EV Route Planning With Charging Reservations and Energy Constraints
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Solution Overview
Problem
Existing vehicle routing systems fail to optimize travel routes for electric vehicles and other vehicles with limited energy sources, as they do not adequately account for charging and refueling requirements, infrastructure availability, and varying costs, leading to inefficient and costly journeys.
Innovation Solution
The development of systems and methods that determine an optimal route by considering vehicle charge and fuel capacities, charging and refueling times, infrastructure compatibility and availability, energy pricing, geographic factors, and consumer preferences, allowing for intelligent route planning and reservation generation at charging stations, while also integrating targeted advertising services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing vehicle routing systems are used, then routes can be determined, but they fail to optimize for electric vehicles with limited energy sources, charging requirements, and infrastructure constraints
Solution Approach 1:
The system performs preliminary calculations of energy consumption, charging requirements, and route feasibility before the vehicle departs. It pre-identifies charging stations along the route and calculates required charge amounts, ensuring energy sufficiency is verified in advance rather than during travel.
Solution Approach 2:
The routing system dynamically adjusts routes based on real-time vehicle energy state, charging infrastructure availability, and changing conditions. It recalculates optimal paths considering variable factors like energy consumption rates, charging station occupancy, and alternative routing options when energy constraints are approached.
2Reliability
If charging stations are identified along the route, then energy replenishment is possible, but travel time increases due to charging stops
Solution Approach 1:
The system pre-calculates the minimum number of charging stops required and their optimal locations along the route. It determines charge amounts needed at each station to reach the destination, minimizing unnecessary stops and optimizing the timing of charging events to reduce overall travel time.
Solution Approach 2:
The system varies charging parameters such as charge rate, duration, and timing based on vehicle battery state, destination requirements, and infrastructure capabilities. It adjusts charging strategies dynamically, using faster charging when available and optimizing charge amounts to avoid both under-charging and excessive waiting time.
3Productivity
If multiple routing parameters are considered, then route optimization improves, but system complexity increases
Solution Approach 1:
The complex routing problem is divided into separate computational modules: energy consumption calculation, charging station identification, route evaluation, and optimization. Each module handles a specific aspect of the problem independently, making the overall system more manageable and maintainable while considering multiple parameters.
Solution Approach 2:
The system introduces intermediary data structures and calculation layers that bridge different parameters. It uses intermediate representations of energy state, route segments, and charging requirements to coordinate between multiple considerations, reducing direct complexity while maintaining comprehensive optimization.
4Reliability
If charging infrastructure availability is accounted for, then realistic routing is achieved, but calculation complexity increases due to sparse and variable infrastructure
Solution Approach 1:
The system pre-load and pre-process charging infrastructure data for the geographic area, organizing station locations, capabilities, and availability information before routing calculations begin. This preliminary preparation reduces real-time computational burden while ensuring accurate assessment of infrastructure constraints.
Solution Approach 2:
The system considers a broader set of charging stations than strictly necessary, evaluating partial routes through different stations to ensure feasibility. It performs excessive calculations of potential charging options to guarantee that at least one viable path exists, then selects the optimal route from these evaluated possibilities.
Data Source
AI summary
Systems and methods are described for determining an optimal path and/or route to a destination for a vehicle. Embodiments of the systems and methods disclosed herein facilitate intelligent route planning to a desired destination by a vehicle. In certain embodiments, a path and/or route to a desired destination is determined that accounts for vehicle charging and/or refueling requirements. Disclosed systems and methods may further generate and distribute reservation information ensuring availability of vehicle charging and/or refueling stations along a selected route. Further embodiments disclosed herein may implement information targeting services in connection with intelligent route planning.