Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle

E. Silvas; N.D. Bergshoeff; T. Hofman; M. Steinbuch

doi:10.1109/VPPC.2014.7007029

Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle

E. Silvas, N.D. Bergshoeff, T. Hofman, M. Steinbuch

Control Systems Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

47 Citations (Scopus)

9 Downloads (Pure)

Abstract

This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

Original language	English
Title of host publication	IEEE Vehicle Power and Propulsion Conference (VPPC), 27-30 Oct. 2014, Coimbra, Portugal
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
ISBN (Print)	978-1-4799-6783-4
DOIs	https://doi.org/10.1109/VPPC.2014.7007029
Publication status	Published - 2015

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10.1109/VPPC.2014.7007029

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title = "Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle",

abstract = "This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.",

author = "E. Silvas and N.D. Bergshoeff and T. Hofman and M. Steinbuch",

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doi = "10.1109/VPPC.2014.7007029",

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Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle. / Silvas, E.; Bergshoeff, N.D.; Hofman, T. et al.
IEEE Vehicle Power and Propulsion Conference (VPPC), 27-30 Oct. 2014, Coimbra, Portugal. Piscataway: Institute of Electrical and Electronics Engineers, 2015. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle

AU - Silvas, E.

AU - Bergshoeff, N.D.

AU - Hofman, T.

AU - Steinbuch, M.

PY - 2015

Y1 - 2015

N2 - This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

AB - This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

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DO - 10.1109/VPPC.2014.7007029

M3 - Conference contribution

SN - 978-1-4799-6783-4

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BT - IEEE Vehicle Power and Propulsion Conference (VPPC), 27-30 Oct. 2014, Coimbra, Portugal

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Comparison of bi-level optimization frameworks for sizing and control of a hybrid electric vehicle

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