inlet angle and outlet width can improve the uniformity of temperature field; the increase of the inlet speed can improve the heat dissipation effect significantly. Compared with the steady temperature field of the initial structure,the average temperature after structure optimization is reduced by 4. 8℃ and the temperature difference is reduced by 15. 8℃,so that the battery can work under reasonable temperature and temperature difference.展开更多
Cell-to-cell variations(CtCV) compromise the electrochemical performance of battery packs, yet the evolutional mechanism and quantitative impacts of CtCV on the pack's fast-charging performance remain unexplored. ...Cell-to-cell variations(CtCV) compromise the electrochemical performance of battery packs, yet the evolutional mechanism and quantitative impacts of CtCV on the pack's fast-charging performance remain unexplored. This knowledge gap is vital for the proliferation of electric vehicles. This study underlies the relationship between CtCV and charging performance by assessing the pack's charge speed, final electric quantity, and temperature consistency. Cell variations and pack status are depicted using 2D parameter diagrams, and an m PnS configured pack model is built upon a decomposed electrode cell model.Variations in three single electric parameters, i.e., capacity(Q), electric quantity(E), and internal resistance(R), and their dual interactions, i.e., E-Q and R-Q, are analyzed carefully. The results indicate that Q variations predominantly affect the final electric quantity of the pack, while R variations impact the charge speed most. With incremental variances in cell parameters, the pack's fast-charging capability first declines linearly and then deteriorates sharply as variations intensify. This research elucidates the correlations between pack charging capabilities and cell variations, providing essential insights for optimizing cell sorting and assembly, battery management design, and charging protocol development for battery packs.展开更多
Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.Ho...Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.展开更多
As the only power source of pure electric vehicles,the performance of battery packs is easily affected by the temperature,and too high or too low temperature will make the performance of battery packs decline.In this ...As the only power source of pure electric vehicles,the performance of battery packs is easily affected by the temperature,and too high or too low temperature will make the performance of battery packs decline.In this study,the thermal analysis finite element modeling of a cast aluminum battery pack and steel battery pack of a pure electric vehicle is established to compare the thermal insulation performance of two kinds of battery packs under high-and low-temperature conditions.The simulation results show that the thermal insulation performance of the two kinds of battery packs meets the design requirements under high-and low-temperature conditions.The external environment of the cell and battery pack mainly transmits heat through heat conduction.Aiming at the problem that the uniform temperature performance of the steel battery pack is lower than that of the cast aluminum battery pack,several optimization solutions are put forward for the insulation design of the steel battery pack,and the optimal solution is obtained by comparing the simulation results.展开更多
Lithium-ion batteries'safe and effective functioning depends on reliable and precise heat control.In this study,we explore the thermal behaviour of a 48-V,30-Ah LiCoO_(2)battery pack utilising an unconventional tr...Lithium-ion batteries'safe and effective functioning depends on reliable and precise heat control.In this study,we explore the thermal behaviour of a 48-V,30-Ah LiCoO_(2)battery pack utilising an unconventional transient thermal analysis technique with a simplified constant heat-generating formula based on the Bernardi equation.This work assessed the effect of several discharge rates and heat transfer coefficients on thermal performance by modelling temperature distribution and heat dissipation inside the battery pack.Heat transfer coefficients 5 W/(m^(2)·K)for natural air convection,10 W/(m^(2)·K)for forced convection of air and discharge rates0.5C,1C,1.5C and 2C on thermal performance were investigated using a sensitivity analysis.The results show that forced convection improves temperature distribution and considerably enhances heat dissipation at a discharge rate of 0.5C.However,the study reveals that advanced thermal management techniques are especially vital.Even forced air convection finds it difficult to maintain temperatures within the optimal range at higher discharge rates,thus emphasizing the need to optimise cooling conditions to guarantee thermal stability and prevent hotspots.The findings underline and offer insightful analysis of the relative impact of discharge rates and cooling conditions on lithium-ion battery pack thermal behaviour.展开更多
Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ...Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology(SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP(dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr(the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.展开更多
Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately estimated.Inconsistency,also known as cell variation,is consi...Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately estimated.Inconsistency,also known as cell variation,is considered a significant evaluation index that greatly affects the degradation of battery pack.This paper proposes a novel joint inconsistency and SOH estimation method under cycling,which fills the gap of joint estimation based on the fast-charging process for electric vehicles.First,fifteen features are extracted from current change points during the partial charging process.Then,a joint estimation system is designed,where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency.A wrapper is used to select the optimal feature subset,and Gaussian process regression is implemented to estimate the SOH.Finally,the estimation performance is assessed by the test data.The results show that the inconsistency evaluation can reflect the aging conditions,and the inconsistency does affect the aging process.The wrapper selection method improves the accuracy of SOH estimation by about 75.8%compared to the traditional filter method when only 10%of data is used for model training.The maximum absolute error and root mean square error are 2.58%and 0.93%,respectively.展开更多
