Kalman Filter Road Grade Estimation Using Accelerometer Gyroscope Velocity
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Solution Overview
Problem
Conventional vehicle road grade estimation techniques are inaccurate, noisy, unreliable, and resource-intensive, requiring additional sensors and processing power, which increases vehicle costs and complexity.
Innovation Solution
A Kalman filter-based road grade estimation system using an accelerometer, gyroscope, and vehicle velocity sensor to estimate road grade, implementing a second-order state space model and solving Riccati equations to determine the Kalman filter gain, allowing for real-time estimation and control of vehicle operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional road grade estimation techniques are used, then road grade can be estimated, but the estimation is inaccurate, noisy, and unreliable
Solution Approach 1:
The patent combines multiple sensor types (accelerometer, gyroscope, velocity sensor) into an integrated estimation system. The accelerometer measures longitudinal acceleration, the gyroscope measures angular pitch rate, and the velocity sensor measures longitudinal velocity. These complementary measurements are fused through a Kalman filter to produce accurate and reliable road grade estimates, resolving the contradiction between measurement precision and reliability.
Solution Approach 2:
The Kalman filter acts as an intermediary that processes and fuses measurements from multiple sensors. It implements a second-order state space model to optimally combine the accelerometer, gyroscope, and velocity sensor data, filtering out noise and inaccuracies while maintaining reliable estimation. This intermediary processing layer transforms individual sensor measurements into a unified, accurate road grade estimate.
2Measurement precision
If additional sensors (such as GPS sensors) are added to improve road grade estimation, then estimation accuracy improves, but vehicle costs and complexity increase
Solution Approach 1:
The patent makes existing sensors serve multiple functions. The accelerometer, gyroscope, and velocity sensor—already present in modern vehicles for other purposes—are utilized for road grade estimation. This multi-functional use of existing sensors achieves accurate estimation without adding GPS sensors or other dedicated road grade measurement devices, thereby avoiding increased vehicle complexity and cost.
Solution Approach 2:
The system uses the vehicle's existing sensor infrastructure to serve the additional function of road grade estimation. Rather than requiring external dedicated sensors, the invention leverages data already being collected by the vehicle's standard sensor suite, making the system self-sufficient and avoiding additional hardware costs.
3Reliability
If additional processor throughput is allocated to improve road grade estimation, then estimation reliability improves, but vehicle costs increase
Solution Approach 1:
The patent employs a second-order state space model with specific parameterizations that optimize computational efficiency. The model structure and Kalman filter implementation are designed to achieve reliable estimation with minimal computational overhead. By carefully selecting and optimizing model parameters, the system achieves high reliability without requiring excessive processor throughput, thus avoiding increased vehicle costs.
Data Source
AI summary
Kalman filter based road grade estimation techniques use models of a longitudinal accelerometer, an angular pitch rate gyroscope, and a velocity sensor and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The proposed Kalman filter formulation is unique in that it uses a mathematical model of the sensors where the gyroscope output is considered as the input and the combined accelerometer and velocity sensor output is considered as the output of the model whose states are to be estimated. By using this unique second-order state space model, a Kalman filter based estimation algorithm is developed to estimate road grade accurately in real-time. This estimated road grade is then being utilized by various vehicle efficiency and/or safety systems to improve vehicle efficiency and/or vehicle safety.