EV Cabin Preconditioning Profiles for Multi-Vehicle Depot Charging
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Solution Overview
Problem
Existing methods for cabin preconditioning in electric vehicles (EVs) at e-Depots are inefficient, leading to energy wastage and State of Charge (SoC) loss, particularly due to uncontrolled and human-error-prone manual processes, and complications arising from power and infrastructure constraints.
Innovation Solution
An automated cabin preconditioning system and method that optimizes energy usage by generating a cabin preconditioning profile based on vehicle data, departure schedules, and the number of connected vehicles, minimizing energy wastage and SoC loss through controlled and timed power delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual preconditioning is used, then operational flexibility is improved, but energy wastage and SoC loss increase due to uncontrolled power delivery
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring vehicle data, departure schedules, and charger availability, then automatically adjusting preconditioning parameters to optimize energy usage while ensuring operational readiness
Solution Approach 2:
The system enables self-service automation where the preconditioning process is triggered and controlled automatically based on detected vehicle connections and scheduled departures, eliminating manual intervention while preventing energy wastage through intelligent power management
2Device complexity
If sequential chargers are used to charge multiple EVs, then infrastructure cost is reduced, but preconditioning time and SoC loss increase due to one-at-a-time charging
Solution Approach 1:
The system performs preliminary actions by automatically triggering preconditioning processes based on detected vehicle connections and scheduled departure times, ensuring vehicles are ready before departure without manual intervention or excessive delays
Solution Approach 2:
The system implements periodic action by alternating power delivery between multiple connected vehicles in a round-robin fashion, enabling sequential chargers to service multiple vehicles efficiently while minimizing total preconditioning time and SoC loss
3Quantity of substance
If preconditioning is performed after battery charging completes, then battery SoC is preserved, but energy costs increase due to unoptimized power usage
Solution Approach 1:
The system applies dynamics by making the preconditioning process adaptive and flexible, allowing power delivery to be dynamically adjusted based on real-time conditions such as vehicle priority, battery charge levels, and grid availability, rather than following a fixed sequence
Solution Approach 2:
The system implements parameter changes by modifying preconditioning parameters such as power delivery rate, duration, and timing based on vehicle-specific requirements and operational priorities, optimizing energy costs while preserving necessary battery charge levels
Data Source
AI summary
A computer implemented method, a cabin preconditioning system, a computer program product, and a charger for preconditioning a cabin of vehicle(s), are provided, and include detecting a connection of the vehicle(s) to the charger, obtaining vehicle data having preconditioning requirements of the vehicles and battery data associated with an energy storage device of the vehicles, and departure schedule associated with each of the vehicle(s) connected to the charger, generating a cabin preconditioning profile for the charger based on the vehicle data, the departure schedule, and a number of the vehicles connected to the charger for preconditioning the cabin of the vehicle(s) connected to the charger.