Method and system for drawing regional energy consumption map of electric vehicle
By constructing a dynamic grid model and a space-time coupled diffusion model, taking into account the various influencing factors of electric vehicle energy consumption, the problems of insufficient accuracy of energy consumption estimation and low scientific layout of charging facilities in the existing technology are solved, and the accurate quantification and visual display of electric vehicle energy consumption is achieved, which improves the practicality and forward-looking nature of the system.
Patent Information
- Application Number
- CN202510237518.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-03
- Publication Date
- 2025-04-01
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology lacks comprehensive consideration of complex variables when estimating the energy consumption of electric vehicles, especially key factors such as road slope, temperature changes and traffic flow, and fails to fully and accurately model. At the same time, the existing energy consumption map is based on a fixed grid system and cannot flexibly adapt to the data point distribution, resulting in data overload in high-density areas and loss of important details in low-density areas.
By obtaining the multi-source data flow generated during the driving of an electric vehicle, and connecting it to the external meteorological interface to obtain the temperature gradient parameters, a dynamic grid generation model is constructed based on the administrative division coding system and the road topology network, and the trajectory points are mapped to the spatial grid unit with hierarchical correlation using the adaptive density adjustment algorithm, and the road slope distribution matrix, temperature gradient parameters and traffic flow weight coefficient are bound. Trajectory continuity analysis is used to perform multi-modal segmentation on the driving segments across the grid boundary, and a mapping relationship between the feature vector and the grid energy consumption contribution degree is established. The dynamic energy consumption reference value is correlated with the distribution density of the charging pile and the traffic flow weight coefficient through the space-time coupled diffusion model to generate a three-dimensional dynamic map.
It realizes accurate quantification and visual display of electric vehicle energy consumption, improves the accuracy and reliability of energy consumption estimation, helps urban planners to rationally configure charging facilities, reduces user mileage anxiety, and promotes the popularization and development of new energy vehicles.