Vehicle Charging Scheduling Based on Routine Travel Prediction
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Solution Overview
Problem
Electric vehicles face challenges with restricted range and prolonged charging times, which can disrupt users' daily schedules and cause inconvenience.
Innovation Solution
A system that determines a vehicle's routine travel behavior, estimates energy requirements, and optimizes charging by scheduling based on historical data, renewable energy availability, and utility rates to ensure seamless operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle battery is charged regularly to ensure uninterrupted operation, then the reliability of vehicle operation is improved, but the charging time increases and disrupts user schedule
Solution Approach 1:
The system performs preliminary analysis of historical travel data, weather conditions, and calendar events to predict future energy requirements and schedule charging sessions in advance during optimal times when the vehicle is parked and electricity rates are lower, thereby ensuring reliability without disrupting user schedules
Solution Approach 2:
The system automatically monitors battery charge levels, predicts energy needs based on learned travel patterns, and initiates charging sessions without user intervention by communicating with charging infrastructure, allowing the vehicle to self-manage its charging schedule to maintain operation reliability
2Ease of operation
If the vehicle user charges at public charging stations, then the convenience of charging location is improved, but the charging time is prolonged
Solution Approach 1:
The system schedules charging sessions in advance during periods when the vehicle is naturally parked (overnight at home, during work hours at destination chargers), eliminating the need for dedicated charging trips and reducing total charging time while maintaining location flexibility
Solution Approach 2:
The system utilizes all available parking opportunities throughout the day and night for charging, transforming intermittent parking events into continuous charging opportunities, thereby reducing total charging time while maintaining access to public charging infrastructure
3Loss of energy
If the system schedules charging based on utility rates and renewable energy availability, then the energy cost is reduced, but the charging schedule complexity increases
Solution Approach 1:
The system automatically receives and processes utility rate information and renewable energy availability data, then autonomously optimizes charging schedules to take advantage of low-rate periods and high renewable availability without requiring user configuration or manual schedule management
Solution Approach 2:
The system continuously monitors actual charging costs, battery state of charge, and changing utility rates, then dynamically adjusts future charging schedules based on this feedback to minimize energy costs while maintaining simple user-facing operation
Data Source
AI summary
A charging management system including a transceiver and a processor is disclosed. The transceiver may receive historical inputs associated with a vehicle. The processor may obtain the historical inputs from the transceiver, and determine a routine travel behavior of the vehicle based on the historical inputs. The processor may further determine a parking and charging location associated with the vehicle based on the routine travel behavior, and estimate a future departure time from the parking and charging location and a future arrival time at the primary parking and charging location based on the routine travel behavior. The processor may further estimate an amount of energy required by the vehicle to travel between the future departure time and the future arrival time based on the routine travel behavior, and perform a predetermined action based on the estimated amount of energy.