Route Energy Prediction Using Environmental Factor Correction
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Solution Overview
Problem
Existing energy prediction methods for vehicles lack accuracy in estimating total necessary energy required for a scheduled travel route, particularly due to variations in energy consumption factors across different sections, leading to reduced correction accuracy and increased software complexity.
Innovation Solution
An energy prediction apparatus that predicts vehicle-speed fluctuation conditions and required travel energy, incorporating environmental variation-factor information to accurately calculate total necessary energy, using generators and an information retrieving unit to reflect environmental influences on predicted energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy consumption is estimated for each predetermined section, then the correction accuracy for each section can be improved, but the correction process becomes complicated and software installability is reduced
Solution Approach 1:
The patent divides the travel route into multiple predetermined sections and estimates energy consumption for each section separately. This segmentation allows for section-specific correction while maintaining a structured approach that prevents excessive complexity by processing each section independently rather than attempting to correct all sections simultaneously.
Solution Approach 2:
The patent applies correction only to sections where environmental variation factors differ from reference conditions, rather than correcting all sections uniformly. This partial action approach improves accuracy for affected sections while avoiding unnecessary processing complexity for sections that do not require correction.
2Measurement precision
If energy consumption is estimated for each energy-consumption factor and each predetermined section, then the correction accuracy can be increased, but the correction process becomes more complicated
Solution Approach 1:
The patent segments both the travel route into sections and the energy consumption into factors (travel-related and travel-unrelated). This dual segmentation allows for precise correction of each factor within each section while maintaining organizational structure that prevents exponential complexity growth.
Solution Approach 2:
The patent selectively applies correction to only those energy consumption factors that are influenced by environmental variation factors, rather than correcting all factors uniformly. This partial correction approach improves accuracy where needed while avoiding unnecessary complexity for factors that do not require environmental adjustment.
3Measurement precision
If environmental variation-factor information is incorporated into the prediction, then the accuracy of total necessary energy prediction is improved, but the computational complexity increases
Solution Approach 1:
The patent retrieves environmental variation-factor information in advance before performing energy consumption calculations. This preliminary action allows the system to prepare correction data ahead of time, reducing real-time computational complexity while maintaining high prediction accuracy when actual energy consumption is compared against predictions.
Data Source
AI summary
In an energy prediction apparatus, a first generator predicts a vehicle-speed fluctuation condition indicative of how a speed of a target vehicle fluctuates over time while the target vehicle will travel along a scheduled travel route to accordingly generate the vehicle-speed fluctuation condition as first predicted information. A second generator predicts, based on the first predicted information, required travel energy required for the target vehicle to travel along the scheduled travel route to accordingly generate the required travel energy as second predicted information. A third generator reflects retrieved environmental variation-factor information on the second predicted information, and predicts, based on the second predicted information on which the environmental variation-factor information has been reflected, total necessary energy required for the target vehicle to travel along the scheduled travel route to accordingly generate the total necessary energy as third predicted information.