Browsing by Author "Basrah, M. Sofian"

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    Chapter 192: Integration of torque blending and slip control using nonlinear model predictive control
    (Unknown, 2016-09-30) Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
    Antilock Braking System (ABS) is an important active safety feature in preventing accidents during emergency braking. Electrified vehicles which include both hydraulic and regenerative braking systems provide the opportunity to implement brake torque blending during slip control operation. This study evaluates the design and implementation of a new torque allocation algorithm using a Nonlinear Model Predictive Control (NMPC) strategy that can run in real-time, with results showing that wheel-locking can be prevented while also permitting for energy recuperation.
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    Wheel slip control with torque blending using linear and nonlinear model predictive control
    (Taylor & Francis, 2017-03-31) Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
    Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.