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and...Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).展开更多
The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach ...The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach to estimate the SOH of battery packs.The approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random noise.Subsequently,the statespace representation of the aging model is incorporated.Additionally,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH estimation.The performance of the proposed method is validated through aging experiments conducted on battery packs.The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.展开更多
Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,tho...Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,those risks can be solved with passive solutions,which require no active cooling or heating.Thecurrentwork aims to optimize the pack design and materials of the type-NCR18650B battery based on a wide range of operation temperature.The lower limit was denoted by cold case while the maximum limit was expressed by hot case.A combined analyticalnumerical approach was developed to model the heat generation inside the battery.A thermal resistance analysis was used to determine the boundary conditions of the numerical model.The governing differential equations for the 1-D heat generation model were solved analytically.The numerical analysis was considered to determine the best battery pack design based on material parameters,number of batteries,and geometrical arrangement.The analytical results revealedthat the cold case canbe selectedas theworst case and thebestmodel wasobtainedusing thehexagonal-shaped 10-battery pack that was covered with Delrin of 1.8 mm in thickness.The numerical results showed that the best model was the hexagonal-shaped 10-battery pack with Delrin of 2 mm in thickness that achieved the largest temperature of−20.6℃ in the cold case.展开更多
In recent years,due to the rapid increase in the number of vehicles in the world,the traditional vehicles using gasoline or diesel as energy have led to serious air pollution and energy depletion.It is urgent to devel...In recent years,due to the rapid increase in the number of vehicles in the world,the traditional vehicles using gasoline or diesel as energy have led to serious air pollution and energy depletion.It is urgent to develop practical clean energy vehicles.The performance of electric vehicle depends on the power battery pack.The working temperature of the battery pack has a great impact on the performance of the battery,so it is necessary to carry out thermal management on the battery pack.Taking a lithium-ion battery as the research object,the temperature field of the battery pack in the charge and discharge state is simulated and analyzed by using CFD simulation software in the way of air cooled heat dissipation,so as to understand the influencing factors of uneven temperature field.At the same time,the development trend of battery temperature can be well predicted through simulation,so as to provide theoretical basis for the design of battery pack.展开更多
The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the opera...The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.展开更多
Increasing the power density and overload capability of the energy-supply units(ESUs)is always one of the most challenging tasks in developing and deploying legged vehicles,especially for heavy-duty legged vehicles,in...Increasing the power density and overload capability of the energy-supply units(ESUs)is always one of the most challenging tasks in developing and deploying legged vehicles,especially for heavy-duty legged vehicles,in which significant power fluctuations in energy supply exist with peak power several times surpassing the average value.Applying ESUs with high power density and high overload can compactly ensure fluctuating power source supply on demand.It can avoid the ultra-high configuration issue,which usually exists in the conventional lithium-ion battery-based or engine-generator-based ESUs.Moreover,it dramatically reduces weight and significantly increases the loading and endurance capabilities of the legged vehicles.In this paper,we present a hybrid energy-supply unit for a heavy-duty legged vehicle combining the discharge characteristics of lithium-ion batteries and peak energy release/absorption characteristics of supercapacitors to adapt the ESU to high overload power fluctuations.The parameters of the lithium-ion battery pack and supercapacitor pack inside the ESU are optimally matched using the genetic algorithm based on the energy consumption model of the heavy-duty legged vehicle.The experiment results exhibit that the legged vehicle with a weight of 4.2 tons can walk at the speed of 5 km/h in a tripod gait under a reduction of 35.39%in weight of the ESU compared to the conventional lithium-ion battery-based solution.展开更多
文摘This paper introduces a kind of substitute bench testing method for vehicle application development and testing method of the test requirements,including battery fast conversion cycle test equipment,enter type incubator,liquid-cooled machine and ancillary equipment composed of a set of test system,through the walk-in constant temperature box to simulate the new energy vehicles under different environmental conditions of the test requirements,Liquid-cooled machine and auxiliary parts to complete the battery thermal management system need cooling fluid conditions,the battery conversion cycle test equipment to simulate the dc fast charging way of filling pile,complete battery thermal management system test,shorten the filling fast charging time and improve battery fast charge security,for troubleshooting and data collection and analysis,Improve work efficiency,save costs,and eliminate customer anxiety about battery life and charging time.
