Dynamic Vehicle State Estimation Filter for Pitch Angle Precision
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Solution Overview
Problem
Existing vehicle state estimation systems face challenges with noise susceptibility and high error levels, particularly in transient conditions, due to large signal-to-noise ratios, which affect the accuracy of pitch angle measurements used for vehicle stability control.
Innovation Solution
A vehicle state estimation apparatus that dynamically adjusts the operating frequency of signal filters based on vehicle operating parameters, using a combination of low-pass and high-pass filters for complementary filtering, to improve estimation accuracy and capture transient states while removing erroneous information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heavy filtering is applied to reduce noise, then measurement precision is improved, but transient state estimation deteriorates
Solution Approach 1:
The filter coefficient is made dynamic rather than fixed, allowing the filter to adapt its characteristics based on current vehicle operating conditions. The controller continuously adjusts the filter coefficient in response to detected transient states, enabling the filter to maintain high precision during steady-state operation while automatically reducing its filtering effect during transient events to preserve estimation accuracy.
Solution Approach 2:
The filter coefficient parameter is changed dynamically based on detected vehicle state transitions. When a transient state is detected through monitoring vehicle dynamics parameters, the controller modifies the filter coefficient to an appropriate value that captures transient behavior. This parameter adaptation allows the system to optimize between noise filtering and transient response based on real-time conditions.
2Device complexity
If a fixed filter coefficient is used, then device complexity is reduced, but adaptability to different operating conditions deteriorates
Solution Approach 1:
The filtering system performs self-adjustment by automatically detecting transient states through vehicle dynamics parameter monitoring and autonomously modifying its own filter coefficient. The controller embedded in the estimation apparatus detects changes in vehicle state and independently adjusts the filtering characteristics without requiring external intervention or complex manual configuration, enabling the system to adapt to different operating conditions while maintaining relatively simple architecture.
Data Source
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AI summary
The present disclosure relates to apparatus (1) for estimation of a vehicle state. The apparatus (1) includes a controller (21) configured to determine a first estimation of the vehicle state in dependence on at least one first vehicle dynamics parameter. A filter coefficient (Fc) is calculated based on a first vehicle operating parameter. An operating frequency of a first signal filter (35) is set in dependence on the determined filter coefficient (Fc) and the first estimation is filtered to generate a first filtered estimation of the vehicle state. The present disclosure also relates to a vehicle; to a dynamic filtering apparatus; to a method of estimating a vehicle state; and to a method of performing dynamic filtering.