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.

Highlights

  • Automotive manufacturers and research centers have been significantly investing resources and efforts into the development of alternative powertrain technologies to lower fuel consumption and pollutant emissions in passenger and commercial vehicles

  • 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

  • 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 mixedinteger linear programs, followed by a discussion of its approximation error

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Summary

Introduction

Automotive manufacturers and research centers have been significantly investing resources and efforts into the development of alternative powertrain technologies to lower fuel consumption and pollutant emissions in passenger and commercial vehicles. In this context, researchers have successfully solved nonlinear OCPs applied to the EMS of HEVs through optimization techniques ranging from dynamic programming to direct and indirect methods. Bengea and DeCarlo (2005) and Sager (2006, 2009) have independently developed the embedding transformation technique that is called combinatorial integral approximation (CIA) decomposition for solving MIOCPs It consists of solving the NLP with a dropped integrality constraint before approximating the relaxed controls in the CIA problem, which is a mixed-integer linear program (MILP).

Model description
Dividing the time horizon into phases
Control variables
Differential states
Outer convexification
Path and vanishing constraints
Prefixing constraints
Minimum dwell time constraints
Problem discretization
Mode transition constraints
Solving combinatorial constrained multiphase mixedinteger control problems
Intuition of CIA decomposition theory
A tailored CIA decomposition algorithm
Numerical results
Exemplary CIA rounding step
Case study with the WLTP driving cycle
Conclusions

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