Vehicle Control Strategy Using Route Frequency Characteristics
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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 can lead to inefficient energy use and unreliable route operations.
Innovation Solution
A method using a control system with processor devices, sensors, and storage media to transform data streams from vehicles 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 methods are used to identify driving patterns, then the system is simpler to implement, but the precision of pattern identification is insufficient
Solution Approach 1:
The patent replaces traditional time-domain analysis methods with frequency-domain analysis using Fast Fourier Transform (FFT). This substitution transforms the approach from analyzing data in the time domain to analyzing it in the frequency domain, enabling more precise identification of periodic driving patterns while maintaining computational efficiency through established signal processing algorithms.
Solution Approach 2:
The patent changes the analysis parameter domain from time to frequency. By applying FFT transformation, the system converts time-series vehicle data into frequency spectrum representations, allowing identification of periodic patterns through frequency components. This parameter transformation enables detection of repeating driving patterns that are not apparent in the time domain.
2Reliability
If more sensors and data streams are collected to improve route characteristics accuracy, then the reliability of vehicle control is improved, but the device complexity and energy consumption increase
Solution Approach 1:
The patent merges multiple data streams from different sensors (GPS, accelerometers, gyroscopes, vehicle sensors) into a unified route characteristics model. By integrating these diverse data sources and analyzing them collectively in the frequency domain, the system achieves reliable route pattern recognition without requiring each individual sensor to be overly complex.
Solution Approach 2:
The patent introduces frequency spectrum analysis as an intermediary processing layer between raw sensor data and control decisions. This intermediary transformation converts complex multi-sensor data into simplified frequency domain representations, making it easier to identify patterns and generate control strategies while reducing the complexity of direct multi-sensor integration.
3Adaptability or versatility
If real-time data processing is performed to adapt to dynamic driving patterns, then the adaptability of vehicle control is improved, but the energy consumption increases
Solution Approach 1:
The patent applies periodic frequency analysis to identify repeating driving patterns along routes. By detecting periodic components in the frequency spectrum, the system can recognize recurring driving behaviors and route characteristics, enabling adaptive control strategies that respond to predictable patterns without requiring continuous high-energy processing of every data point.
Solution Approach 2:
The patent performs preliminary frequency domain transformation and pattern identification during data collection phases. By pre-processing data to extract characteristic frequency patterns and storing these route characteristics, the system reduces the computational energy required during real-time operation, as the heavy lifting of pattern recognition is done in advance.
Data Source
AI summary
The present disclosure relates to a method for generating a part of a vehicle control strategy for a vehicle following a route to a destination, said route extending through one or more route segments, the method comprising: obtaining one or more data streams over time from the vehicle, said one or more data streams containing at least one measurable data parameter; transforming each time sequence of one type of obtained data streams to a frequency representation; identifying, one or more relevant components of the frequency spectrum; quantifying the identified one or more relevant components of the frequency spectrum; associating the identified and quantified relevant components of the frequency spectrum with a given road segment of the route; combining 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.