Autonomous Vehicle Fueling Route Planning Under Trip Constraints
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Solution Overview
Problem
Current systems lack the ability to intelligently optimize fueling routes for autonomous vehicles (AVs) to minimize fuel consumption and comply with operational goals, particularly in ride-hailing and sharing scenarios where frequent short trips and varying fuel prices complicate efficient fuel management.
Innovation Solution
The implementation of systems and methods that analyze trip schedules, evaluate fueling options based on vehicle and fueling constraints, and generate optimized routes using mixed-integer programming to determine the best fueling locations and amounts, incorporating factors like fuel type, price, wait time, and idle time, while allowing for opportunistic fueling and overriding conditions such as high revenue potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional fueling methods are used for ride-hailing vehicles, then fueling can be performed at any location, but fuel costs increase and operational efficiency decreases due to frequent manual fueling tasks
Solution Approach 1:
The system performs preliminary actions by analyzing the trip schedule in advance to predict future fuel needs and identify optimal fueling opportunities before they occur. The fueling route is planned ahead of time, allowing the vehicle to fuel during scheduled idle periods rather than reacting to low fuel levels, thereby reducing both cost and operational disruption.
Solution Approach 2:
The system enables self-service by automatically analyzing trip schedules, predicting fuel requirements, and generating optimized fueling routes without human intervention. The autonomous vehicle independently makes fueling decisions based on predefined constraints and objectives, eliminating the need for manual fueling tasks and improving operational efficiency.
2Reliability
If fueling occurs frequently during the day to maintain operational readiness, then fuel levels remain optimal, but idle time increases and profitability decreases
Solution Approach 1:
The system performs preliminary analysis of the trip schedule to predict when fueling will be needed and identifies optimal fueling windows during scheduled idle periods. By planning fueling actions in advance rather than reacting to fuel levels, the system eliminates unnecessary idle time while maintaining readiness.
Solution Approach 2:
The system uses feedback from the trip schedule and current fuel level to dynamically adjust fueling decisions. It continuously monitors the vehicle's operational status and fuel consumption patterns, using this feedback to optimize the timing and location of fueling events, thereby minimizing idle time while ensuring fuel availability.
3Device complexity
If manual fueling management is used, then implementation is simple, but intelligent route optimization for fuel consumption cannot be achieved
Solution Approach 1:
The system enables self-service by automatically analyzing trip schedules, predicting fuel requirements, and generating optimized fueling routes without human intervention. This automated approach replaces simple manual management with intelligent decision-making that reduces fuel consumption through data-driven route optimization.
Solution Approach 2:
The system applies parameter changes by using mixed-integer programming to optimize multiple variables simultaneously, including fueling location, timing, amount, and route selection. By dynamically adjusting these parameters based on trip schedule constraints and fuel price variations, the system achieves intelligent fuel consumption optimization that would be impossible with manual management.
4Ease of operation
If trip schedules are planned in advance, then preview and planning activities are feasible, but adaptability to spontaneous changes decreases
Solution Approach 1:
The system applies dynamics by making the fueling plan adaptable and flexible rather than static. While the initial plan is created based on the scheduled trip schedule, the system can dynamically adjust fueling decisions when spontaneous changes occur, such as adding impromptu trips or changing destinations, by re-evaluating the trip schedule and regenerating the fueling route as needed.
Data Source
AI summary
Opportunistic fueling for car hailing autonomous vehicles are disclosed herein. An example method includes evaluating a trip schedule having one or more destinations for a vehicle, calculating a plurality of potential routes based on the trip schedule with fueling options along a proposed path of travel for the vehicle, applying one or more vehicle fueling constraints, selecting an optimized route from the plurality of potential routes for the vehicle using the one or more vehicle fueling constraints, and completing the trip schedule using the optimized route.