EV Charging and Cabin Conditioning Using Transit Arrival Tracking
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Solution Overview
Problem
Existing electric vehicle (EV) charging systems at public transportation hubs require human planning and intervention for optimal charging and cabin conditioning, failing to account for uncertainty in arrival times and weather conditions, leading to inefficient and inconvenient charging experiences.
Innovation Solution
An automated EV conditioning system that integrates with public transportation schedules to optimize charging based on expected arrival times, weather forecasts, and desired cabin conditions, using an algorithm to calculate necessary energy delivery and cabin conditioning without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual scheduling of charging and cabin conditioning is implemented, then the vehicle can be ready at a specific time, but it requires human planning and intervention which is inconvenient and may be forgotten
Solution Approach 1:
The system automatically schedules charging and cabin conditioning based on public transportation arrival times without requiring manual user input. The automated mechanism monitors transportation schedules and independently controls charging parameters, eliminating the need for users to remember or manually set charging schedules.
Solution Approach 2:
The system integrates real-time public transportation schedule data and weather forecast information to dynamically adjust charging and conditioning schedules. This feedback loop ensures the vehicle is ready precisely when the driver arrives, adapting to changes in arrival times and environmental conditions.
2Loss of energy
If charging is optimized for minimum cost based on single-day baseline, then charging costs are reduced, but the vehicle may not be ready when the driver arrives and cabin temperature may be extreme
Solution Approach 1:
The system performs preliminary charging and cabin conditioning actions based on predicted arrival times from public transportation schedules. By pre-scheduling these actions before the driver arrives, the system ensures vehicle readiness and comfortable cabin temperature while optimizing charging timing to minimize costs.
Solution Approach 2:
The charging and conditioning schedule is dynamically adjusted based on real-time updates of transportation itineraries and weather forecasts. The system flexibly modifies charging power and timing to balance cost optimization with ensuring vehicle readiness and cabin comfort according to actual arrival conditions.
3Reliability
If charging power is increased to ensure vehicle readiness, then the vehicle is guaranteed to be ready, but energy waste increases when driver arrival is delayed
Solution Approach 1:
The system dynamically adjusts charging power levels based on real-time transportation schedule updates and predicted arrival times. When arrival is expected sooner, charging power is reduced; when delays are anticipated, charging power is increased accordingly. This dynamic adjustment eliminates energy waste from premature charging while ensuring vehicle readiness.
Solution Approach 2:
The system changes charging parameters (power level, timing, duration) based on varying conditions including transportation delays and weather forecasts. By adapting these parameters in real-time, the system optimizes the balance between ensuring vehicle readiness and minimizing energy waste from premature or excessive charging.
4Reliability
If snow melting is performed early to ensure vehicle accessibility, then the vehicle is accessible when needed, but energy is wasted if the driver arrives much later than expected
Solution Approach 1:
The system dynamically controls snow melting operations based on real-time transportation schedule updates and predicted arrival times. Heating power is adjusted proportionally to the time until expected arrival, and melting operations are postponed if significant delays are detected in the transportation itinerary, thereby eliminating energy waste from premature snow removal.
Solution Approach 2:
The system changes snow melting parameters (temperature, power level, duration) based on varying arrival time predictions and weather conditions. By adapting these parameters to actual conditions, the system ensures vehicle accessibility when needed while minimizing energy consumption from premature or excessive snow melting operations.
Data Source
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AI summary
An automated EV conditioning system (105) based on public transportation information of an EV driver (110) is provided. It comprises an EVSE (112) configured to charge a battery (115) of an EV (117), a CSMS (120) coupled to the EVSE (112), a public transportation tracking platform (122) coupled to the CSMS (120) for tracking an EV location (125), a public transportation (127), and/or an EV driver phone (130) through an App (132), which can confirm the EV driver (110) actually hopped on a train (135) or a bus and an automated mechanism (140) including an algorithm (145) to take into consideration public transportation schedules (147) into EV charging by the EVSE (112). The platform (122) is coupled to the public transportation (127) such that the EV driver (110) arriving via the public transportation (127) expects the EV (117) ready to travel. The system permits the EV driver (110) to only set a single target SoC (150) in the EV (117) or an App (132) and have the EV (117) actually with that SoC and with a conditioned cabin automatically (155).