For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate p...Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external pressures.Atomic force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external pressure.Then,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium thermodynamics.The results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of SEI.Moreover,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge reactions.By integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate it.This study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.展开更多
External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batte...External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.展开更多
Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a c...Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.展开更多
State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have p...State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.展开更多
Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy man...Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.展开更多
Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offe...Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and recycling.Firstly,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency.Secondly,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic issues.In the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery technologies.Particularly,we introduce several global leading recyclers to illustrate their industrial processes and technical intricacies.Furthermore,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal framework.We hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.展开更多
Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the R...Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.展开更多
Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using b...Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models.In addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging data.We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves.Correlation analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional flexibility.The validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 mAh.Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data.Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method.Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation.Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.展开更多
Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density,long lifespan,and high efficiency.However,the manufacturing defects,caused by production flaws and r...Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density,long lifespan,and high efficiency.However,the manufacturing defects,caused by production flaws and raw material impurities can accelerate battery degradation.In extreme cases,these defects may result in severe safety incidents,such as thermal runaway.Metal foreign matter is one of the main types of manufacturing defects,frequently causing internal short circuits in lithium-ion batteries.Among these,copper particles are the most common contaminants.This paper addresses the safety risks posed by manufacturing defects in lithium-ion batteries,analyzes their classification and associated hazards,and reviews the research on metal foreign matter defects,with a focus on copper particle contamination.Furthermore,we summarize the detection methods to identify defective batteries and propose future research directions to address metal foreign matter defects.展开更多
Manufacturing defects in lithium-ion batteries are a major cause of thermal runaway,with copper foreign matter being one of the most common defects on battery production lines.Such defects can induce internal short ci...Manufacturing defects in lithium-ion batteries are a major cause of thermal runaway,with copper foreign matter being one of the most common defects on battery production lines.Such defects can induce internal short circuits(ISCs)that may trigger thermal runaway,posing significant safety risks.The occurrence of ISCs in copper defect batteries is closely associated with the charging stages during formation and cycling processes.However,the abnormal characteristics during these processes are not yet fully understood,and existing methods for detecting copper matter in batteries primarily rely on offline self-discharge measurements.In this study,a detailed analysis of abnormal current and voltage characteristics in copper defect batteries during formation and cycling is conducted,a multi-stage defect detection method is proposed.The proposed method achieves detection rates of 84.2%in the formation stage,84.2%in the single-cycle stage,and 68.4%in the multi-cycle stage.Using this multi-stage detection method,all copper defect batteries,including those prone to sudden ISCs,are successfully identified.Furthermore,the proposed method requires no complex calculations or additional equipment and relies only on standard current and voltage data collected during formation and cycling.This provides an efficient and practical solution for detecting copper foreign matter defects in lithium-ion batteries,thereby enhancing overall battery safety.展开更多
The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading...The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading to fire or thermal runaway.This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in EVs.In the proposed method,the EKF is used to estimate the state of charge(SOC)of the faulty cell by adjusting a gain matrix based on real-time measured voltages.The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults,and the soft SC resistance values are further identified to indicate the degree of fault severity.Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values.The experimental data are acquired to validate the proposed soft SC fault diagnosis method.The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.展开更多
An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the re...An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.展开更多
Solar energy plays an important role in the global energy framework for future.Comparing with conventional generation systems using fossil fuels,the cost structure of photovoltaic(PV)systems is different:the capital c...Solar energy plays an important role in the global energy framework for future.Comparing with conventional generation systems using fossil fuels,the cost structure of photovoltaic(PV)systems is different:the capital cost is higher while the operation cost is negligible.Reliabilities of the PV system can also influence the cost for producing electricity.Investors,planners and regulators require deep insight into the return and cost of a PV project.A reliability based economical assessment of largescale PV systems has been conducted utilizing Universal Generating Function(UGF)techniques.The reliability models of solar panel arrays,PV inverters and energy production units(EPUs)are represented as the corresponding UGFs.The expected energy production models for different PV system configurations have also been developed.The expected unit cost of electricity has been calculated to provide informative metrics for making optimal decisions.The proposed method has been applied to determine the PV system configuration which provides electricity for a water purification process.展开更多
