Dynamic Buffer Adjustment for Electric Vehicle Schedule Planning
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Solution Overview
Problem
Current techniques for planning transportation and charging schedules for motor-driven vehicles, such as electric automobiles, often result in low transportation efficiency due to large buffer values set to account for prediction errors, leading to a higher need for schedule corrections.
Innovation Solution
A schedule planning apparatus that evaluates multiple schedule combinations based on prediction parameter values and constraint parameter values, optimizing both transportation efficiency and ease of schedule correction by outputting information on high-efficiency and easily correctible schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large buffer values are set for prediction parameter values to account for prediction errors, then the schedule does not need to be corrected even if prediction fails, but the transportation efficiency becomes low
Solution Approach 1:
The patent applies dynamics by making the buffer value adjustable rather than fixed. The buffer value is determined dynamically based on the relationship between prediction values and actual values from past data. The system learns from historical prediction errors and adjusts buffer values accordingly, allowing the schedule to adapt to actual performance patterns while maintaining efficiency.
Solution Approach 2:
The patent changes the parameter of buffer value from a large fixed value to a dynamically determined value based on prediction accuracy. By analyzing the relationship between prediction values and actual values, the system determines appropriate buffer values that account for prediction errors without unnecessarily inflating resource allocation, thus resolving the contradiction between reliability and productivity.
2Productivity
If buffer values are reduced to improve transportation efficiency, then efficiency improves, but the necessity of schedule correction increases
Solution Approach 1:
The patent implements feedback by continuously monitoring the relationship between prediction values and actual values, and using this information to adjust buffer values. The system learns from past prediction accuracy and provides feedback to optimize future buffer value settings, enabling efficient schedules while maintaining appropriate correction margins based on actual performance patterns.
Solution Approach 2:
The patent performs preliminary action by determining buffer values in advance based on historical prediction accuracy data. Rather than using large fixed buffers, the system pre-calculates appropriate buffer values based on learned prediction error patterns, allowing efficient schedule planning while accounting for expected prediction deviations.
3Reliability
If the number of necessary vehicles and operation time are estimated on the large side to account for prediction failure, then schedule correction is not needed, but the schedule has low transportation efficiency
Solution Approach 1:
The patent changes the parameters of vehicle count and operation time from overestimated fixed values to dynamically determined values. By baseing these estimates on historical prediction accuracy and actual performance data, the system determines appropriate buffer values that provide robustness without unnecessary overallocation of resources, thus improving transportation efficiency while maintaining schedule reliability.
Solution Approach 2:
The patent applies dynamics by making vehicle estimates and operation time estimates adaptive rather than static. The system learns from past prediction accuracy and adjusts these parameters dynamically, allowing the schedule to be robust against prediction errors without permanently overallocating resources, thereby resolving the contradiction between reliability and productivity.
Data Source
AI summary
A schedule planning apparatus plans, based on a plurality of parameter values, a schedule combination including a transportation schedule and a charging schedule, evaluates, for each of the one or the plurality of planned schedule combinations, efficiency of transportation and easiness of schedule correction, and outputs information based on at least a part of a schedule combination in which the efficiency and the correction easiness are relatively high. For each of one or a plurality of kinds of constraint parameter value combinations, the constraint parameter value combination is a combination including a plurality of constraint parameter values respectively corresponding to the plurality of constraint parameter names. A constraint parameter value of at least one constraint parameter name affects the correction easiness.