The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging...The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their correspon...The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.展开更多
Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention ...Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
An accurate state of charge(SOC)estimation of lithium-ion batteries underpins a safe and optimized operation of the system.In recent years,deep learning-based SOC estimation has made significant progress.In order to p...An accurate state of charge(SOC)estimation of lithium-ion batteries underpins a safe and optimized operation of the system.In recent years,deep learning-based SOC estimation has made significant progress.In order to provide researchers in this rapidly advancing field a comprehensive overview of the state of the art,this paper carries out a structured review on deep learning-based SOC estimation of lithium-ion batteries.A detailed taxonomy of SOC estimation approaches and popularly used public datasets is provided as an introduction to the technical background.A systematic walk-through of the existing deep learning-based SOC estimation approaches,together with the frequently applied optimization strategies,is presented,where we also appeal for a standardized evaluation protocol in this field.As highlight,the current trends and emerging perspectives are pointed out and discussed in detail,including physics-informed neural networks(PINNs),multi-task learning(MTL),few-shot learning,and continual learning.We believe this work could not only provide the researchers and practitioners new to this topic with a clear and detailed manual to start with,but also point out the emerging perspectives for further cutting-edge studies towards a smarter battery management system.展开更多
Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods ...Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems,leading to inaccuracies that compromise the efficiency and reliability of electric vehicles.This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks.Specifically,teaching-learning based optimization(TLBO)is employed to optimize the weights and biases of the deep neural networks model,enhancing estimation accuracy.The proposed TLBO-deep neural networks(TLBO-DNNs)method was evaluated on a dataset of 1,064,000 samples,with performance assessed using mean absolute error(MAE),root mean square error(RMSE),and convergence value.The TLBO-DNNs model achieved an MAE of 3.4480,an RMSE of 4.6487,and a convergence value of 0.0328,outperforming other hybrid approaches.These include the barnacle mating optimizer-deep neural networks(BMO-DNNs)with an MAE of 5.3848,an RMSE of 7.0395,and a convergence value of 0.0492;the evolutionary mating algorithm-deep neural networks(EMA-DNNs)with an MAE of 7.6127,an RMSE of 11.2287,and a convergence value of 0.0536;and the particle swarm optimization-deep neural networks(PSO-DNNs)with an MAE of 4.3089,an RMSE of 5.9672,and a convergence value of 0.0345.Additionally,the TLBO-DNNs approach outperformed standalone models,including the autoregressive integrated moving average(ARIMA)model(MAE:14.3301,RMSE:7.0697)and support vector machines(MAE:6.0065,RMSE:8.0360).This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles,contributing to improved efficiency and reliability in electric vehicle operations.展开更多
Model-based strategies for estimating the state-of-charge(SOC)of Li-ion batteries are essential in real-time applications,such as electric vehicles and large-scale energy storage.However,based on existing models,devel...Model-based strategies for estimating the state-of-charge(SOC)of Li-ion batteries are essential in real-time applications,such as electric vehicles and large-scale energy storage.However,based on existing models,developing estimation methods with strong robustness to initial and cumulative errors,high SOC estimation accuracy,and adaptability to sparse data remains challenging.Herein,the modeling principles of the gas-liquid dynamics model are systematically clarified,and a SOC estimation method based on this model and a dual extended Kalman filter with a watchdog function is proposed.The proposed method is comprehensively compared with general extended Kalman filter and dual extended Kalman filter methods under five working conditions.The results indicate that all three methods based on the gas-liquid dynamics model have good estimation accuracy,with a maximum SOC error of 0.016 under correct initial conditions.But the proposed method has significant advantages in robustness to large initial errors,cumulative errors,and sparse data.This study provides new insights into efficient online SOC estimation.展开更多
A model based method which recruited the extended Kalman filter （EKF） to estimate the full state of charge （SOC） of Li-ion battery was proposed. The underlying dynamic behavior of the cell pack was described based...A model based method which recruited the extended Kalman filter （EKF） to estimate the full state of charge （SOC） of Li-ion battery was proposed. The underlying dynamic behavior of the cell pack was described based on an equivalent circuit comprising of two capacitors and three resistors. Measurements in two tests were applied to compare the SOC estimated by model based EKF estimation with the SOC calculated by coulomb counting. Results have shown that the proposed method is able to perform a good estimation of the SOC of battery packs. Moreover, a corresponding battery management systems （BMS） including software and hardware based on this method was designed.展开更多
