EV Load Demand Prediction via Transport Simulator Integration
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Solution Overview
Problem
Existing methods for predicting Electric Vehicle (EV) load demand on an electric grid lack accuracy due to insufficient consideration of traffic demand, EV models, and regenerative braking, which are crucial for understanding the spatial distribution of EV load on the grid.
Innovation Solution
A method and system that integrate an EV model with a transport simulator to predict temporal-spatial distribution of EV load demand by tracking the State of Charge (SOC) of batteries in real-time, taking into account velocity, acceleration, and traffic conditions, and aggregating the data to provide a detailed impact on the electric grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If statistical methods are used for prediction, then the method is simple to implement, but the prediction accuracy is limited
Solution Approach 1:
The patent merges multiple data sources including traffic demand data, EV model data, road network data, and demographic distribution data into a unified prediction framework. This integration allows the system to leverage diverse inputs to improve prediction accuracy while maintaining a cohesive implementation structure.
Solution Approach 2:
The prediction system is designed to handle multiple types of inputs (traffic data, EV models, road network, demographics) and produce comprehensive predictions that can serve various purposes. This multi-functional approach enables the system to adapt to different prediction needs while using a single integrated framework.
2Device complexity
If a coarse-grained EV model is used, then the model is simpler to compute, but it cannot capture regenerative braking effects
Solution Approach 1:
The patent changes the level of detail in EV model parameters to capture regenerative braking effects. By incorporating fine-grained parameters such as braking energy recovery, motor efficiency variations, and detailed powertrain characteristics, the model accurately tracks battery status while accounting for regenerative braking without becoming computationally intractable.
3Quantity of substance
If aggregate level load analysis is performed, then supply-side problems can be identified, but spatial distribution hotspots cannot be detected
Solution Approach 1:
The patent segments the aggregate load analysis into spatial components by dividing the service area into discrete locations or zones. This segmentation allows the system to maintain overall load information while simultaneously identifying spatial distribution patterns and hotspots at granular locations, resolving the contradiction between aggregate and detailed analysis.
Data Source
AI summary
Method and system for predicting temporal-spatial distribution of load demand on an electric grid due to a plurality of Electric Vehicles (EVs) is described. The method includes creating an EV load demand (EVLD) model for a Region of Interest (ROI) serviced by the electric grid, wherein the EVLD model integrates an EV model and a transport simulator simulating EV traffic conditions for the ROI. Further, the method includes computing the load demand in time and space in terms of State of Charge (SOC) of a battery for each EV among the plurality of EVs in the ROI, based on the EVLD model. Furthermore, the method includes aggregating the computed the load demand, in terms of the SOC, of each EV in time domain and space domain to create a temporal-spatial impact of the load demand by the plurality of EVs on the electric grid for the ROI.