Electric Vehicle Route Score Computation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated routing systems for electric vehicles are sub-optimal due to their reliance on criteria suited for gasoline or diesel vehicles, failing to account for the unique characteristics of electric vehicles and fleets, and do not incorporate real-time external factors like weather and topology, leading to inefficient route selection and increased operational costs.
Innovation Solution
A system and method that computes a route score for electric vehicles and fleets by integrating processors to evaluate route path, traffic, and environmental factors in real-time, including topological, scheduled activity, traffic, and environmental parameters, to determine an overall route score for individual vehicles and fleets, optimizing route selection through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated routing systems are used for electric vehicles, then route selection is automated, but the routing optimization is sub-optimal because the systems rely on criteria suited for gasoline or diesel vehicles rather than electric vehicle characteristics
Solution Approach 1:
The system changes the evaluation parameters from traditional gasoline/diesel vehicle metrics to electric vehicle-specific parameters including battery state of charge, charging station locations, regenerative braking potential, and energy consumption rates. This allows the routing system to optimize for EV characteristics rather than conventional vehicle parameters.
Solution Approach 2:
The routing system is designed to serve multiple functions: it optimizes for individual EV routes, fleet-wide efficiency, real-time traffic conditions, and environmental factors. This multi-functional approach allows the same system to adapt to various EV operational scenarios and characteristics.
2Adaptability or versatility
If manual route selection is used, then human judgment can be applied, but significant human intervention is required leading to inefficient outcomes and increased operational costs
Solution Approach 1:
The system enables self-service routing by automatically computing optimal routes for electric vehicles without requiring manual human intervention. The automated system evaluates multiple routes, considers EV-specific parameters, and selects the optimal path independently, eliminating time-consuming manual processes while maintaining or improving outcome quality.
Solution Approach 2:
The system incorporates real-time feedback loops that monitor battery charge levels, traffic conditions, and route performance, automatically adjusting route recommendations. This continuous feedback mechanism replaces manual monitoring and decision-making with automated adaptive routing that responds dynamically to changing conditions.
3Ease of manufacture
If simplistic routing factors such as route gradient are used, then the routing system is easy to implement, but the system cannot incorporate real-time external factors such as weather and topology that affect operational costs
Solution Approach 1:
The system segments the routing evaluation into multiple independent modules: topological analysis, environmental condition assessment, traffic pattern evaluation, and energy consumption calculation. Each module processes specific factors separately and combines results to produce comprehensive route scores, making the complex system manageable while incorporating diverse real-time factors.
Solution Approach 2:
The system performs preliminary assessments of multiple potential routes by pre-calculating energy requirements, identifying charging station locations, and evaluating environmental conditions before final route selection. This advance preparation ensures that all relevant factors are considered without requiring complex real-time computations during actual routing decisions.
Data Source
AI summary
A system and method for evaluation of a route score for an electric vehicle are disclosed. The system includes a route path characteristic score computation subsystem configured to compute a route path characteristic score of one or more routes between two or more distant locations based on one or more topological parameters and one or more scheduled activity parameters, a traffic score computation subsystem configured to compute a traffic score in real-time, an environment score computation subsystem configured to compute an environment score in the real-time, an overall route score computation subsystem configured to compute an overall route score in the real-time, a vehicle-level route score computation subsystem configured to compute a vehicle-level route score for a particular electric vehicle, an overall fleet-level route score computation subsystem configured to compute an overall fleet-level route score after aggregating the vehicle-level route score.