Accurate state of charge(SOC)estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles,and it is also a key technology component in battery manag...Accurate state of charge(SOC)estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles,and it is also a key technology component in battery management systems.In recent years,lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity.However,these methods commonly face the issue of poor model generalization and limited robustness.To address such issues,this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression(SA-SVR)combined with minimum error entropy based extended Kalman filter(MEE-EKF)algorithm.Firstly,a probability-based SA algorithm is employed to optimize the internal parameters of the SVR,thereby enhancing the precision of original SOC estimation.Secondly,utilizing the framework of the Kalman filter,the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF,while the ampere-hour integral physical model serves as the state equation,effectively attenuating the measurement noise,enhancing the estimation accuracy,and improving generalization ability.The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training.The results demonstrate that the proposed method achieves a mean absolute error below 0.60%and a root mean square error below 0.73%across all operating conditions,showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms.The high precision and generalization capability of the proposed method are evident,ensuring accurate SOC estimation for electric vehicles.展开更多
The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network t...The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.展开更多
Modeling and state of charge(SOC)estimation of Lithium cells are crucial techniques of the lithium battery management system.The modeling is extremely complicated as the operating status of lithium battery is affected...Modeling and state of charge(SOC)estimation of Lithium cells are crucial techniques of the lithium battery management system.The modeling is extremely complicated as the operating status of lithium battery is affected by temperature,current,cycle number,discharge depth and other factors.This paper studies the modeling of lithium iron phosphate battery based on the Thevenin’s equivalent circuit and a method to identify the open circuit voltage,resistance and capacitance in the model is proposed.To improve the accuracy of the lithium battery model,a capacity estimation algorithm considering the capacity loss during the battery’s life cycle.In addition,this paper solves the SOC estimation issue of the lithium battery caused by the uncertain noise using the extended Kalman filtering(EKF)algorithm.A simulation model of actual lithium batteries is designed in Matlab/Simulink and the simulation results verify the accuracy of the model under different operating modes.展开更多
While several recent studies have focused on elimi-nating the imbalance of energy stored in series-connected battery cells,very little attention has been given to balancing the energy stored in parallel-connected batt...While several recent studies have focused on elimi-nating the imbalance of energy stored in series-connected battery cells,very little attention has been given to balancing the energy stored in parallel-connected battery cells.As such,this paper aims at presenting a new balancing approach for parallel LiFePO_(4) battery cells.In this regard,a Backpropagation Neural Network(BPNN)based technique is employed to develop a Battery Management System(BMS)that can assess the charging status of all cells and control its operations through a DC/DC Buck-Boost converter.Simulation results demonstrate the effectiveness of the proposed approach in balancing the energy stored in parallel-connected battery cells in which the state of charge(SoC)estimation error is found to be only 1.15%.展开更多
Battery Energy Storage Systems(BESS)are the backbone of modern power grids.They allow for the increase of energy storage,peak shaving,or backup power.Due to their complexity and dynamics,BESS require high-advanced man...Battery Energy Storage Systems(BESS)are the backbone of modern power grids.They allow for the increase of energy storage,peak shaving,or backup power.Due to their complexity and dynamics,BESS require high-advanced management methods to optimise its performance.This paper focuses on the integration of Artificial Intelligence(AI)into BESS,discussing three main pillars:system stability,battery usage optimisation,and predictive maintenance.The emergence of Artificial Intelligence and in particular deep learning,reinforcement learning,and neural networks,brings significant improvements in the modelling of complex reaction mechanisms,the adaptation to real-time data,and predictive maintenance.By analysing large datasets from various sources,AI can increase the precision of State of Charge(SOC)estimation,reduce maintenance costs,and improve the reliability of the system.The comparison with different case studies underlines the potential implementation of AI in real-life applications,which brings cost savings and increased system efficiency.This paper concludes that the power of AI enables new techniques for BESS management,and it would bring major benefits in the construction of more powerful and resilient energy systems as a whole.展开更多
基金supported by research on value model and technology application of patent operation of science and technology project(52094020000U)National Natural Science Foundation of China(52177193).
