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In vehicle dynamics modelling, the road profile is generally treated in one of two ways; either the gradient is a property that changes over a length scale far greater than that of the vehicle's wheelbase, or as a very detailed road surface model for determining the behaviour of vehicle suspensions. Occasionally, for modelling the behaviour of off-road vehicles, step-climbing manoeuvres are modelled. We propose an extension of these step-climbing models to a general, continuously varying road gradient model for cases where the distance over which the large gradient change occurs are of similar length-scale as the vehicle wheelbase. The motivation behind this work comes from a road gradient and vehicle mass estimation problem where it was noticed that very sudden gradient changes have a significant impact on the powertrain, but in a way that is not proportional to the attitude change of the vehicle.

Hybrid and electric vehicle (H/EV) technology is already well established in the automotive industry and a great majority of car manufacturers offer vehicles with alternative propulsion systems (hybrid or electric - H/E). This advancement, however, does not mean that all technical aspects of H/E propulsion systems have already been encapsulated or even fully understood. This statement is specifically valid for regenerative braking technology. In order to regenerate the maximum possible energy, which may be limited in real applications (e.g. by the charging ratio of the energy storage device(s)), the interaction of regenerative braking and the active driving safety systems (ADSSs) such as the anti-lock braking system (ABS) needs to be taken in to account. For maximum recaptured energy via electric motor (E-Motor) braking, the use of regenerative braking, which generates decelerations greater than 0.1g, should be deployed.

We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS).

This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate.