EV Charging Route Planning With Nonlinear Charge-Time Modeling
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Solution Overview
Problem
Electric vehicle drivers face challenges in locating compatible charging stations and planning trips due to the rarity of charging stations, varying equipment, and non-linear charging rates, which complicates accurate trip planning.
Innovation Solution
A system generates navigation routes for electric vehicles that consider non-linear charge-time relationships by pre-computing a navigation graph with charging stations as nodes and edges, using a minimum spanning tree and bipartite graphs to optimize travel time with charging stops, and adjusts for factors like vehicle type and weather.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a highly connected graph with many charging station connections is used, then route options increase, but computation time and system complexity increase
Solution Approach 1:
The patent extracts only the essential connections needed for efficient routing by computing a minimum spanning tree (MST) from the highly connected graph. This MST contains the minimum set of edges that connect all charging stations, eliminating redundant connections while preserving route options. The system then selectively adds back only those edges that provide significantly better paths, balancing graph simplicity with routing versatility.
Solution Approach 2:
The system performs preliminary computation of the minimum spanning tree and selective edge addition offline before actual navigation requests. This pre-computation creates an optimized navigation graph that balances completeness and efficiency, avoiding the need to process the full highly connected graph in real-time while still providing comprehensive route options.
2Productivity
If charging rates are assumed to be linear, then computation is simpler, but accuracy in estimating charge time decreases
Solution Approach 1:
The patent transforms the non-linear charging relationship into a parameterized model where charge time is calculated as a function of the charging curve characteristics. By incorporating the actual non-linear charging rates into the graph edge weights, the system accurately reflects real-world charging behavior while maintaining computational efficiency through the pre-computed navigation graph structure.
3Adaptability or versatility
If all possible charging station connections are included in the navigation graph, then route flexibility increases, but the time to generate navigation directions increases
Solution Approach 1:
The patent extracts the essential routing information by computing a minimum spanning tree that connects all charging stations with the minimum number of edges. This MST provides a simplified graph structure that maintains route flexibility while dramatically reducing the number of connections that need to be evaluated during navigation generation, thus reducing computation time.
Solution Approach 2:
The system performs the complex graph optimization work in advance by pre-computing the MST and selectively adding back only the most beneficial edges. This preliminary action creates a streamlined navigation graph that can be quickly queried for routing decisions without sacrificing route flexibility, as the essential connectivity patterns are already established.
Data Source
AI summary
To navigate an electric vehicle from a starting location to a destination, a system identifies multiple charging stations between the starting location and the destination and determining a navigation route that requires a least amount of time for the electric vehicle to travel from the starting location to the destination via one or more of the charging stations, including determining a non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time.