文摘The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金the financial support from the China Scholarship Council(CSC)(No.202207550010)。
文摘The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.
基金Open Access Publication Fund of TU Berlin for the support.
文摘An accurate state of charge(SOC)estimation of lithium-ion batteries underpins a safe and optimized operation of the system.In recent years,deep learning-based SOC estimation has made significant progress.In order to provide researchers in this rapidly advancing field a comprehensive overview of the state of the art,this paper carries out a structured review on deep learning-based SOC estimation of lithium-ion batteries.A detailed taxonomy of SOC estimation approaches and popularly used public datasets is provided as an introduction to the technical background.A systematic walk-through of the existing deep learning-based SOC estimation approaches,together with the frequently applied optimization strategies,is presented,where we also appeal for a standardized evaluation protocol in this field.As highlight,the current trends and emerging perspectives are pointed out and discussed in detail,including physics-informed neural networks(PINNs),multi-task learning(MTL),few-shot learning,and continual learning.We believe this work could not only provide the researchers and practitioners new to this topic with a clear and detailed manual to start with,but also point out the emerging perspectives for further cutting-edge studies towards a smarter battery management system.
基金supported by Ministry of Higher Education Malaysia(MOHE)under Fundamental Research Grant Scheme(Grant No.:FRGS/1/2024/ICT02/UMP/02/).
文摘Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems,leading to inaccuracies that compromise the efficiency and reliability of electric vehicles.This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks.Specifically,teaching-learning based optimization(TLBO)is employed to optimize the weights and biases of the deep neural networks model,enhancing estimation accuracy.The proposed TLBO-deep neural networks(TLBO-DNNs)method was evaluated on a dataset of 1,064,000 samples,with performance assessed using mean absolute error(MAE),root mean square error(RMSE),and convergence value.The TLBO-DNNs model achieved an MAE of 3.4480,an RMSE of 4.6487,and a convergence value of 0.0328,outperforming other hybrid approaches.These include the barnacle mating optimizer-deep neural networks(BMO-DNNs)with an MAE of 5.3848,an RMSE of 7.0395,and a convergence value of 0.0492;the evolutionary mating algorithm-deep neural networks(EMA-DNNs)with an MAE of 7.6127,an RMSE of 11.2287,and a convergence value of 0.0536;and the particle swarm optimization-deep neural networks(PSO-DNNs)with an MAE of 4.3089,an RMSE of 5.9672,and a convergence value of 0.0345.Additionally,the TLBO-DNNs approach outperformed standalone models,including the autoregressive integrated moving average(ARIMA)model(MAE:14.3301,RMSE:7.0697)and support vector machines(MAE:6.0065,RMSE:8.0360).This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles,contributing to improved efficiency and reliability in electric vehicle operations.
基金supported by Natural Science Research of Jiangsu Higher Education Institutions of China(22KJB460015)the Doctoral Research Start-up Foundation by Huaiyin Institute of Technology(Z301B22530)+1 种基金the Foundation for Jiangsu key Laboratory of Traffic and Transportation Security(TTS2021-04)the China Postdoctoral Science Foundation(2023M731685).
文摘Model-based strategies for estimating the state-of-charge(SOC)of Li-ion batteries are essential in real-time applications,such as electric vehicles and large-scale energy storage.However,based on existing models,developing estimation methods with strong robustness to initial and cumulative errors,high SOC estimation accuracy,and adaptability to sparse data remains challenging.Herein,the modeling principles of the gas-liquid dynamics model are systematically clarified,and a SOC estimation method based on this model and a dual extended Kalman filter with a watchdog function is proposed.The proposed method is comprehensively compared with general extended Kalman filter and dual extended Kalman filter methods under five working conditions.The results indicate that all three methods based on the gas-liquid dynamics model have good estimation accuracy,with a maximum SOC error of 0.016 under correct initial conditions.But the proposed method has significant advantages in robustness to large initial errors,cumulative errors,and sparse data.This study provides new insights into efficient online SOC estimation.
