EV Route Assignment Based on Driving Behavior and Battery Use
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Solution Overview
Problem
Electric vehicles face power shortages and increased charging times due to inefficient route management, leading to travel delays and customer complaints, as poor driving behavior affects battery consumption and route planning.
Innovation Solution
A route management system that detects the driving state of electric vehicles, analyzes driving behavior, and assigns routes with optimal average distances between stations to optimize energy use, selecting routes with longer or shorter distances based on energy consumption, recharging state, acceleration, and mileage efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If drivers operate electric vehicles without route optimization based on driving behavior, then operational flexibility is maintained, but battery power consumption increases and charging frequency increases
Solution Approach 1:
The system changes the parameter of route selection by using driving behavior indices to dynamically select routes with different average distances between stations. Drivers with poor driving behavior (higher energy consumption) are assigned routes with shorter average distances, while drivers with good behavior are assigned routes with longer average distances, thereby optimizing overall battery power consumption across the fleet.
2Reliability
If routes are assigned without considering driving behavior, then route assignment simplicity is maintained, but power shortage and missed shifts occur
Solution Approach 1:
The system implements feedback by continuously monitoring driving state information (energy consumption, acceleration, deceleration, mileage) and using this feedback to calculate driving behavior indices. These indices are then fed back into the route assignment process, creating a closed-loop system that dynamically adjusts route assignments to ensure reliable completion of route tasks while preventing power shortages and missed shifts.
3Reliability
If charging frequency is increased to accommodate poor driving behavior, then power shortage is prevented, but travel delay and customer complaints increase
Solution Approach 1:
The system takes preliminary action by assigning optimized routes to drivers based on their driving behavior indices before they start their routes. This proactive approach prevents power shortages from occurring in the first place, eliminating the need for frequent unplanned charging stops and thereby preventing travel delays and customer complaints.
4Productivity
If average distance between stations is increased, then route efficiency is improved, but power consumption per route increases
Solution Approach 1:
The system applies local quality by tailoring the average distance between stations to each driver's specific driving behavior. Routes are not uniformly designed but rather customized in terms of average distance based on local driver characteristics. This ensures that each driver operates on a route appropriately matched to their energy consumption patterns, optimizing overall fleet efficiency.
Data Source
AI summary
Route management methods and systems for electric vehicles are provided. First, a driving monitoring system of an electric vehicle is used to detect a driving state of the electric vehicle. Then, the driving state is transmitted to the server via a network by the driving monitoring system. The server performs an analysis according to the driving state to obtain a driving behavior index of a driver corresponding to the electric vehicle. Then, a plurality of routes are provided in the server, wherein each route includes a plurality of stations, and average distance between two stations. The server selects one of the routes according to the driving behavior index, and assigns the selected route to the driver of the electric vehicle.