[go: up one dir, main page]

Tian et al., 2022 - Google Patents

Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

Tian et al., 2022

View PDF
Document ID
1399410256436512755
Author
Tian S
Li C
Lv Q
Li J
Publication year
Publication venue
IET intelligent transport systems

External Links

Snippet

Accurately predicting the remaining mileage of electric vehicles (EVs) can effectively alleviate user's mileage anxiety and develop refinement of energy management strategy. However, traditional prediction methods not only consume time and resources, but also …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism

Similar Documents

Publication Publication Date Title
Ullah et al. Electric vehicle energy consumption prediction using stacked generalization: An ensemble learning approach
Yao et al. A novel graph-based framework for state of health prediction of lithium-ion battery
Ullah et al. Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction
Gao et al. Machine learning toward advanced energy storage devices and systems
Sun et al. A machine learning method for predicting driving range of battery electric vehicles
Ullah et al. Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations
Ping et al. Impact of driver behavior on fuel consumption: Classification, evaluation and prediction using machine learning
Tian et al. Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion
Naresh et al. Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions
Zhi et al. A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR
Babu et al. Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms
Vasant et al. Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques
Wang et al. Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
Li et al. Spatial-temporal attention mechanism and graph convolutional networks for destination prediction
Maity et al. Data-driven probabilistic energy consumption estimation for battery electric vehicles with model uncertainty
Qiang et al. Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization
Zhu et al. State of Health Estimation of Lithium‐Ion Battery Using Time Convolution Memory Neural Network
CN118211045A (en) Battery selection method, device, storage medium and equipment for power exchange station
Wang et al. Adaptive multi-personalized federated learning for state of health estimation of multiple batteries
Lin et al. Instantaneous energy consumption estimation for electric buses with a multi-model fusion method
Wang et al. A predictive energy management strategy for plug-in hybrid electric vehicles using real-time traffic based reference SOC planning
Saeed et al. A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm
Mishra et al. Exploratory data analysis for electric vehicle driving range prediction: Insights and evaluation
CN114444803A (en) Method, system, terminal and medium for predicting charging load of electric vehicle charging station
Li et al. Eco-pinn: A physics-informed neural network for eco-toll estimation