A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].T...A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.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
The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,whi...The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.展开更多
It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous s...It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.展开更多
Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of ...Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.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
Accurate estimation of lithium-ion battery state of charge(SOC)is crucial for the safe and efficient operation of electric vehicles(EVs).However,both data-driven and model-driven SOC estimation methods face significan...Accurate estimation of lithium-ion battery state of charge(SOC)is crucial for the safe and efficient operation of electric vehicles(EVs).However,both data-driven and model-driven SOC estimation methods face significant challenges under battery aging,which alters internal resistance and electrochemical properties,especially across complex aging trajectories.Most existing deep learning and model-based approaches operate in an open-loop manner,lacking mechanisms for uncertainty quantification,accuracy prediction,or adaptive correction—leading to uncontrolled estimation errors during aging.To address this,we propose an innovative closed-loop SOC estimation framework that integrates active learning with uncertainty-aware correction into deep learning networks,enabling real-time feedback on SOC prediction confidence levels without the need for additional sensors or reference data.Specifically,we quantify the performance degradation of mainstream data-driven methods,including long short-term memory(LSTM)networks and Gaussian process regression(GPR),under complex aging paths.We demonstrate that our model-disagreement-based active learning correction strategy maintains robustness throughout the battery lifecycle.Experimental results show that with only four active retraining sessions over the full aging process,our method reduces average SOC estimation error to below 1.5%,and maximum cycle-based average error to below 2%.This work establishes a path toward uncertaintyinformed,lifecycle-resilient,and data-efficient SOC estimation,marking a significant advancement in battery management systems for real-world EV applications.展开更多
With the rise of the electric vehicle industry,as the power source of electric vehicles,lithium battery has become a research hotspot.The state of charge(SOC)estimation and modelling of lithium battery are studied in ...With the rise of the electric vehicle industry,as the power source of electric vehicles,lithium battery has become a research hotspot.The state of charge(SOC)estimation and modelling of lithium battery are studied in this paper.The ampere-hour(Ah)integration method based on external characteristics is analyzed,and the open-circuit voltage(OCV)method is studied.The two methods are combined to estimate SOC.Considering the accuracy and complexity of the model,the second-order RC equivalent circuit model of lithium battery is selected.Pulse discharge and exponential fitting of lithium battery are used to obtain corresponding parameters.The simulation is carried out by using fixed resistance capacitance and variable resistance capacitor respectively.The accuracy of variable resistance and capacitance model is 2.9%,which verifies the validity of the proposed model.展开更多
This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the...This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the nonlinear multiagent system,a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased(MVU)estimation of the unknown inputs and states.Compared with the conventional decentralized EKF-based unknown input filter,the proposed distributed filter has a more relaxed existence condition of the filter,which makes it more applicable in reality.This new type of filter is then successfully applied to the simultaneous estimation of state of charge(SOC)and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.展开更多
The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference...The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.展开更多
The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of ...The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.展开更多
The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical f...The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical factor that obstructs the commercialization of the hybrid system.In this study,based on finite element ideas,a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell.As for the lithium battery,previous energy management studies generally only focused on the state of charge,however,the state of health is also a key parameter for lithium batteries.In this paper,considering the strong coupling between variables,we use a particle filter algorithm to jointly estimate the state of charge and state of health.To reasonably distribute and manage the energy in the hybrid system,a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery.Finally,our algorithm is verified by using the DSPACE simulation platform.The results show that the hybrid system with an energy management strategy not only meets the real-time sharply changing demands,but also ensures the hybrid system is working efficiently and safely.展开更多
文摘A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.
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supported by the National Key R.D Program of China(2021YFB2401904)the Joint Fund project of the National Natural Science Foundation of China(U21A20485)+1 种基金the National Natural Science Foundation of China(61976175)the Key Laboratory Project of Shaanxi Provincial Education Department Scientific Research Projects(20JS109)。
文摘The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University(KKU)for funding this research project Number(R.G.P.2/133/43).
文摘It is critical to have precise data about Lithium-ion batteries,such as the State-of-Charge(SoC),to maintain a safe and consistent functioning of battery packs in energy storage systems of electric vehicles.Numerous strategies for estimating battery SoC,such as by including the coulomb counting and Kalman filter,have been established.As a result of the differences in parameter values between each cell,when these methods are applied to highcapacity battery packs,it has difficulties sustaining the prediction accuracy of overall cells.As a result of aging,the variation in the parameters of each cell is higher as more time is spent in operation.It is suggested in this study to establish an SoC estimate model for a Lithium-ion battery by employing an enhanced Deep Neural Network(DNN)approach.This is because the proposed DNN has a substantial hidden layer,which can accurately predict the SoC of an unknown driving cycle during training,making it ideal for SoC estimation.To evaluate the nonlinearities between voltage and current at various SoCs and temperatures,the proposed DNN is applied.Using current and voltage data measured at various temperatures throughout discharge/charge cycles is necessary for training and testing purposes.When the method has been thoroughly trained with the data collected,it is used for additional cells cycle tests to predict their SoC.The simulation has been conducted for two different Li-ion battery datasets.According to the experimental data,the suggested DNN-based SoC estimate approach produces a low mean absolute error and root-mean-square-error values,say less than 5%errors.