1.Introduction Electric vehicles(EVs)are playing an increasingly important role in decarbonizing the transportation sector.They constitute a promising solution to a set of global challenges such as climate change and ...1.Introduction Electric vehicles(EVs)are playing an increasingly important role in decarbonizing the transportation sector.They constitute a promising solution to a set of global challenges such as climate change and air pollution.EVs are an integration of a wide spectrum of techniques,such as battery monitoring,battery safety and vehicle energy management.In this regard,the EV development still faces significant challenges,which necessitate innovations in EV technologies.Given this,Green Energy and Intelligent Transportation(GEITS)organizes a special issue of“Key Technologies for Electric Vehicles”that attempts to advance knowledge in the area of EVs and provides a platform for researchers and engineers to share recent research results and discuss critical challenges in this field.A wide spectrum of topics are discussed,including but not limited to the following.展开更多
In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approxima...In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.展开更多
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported by the National Key R&D Program of China(Grant No.2023YFB2503800)。
文摘Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and safety.This study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external pressures.Atomic force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external pressure.Then,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium thermodynamics.The results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of SEI.Moreover,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge reactions.By integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate it.This study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.
基金support by the National Key Researchand Development Program of China(2018YFBO104100).
文摘External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.
基金supported by the National Key R&D Program of China(2021YFB2402002)the National Natural Science Foundation of China(51922006 and 51877009)+1 种基金the China Postdoctoral Science Foundation(BX2021035 and 2022M710379)the Beijing Natural Science Foundation(Grant No.L223013)。
文摘Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems.However,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data collection.In response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input.More importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training data.Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method.The results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are given.However,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are available.In this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed method.Our method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.
基金Beijing Municipal Natural Science Foundation of China(Grant No.3182035)National Natural Science Foundation of China(Grant No.51877009).
文摘State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles.Battery fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC estimation.In this article,we compare five state-of-the-art FOMs in terms of SOC estimation.To this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM parameters.Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy.The model R(RQ)W shows superior identification accuracy than the other four FOMs.Secondly,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current drifts.The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs.Although more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory accuracy.Validation results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other models.Moreover,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.
基金This work was supported by the National Key Research and Development Program of China(2017YFB0103802)the National Natural Science Foundation of China(51922006 and 51707011).
文摘Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.
基金supported by an Australian Government Research Training Program Scholarship offered to the first author of this study。
文摘Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and recycling.Firstly,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency.Secondly,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic issues.In the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery technologies.Particularly,we introduce several global leading recyclers to illustrate their industrial processes and technical intricacies.Furthermore,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal framework.We hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.
基金Supported by National Key R&D Program of China(Grant No.2021YFB2402002)Beijing Municipal Natural Science Foundation of China(Grant No.L223013).
文摘Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.
基金This work was supported by the National Key R&D Program of China(2021YFB2402002)the Beijing Natural Science Foundation(L223013)the Chongqing Automobile Collaborative Innovation Centre(No.2022CDJDX-004).
文摘Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries.The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models.In addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging data.We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves.Correlation analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional flexibility.The validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 mAh.Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data.Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method.Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation.Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.
基金supported by the National Key R&D Program of China(2021YFB2402002)Beijing Natural Science Foundation(Grant No.L223013).
文摘Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density,long lifespan,and high efficiency.However,the manufacturing defects,caused by production flaws and raw material impurities can accelerate battery degradation.In extreme cases,these defects may result in severe safety incidents,such as thermal runaway.Metal foreign matter is one of the main types of manufacturing defects,frequently causing internal short circuits in lithium-ion batteries.Among these,copper particles are the most common contaminants.This paper addresses the safety risks posed by manufacturing defects in lithium-ion batteries,analyzes their classification and associated hazards,and reviews the research on metal foreign matter defects,with a focus on copper particle contamination.Furthermore,we summarize the detection methods to identify defective batteries and propose future research directions to address metal foreign matter defects.
