Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate p...Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.展开更多
Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV ...Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.展开更多
With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also ...With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also crucial for improving user satisfaction and operational efficiency.This study aims to predict the charging behavior of EV users using Large Language Models(LLMs).Unlike traditional methods such as Long Short-Term Memory(LSTM)and XGBoost,or single-task prediction models,our proposed model,EVCharging-GPT,is the first to integrate the text generation capabilities of LLMs with a multi-task learning framework for EV user behavior prediction.We construct an EV user charging data processing flow and create a dataset of real scenarios for fine-tuning and testing the model.By carefully designing prompt templates,we transform the charging behavior prediction task into a text-to-text format,allowing the model to leverage its rich pre-trained knowledge base to make effective predictions.Additionally,we integrate temporal and static categorical features through natural language prompts and employ LoRA(Low-Rank Adaptation)technology to achieve efficient domain adaptation.To verify the effectiveness of the EVCharging-GPT model,we conduct extensive comparative experiments with various LLMs and traditional models.The results demonstrate the potential of the LLM-based approach for predicting user behavior in EVs and provide a solid foundation for future research and applications.展开更多
The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuati...The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations.Compared with EV cluster-layer prediction,predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables(e.g.,weather and holidays)and prediction accuracy,thereby imposing higher robustness requirements on prediction algorithms.An individual-user EV charging demand prediction method that in-tegrates multisource data with a dual-layer clustering approach and a light gradient boosting machine(LightGBM)is proposed in this study to address these technical challenges.First,a multisource dataset that incorporates user charging behavior data and exogenous variables(meteorological factors and date types)is constructed.A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed,thereby establishing a classi-fication feature space that characterizes different charging types and user groups.A predictive model is subse-quently developed using the LightGBM algorithm,which directly incorporates classification features as its inputs,effectively mitigating the information loss associated with the traditional categorical variable encoding process.Finally,employing EV users from a typical residential community in northern China as an empirical case,comparative experiments are performed to validate the proposed method,demonstrating its effectiveness at improving prediction accuracy.展开更多
Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction ...Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.展开更多
Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy development.This study introduces IsoFusion,a deep learning model designed to ...Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy development.This study introduces IsoFusion,a deep learning model designed to address isotope detection in raw mass spectra.Rather than directly applying convolutional layers to all signal and noise peaks,IsoFusion employs a trial-and-error strategy.First,it investigates all potential charge states(trials)and collects signal peaks around expected m/z values for each trial.Then,convolutional layers extract features from each trial,which are fused to identify the correct one.Finally,the reparameterization trick predicts isotope features based on this correct trial.A key advantage of IsoFusion is shared model parameters across all trials,enhancing feature learning for less common charge states using data from prevalent ones.Our results show a significant accuracy improvement for charge state 5,reaching 99.42%,compared to DeepIso’s 43.36%.Moreover,IsoFusion achieves a 97.33%detection accuracy for isotopes,with 2.4%of detected isotopes previously unidentified by four commonly used methods.展开更多
基金supported in part by the Chunhui Project of the Ministry of Education of China(HZKY20220429)the Department of Science&Technology of Liaoning Province(2022-MS-300)the Educational Department of Liaoning Province(LJKMZ20220561)
文摘Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.
基金supported by the Unveiling and Leading Projects of Gansu Provincial Department of Transportation(JT-JJ-2023-008).
文摘Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.
基金supported by the National Natural Science Foundation of China(No.62276026).
文摘With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also crucial for improving user satisfaction and operational efficiency.This study aims to predict the charging behavior of EV users using Large Language Models(LLMs).Unlike traditional methods such as Long Short-Term Memory(LSTM)and XGBoost,or single-task prediction models,our proposed model,EVCharging-GPT,is the first to integrate the text generation capabilities of LLMs with a multi-task learning framework for EV user behavior prediction.We construct an EV user charging data processing flow and create a dataset of real scenarios for fine-tuning and testing the model.By carefully designing prompt templates,we transform the charging behavior prediction task into a text-to-text format,allowing the model to leverage its rich pre-trained knowledge base to make effective predictions.Additionally,we integrate temporal and static categorical features through natural language prompts and employ LoRA(Low-Rank Adaptation)technology to achieve efficient domain adaptation.To verify the effectiveness of the EVCharging-GPT model,we conduct extensive comparative experiments with various LLMs and traditional models.The results demonstrate the potential of the LLM-based approach for predicting user behavior in EVs and provide a solid foundation for future research and applications.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB2403900.
文摘The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations.Compared with EV cluster-layer prediction,predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables(e.g.,weather and holidays)and prediction accuracy,thereby imposing higher robustness requirements on prediction algorithms.An individual-user EV charging demand prediction method that in-tegrates multisource data with a dual-layer clustering approach and a light gradient boosting machine(LightGBM)is proposed in this study to address these technical challenges.First,a multisource dataset that incorporates user charging behavior data and exogenous variables(meteorological factors and date types)is constructed.A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed,thereby establishing a classi-fication feature space that characterizes different charging types and user groups.A predictive model is subse-quently developed using the LightGBM algorithm,which directly incorporates classification features as its inputs,effectively mitigating the information loss associated with the traditional categorical variable encoding process.Finally,employing EV users from a typical residential community in northern China as an empirical case,comparative experiments are performed to validate the proposed method,demonstrating its effectiveness at improving prediction accuracy.
基金supported by Science and Technology Innovation Key R&D Program of Chongqing(CSTB2023TIAD-STX0024)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant number KJQN202201121).
文摘Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.
基金supported by the National Natural Science Foundation of China(Nos.62072283 and 62072435).
文摘Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy development.This study introduces IsoFusion,a deep learning model designed to address isotope detection in raw mass spectra.Rather than directly applying convolutional layers to all signal and noise peaks,IsoFusion employs a trial-and-error strategy.First,it investigates all potential charge states(trials)and collects signal peaks around expected m/z values for each trial.Then,convolutional layers extract features from each trial,which are fused to identify the correct one.Finally,the reparameterization trick predicts isotope features based on this correct trial.A key advantage of IsoFusion is shared model parameters across all trials,enhancing feature learning for less common charge states using data from prevalent ones.Our results show a significant accuracy improvement for charge state 5,reaching 99.42%,compared to DeepIso’s 43.36%.Moreover,IsoFusion achieves a 97.33%detection accuracy for isotopes,with 2.4%of detected isotopes previously unidentified by four commonly used methods.