文摘A model based method which recruited the extended Kalman filter （EKF） to estimate the full state of charge （SOC） of Li-ion battery was proposed. The underlying dynamic behavior of the cell pack was described based on an equivalent circuit comprising of two capacitors and three resistors. Measurements in two tests were applied to compare the SOC estimated by model based EKF estimation with the SOC calculated by coulomb counting. Results have shown that the proposed method is able to perform a good estimation of the SOC of battery packs. Moreover, a corresponding battery management systems （BMS） including software and hardware based on this method was designed.
基金Key Research and Development Program of Shaanxi Province(2023-GHYB-05 and 2023-YBSF-104).
文摘Accurate state of charge(SOC)estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles,and it is also a key technology component in battery management systems.In recent years,lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity.However,these methods commonly face the issue of poor model generalization and limited robustness.To address such issues,this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression(SA-SVR)combined with minimum error entropy based extended Kalman filter(MEE-EKF)algorithm.Firstly,a probability-based SA algorithm is employed to optimize the internal parameters of the SVR,thereby enhancing the precision of original SOC estimation.Secondly,utilizing the framework of the Kalman filter,the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF,while the ampere-hour integral physical model serves as the state equation,effectively attenuating the measurement noise,enhancing the estimation accuracy,and improving generalization ability.The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training.The results demonstrate that the proposed method achieves a mean absolute error below 0.60%and a root mean square error below 0.73%across all operating conditions,showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms.The high precision and generalization capability of the proposed method are evident,ensuring accurate SOC estimation for electric vehicles.
文摘The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.
基金This work is supported in part by Open Fund of State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(DGB51201700424)Industrial Innovation of Jilin Province Development and Reform Commission(2017C017-2)Jilin Provincial“13th Five-Year Plan”Science and Technology Project([2016]88).
文摘Modeling and state of charge(SOC)estimation of Lithium cells are crucial techniques of the lithium battery management system.The modeling is extremely complicated as the operating status of lithium battery is affected by temperature,current,cycle number,discharge depth and other factors.This paper studies the modeling of lithium iron phosphate battery based on the Thevenin’s equivalent circuit and a method to identify the open circuit voltage,resistance and capacitance in the model is proposed.To improve the accuracy of the lithium battery model,a capacity estimation algorithm considering the capacity loss during the battery’s life cycle.In addition,this paper solves the SOC estimation issue of the lithium battery caused by the uncertain noise using the extended Kalman filtering(EKF)algorithm.A simulation model of actual lithium batteries is designed in Matlab/Simulink and the simulation results verify the accuracy of the model under different operating modes.
基金research and innovation management center(RIMC)UNIMAS via Fundamental Research Grant Scheme FRGS/1/2017/TK10/UNIMAS/03/1,Ministry of Higher Education,Malaysia.
文摘While several recent studies have focused on elimi-nating the imbalance of energy stored in series-connected battery cells,very little attention has been given to balancing the energy stored in parallel-connected battery cells.As such,this paper aims at presenting a new balancing approach for parallel LiFePO_(4) battery cells.In this regard,a Backpropagation Neural Network(BPNN)based technique is employed to develop a Battery Management System(BMS)that can assess the charging status of all cells and control its operations through a DC/DC Buck-Boost converter.Simulation results demonstrate the effectiveness of the proposed approach in balancing the energy stored in parallel-connected battery cells in which the state of charge(SoC)estimation error is found to be only 1.15%.
文摘Battery Energy Storage Systems(BESS)are the backbone of modern power grids.They allow for the increase of energy storage,peak shaving,or backup power.Due to their complexity and dynamics,BESS require high-advanced management methods to optimise its performance.This paper focuses on the integration of Artificial Intelligence(AI)into BESS,discussing three main pillars:system stability,battery usage optimisation,and predictive maintenance.The emergence of Artificial Intelligence and in particular deep learning,reinforcement learning,and neural networks,brings significant improvements in the modelling of complex reaction mechanisms,the adaptation to real-time data,and predictive maintenance.By analysing large datasets from various sources,AI can increase the precision of State of Charge(SOC)estimation,reduce maintenance costs,and improve the reliability of the system.The comparison with different case studies underlines the potential implementation of AI in real-life applications,which brings cost savings and increased system efficiency.This paper concludes that the power of AI enables new techniques for BESS management,and it would bring major benefits in the construction of more powerful and resilient energy systems as a whole.