基金Supported by National Natural Science Foundation of China(Grant Nos.51875054,U1864212)Graduate Research and Innovation Foundation of Chongqing+2 种基金China(Grant No.CYS20018)Chongqing Municipal Natural Science Foundation for Distinguished Young Scholars of China(Grant No.cstc2019jcyjjq X0016)Chongqing Science and Technology Bureau of China。
文摘Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.
基金China Scholarship Council for the support during his study and research。
文摘The increasing gross weight of electric Unmanned Aerial Vehicle(UAV) poses a challenge in practical applications. The range and endurance of the electric UAV are limited by the fixed mass of the battery package. In this work, a design optimization method for the battery package topology of small electric UAV is proposed to enhance the performance. To improve the accuracy of the method, the dynamic battery model and simplified electric component models are presented.These models are utilized by the trajectory optimization method, which takes the dynamic characteristic into consideration to calculate the aircraft performance. The direct optimal control method is used for solving the trajectory optimization problem, and this method is tested on a small blended-wing-body electric aircraft. The test result shows that the range and energy-consumption are mainly influenced by the parallel topology of the battery package, while the flight time in climb phase is more sensitive to the series topology. It is deduced that the range-and energy-optimal design points can be considered concurrently in design optimization. The work proves the feasibility of integrating the trajectory optimization and battery package design.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB3305403)Project of basic research funds for central universities(2022CDJDX006)+1 种基金Talent Plan Project of Chongqing(No.cstc2021ycjhbgzxm0295)National Natural Science Foundation of China(No.52111530194)。
文摘Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.
基金sponsored by the Science and Technology Program of State Grid Corporation of China(4000-202355090A-1-1ZN)。
文摘The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.
基金supported by the National Natural Science Foundation of China(Grant Nos.61004092 and 51007088)the National High Technology Research and Development Program of China(Grant Nos.2011AA11A251 and 2011AA11A262)+1 种基金the International Science&Technology Cooperation Program of China(Grant Nos.2010DFA72760 and 2011DFA70570)the Research Foundation of National Engineering Laboratory for Electric Vehicles,China(GrantNo.2012-NELEV-03)
文摘The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs. Firstly, in this paper we make use of an accelerated life test and a statistical analysis method to establish the capacity accelerated degradation model under three constant stress parameters according to the degradation data, which are charge rate, discharge rate, and operating temperature, and then we propose a capacity degradation model according to the current residual capacity of a Li-ion cell under dynamic stress parameters. Secondly, we analyze the charge and discharge process of a series power battery pack and interpret the correlation between the capacity degradations of the battery pack and its charge/discharge rate. According to this cycling condition, we establish a capacity degradation model of a series power battery pack under inconsistent capacity of cells, and analyze the degradation mechanism with capacity variance and operating temperature difference. The comparative analysis of test results shows that the inconsistent operating temperatures of cells in the series power battery pack are the main cause of its degradation; when the difference between inconsistent temperatures is narrowed by 5 ℃, the cycle life can be improved by more than 50%. Therefore, it effectively improves the cycle life of the series battery pack to reasonably assemble the batteries according to their capacities and to narrow the differences in operating temperature among cells.
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Supported by the National Natural Science Foundation of China(51507012)Beijing Nova Program(Z171100001117063)
文摘In order to solve the problems of high temperature and inconsistency in the operation of electric vehicle（ EV） battery pack,computational fluid dynamics（ CFD） simulation method is used to simulate and optimize the heat dissipation of battery pack. The heat generation rate at different discharge magnifications is identified by establishing the heat generation model of the battery. In the forced air cooling mode,the Fluent software is used to compare the effects of different inlet and outlet directions,inlet angles,outlet angles,outlet sizes and inlet air speeds on heat dissipation. The simulation results show that the heat dissipation effect of the structure with the inlet and outlet on the same side is better than that on the different sides; the appropriate inlet angle and outlet width can improve the uniformity of temperature field; the increase of the inlet speed can improve the heat dissipation effect significantly. Compared with the steady temperature field of the initial structure,the average temperature after structure optimization is reduced by 4. 8℃ and the temperature difference is reduced by 15. 8℃,so that the battery can work under reasonable temperature and temperature difference.
