The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interfe...The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interferences.This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments.By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the catenary.To improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and KF.In this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy rules.The inputs of the model include time,temperature,and historical displacement,while the output is the predicted displacement.Furthermore,the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance.These modifications contribute to more accurate predictions.Lastly,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method.The test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.展开更多
The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and anal...The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and analyse its aging characteristics,accurate modelling of VRFB is crucial.In this paper,a hybrid physics-based and data-driven modelling framework is proposed for VRFB.First,a reduced-order electrochemical model for VRFB is established considering two main aging mechanisms:electrolyte volume transfer and ion crossover.Then,two key empirical parameters related to the aging dynamic are fully analysed.Finally,a Kolmogorov-Arnold network(KAN)is constructed with prior information from the electrochemical model to produce high-precision voltage prediction.A real-world test platform is built to validate the proposed method.It achieves the maximum prediction error of less than 1%in short,middle,and long-term aging experiments.展开更多
In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel...In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.展开更多
With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging mo...With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).展开更多
基金supported by the Science and Technology Research Project of Henan Province (No.222102210087)the Science and Technology Research Project of Henan Province (No.222102220102).
文摘The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interferences.This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments.By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the catenary.To improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and KF.In this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy rules.The inputs of the model include time,temperature,and historical displacement,while the output is the predicted displacement.Furthermore,the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance.These modifications contribute to more accurate predictions.Lastly,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method.The test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.
基金supported by the National Natural Science Foundation of China[grant number 62325306][grant number62273237].
文摘The vanadium redox flow battery(VRFB)is an emerging energy storage technology featuring long cycle life.During its operation,VRFB requires periodic maintenance to restore its capacity.To thoroughly understand and analyse its aging characteristics,accurate modelling of VRFB is crucial.In this paper,a hybrid physics-based and data-driven modelling framework is proposed for VRFB.First,a reduced-order electrochemical model for VRFB is established considering two main aging mechanisms:electrolyte volume transfer and ion crossover.Then,two key empirical parameters related to the aging dynamic are fully analysed.Finally,a Kolmogorov-Arnold network(KAN)is constructed with prior information from the electrochemical model to produce high-precision voltage prediction.A real-world test platform is built to validate the proposed method.It achieves the maximum prediction error of less than 1%in short,middle,and long-term aging experiments.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algorithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58%compared to LSTM,PSO-LSTM,and ACO-LSTM.
基金the National Natural Science Foundation of China(No.52172400).
文摘With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).
