Vehicle Mass and Road Slope Estimation Using Kalman Filter
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Solution Overview
Problem
Existing methods for real-time vehicle mass and road slope estimation are complex, computationally expensive, and require additional sensors, making them unsuitable for economy-priced vehicles, which need simple, accurate, fast, robust, and inexpensive solutions with minimal instrumentation.
Innovation Solution
A real-time estimator using a linear Kalman filter for road slope estimation and a recursive least squares algorithm with a forgetting factor for vehicle mass estimation, based on vehicle propulsion system states, driveline signals, and available sensors like wheel speed and longitudinal accelerometers, to provide independent and integrated estimates that are robust to disturbances and model uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based estimation approaches are used for vehicle mass and road slope, then measurement precision is improved, but device complexity increases and computational cost rises
Solution Approach 1:
The patent segments the estimation problem into two independent parts: road slope estimation using an Extended Kalman Filter (EKF) and vehicle mass estimation using Recursive Least Squares (RLS). This segmentation allows each algorithm to be optimized independently, reducing overall complexity while maintaining accuracy. The EKF handles the dynamic road slope estimation, while RLS focuses on mass estimation, avoiding the need for a single complex integrated model.
Solution Approach 2:
The patent extracts and removes the aerodynamic drag coefficient from the simultaneous estimation problem, estimating it separately using a dedicated RLS algorithm. This extraction simplifies the main mass and slope estimation by eliminating a complex time-varying parameter from the primary estimation equations, reducing computational burden while maintaining accuracy through separate estimation.
2Measurement precision
If simultaneous estimation of mass and slope is implemented, then measurement precision is improved, but use of energy increases due to computational complexity
Solution Approach 1:
The patent divides the simultaneous estimation into separate sequential estimations: first estimating road slope with EKF, then using that slope information to estimate vehicle mass with RLS. This segmentation reduces computational energy by avoiding the matrix inversions and complex calculations required for true simultaneous estimation of all parameters, while still achieving simultaneous availability of both estimates through sequential processing.
Solution Approach 2:
The patent implements dynamic adaptation of the estimation algorithms by using a forgetting factor in the RLS algorithm that adjusts based on signal conditions, and by dynamically selecting when to update mass estimates based on available excitation. This dynamic approach reduces computational energy by performing full updates only when necessary, rather than continuously at fixed intervals.
3Reliability
If robust estimation algorithms are used to handle disturbances and model uncertainties, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the estimated road slope is continuously fed back into the mass estimation algorithm to compensate for gravitational effects, and where the estimated mass is fed back into the slope estimation to improve accuracy. This feedback structure provides robustness to disturbances and model uncertainties while maintaining relatively simple algorithmic structures through iterative refinement rather than complex preprocessing.
Solution Approach 2:
The estimation algorithms use the vehicle's own operational data (acceleration, speed, torque, brake pressure) to self-correct and improve estimates without requiring external reference measurements or additional sensors. The EKF uses vehicle dynamics models to self-correct slope estimates, while RLS uses propulsion system data to self-correct mass estimates, providing robustness through self-validation rather than external verification systems.
Data Source
AI summary
Automotive electronic control unit programmed to realtime estimate either or both of vehicle mass and road slope, wherein; a. road slope, is estimated; a1. when vehicle is considered stopped based on an accelerometer signal indicative of vehicle acceleration, wherein the vehicle is considered stopped in the presence of substantially zero values of a speed signal indicative of vehicle speed, and a2. when vehicle is in rectilinear and curvilinear motion by implementing a road slope observer based on a linear Kalman filter, which is designed to: a21. operate based on signals indicative of vehicle speed and acceleration, and a22. compensate for accelerometric disturbances due to; a221. vehicle static pitch resulting from vehicle load distribution, and a222. vehicle dynamic pitch due to acceleration to which vehicle is subjected during motion, and a223. accelerometric disturbance components due to vehicle lateral dynamics; b. vehicle mass is estimated: b1. when vehicle is in motion, and b2. based on a recursive least square algorithm with forgetting factor, and b3. based on an accelerometric signal indicative of vehicle acceleration, on a vehicle speed signal, and other signals representing a vehicle propulsive/resistive torque, and b4. at different low gears, to provide a mass estimation and an associated variance for each gear, and b5. based on mass estimations and corresponding variances for each gear, and b6. compensating for accelerometer disturbances due to: b61, vehicle dynamic pitch; and b62. accelerometric disturbance components due to vehicle lateral dynamics; and b7. minimizing uncertainties on propulsive/resistive torque due to gear efficiency and rolling resistance.