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
Remaining time prediction of business processes plays an important role in resource scheduling and plan making.The structural features of single process instance and the concurrent running of multiple process instance...Remaining time prediction of business processes plays an important role in resource scheduling and plan making.The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction.Existing prediction methods does not take full advantage of these two aspects into consideration.To address this issue,a new prediction method based on trace representation is proposed.More specifically,we first associate the prefix set generated by the event log to different states of the transition system,and encode the structural features of the prefixes in the state.Then,an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system.Next,states in the extended transition system are partitioned by the different lengths of the states,which considers concurrency among multiple process instances.Finally,the long short-term memory(LSTM)deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances.By extensive experimental evaluation using synthetic event logs and reallife event logs,we show that the proposed method outperforms existing baseline methods.展开更多
The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board...The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.展开更多
The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,...The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process,which complicates the estimation of RCT in actual scenarios.Hence,this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times.Through an in-depth analysis of multi-scenario charging data,the RCT of the charging process is estimated using the temporal convolutional network(TCN)model,which has a strong generalization ability.Additionally,a dynamic learning rate(DLR)mechanism and an early stopping strategy(ES)are designed in the TCN model(DLR-ES TCN)for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy.Finally,compared with the traditional TCN model and four common deep learning models under three different scenarios,the experimental results show the mean absolute percentage error(MAPE)of the proposed method is less than 2%,indicating better accuracy and stability.This research can improve the safety monitoring of power batteries when applied to various target domains.展开更多
Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite commu...Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.展开更多
Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autore...Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.展开更多
At the invitation of the Government of the People's Republic of China,Samdech Akka Moha Sena Padei Techo Hun Sen,Prime Minister of the Kingdom of Cambodia,paid an official visit to the People's Republic of Chi...At the invitation of the Government of the People's Republic of China,Samdech Akka Moha Sena Padei Techo Hun Sen,Prime Minister of the Kingdom of Cambodia,paid an official visit to the People's Republic of China from February 9 to II,2023.During the visit,President Xi Jinping met with Samdech Techo Prime Minister Hun Sen,having in-depth exchanges on building a China-Cambodia Community with a Shared Future in the New Era and intermational and regional issues of shared interest,charting the course for future China-Cambodia relations.展开更多
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supported by National Natural Science Foundation of China(No.U1931207 and No.61702306)Sci.&Tech.Development Fund of Shandong Province of China(No.ZR2019LZH001,No.ZR2017BF015 and No.ZR2017MF027)+4 种基金the Humanities and Social Science Research Project of the Ministry of Education(No.18YJAZH017)Shandong Chongqing Science and technology cooperation project(No.cstc2020jscx-lyjsAX0008)Sci.&Tech.Development Fund of Qingdao(No.21-1-5-zlyj-1-zc)the Taishan Scholar Program of Shandong ProvinceSDUST Research Fund(No.2015TDJH102 and No.2019KJN024).
文摘Remaining time prediction of business processes plays an important role in resource scheduling and plan making.The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction.Existing prediction methods does not take full advantage of these two aspects into consideration.To address this issue,a new prediction method based on trace representation is proposed.More specifically,we first associate the prefix set generated by the event log to different states of the transition system,and encode the structural features of the prefixes in the state.Then,an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system.Next,states in the extended transition system are partitioned by the different lengths of the states,which considers concurrency among multiple process instances.Finally,the long short-term memory(LSTM)deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances.By extensive experimental evaluation using synthetic event logs and reallife event logs,we show that the proposed method outperforms existing baseline methods.
基金supported by the Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project(Grant No.2022YFB4300500).
文摘The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.
基金supported in part by the National Natural Science Foundation of China(Grant No.5217051006)the Shandong Province Natural Science Foundation(Grant No.ZR2021ME223)+1 种基金the Yantai Science and Technology Planning Project(Grant No.2022GCCRC158)the Graduate Innovation Foundation of Yantai University,GIFYTU(Grant No.GGIFYTU2349).
文摘The accurate estimation of the remaining charge time(RCT)is essential in a battery management system(BMS),because it guarantees the safety and dependability of the power battery systems of new energy vehicles.However,the direct estimation of RCT is challenging because of the variability of actual charging scenarios and the complex charging process,which complicates the estimation of RCT in actual scenarios.Hence,this paper proposes an estimation framework based on deep learning for multi-scenario charging data to estimate the remaining charging times.Through an in-depth analysis of multi-scenario charging data,the RCT of the charging process is estimated using the temporal convolutional network(TCN)model,which has a strong generalization ability.Additionally,a dynamic learning rate(DLR)mechanism and an early stopping strategy(ES)are designed in the TCN model(DLR-ES TCN)for the nonlinear characteristics of the battery system to balance the relationship between model convergence speed and accuracy.Finally,compared with the traditional TCN model and four common deep learning models under three different scenarios,the experimental results show the mean absolute percentage error(MAPE)of the proposed method is less than 2%,indicating better accuracy and stability.This research can improve the safety monitoring of power batteries when applied to various target domains.
基金Supported by the National Key Research and Development Program of China(No.2019YFB1803101)the Natural Science Foundation of Shanghai(No.19ZR1467200).
文摘Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.
文摘Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.
文摘At the invitation of the Government of the People's Republic of China,Samdech Akka Moha Sena Padei Techo Hun Sen,Prime Minister of the Kingdom of Cambodia,paid an official visit to the People's Republic of China from February 9 to II,2023.During the visit,President Xi Jinping met with Samdech Techo Prime Minister Hun Sen,having in-depth exchanges on building a China-Cambodia Community with a Shared Future in the New Era and intermational and regional issues of shared interest,charting the course for future China-Cambodia relations.
