Vehicle Longitudinal Acceleration Research Articles

Multi-objective optimization for automotive performance

A multi-objective optimisation procedure has been developed to optimise the vehicle longitudinal acceleration time, steady-state fuel economy, ride quality and steering stability. A 5-speed manual transmission vehicle, with the engine at wide-open throttle, was adopted for the prediction of the 0–60 mph acceleration time. A simplified fuel economy model was used to simulate the fuel consumption under steady vehicle speed. The optimisation algorithm selected is the Modified Method of Feasible Directions due to its high reliability and efficiency. A notable feature of the optimisation process adopted is that the algorithm can compromise between the competing performances while meeting the requirements for the handling quality of the vehicle. The optimisation programme, which is coded with FORTRAN 77, is capable of optimising the design parameters, such as, transmission gear ratios, tyre radius, driving axle ratio, vehicle weight, wheel base and position of the centre of gravity. The programme is verified by typical vehicle data.

ADAPTIVE EMERGENCY BRAKING CONTROL WITH OBSERVER-BASED DYNAMIC TIRE/ROAD FRICTION MODEL AND UNDERESTIMATION OF FRICTION COEFFICIENT

An adaptive control scheme for emergency braking of vehicles is designed based on a LuGre dynamic model for the tire/road friction. The wheel angular speed and longitudinal vehicle acceleration information are used to design an observer-estimator for vehicle velocity, friction internal state and friction parameters. A Lyapunov-based stabilizing controller is designed to achieve near maximum braking capability of the vehicle. Underestimation of the friction coefficient is guaranteed by a proper choice of adaptation gains and initial conditions of the friction parameters. Simulation results show that both the state estimation and the friction parameters converge to the true values and that vehicle stops rapidly.

An Extended Adaptive Kalman Filter for Real-time State Estimation of Vehicle Handling Dynamics

This paper considers a method for estimating vehicle handling dynamic states in real-time, using a reduced sensor set; the information is essential for vehicle handling stability control and is also valuable in chassis design evaluation. An extended (nonlinear) Kalman filter is designed to estimate the rapidly varying handling state vector. This employs a low order (4 DOF) handling model which is augmented to include adaptive states (cornering stiffnesses) to compensate for tyre force nonlinearities. The adaptation is driven by steer-induced variations in the longitudinal vehicle acceleration. The observer is compared with an equivalent linear, model-invariant Kalman filter. Both filters are designed and tested against data from a high order source model which simulates six degrees of freedom for the vehicle body, and employs a combined-slip Pacejka tyre model. A performance comparison is presented, which shows promising results for the extended filter, given a sensor set comprising three accelerometers only. The study also presents an insight into the effect of correlated error sources in this application, and it concludes with a discussion of the new observer's practical viability.

Feedback linearizing control of vehicle longitudinal acceleration

The application of recently developed feedback linearization techniques to controlling the powertrain of an automobile is discussed. The objective of powertrain control is to regulate the output torque of the engine and transmission system to achieve a desired longitudinal acceleration of the vehicle. With higher model order and increasing system complexity, the design process for feedback linearization becomes harder and more tedious. However, the design process can be simplified for a class of nonlinear systems including the automobile powertrain. Simplification of the design process is accomplished by incorporating the single perturbation technique into the input-output linearization technique. Furthermore, the controller obtained by the simplified linearization technique is computationally simpler than the one obtained by the conventional linearization technique. Nonetheless, it provides good dynamic performance for the powertrain system. >