基金supported by the National Natural Science Foundation of China under No. 52177217the Postdoctoral Innovative Talents Support Program under No. BX20240232。
文摘Cell-to-cell variations(CtCV) compromise the electrochemical performance of battery packs, yet the evolutional mechanism and quantitative impacts of CtCV on the pack's fast-charging performance remain unexplored. This knowledge gap is vital for the proliferation of electric vehicles. This study underlies the relationship between CtCV and charging performance by assessing the pack's charge speed, final electric quantity, and temperature consistency. Cell variations and pack status are depicted using 2D parameter diagrams, and an m PnS configured pack model is built upon a decomposed electrode cell model.Variations in three single electric parameters, i.e., capacity(Q), electric quantity(E), and internal resistance(R), and their dual interactions, i.e., E-Q and R-Q, are analyzed carefully. The results indicate that Q variations predominantly affect the final electric quantity of the pack, while R variations impact the charge speed most. With incremental variances in cell parameters, the pack's fast-charging capability first declines linearly and then deteriorates sharply as variations intensify. This research elucidates the correlations between pack charging capabilities and cell variations, providing essential insights for optimizing cell sorting and assembly, battery management design, and charging protocol development for battery packs.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[Grant No.52222708]the Natural Science Foundation of Beijing Municipality[Grant No.3212033]。
文摘Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.
文摘As the only power source of pure electric vehicles,the performance of battery packs is easily affected by the temperature,and too high or too low temperature will make the performance of battery packs decline.In this study,the thermal analysis finite element modeling of a cast aluminum battery pack and steel battery pack of a pure electric vehicle is established to compare the thermal insulation performance of two kinds of battery packs under high-and low-temperature conditions.The simulation results show that the thermal insulation performance of the two kinds of battery packs meets the design requirements under high-and low-temperature conditions.The external environment of the cell and battery pack mainly transmits heat through heat conduction.Aiming at the problem that the uniform temperature performance of the steel battery pack is lower than that of the cast aluminum battery pack,several optimization solutions are put forward for the insulation design of the steel battery pack,and the optimal solution is obtained by comparing the simulation results.
文摘Lithium-ion batteries'safe and effective functioning depends on reliable and precise heat control.In this study,we explore the thermal behaviour of a 48-V,30-Ah LiCoO_(2)battery pack utilising an unconventional transient thermal analysis technique with a simplified constant heat-generating formula based on the Bernardi equation.This work assessed the effect of several discharge rates and heat transfer coefficients on thermal performance by modelling temperature distribution and heat dissipation inside the battery pack.Heat transfer coefficients 5 W/(m^(2)·K)for natural air convection,10 W/(m^(2)·K)for forced convection of air and discharge rates0.5C,1C,1.5C and 2C on thermal performance were investigated using a sensitivity analysis.The results show that forced convection improves temperature distribution and considerably enhances heat dissipation at a discharge rate of 0.5C.However,the study reveals that advanced thermal management techniques are especially vital.Even forced air convection finds it difficult to maintain temperatures within the optimal range at higher discharge rates,thus emphasizing the need to optimise cooling conditions to guarantee thermal stability and prevent hotspots.The findings underline and offer insightful analysis of the relative impact of discharge rates and cooling conditions on lithium-ion battery pack thermal behaviour.
基金supported by the National Natural Science Foundation of China (Grant No. U1564205)the Ministry of Science and Technology of China (Grant No. 2016YFE0102200)funded by China Scholarship Council
文摘Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology(SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP(dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr(the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.51875054 and Grant No.U1864212)Graduate research and innovation foundation of Chongqing,China(Grant No.CYS20018)Chongqing Natural Science Foundation for Distinguished Young Scholars(Grant No.cstc2019jcyjjq0010),and Chongqing Science and Technology Bureau,China.
