EV Route Planning with Predicted Charging Availability
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Solution Overview
Problem
Users of electric vehicles face challenges in planning routes that consider their vehicle's charging needs, the availability of charging stations, and personal preferences for locations of interest, as existing methods do not efficiently integrate real-time and predicted charging station availability with user-specific preferences and interests.
Innovation Solution
A computer system that receives destination and user information, determines the electric vehicle's travel distance, predicts charging station availability, and recommends routes and charging schedules based on travel distance, predicted charging station availability, and user preferences, incorporating real-time data and historical charging location data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually plan routes and find charging locations, then they can control their charging schedule, but the process is complex and time-consuming
Solution Approach 1:
The system automatically performs route planning and charging location identification without requiring manual user input. The computer system receives destination information, determines the vehicle's travel distance capability, predicts charging station availability, and autonomously generates route options with charging schedules, eliminating the need for users to manually plan their journeys.
Solution Approach 2:
The system performs preliminary actions by predicting charging station availability in advance and pre-calculating optimal route options before the user needs to travel. The computer system analyzes real-time data and historical charging location data to determine future charging availability, allowing users to plan ahead without needing to manually search for charging locations.
2Reliability
If users consider multiple factors like charging availability and locations of interest, then the route planning becomes more accurate, but the system complexity increases
Solution Approach 1:
The system merges multiple data sources and consideration factors into a unified route planning process. It combines real-time charging station availability data, historical charging location data, user preferences, and locations of interest to generate comprehensive route options. The computer system integrates these diverse data types and evaluates multiple factors simultaneously to produce accurate and reliable route recommendations.
Solution Approach 2:
The computer system acts as an intermediary that processes and synthesizes multiple data sources. It receives destination information, user preferences, and charging station data, then mediates between these different data types to generate coordinated route options. The system serves as an intermediate layer that harmonizes various considerations (charging availability, locations of interest, travel distance) into a unified planning output.
3Reliability
If the system predicts charging station availability using real-time and historical data, then the charging schedule becomes more reliable, but the data processing requirements increase
Solution Approach 1:
The system performs preliminary data analysis and availability predictions before route planning is needed. By pre-processing historical charging location data and analyzing patterns in advance, the system can quickly generate reliable predictions without requiring extensive real-time computational resources during the actual route planning process.
Solution Approach 2:
The system changes the parameters of data processing by using historical data patterns and statistical models to predict future charging availability. Instead of processing every real-time data point individually, the system transforms the data into predictive parameters that can be efficiently processed, reducing the computational energy required while maintaining prediction reliability.
Data Source
AI summary
A method, system, and computer program product for determining directional guidance for vehicles includes receiving a destination location and user information data related to a user of the vehicle. The method further includes determining the distance the vehicle can travel and predicting the availability of charging stations at a plurality of charging locations based on real time data and historical data. The method additionally includes determining relevant locations of interest to the user located near the plurality of charging locations based on the user information data. The method also includes determining and recommending route options and charging schedules to reach the destination location considering the distance the vehicle can travel, the predicted availability of charging stations, the relevant locations of interest, the total travel time, availability of locations of interest, user preferences, and charging location data.