On-Demand EV Fleet Charging Schedules Based on Trip Demand
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Solution Overview
Problem
On-demand fleets face challenges in optimizing the control of electric vehicle (EV) charging patterns and energy use due to their highly variable operating schedules, leading to inefficiencies and increased costs.
Innovation Solution
An EV fleet control system that includes an optimizer and predictors to generate control information for optimizing EV charging schedules and energy use based on predicted trip demand and state of charge (SOC) information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EVs in on-demand fleets charge based on traditional schedules or driver discretion, then charging operations are simple to manage, but charging efficiency is low and costs increase
Solution Approach 1:
The system performs preliminary actions by predicting future trip demand and energy requirements before charging occurs. The optimization module pre-calculates charging schedules based on forecasted fleet needs, allowing EVs to charge at optimal times rather than reacting to immediate demands, thereby improving charging efficiency while maintaining manageable complexity through automated planning
Solution Approach 2:
The system implements continuous feedback loops where actual trip demand, energy consumption, and charging performance data are fed back to the optimization module. This feedback mechanism allows the system to learn from past performance and adjust charging schedules dynamically, improving efficiency over time while the automated nature of the feedback process prevents complexity from escalating
2Loss of time
If the fleet uses more fast chargers to reduce charging time, then EV availability increases, but energy costs and infrastructure investment increase
Solution Approach 1:
The system dynamically adjusts charging strategies based on real-time and predicted conditions. Rather than relying statically on fast chargers, the optimization module flexibly schedules charging across both fast and slow chargers, adjusting the mix based on predicted trip demand, current EV battery states, and energy pricing signals. This dynamic approach reduces overall charging time losses while minimizing dependence on expensive fast charging infrastructure
Solution Approach 2:
The system changes operational parameters by optimizing charging schedules based on predicted energy prices and fleet demand patterns. It adjusts charging rates, timing, and location parameters to balance speed requirements against energy costs, using prediction data to identify optimal moments to utilize fast chargers versus slower, cheaper charging options, thereby reducing both time loss and energy cost
3Productivity
If the fleet optimizes charging based on predicted trip demand, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the complex optimization task into distinct functional modules: prediction modules that forecast trip demand and energy requirements, optimization modules that calculate charging schedules, and execution modules that implement charging actions. This segmentation allows each module to specialize in specific functions, improving overall operational efficiency while managing system complexity through modular architecture
Solution Approach 2:
The optimization module acts as an intermediary between the prediction system and the actual charging infrastructure. It translates predicted trip demand into actionable charging schedules, mediating between the abstract predictions and concrete charging operations. This intermediary layer simplifies the overall system by centralizing the complex decision-making logic in a dedicated component rather than distributing complexity across multiple systems
4Reliability
If EVs charge during high-demand periods to be available, then service responsiveness improves, but energy costs increase
Solution Approach 1:
The system performs preliminary charging actions during low-demand periods based on predicted future service needs. Rather than charging during high-demand periods when energy costs are higher, the optimization module schedules charging in advance during off-peak times, ensuring EVs are ready for anticipated high-demand periods while avoiding expensive energy purchases
Solution Approach 2:
The system uses feedback from actual service demand patterns and energy cost variations to continuously refine charging schedules. By monitoring when high-demand periods actually occur and what energy costs are incurred, the optimization module learns to predict and prepare in advance, maintaining service availability while progressively reducing energy costs through improved timing of charging actions
Data Source
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AI summary
Systems and methods for providing control in relation to electric vehicles (EVs) in an on-demand fleet of vehicles are provided. An on-demand fleet receives requests for trips that are unscheduled, which creates challenges for the fleet operator in managing and controlling fleet vehicles. A system receives information relating to EVs in the fleet and trip demand information, and provides control in relation to the fleet including generating control information based on the EV and trip demand information. The control information includes EV charging schedule information including indications of EVs to perform charging during a given time interval. The control information is transmitted for use by computing devices associated with the EVs for use in controlling the EVs.