基金Supported by the National Key R&D Program of China(2024YFB2505003).
文摘Manufacturing defects in lithium-ion batteries are a major cause of thermal runaway,with copper foreign matter being one of the most common defects on battery production lines.Such defects can induce internal short circuits(ISCs)that may trigger thermal runaway,posing significant safety risks.The occurrence of ISCs in copper defect batteries is closely associated with the charging stages during formation and cycling processes.However,the abnormal characteristics during these processes are not yet fully understood,and existing methods for detecting copper matter in batteries primarily rely on offline self-discharge measurements.In this study,a detailed analysis of abnormal current and voltage characteristics in copper defect batteries during formation and cycling is conducted,a multi-stage defect detection method is proposed.The proposed method achieves detection rates of 84.2%in the formation stage,84.2%in the single-cycle stage,and 68.4%in the multi-cycle stage.Using this multi-stage detection method,all copper defect batteries,including those prone to sudden ISCs,are successfully identified.Furthermore,the proposed method requires no complex calculations or additional equipment and relies only on standard current and voltage data collected during formation and cycling.This provides an efficient and practical solution for detecting copper foreign matter defects in lithium-ion batteries,thereby enhancing overall battery safety.
基金This work was supported by the National Natural Science Foundation of China(Grant No.51922006,51877009).
文摘The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading to fire or thermal runaway.This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in EVs.In the proposed method,the EKF is used to estimate the state of charge(SOC)of the faulty cell by adjusting a gain matrix based on real-time measured voltages.The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults,and the soft SC resistance values are further identified to indicate the degree of fault severity.Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values.The experimental data are acquired to validate the proposed soft SC fault diagnosis method.The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.
基金supported by the National Science Foundation for Excellent Young Scholars of China(Grant No.51922006).
文摘An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric vehicles.In this paper,we address the requirements to achieve better EMS performances for a HESS.First,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)algorithm.Secondly,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and analyzed.For online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS.Thirdly,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery life.Finally,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.
文摘Solar energy plays an important role in the global energy framework for future.Comparing with conventional generation systems using fossil fuels,the cost structure of photovoltaic(PV)systems is different:the capital cost is higher while the operation cost is negligible.Reliabilities of the PV system can also influence the cost for producing electricity.Investors,planners and regulators require deep insight into the return and cost of a PV project.A reliability based economical assessment of largescale PV systems has been conducted utilizing Universal Generating Function(UGF)techniques.The reliability models of solar panel arrays,PV inverters and energy production units(EPUs)are represented as the corresponding UGFs.The expected energy production models for different PV system configurations have also been developed.The expected unit cost of electricity has been calculated to provide informative metrics for making optimal decisions.The proposed method has been applied to determine the PV system configuration which provides electricity for a water purification process.
基金Beijing Natural Science Foundation(Grant No.L223013)National Natural Science Foundation of China(Grant No.52107222).
文摘1.Introduction Electric vehicles(EVs)are playing an increasingly important role in decarbonizing the transportation sector.They constitute a promising solution to a set of global challenges such as climate change and air pollution.EVs are an integration of a wide spectrum of techniques,such as battery monitoring,battery safety and vehicle energy management.In this regard,the EV development still faces significant challenges,which necessitate innovations in EV technologies.Given this,Green Energy and Intelligent Transportation(GEITS)organizes a special issue of“Key Technologies for Electric Vehicles”that attempts to advance knowledge in the area of EVs and provides a platform for researchers and engineers to share recent research results and discuss critical challenges in this field.A wide spectrum of topics are discussed,including but not limited to the following.
基金Natural Science Program of Shandong Province(Grant No.ZR2020ME209)National Natural Science Foundation of China(Grant No.52177210)China Postdoctoral Science Foundation(Grant No.2021M690740).
文摘In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.
