EV Block Scheduling With Intraday Charging Route Optimization
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Solution Overview
Problem
Current route scheduling methods for electric vehicles lack sophistication in optimizing EV utilization, particularly in considering battery capacity, charging times, and intraday charging, leading to suboptimal fleet management and increased carbon emissions.
Innovation Solution
A computer-implemented method and system for block scheduling that incorporates intraday charging by adding dummy routes to the scheduling process, allowing for increased State of Charge (SoC) during the day, which enables EVs to be assigned to longer routes and optimize energy utilization through a Mixed Integer Linear Programming (MILP) optimization model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling or basic algorithms are used for EV route allocation, then implementation simplicity is maintained, but EV utilization optimization is insufficient
Solution Approach 1:
The scheduling problem is segmented into discrete route blocks that can be independently evaluated and assigned. Each route is broken down into manageable segments with specific energy requirements, allowing the optimization algorithm to process and allocate routes systematically rather than as a monolithic problem.
Solution Approach 2:
The system performs preliminary calculations of energy consumption, charging requirements, and route feasibility before final assignment. By pre-processing route data and EV battery states, the algorithm prepares optimization inputs in advance, enabling more sophisticated optimization without increasing real-time computational complexity.
2Duration of action of moving object
If intraday charging is incorporated into route scheduling, then EV utilization and route length are improved, but scheduling complexity and computational requirements increase
Solution Approach 1:
The scheduling model adds a temporal dimension by incorporating charging time periods as separate route blocks. This transforms the problem from simple spatial route assignment to a time-space optimization where charging events are scheduled alongside driving routes, enabling longer operational durations through strategic intraday charging.
Solution Approach 2:
The system dynamically adjusts route assignments based on real-time battery state of charge levels and charging availability. The scheduling algorithm continuously evaluates which EVs are ready for new routes versus which need charging, creating a dynamic allocation system that adapts to changing energy states throughout the day.
3Object-generated harmful factors
If basic scheduling algorithms are used, then computational resources are conserved, but carbon emissions reduction potential is lost
Solution Approach 1:
The system implements feedback loops that monitor actual energy consumption versus predicted consumption, tracking carbon emissions metrics, and adjusting future route assignments accordingly. This automated feedback mechanism continuously optimizes for emissions reduction by learning from operational data and refining scheduling decisions.
Solution Approach 2:
The optimization algorithm adjusts multiple parameters simultaneously including route selection, charging timing, EV assignment, and operational schedules to minimize carbon emissions. By changing these parameters in coordinated fashion through automated optimization, the system achieves significant emissions reduction that manual scheduling cannot accomplish.
4Productivity
If route scheduling does not account for battery capacity constraints, then route assignment flexibility is maintained, but energy management efficiency deteriorates
Solution Approach 1:
The system performs preliminary energy audits of each route to determine exact battery requirements before assignment. By calculating energy consumption in advance and matching it against available battery capacity and charging opportunities, the system ensures efficient energy utilization while maintaining flexibility to assign routes to appropriate EVs based on their energy states.
Data Source
AI summary
A system and method for block scheduling for an electric vehicle are provided. The method includes obtaining route information including designated routes and their distances in a defined operation period for the EV, and battery information including a consumption factor and a charging rate for a battery of the EV. The method further includes adding dummy routes representative of a predefined charging time period for the battery in the defined operation period. The method further includes defining a dummy factor having a first value for each of the designated routes for the energy being consumed, and a second value for each of the dummy routes for the energy being replenished. The method further includes determining a schedule for the designated routes and the dummy routes for the EV using an optimization model having constraints for the route information, the battery information and the dummy factor.