Telematics Server Route Planning for Electric Vehicle Charging
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Solution Overview
Problem
Electric vehicle drivers face challenges in determining optimal charging times due to varying driving patterns and changing electric power costs at charging stations, as current systems only provide information on present power levels without considering expected power consumption and costs based on driving habits.
Innovation Solution
A method and system that calculates expected power consumption and provides driving route information for electric vehicles by analyzing driving patterns, current position, and destination, incorporating telematics services to offer candidate routes and charging station information, allowing drivers to select routes and stations based on expected costs and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If only present power amount information is provided to drivers, then the system complexity is low, but drivers cannot determine optimal charging time points
Solution Approach 1:
The system performs preliminary calculations of expected power consumption for multiple candidate routes before the driver makes a charging decision. By pre-calculating power consumption data based on driving patterns and route characteristics, the system provides comprehensive charging decision information in advance, enabling drivers to determine optimal charging time points without requiring complex real-time analysis.
Solution Approach 2:
The telematics server acts as an intermediary between the vehicle's power management system and the driver. It collects driving pattern data, calculates expected power consumption for candidate routes, and presents processed information to the driver. This intermediary processing transforms raw power amount data into meaningful charging recommendations, reducing the information loss without requiring the driver's device to perform complex calculations.
2Measurement precision
If expected power consumption is calculated for multiple candidate routes, then charging decision accuracy is improved, but calculation time and processing load increase
Solution Approach 1:
The system pre-calculates and stores power consumption characteristics for multiple candidate routes based on historical driving patterns and route features. When a charging decision is needed, the system retrieves and compares pre-computed expected power consumption values rather than performing full calculations in real-time. This approach maintains high prediction accuracy while significantly reducing calculation time.
Solution Approach 2:
The system focuses calculation resources on the specific candidate routes that are most relevant to the driver's current situation. By identifying and prioritizing calculation for a limited number of plausible candidate routes rather than all possible routes, the system achieves accurate power consumption predictions for the most important options while minimizing overall calculation time and processing load.
3Measurement precision
If driving pattern analysis is incorporated into power consumption calculation, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The telematics server serves as an intermediary that handles the complex task of analyzing driving patterns and translating them into power consumption predictions. The server collects raw driving data, applies pattern recognition algorithms, and generates expected power consumption values for candidate routes. This intermediary approach improves prediction accuracy by incorporating driving pattern analysis while keeping the complexity contained in the server's processing rather than requiring complex devices in the vehicle or driver's terminal.
Data Source
AI summary
A method and a system implemented for providing driving route information of an electric vehicle to a telematics terminal by a telematics server may include checking a driving pattern of a driver corresponding to the electric vehicle; checking information on a current position and a destination of the electric vehicle; calculating at least one candidate route from the current position to the destination; calculating expected power consumption corresponding to each of the at least one candidate routes based on the driving pattern, the current position, and the destination; and transmitting information on the at least one candidate route and the expected power consumption corresponding thereto to the telematics terminal.