EV Charging Window Scheduling for On-Demand Transport Demand
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Solution Overview
Problem
Electric vehicle (EV) drivers face inefficiencies in servicing on-demand transportation requests due to the time required for charging, which can lead to downtime and reduced earnings, as EV charging stations are less frequent and often take longer than conventional fueling.
Innovation Solution
A network computer system determines optimal sub-intervals for EV charging based on forecasted demand and the operational range of EVs, ensuring that service providers can efficiently match transport requests and minimize downtime by recommending charging times that maximize earnings and reduce charging costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If EV drivers charge their vehicles at EV charging stations, then the vehicle battery charge level is improved, but the service time is increased due to charging taking up to an hour or more
Solution Approach 1:
The system performs preliminary forecasting of transport demand to identify low-demand time periods before the driver needs to charge. By predicting future demand patterns, the system can recommend charging times in advance that minimize service disruption, allowing drivers to plan charging during periods when fewer requests are expected.
Solution Approach 2:
The system continuously monitors actual transport demand against forecasted demand and uses this feedback to refine charging recommendations. By comparing predicted versus actual demand patterns, the system learns from real-world data to improve future charging time suggestions, adapting to changing demand patterns and driver behavior.
2Productivity
If EV drivers accept more transport requests, then earnings are improved, but vehicle operational range is depleted faster
Solution Approach 1:
The system forecasts transport demand in advance to identify optimal charging windows before the driver depletes their operational range. By predicting when demand will be low, the system can suggest charging times that prevent range depletion during high-demand periods, ensuring drivers maintain sufficient range to accept profitable requests.
Solution Approach 2:
The system dynamically adjusts charging recommendations based on multiple parameters including current battery charge level, forecasted demand, historical driver behavior, and earnings potential. By changing these parameters in real-time, the system optimizes the balance between maintaining operational range and maximizing earnings opportunities.
3Adaptability or versatility
If EV charging stations are made more accessible, then charging availability is improved, but the infrastructure complexity is increased
Solution Approach 1:
The system integrates multiple functions into a single platform: demand forecasting, charging station location identification, real-time availability checking, and optimized routing. By combining these functions, the system provides comprehensive charging support without requiring separate infrastructure for each function, reducing overall system complexity while improving charging accessibility.
Solution Approach 2:
The system acts as an intermediary between drivers and the complex charging infrastructure network. Rather than requiring drivers to directly navigate complex infrastructure details, the system translates infrastructure availability and characteristics into simple, actionable charging recommendations, effectively mediating between the driver's needs and the complex charging network.
Data Source
AI summary
A network computer system determines an upcoming session during which the service provider is expected to utilize an on-demand transport service to provide, or be available to provide, transport services. The network system determines that a vehicle operated by a service provider will likely be charged during the upcoming session time. Further, the network system forecasts a demand for a service provider to provide transport services at each of a plurality of sub-intervals of the upcoming session time. The network system determines one or more sub-intervals of the plurality of sub-intervals for the service provider to charge the vehicle in order to optimize an objective of the service provider, based at least in part on the forecasted demand during one or more of the multiple sub-intervals.