文摘Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately estimated.Inconsistency,also known as cell variation,is considered a significant evaluation index that greatly affects the degradation of battery pack.This paper proposes a novel joint inconsistency and SOH estimation method under cycling,which fills the gap of joint estimation based on the fast-charging process for electric vehicles.First,fifteen features are extracted from current change points during the partial charging process.Then,a joint estimation system is designed,where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency.A wrapper is used to select the optimal feature subset,and Gaussian process regression is implemented to estimate the SOH.Finally,the estimation performance is assessed by the test data.The results show that the inconsistency evaluation can reflect the aging conditions,and the inconsistency does affect the aging process.The wrapper selection method improves the accuracy of SOH estimation by about 75.8%compared to the traditional filter method when only 10%of data is used for model training.The maximum absolute error and root mean square error are 2.58%and 0.93%,respectively.
文摘Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).
基金supported by the National Natural Science Foundation of China(Grant No.62303007)Doctoral Research Start-up Funding(Grant No.S020318015/028)China Postdoctoral Science Foundation(No.2023M741452).
文摘The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach to estimate the SOH of battery packs.The approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random noise.Subsequently,the statespace representation of the aging model is incorporated.Additionally,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH estimation.The performance of the proposed method is validated through aging experiments conducted on battery packs.The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.
文摘Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures.Depending on the battery package design,those risks can be solved with passive solutions,which require no active cooling or heating.Thecurrentwork aims to optimize the pack design and materials of the type-NCR18650B battery based on a wide range of operation temperature.The lower limit was denoted by cold case while the maximum limit was expressed by hot case.A combined analyticalnumerical approach was developed to model the heat generation inside the battery.A thermal resistance analysis was used to determine the boundary conditions of the numerical model.The governing differential equations for the 1-D heat generation model were solved analytically.The numerical analysis was considered to determine the best battery pack design based on material parameters,number of batteries,and geometrical arrangement.The analytical results revealedthat the cold case canbe selectedas theworst case and thebestmodel wasobtainedusing thehexagonal-shaped 10-battery pack that was covered with Delrin of 1.8 mm in thickness.The numerical results showed that the best model was the hexagonal-shaped 10-battery pack with Delrin of 2 mm in thickness that achieved the largest temperature of−20.6℃ in the cold case.
文摘In recent years,due to the rapid increase in the number of vehicles in the world,the traditional vehicles using gasoline or diesel as energy have led to serious air pollution and energy depletion.It is urgent to develop practical clean energy vehicles.The performance of electric vehicle depends on the power battery pack.The working temperature of the battery pack has a great impact on the performance of the battery,so it is necessary to carry out thermal management on the battery pack.Taking a lithium-ion battery as the research object,the temperature field of the battery pack in the charge and discharge state is simulated and analyzed by using CFD simulation software in the way of air cooled heat dissipation,so as to understand the influencing factors of uneven temperature field.At the same time,the development trend of battery temperature can be well predicted through simulation,so as to provide theoretical basis for the design of battery pack.
基金Supported by the National Natural Science Foundation of China(51507012)Beijing Nova Program(Z171100001117063)
文摘The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.
基金supported in part by the National Key R&D Program of China under Grant No.2019YFB1309502.
文摘Increasing the power density and overload capability of the energy-supply units(ESUs)is always one of the most challenging tasks in developing and deploying legged vehicles,especially for heavy-duty legged vehicles,in which significant power fluctuations in energy supply exist with peak power several times surpassing the average value.Applying ESUs with high power density and high overload can compactly ensure fluctuating power source supply on demand.It can avoid the ultra-high configuration issue,which usually exists in the conventional lithium-ion battery-based or engine-generator-based ESUs.Moreover,it dramatically reduces weight and significantly increases the loading and endurance capabilities of the legged vehicles.In this paper,we present a hybrid energy-supply unit for a heavy-duty legged vehicle combining the discharge characteristics of lithium-ion batteries and peak energy release/absorption characteristics of supercapacitors to adapt the ESU to high overload power fluctuations.The parameters of the lithium-ion battery pack and supercapacitor pack inside the ESU are optimally matched using the genetic algorithm based on the energy consumption model of the heavy-duty legged vehicle.The experiment results exhibit that the legged vehicle with a weight of 4.2 tons can walk at the speed of 5 km/h in a tripod gait under a reduction of 35.39%in weight of the ESU compared to the conventional lithium-ion battery-based solution.
