EV Fleet Charging Reserve Profiles for On-Time Readiness
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Solution Overview
Problem
Electric vehicle fleets face uncertainties in scheduled charging due to environmental disturbances and changes in energy demand, leading to potential delays and increased peak power demand, which existing solutions fail to adequately address.
Innovation Solution
The implementation of an energy-time reserve system that associates power infrastructure assets with energy-reserve profiles, which decrease available charging power over time, and uses optimization models to determine optimal operating configurations for power infrastructure sites, mitigating uncertainties and improving on-time charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If deterministic charging strategy minimizes electricity costs by waiting until the last minute to charge, then energy cost is reduced, but reliability of on-time charging deteriorates due to unexpected disturbances
Solution Approach 1:
The system performs preliminary charging actions by establishing an energy-time reserve before the scheduled departure time. The optimization model proactively allocates charging energy and time buffers in advance, rather than waiting until the last minute, thereby maintaining both cost efficiency and reliability under uncertainty.
Solution Approach 2:
The system creates an energy-time reserve that acts as a cushion against unexpected disturbances. This reserve includes both energy buffer and time buffer components that protect the charging schedule from disruptions such as vehicle arrivals, power constraints, or equipment failures, ensuring on-time charging reliability.
2Reliability
If electric vehicles are charged to full state of charge as soon as possible to mitigate uncertainty, then on-time charging reliability is improved, but peak power demand increases
Solution Approach 1:
The system dynamically adjusts the charging power profile over time rather than applying constant maximum power. The optimization model determines a time-varying power schedule that charges vehicles reliably while spreading the power demand across different time periods, thereby reducing peak power demand.
Solution Approach 2:
The system changes the charging parameters (power level, timing) based on the optimization model's solution. Instead of fixed maximum power charging, the model adjusts power levels and timing parameters to balance reliability requirements with peak power demand constraints.
3Reliability
If time buffer is subtracted from deadline to anticipate delays, then on-time charging reliability is improved, but productivity deteriorates due to wasted charging time
Solution Approach 1:
The system performs preliminary charging actions by establishing an energy-time reserve before the scheduled departure time. The optimization model proactively allocates charging energy and time buffers in advance, rather than waiting until the last minute, thereby maintaining both cost efficiency and reliability under uncertainty.
Solution Approach 2:
The system applies partial charging action by charging vehicles to the necessary state of charge rather than always to full charge. The optimization model determines the precise charging amount needed, avoiding excessive charging that would waste time and energy, while still ensuring reliability under uncertainty.
4Measurement precision
If complex digitization and real-time planning applications are implemented to increase visibility into EV fleet status, then measurement precision is improved, but device complexity and maintenance cost increase
Solution Approach 1:
The control system performs self-service by using the optimization model to automatically determine charging schedules and manage the energy-time reserve. The system uses existing data about vehicle departures and charging requirements to autonomously optimize charging, reducing the need for complex external monitoring and control infrastructure.
Data Source
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AI summary
Electric vehicle supply equipment (EVSE) in an electric vehicle (EV) depot can be controlled to charge electric vehicles by their scheduled departure times. However, disturbances and other uncertainties can unexpectedly change the power required for charging over any given time period. Disclosed embodiments utilize energy-reserve profiles, associated with power infrastructure assets, as constraints in an optimization model to prevent the optimization from relying too heavily on last-minute charging, while still providing the optimization model with flexibility to balance competing objectives, in a fast and scalable manner. Each energy-reserve profile may decrease energy available to the associated power infrastructure asset over time, by increasing an energy reserve over time.