This study considers the problem of computing a non-causal minimum-fuel energy management strategy for a hybrid electric vehicle on a given driving cycle. Specifically, we address the multiphase mixed-integer nonlinear optimal control problem that arises when the optimal gear choice, torque split and engine on/off controls are sought in off-line evaluations. We propose an efficient model by introducing vanishing constraints and a phase specific right-hand side function that accounts for the different powertrain operating modes. The gearbox and driveability requirements translate into combinatorial constraints. These constraints have not been included in previous research; however, they are part of the algorithmic framework for this investigation. We devise a tailored algorithm to solve this problem by extending the combinatorial integral approximation (CIA) technique that breaks down the original mixed-integer nonlinear program into a sequence of nonlinear programs and mixed-integer linear programs, followed by a discussion of its approximation error. Finally, numerical results illustrate the proposed algorithm in terms of solution quality and run time.

Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles

Robuschi N.;Braghin F.
2021-01-01

Abstract

This study considers the problem of computing a non-causal minimum-fuel energy management strategy for a hybrid electric vehicle on a given driving cycle. Specifically, we address the multiphase mixed-integer nonlinear optimal control problem that arises when the optimal gear choice, torque split and engine on/off controls are sought in off-line evaluations. We propose an efficient model by introducing vanishing constraints and a phase specific right-hand side function that accounts for the different powertrain operating modes. The gearbox and driveability requirements translate into combinatorial constraints. These constraints have not been included in previous research; however, they are part of the algorithmic framework for this investigation. We devise a tailored algorithm to solve this problem by extending the combinatorial integral approximation (CIA) technique that breaks down the original mixed-integer nonlinear program into a sequence of nonlinear programs and mixed-integer linear programs, followed by a discussion of its approximation error. Finally, numerical results illustrate the proposed algorithm in terms of solution quality and run time.
2021
Combinatorial constraints
Combinatorial integral approximation
Energy management
Hybrid electric vehicles
Hybrid powertrains
Mixed-integer nonlinear optimal control
Nonlinear programming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1163431
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