Parametric optimization of hybrid car engines

We consider the problem of optimal design of hybrid car engines which combine thermic and electric power. The optimal configuration of the different motors composing the hybrid system involves the choice of certain design parameters. For a given configuration, the goal is to minimize the fuel consumption along a trajectory. This is an optimal control problem with one state variable. The simultaneous optimization of design parameters and trajectories can be formulated as a bilevel optimization problem. The lower level computes the optimal control for a given architecture. The higher level seeks for the optimal design parameters by solving a nonconvex nonsmooth optimization problem with a bundle method

Linking model predictive control (MPC) and system simulation tools to support automotive system architecture choices

International audienceToday's automotive industry must introduce advanced powertrain technologies as a consequence of the stringent environmental regulations and strong market expectations. This leads to the increase of vehicle variants and to the growth of the powertrain architectures complexity. Hence, the development of hardware and software can no longer be decoupled. Those activities have to run in parallel, starting from simulation and advanced engineering, continuing to the detailed engineering phase, and ending in validation and calibration. In the so-called Model-Based Systems Engineering (MBSE) approach, simulation models including both hardware and control systems are used to first make decisions on possible architectures. This paper presents a new MBSE approach that allows powertrain hardware selection to occur early during the simulation stages. The process combines Model Predictive Control (MPC) for the control system with a physical modeling software package for the hardware. This combination of MPC and physical modeling addresses several practical difficulties that typically hinder attempts at MBSE for hardware selection

Hybrid open-loop closed-loop path-following control with preliminary feedback

Programme 5 : traitement du signal, automatique et productiqueSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1994 n.2173 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Maneuver-based obstacle-avoiding trajectory optimization

Available at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1993 n.1955 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc