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
The zero point of charge (ZPC) and the remaining charge σp at ZPC are two important parameters characterizing surface charge of red soils.Fourteen red soil samples of different soil type and parent material were trea...The zero point of charge (ZPC) and the remaining charge σp at ZPC are two important parameters characterizing surface charge of red soils.Fourteen red soil samples of different soil type and parent material were treated with dithionite-citrate-dicarbonate (DCB) and Na2CO3 respectively.ZPC and σp of the samples in three indifferent electrolytes (NaCl,Na2SO4,and NaH2PO4) were determined.Kaolinite was used as reference.The results showed that ZPC of red soils was affected by the composition of parent materials and clay minerals and in significantly positive correlation with the content of total iron oxide (Fet),free iron oxide (Fed),amorphous iron oxide (Feo),aluminum oxide (Alo) and clay,but it was negatively correlated with the content of total silica (Sit).The σp of red soils was also markedly influenced by mineral components.Organic components were also contributing factor to the value of σp.The surface charges of red soils were evidently affected by the constitution of the electrolytes.Specific adsorption of anions in the electrolytes tended to make the ZPC of red soils shift to a higher pH value and to increase positive surface charges of the soils,thus leading to change of the σp value and decrease of the remaining net negative charges,even to the soils becoming net positive charge carriers.The effect of phosphate anion was greater than that of sulfate ion.展开更多
Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the R...Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.展开更多
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Project supported by the National Natural Science Fundation of China
文摘The zero point of charge (ZPC) and the remaining charge σp at ZPC are two important parameters characterizing surface charge of red soils.Fourteen red soil samples of different soil type and parent material were treated with dithionite-citrate-dicarbonate (DCB) and Na2CO3 respectively.ZPC and σp of the samples in three indifferent electrolytes (NaCl,Na2SO4,and NaH2PO4) were determined.Kaolinite was used as reference.The results showed that ZPC of red soils was affected by the composition of parent materials and clay minerals and in significantly positive correlation with the content of total iron oxide (Fet),free iron oxide (Fed),amorphous iron oxide (Feo),aluminum oxide (Alo) and clay,but it was negatively correlated with the content of total silica (Sit).The σp of red soils was also markedly influenced by mineral components.Organic components were also contributing factor to the value of σp.The surface charges of red soils were evidently affected by the constitution of the electrolytes.Specific adsorption of anions in the electrolytes tended to make the ZPC of red soils shift to a higher pH value and to increase positive surface charges of the soils,thus leading to change of the σp value and decrease of the remaining net negative charges,even to the soils becoming net positive charge carriers.The effect of phosphate anion was greater than that of sulfate ion.
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%.
