EV Route Planning Using Distance-to-Time Ratio
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Solution Overview
Problem
Current route scheduling methods for electric vehicles (EVs) lack sophistication in optimizing EV utilization, considering factors like battery capacity, charging times, energy consumption, and constraints, leading to inefficient use of EVs in commercial fleets.
Innovation Solution
The proposed method schedules EVs based on a distance to time ratio (dttr) of routes, allowing for more efficient allocation of EVs by considering both distance and time, thereby enhancing EV utilization and scalability with larger fleets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling or basic algorithms are used to allocate EVs to routes, then the scheduling process is simple to implement, but EV utilization is not optimized and operational efficiency is reduced
Solution Approach 1:
The patent transforms the scheduling approach by changing the key parameter from distance-based allocation to distance-to-time-ratio (dttr) based allocation. This parameter transformation enables the system to optimize EV utilization by considering both distance and time requirements, allowing EVs to cover more routes within available time windows while accounting for battery constraints.
Solution Approach 2:
The system performs preliminary calculations of dttr for all routes before actual scheduling occurs. By pre-computing these ratios and sorting routes accordingly, the system prepares optimized route sequences in advance, enabling efficient real-time scheduling decisions without complex runtime optimization.
2Productivity
If routes are allocated based only on distance, then the allocation process is simple, but EVs cannot cover maximum number of routes within time constraints
Solution Approach 1:
The patent introduces a new composite parameter dttr (distance-to-time-ratio) that combines both distance and time dimensions into a single metric. Routes are sorted and allocated based on this ratio, ensuring that EVs prioritize routes that maximize distance coverage relative to time consumption, thereby increasing the number of routes covered within available time windows.
Solution Approach 2:
The system transitions from one-dimensional distance-based routing to a two-dimensional optimization by incorporating time as an additional dimension. The dttr parameter effectively captures the trade-off between distance and time, allowing the scheduling system to operate in this expanded parameter space for optimal route allocation.
3Quantity of substance
If the fleet size increases, then more routes can be covered, but the complexity of scheduling and scalability of the solution increases
Solution Approach 1:
The system performs preliminary sorting of routes by dttr and pre-determines optimal route sequences for different EV types. This pre-computation creates a scalable framework where adding more EVs to the fleet simply requires allocating them to pre-sorted route sequences, rather than re-optimizing the entire scheduling problem from scratch.
Solution Approach 2:
The patent segments the route set into ordered sequences based on dttr values, creating modular route groups that can be independently allocated to different EVs. This segmentation allows the scheduling system to handle large fleets by distributing pre-sorted route segments to multiple vehicles, reducing the computational complexity of managing large-scale fleets.
4Reliability
If basic scheduling algorithms are used, then computational resources required are low, but battery capacity constraints and charging requirements are not properly considered
Solution Approach 1:
The system performs preliminary filtering of routes based on battery capacity constraints and dttr calculations before detailed scheduling. By pre-identifying feasible routes that meet energy requirements and sorting them by efficiency metrics, the system reduces the computational search space, allowing sophisticated constraint checking with minimal additional computational energy consumption.
Data Source
AI summary
A system, method and computer program product for route planning of electric vehicles (EVs) is provided. The method includes receiving a plurality of parameters associated with an EV fleet. The method includes determining a dttr for each route of a plurality of routes based on the route information associated with each EV of the EV fleet. Further, the method includes determining a set of EVs from the EV fleet based on a validity of each EV of the EV fleet and mapping the set of EVs to a set of the routes of the plurality of routes based on the dttr for each route of the plurality of routes. The method also includes scheduling the set of EVs to the set of the routes based on the mapping.