Vehicle Route Segment Control Using Frequency-Based Driving Patterns
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Solution Overview
Problem
Current vehicle control systems struggle to accurately predict and adapt to dynamic driving patterns, especially for heavy-duty electric vehicles, which affects energy efficiency and reliable transportation.
Innovation Solution
A method using a control system with processor devices and sensors to transform data streams into frequency representations, identify relevant components, and associate them with road segments, creating route characteristics records for improved vehicle control strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-domain analysis is used to identify driving patterns, then the system is simpler to implement, but the accuracy and precision of pattern recognition is insufficient
Solution Approach 1:
The patent replaces traditional time-domain signal processing methods with frequency-domain analysis using Fourier transforms. This substitution enables more accurate identification of periodic driving patterns by transforming temporal data into frequency spectra, where recurring patterns appear as distinct frequency peaks. The frequency-domain approach provides superior pattern recognition accuracy while maintaining computational feasibility through standard signal processing algorithms.
2Adaptability or versatility
If more sensor data and processing are used to improve vehicle control adaptability, then the control strategy becomes more accurate, but the computational load and system complexity increases
Solution Approach 1:
The patent extracts only the most relevant features from multi-sensor data by identifying dominant frequency components in the frequency spectrum. Instead of processing all raw sensor data, the system extracts key periodic patterns that represent typical driving behaviors on specific route segments. This extraction approach maintains high adaptability by capturing essential driving patterns while significantly reducing computational complexity by focusing only on significant frequency components rather than all possible data variations.
Solution Approach 2:
The patent transforms the analysis from time-domain parameters to frequency-domain parameters through Fourier transformation. This parameter change enables the system to identify periodic patterns more effectively by representing driving behaviors as frequency spectra. The transformation allows the control system to adapt to varying driving conditions by analyzing frequency characteristics of route segments, providing enhanced adaptability with manageable computational requirements.
3Measurement precision
If historical route data is collected and analyzed for each segment, then the prediction accuracy improves, but the data processing time and storage requirements increase
Solution Approach 1:
The patent divides the vehicle route into multiple segments and applies frequency-domain analysis to each segment independently. By segmenting the route, the system can identify specific periodic patterns characteristic of each segment without processing the entire route as one large dataset. This segmentation approach improves prediction accuracy for each individual segment while reducing overall processing time, as each segment can be analyzed separately and efficiently using standardized frequency transformation methods.
Data Source
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AI summary
The present disclosure relates to a method (100) for generating a part of a vehicle control strategy (110) for a vehicle following a route (102) to a destination (104), said route extending through one or more route segments (103a to 103n), the method comprising: obtaining (S1) one or more data streams (40, 40a to 40n) over time from the vehicle, said one or more data streams containing at least one measurable data parameter (42, 42a to 42n); transforming (S2) each time sequence of one type of obtained data streams to a frequency representation; identifying (S3), one or more relevant components (50) of the frequency spectrum; quantifying (S4) the identified one or more relevant components of the frequency spectrum; associating (S5) the identified and quantified relevant components of the frequency spectrum with a given road segment of the route; combining (S6) the associated, quantified and identified relevant components of the frequency spectrum for the given road segment from different types of data streams into one route characteristics record (120a to 120n).