A Novel Kalman Filter Based Road Grade Estimation Method

Accurate, robust and real-time estimation of road grade is extremely important in vehicle control (battery management, transmission shift scheduling, distance-to-empty prediction, anti-lock braking system, collision avoidance, stability control, etc.) to improve safety, stability, efficiency and performance. This paper presents a novel Kalman filter based road grade estimation method using measurements from an accelerometer, gyroscope and tachometer. The accelerometer measures the components of the vehicle acceleration (including the components of the acceleration due to gravity), and the measurements provided by the accelerometer are almost drift free but heavily corrupted by measurement noises. The gyroscope measures the components of the angular velocity of the vehicle, and the measurements provided by the gyroscope are quite clean but disturbed by gyroscope biases. The tachometer measures the longitudinal vehicle velocity, and the measurement provided by the tachometer is also corrupted by measurement noise. The Kalman filter uses the model of the sensors and their outputs, and fuses the sensor measurements to optimally estimate the road grade.