PEV Departure Time Estimation via Connector Removal History
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Solution Overview
Problem
Current methods for estimating a plug-in electric vehicle's (PEV) departure time are inaccurate, making it difficult for owners to ensure their vehicle is charged prior to departure, which is crucial for minimizing charging costs and ensuring sufficient charge for daily journeys.
Innovation Solution
A system that analyzes historical data on when the charging connector is removed from the PEV's inlet to estimate the departure time, using this information to optimize charging schedules and account for variations on different days, including weekdays, weekends, and holidays, while also considering Time-of-Use rate structures to minimize charging costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to estimate departure time, then the estimation process is simple, but the accuracy of departure time estimation is poor
Solution Approach 1:
The system continuously monitors when the charging connector is actually removed from the inlet and uses this feedback to refine and update the estimated departure time predictions. This closed-loop feedback mechanism improves accuracy by comparing predicted versus actual departure times and adjusting future estimates accordingly.
Solution Approach 2:
The system automatically tracks and records the actual departure times when the connector is removed, using this self-collected data to improve its own estimation accuracy without requiring external intervention or manual input from the user.
2Loss of energy
If charging is optimized based on accurate departure time estimation, then charging cost is minimized, but the requirement for historical data and processing increases
Solution Approach 1:
The system extracts only the essential information needed for cost optimization - specifically the timing patterns of connector removal - from the larger body of available historical data. By focusing on this key parameter rather than processing all possible vehicle operating data, the system achieves cost minimization without requiring excessive data storage or processing capacity.
Solution Approach 2:
The system performs preliminary analysis of historical charging patterns to establish predicted departure times before the actual charging decision is made. This advance preparation allows the charging schedule to be optimized in advance based on predicted needs, reducing the need for complex real-time adjustments and minimizing charging costs through proactive planning.
3Measurement precision
If the system accounts for variations on different days (weekdays, weekends, holidays), then the accuracy of departure time estimation is improved, but the complexity of categorizing and processing history data increases
Solution Approach 1:
The system segments historical data into distinct categories based on day type (weekdays, weekends, holidays) and applies separate analysis to each segment. This segmentation allows the system to capture the different charging patterns that occur on different types of days while maintaining manageable complexity by treating each category independently rather than attempting to analyze all data uniformly.
Solution Approach 2:
The system applies different estimation approaches and parameters tailored to specific day types - using patterns appropriate for weekdays versus weekends versus holidays. This local quality approach recognizes that departure time patterns vary by day type and optimizes the estimation algorithm for each specific context, improving overall accuracy without requiring a single overly complex universal model.
Data Source
AI summary
The disclosure includes a system and method for determining a departure time for a Plug-in Electric Vehicle (“PEV”). The method may include analyzing power meter data to determine that a connector of a control device is coupled to an inlet of the PEV. The method may include charging a battery set of the PEV with electricity. The method may include determining a next day of a next journey for the PEV based on clock data provided by a clock. The clock data may describe time information describing a present day and a category of the next day. The method may include analyzing history data associated with the category of the next day to determine a habitual time associated with the category of the next day. The method may include estimating that a departure time for the next journey is substantially equal to the habitual time.