Fleet Telemetry EV Charging Control for Load-Constrained Panels
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Solution Overview
Problem
The increasing adoption of electric vehicles poses challenges to the electrical infrastructure, leading to potential overloading, circuit tripping, and safety hazards due to unscheduled and unmanaged charging loads.
Innovation Solution
A system utilizing fleet-based telemetry and intelligent EVSE units to collect and analyze data from a fleet of electric vehicles, predicting energy needs, and adjusting charging schedules to optimize energy usage and reduce peak loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric vehicle charging is increased to meet growing demand, then charging capacity and vehicle adoption are improved, but electrical infrastructure overload and safety hazards worsen
Solution Approach 1:
The system dynamically adjusts charging parameters including charge rate, charging schedule, and power allocation based on real-time monitoring of infrastructure load, vehicle battery states, and grid conditions. This dynamic control enables the system to optimize charging capacity while preventing infrastructure overload through adaptive response to changing conditions
Solution Approach 2:
The system implements continuous feedback loops by monitoring electrical load on the infrastructure, battery charge levels, and charging progress. This feedback enables real-time adjustments to charging parameters, allowing the system to maintain high charging capacity while preventing infrastructure overload through closed-loop control
2Speed
If maximum charging load is applied to meet demand, then charging speed is improved, but circuit tripping and device malfunction worsen
Solution Approach 1:
The system dynamically modulates charging speed based on real-time assessment of infrastructure capacity and vehicle needs. By adjusting charge rates adaptively rather than applying maximum load continuously, the system maintains high charging speeds when safe while preventing circuit tripping and ensuring system stability
Solution Approach 2:
The system performs preliminary assessment of infrastructure capacity and vehicle requirements before initiating charging. This advance planning allows the system to set appropriate charging parameters that maximize speed while staying within safe operational limits, preventing circuit tripping before it occurs
3Reliability
If charging is scheduled and managed intelligently, then infrastructure safety is improved, but system complexity worsens
Solution Approach 1:
The system implements self-service capabilities where the charging infrastructure autonomously monitors its own load conditions, assesses vehicle requirements, and adjusts charging parameters without external intervention. This self-management approach improves safety while minimizing the complexity of external control systems
Solution Approach 2:
The system integrates multiple functions including load monitoring, battery management, charging control, and safety monitoring into a unified platform. This multi-functionality approach improves infrastructure safety through comprehensive monitoring while reducing overall system complexity by consolidating control functions
Data Source
AI summary
A remote computer server communicates with a fleet of electric vehicles, and gathers telemetry data from the fleet of electric vehicles. An intelligent EVSE unit and/or a DC fast charging unit communicates with the remote server, and charges an electric vehicle based at least in part on the telemetry data from the fleet of electric vehicles. The remote computer server can generate charging instructions based at least in part on the telemetry data gathered from the fleet of electric vehicles. The intelligent EVSE unit and/or the DC fast charging unit receive the charging instructions, and charge the electric vehicle based at least in part on the charging instructions, the telemetry data, and/or an existent electrical load associated with an electrical panel of a house or a building.