Connected Vehicle Travelable Range Prediction Using Server Aggregated Data
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Solution Overview
Problem
The accuracy of calculating the travelable range in connected vehicles is compromised due to the difficulty in predicting electric power consumption rates for future routes, leading to a discrepancy between calculated and actual ranges.
Innovation Solution
A connected vehicle system that communicates with a server to calculate travelable range using both local and aggregated data from multiple vehicles, ensuring a more accurate prediction by comparing individual vehicle data with collective data from the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the travelable range is calculated using only the traveling results of the vehicle of interest, then the calculation is simple and fast, but the accuracy of the predicted travelable range is low
Solution Approach 1:
A server acts as an intermediary between multiple vehicles and the travelable range calculation system. The server collects traveling results from multiple vehicles, processes this data to generate average consumption rates, and provides this refined data back to individual vehicles. This intermediary structure enables accurate predictions without requiring each vehicle to independently collect and process large amounts of data, thus improving accuracy while managing system complexity centrally.
Solution Approach 2:
The system implements feedback by continuously collecting actual traveling results from multiple vehicles, comparing them with predicted values, and using this feedback to refine the average consumption rates stored on the server. This feedback loop ensures that the travelable range predictions become progressively more accurate over time as the system learns from real-world data across the vehicle fleet.
2Reliability
If the travelable range is calculated using only the traveling results of the vehicle of interest, then the system structure is simple, but the reliability of the travelable range estimate is low
Solution Approach 1:
The system merges traveling results from multiple vehicles to create a collective data set that is more reliable than individual vehicle data alone. By combining data from multiple sources and calculating average consumption rates, the system produces more reliable travelable range estimates. The server consolidates this merged data and makes it available to individual vehicles, enhancing reliability without requiring complex processing in each vehicle.
3Measurement precision
If the travelable range is calculated using both first data from the vehicle and second data from the server, then the accuracy is improved, but the data processing time increases
Solution Approach 1:
The server performs preliminary processing of traveling results from multiple vehicles in advance, calculating and storing average consumption rates before they are needed by individual vehicles. When a vehicle needs a travelable range prediction, it simply retrieves pre-computed data from the server rather than performing complex calculations in real-time. This preliminary action significantly reduces the time required for data processing while maintaining high accuracy.
Data Source
AI summary
A connected vehicle is able to communicate with a server. The server is configured to collect result data indicating traveling results of a plurality of vehicles. The connected vehicle includes a communication device configured to receive data from the server and a control device configured to calculate a travelable range of the connected vehicle. The control device is configured to calculate the travelable range based on data in which the travelable range is shorter among first data and second data. The first data is calculated based on traveling results of the connected vehicle and is correlated with the travelable range. The second data is calculated based on the result data collected in the server and is correlated with the travelable range.