Optimal low-level control strategies for a high-performance hybrid electric power unit

Camillo Balerna, Nicolas Lanzetti, Mauro Salazar, Alberto Cerofolini and Christopher Onder

Applied Energy, 2020, vol. 276, issue C, No S0306261920307601

Abstract: In this paper we present models and optimization algorithms to compute the optimal low-level control strategies for hybrid electric powertrains. Specifically, we study the minimum-fuel operation of a turbocharged internal combustion engine coupled to an electrical energy recovery system, consisting of a battery and two motors connected to the turbocharger and to the wheels, respectively. First, we combine physics-based modeling approaches with neural networks to identify a piecewise affine model of the power unit accounting for the internal dynamics of the engine, and formulate the minimum-fuel control problem for a given driving cycle. Second, we parse the control problem to a mixed-integer linear program that can be solved with off-the-shelf optimization algorithms that guarantee global optimality of the solution. Finally, we validate our model against a high fidelity nonlinear simulator and showcase the presented framework with a case-study for racing applications. Our results show that cylinder deactivation and turbocharger electrification can decrease fuel consumption up to 4% and 8%, respectively.

Keywords: Hybrid electric vehicles; Optimal control; Neural networks; Turbocharger; Mixed-integer optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2020.115248

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