基金Under the auspices of National High-tech R&D Program of China(No.2013AA102301)National Natural Science Foundation of China(No.71503148)
文摘Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.
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funding support by the National Key R&D Program of China(Grant No.2022YFE0208000)the Shanghai Key Laboratory of Aerodynamics and Thermal Environment Simulation for Ground Vehicles(Grant No.23DZ2229029)+1 种基金the Shanghai Automotive Wind Tunnel Technical Service Platform(Grant No.19DZ2290400)the Fundamental Research Funds for the Central Universities.N.M.gratefully acknowledges the support of the Interna-tional Institute for Carbon Neutral Energy Research,sponsored by the Japanese Ministry of Education,Culture,Sports,Science and Technology.
文摘Accurate estimation of lithium-ion battery state of charge(SOC)is crucial for the safe and efficient operation of electric vehicles(EVs).However,both data-driven and model-driven SOC estimation methods face significant challenges under battery aging,which alters internal resistance and electrochemical properties,especially across complex aging trajectories.Most existing deep learning and model-based approaches operate in an open-loop manner,lacking mechanisms for uncertainty quantification,accuracy prediction,or adaptive correction—leading to uncontrolled estimation errors during aging.To address this,we propose an innovative closed-loop SOC estimation framework that integrates active learning with uncertainty-aware correction into deep learning networks,enabling real-time feedback on SOC prediction confidence levels without the need for additional sensors or reference data.Specifically,we quantify the performance degradation of mainstream data-driven methods,including long short-term memory(LSTM)networks and Gaussian process regression(GPR),under complex aging paths.We demonstrate that our model-disagreement-based active learning correction strategy maintains robustness throughout the battery lifecycle.Experimental results show that with only four active retraining sessions over the full aging process,our method reduces average SOC estimation error to below 1.5%,and maximum cycle-based average error to below 2%.This work establishes a path toward uncertaintyinformed,lifecycle-resilient,and data-efficient SOC estimation,marking a significant advancement in battery management systems for real-world EV applications.
基金Project(51507073)supported by the National Natural Science Foundation of China。
文摘With the rise of the electric vehicle industry,as the power source of electric vehicles,lithium battery has become a research hotspot.The state of charge(SOC)estimation and modelling of lithium battery are studied in this paper.The ampere-hour(Ah)integration method based on external characteristics is analyzed,and the open-circuit voltage(OCV)method is studied.The two methods are combined to estimate SOC.Considering the accuracy and complexity of the model,the second-order RC equivalent circuit model of lithium battery is selected.Pulse discharge and exponential fitting of lithium battery are used to obtain corresponding parameters.The simulation is carried out by using fixed resistance capacitance and variable resistance capacitor respectively.The accuracy of variable resistance and capacitance model is 2.9%,which verifies the validity of the proposed model.
基金supported by the National Natural Science Foundation of China(No.61822308 and No.61751307)the Natural Science Foundation of Shandong Province(No.JQ201812)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the nonlinear multiagent system,a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased(MVU)estimation of the unknown inputs and states.Compared with the conventional decentralized EKF-based unknown input filter,the proposed distributed filter has a more relaxed existence condition of the filter,which makes it more applicable in reality.This new type of filter is then successfully applied to the simultaneous estimation of state of charge(SOC)and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.
基金National Natural Science Foundation of China(NSFC,Grant No.52107230)Fundamental Research Funds for the Central Universities and the Major State Basic Research Development Program of China。
文摘The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2022MS015)。
文摘The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.
基金supported by the National Key Research and Development Program of China(2024YFB4007502)The Foundation for Outstanding Research Groups of Hubei Province of China(2025AFA012)Hubei Provincial Natural Science Foundation,China(2024AFB226)。
文摘The research of the fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission.However,the lower efficiency of energy management has been a critical factor that obstructs the commercialization of the hybrid system.In this study,based on finite element ideas,a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell.As for the lithium battery,previous energy management studies generally only focused on the state of charge,however,the state of health is also a key parameter for lithium batteries.In this paper,considering the strong coupling between variables,we use a particle filter algorithm to jointly estimate the state of charge and state of health.To reasonably distribute and manage the energy in the hybrid system,a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery.Finally,our algorithm is verified by using the DSPACE simulation platform.The results show that the hybrid system with an energy management strategy not only meets the real-time sharply changing demands,but also ensures the hybrid system is working efficiently and safely.
