Smart Grid EV Tolling System with Privacy Trust Levels
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Solution Overview
Problem
Current systems for assessing roadway fees for electric and hybrid-electric vehicles face challenges such as automation deficiencies, broad application of usage fees, privacy concerns, lack of standardization, and interjurisdictional settlement issues, particularly in densely or sparsely populated areas.
Innovation Solution
The implementation of a system utilizing an electric utility's smart grid communication network to automate the assessment of fees by tracking vehicle position, road classes, and waypoints, with user-selectable trust levels for privacy protection, and a method for calculating and remitting usage fees to jurisdictional authorities through electric vehicle charging equipment and utility billing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual mileage reporting or cellular data transmission is used, then automation is reduced, but system complexity and cost increase
Solution Approach 1:
The system enables self-service automation by having the vehicle's onboard computer automatically report mileage data to the tolling authority without requiring manual user input or separate cellular data plans. The vehicle communicates its location and mileage information autonomously through the tolling system's infrastructure.
Solution Approach 2:
The tolling system is designed to serve multiple functions: it collects toll fees, tracks mileage for fee calculation, and communicates with vehicles using existing infrastructure. This multi-functionality reduces the need for separate specialized systems while maintaining high automation levels.
2Measurement precision
If specific coordinate data for routes traveled is collected, then mileage tracking accuracy is improved, but privacy concerns increase
Solution Approach 1:
The system extracts only the essential data needed for toll calculation (mileage and route class) while excluding unnecessary personal information. By taking out only what is required for the primary function, the system maintains accuracy while minimizing privacy intrusion.
Solution Approach 2:
The system applies different levels of data collection based on local requirements and user preferences. Rather than uniformly collecting all possible data, it adapts the data collection scope to match specific jurisdictional needs and user privacy settings, reducing overall privacy concerns while maintaining necessary tracking accuracy.
3Productivity
If tolling and congestion pricing are implemented, then roadway usage fee collection is improved, but applicability varies by population density
Solution Approach 1:
The system dynamically adjusts its operation based on local conditions such as population density and roadway type. It can operate in different modes (tolling, congestion pricing, or mileage-based fees) depending on the specific jurisdiction's needs, making it adaptable to both densely and sparsely populated areas while maintaining efficient fee collection.
Solution Approach 2:
The system allows jurisdictions to change key parameters such as fee rates, mileage thresholds, and route classifications to suit local conditions. This flexibility enables the same core system to be effectively applied across diverse geographic and demographic contexts, from urban congestion zones to rural roadway networks.
4Productivity
If broad based application of usage fees is implemented, then fee collection coverage is improved, but fairness decreases
Solution Approach 1:
The system applies different fee structures and rules to different roadways and usage scenarios rather than a uniform broad-based approach. By tailoring the fee application to specific local conditions and roadway types, it achieves comprehensive coverage while maintaining fairness through context-appropriate fee assessment.
Solution Approach 2:
The system segments the roadway network into different classes and categories, applying appropriate fee structures to each segment. This segmentation allows broad-based collection coverage across all road types while ensuring fairness by matching fee levels to the actual cost and value of using different roadway segments.
Data Source
AI summary
Apparatuses and methods for the assessment of electric vehicle usage fees for electric and hybrid-electric vehicles usage of roadways and waypoints over publicly or privately funded thoroughfares are disclosed herein. Exemplary implementations address automated systems of assessing fees charged for roadway and waypoint usage as applied to vehicle mileage traveled over functionally classified thoroughfares, collection of usage charges, settlement of payments to jurisdictional authorities, and/or periodic reconciliation of vehicle mileage traveled. The implementations include electric vehicles with user interfaces that have selectable trust level inputs, systems to calculate and store position information, road classes and waypoints travelled, and vehicles and users information. In one implementation(s) the electric vehicle transmits the report through a local area network based on the selected trust level, to a remotely located receiver node.