AI EV Routing With Charging Stops and Energy Constraints
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Solution Overview
Problem
Current systems for managing the charging of electric vehicles lack efficient routing algorithms that consider multiple factors such as battery charging-replacement station locations, travel time, roadway conditions, traffic congestion, and energy usage, leading to suboptimal route selection and increased range anxiety.
Innovation Solution
The implementation of a system utilizing specifically programmed computer machines with artificial intelligence expert systems for battery energy management and navigation route control. This system evaluates and selects routes based on battery energy parameters and route guidance parameters, considering various factors including expected travel time, energy requirements, and traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing systems are used for electric vehicles, then the system complexity is low, but the routing optimization considering multiple factors (charging stations, traffic, energy usage) is insufficient
Solution Approach 1:
The routing system is segmented into multiple specialized modules: energy consumption calculator, charging station locator, traffic condition analyzer, and route optimizer. Each module handles a specific aspect of the routing problem, allowing the system to process multiple factors simultaneously without becoming unmanageably complex.
Solution Approach 2:
An intermediary computing system is introduced that receives data from various sources (vehicles, charging stations, traffic sensors) and processes routing calculations centrally. This intermediary acts as a mediator between data collection and route guidance, enabling sophisticated optimization without requiring complex onboard processing in each vehicle.
2Reliability
If more factors are considered in routing (charging stations, traffic, energy usage), then the routing quality improves, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-mapping charging station locations, pre-calculating energy consumption for different routes, and pre-analyzing traffic patterns. This advance preparation allows the routing system to quickly retrieve and compare pre-processed data rather than calculating everything in real-time, reducing computational processing time while maintaining high routing accuracy.
Solution Approach 2:
The system implements feedback mechanisms where actual energy consumption, charging station availability, and traffic conditions are continuously monitored and fed back into the routing algorithm. This feedback loop allows the system to refine and adjust routes dynamically, improving accuracy over time while using efficient algorithms that minimize computational overhead.
3Reliability
If real-time data processing is implemented for traffic and charging stations, then the routing becomes more accurate, but the energy consumption of the routing system increases
Solution Approach 1:
The routing system is designed as a multi-functional platform that processes multiple types of data (traffic, charging stations, energy consumption, weather) through a single unified algorithmic framework. This universal approach allows the system to handle real-time data processing efficiently by reusing computational resources and data structures across different functions, minimizing overall energy consumption while maintaining high routing accuracy.
Data Source
AI summary
The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.