Integrated optimal design for hybrid electric vehicles

Silvas, E.

Published: 30/11/2015

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Citation for published version (APA):Silvas, E. (2015). Integrated optimal design for hybrid electric vehicles Eindhoven: Technische UniversiteitEindhoven

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Integrated Optimal Design for Hybrid Electric Vehicles

Emilia Silvas

2015

The research reported in this thesis is carried out as part of the HTAS (High-Tech AutomotiveSystems) Hybrid Innovations for Trucks project (HATASI10002). The research has beenperformed in the Control Systems Technology Group of Mechanical Engineering at EindhovenUniversity of Technology, with support from DAF Trucks N.V. in Eindhoven, as the industrialpartner, and Agentschap NL.

Integrated Optimal Design for Hybrid Electric Vehicles by Emilia Silvas - Eindhoven Universityof Technology 2015 - PhD Thesis.

A catalogue record is available from the Eindhoven University of Technology Library.ISBN: 978-90-386-3968-0

Cover Design: Emilia Silvas.Reproduction: CPI Koninklijke Wohrmann, Zutphen, The Netherlands.

Copyright c 2015 by Emilia Silvas. All rights reserved.

Integrated Optimal Design forHybrid Electric Vehicles

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan deTechnische Universiteit Eindhoven, op gezag vande rector magnificus, prof.dr.ir. F.P.T. Baaijens,

voor een commissie aangewezen door het Collegevoor Promoties, in het openbaar te verdedigenop maandag 30 november 2015 om 16.00 uur

door

Emilia Silvas

geboren te Novaci, Roemenie

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de pro-motiecommissie is als volgt:

voorzitter: prof. dr. L.P.H. de Goeypromotor: prof. dr. ir. M. Steinbuchco-promotor: dr. ir. T. Hofmanleden: prof. dr. H. Peng University of Michigan, USA

prof. dr. B. Egardt Chalmers University of Technology, SEprof. dr. ir. J. Hellendoorn Delft University of Technology, NLir. H. Voets DAF Trucks N.V., NLprof. dr. M.G.J. van den Brand

Het onderzoek dat in dit proefschrift / proefontwerp wordt beschreven is uitgevoerd inovereenstemming met de TU/e Gedragscode Wetenschapsbeoefening.

NOTATION

Abbreviations

Abbreviation Description ReferenceCO2 Carbon Dioxide p. 1HD Heavy Duty p. 1HEV Hybrid Electric Vehicle p. 3OEM Original Equipament Manufacturer p. 3HIT Hybrid Innovation in Trucks p. 2EM Electric Machine p. 3EMS Energy Management System p. 5NOx Nitric Oxide and Nitrogen Dioxide p. 4PM Particulate Matter p. 4EPA US Environmental Protection Agency p. 5NEDC New European Driving Cycle p. 5BEV Battery Electric Vehicle p. 18SLD System Level Design p. 23FD Finite Domains p. 25ECMS Equivalent Consumption Minimization Strategy p. 27(S)DP (Stochastic) Dynamic Programming p. 27RB Rule Based p. 27MPC Model Predictive Control p. 27GA Genetic Algorithms p. 32CSP Constraint Satisfaction Problem p. 44PBS Platform Based Design p. 45BB Branch and Bound p. 53CLP Constraint Logic Programming p. 53TPM Transition Probability Matrix p. 95MCMC Markov Chain Monte Carlo technique p. 99

i

ii NOTATION

Symbols

Symbols Description ReferenceT Topology p. 25V Vertex / node p. 22E Edge p. 22T A set of elements of type T p. 22D Domain of a variable p. 22X Variables p. 6J Optimization target/function p. 6C Battery capacity p. 19Pm Electric motor power p. 64 Driving cycle p. 6Pi j The probability of going from current state i to next state j p. 98F Transition probability matrix p. 98 Optimization target p. 22M Velocity classes p. 99N Road slope classes p. 99O Acceleration classes p. 104 Node type p. 22c Constraint function p. 22 Gear number p. 64Superscripts p. 22p Possible p. 25fe Feasible p. 25f Functionality p. 22c Control p. 19

Special Symbols

Symbols Description Reference, Subset of p. 25(, 6 Not a subset of p. 45, Inequality (smaller/greater or equal to) p. 6A

B The union of two sets A and B: {x : x A or x B} p. 25ni=1 xi Sum over i; i = 1,2, ...,n (= x1 + x2 + ...xn) p. 48ni=1 xi Product over i; i = 1,2, ...,n (= x1x2...xn) p. 25a || b The concatenation of vector a with vector b p. 100

SOCIETAL SUMMARY

Integrated Optimal Design for Hybrid Electric Vehicles

Increasing levels of air emissions, regardless of their source, harm the planet, both onthe short, as well as on the long term. In the last decades significant increases in globalemissions were measured that contributed to the growth of greenhouse gas emissionsand global warming. For example, only between 1990 and 2007, CO2 emissions fromtransport (land, water and air) increased by 45%. To constrain climate changes, theseemission levels must be reduced. To this end, in the recent past, electric and hybridcars have entered the market, especially, in the passenger vehicle category. With provenbenefits, these new power trains will enter other markets as well, be it commercial trucks,buses, boats, ships and so forth.

In this thesis, the design of hybrid electric vehicles is studied, to provide efficient so-lutions (low energy and fuel consumption), with affordable and competitive prices. Inparticular, the research focuses on solutions suitable for long-haul heavy-duty trucks.To find the optimal design for a hybrid electric vehicle, its architecture, the componentsused (their sizes and technologies), the driving cycle and the optimal control algorithmare investigated. Starting from a set of components, a method for finding all possiblearchitectures of hybrid electric vehicles is introduced. Then, the design problem is for-mulated as an optimization problem on several levels and with multiple objectives. Theresults presented in this thesis demonstrate significant potential for reducing the fuelconsumption and emission by introducing new hybrid architectures and by integrated theoptimal sizing and control of components. The design methods introduced here for hy-brid electric trucks can be easily used in the design of other transport systems, optimizingthe prototyping process and eliminating costly redesigns.

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SUMMARY

Integrated Optimal Design for Hybrid Electric Vehicles

Current challenges for newly developed vehicles are addressed in various transportationsectors, with hybrid power trains, as viable solutions. These challanges include strictlegislations on CO2 or the foreseen future-lack of oil. Having more than one source ofpower, hybrid power trains give birth to a large design space for the physical system andincrease the complexity of the controller. The strong coupling between the parameters ofthe physical system (e.g., topology) and the parameters of the controller transforms theproblem into a multi-level problem that, if solved sequentially, is by definition subopti-mal. To obtain an optimal system design, the physical system and its controller shouldbe designed in an integrated manner.

The design of a hybrid electric vehicle (HEV) can be formulated as a multi-objectiveoptimization problem that spreads over multiple levels (technology, topology, size andcontrol). In the last decade, studies have shown that, by integrating these optimizationlevels fuel and energy benefits are obtained, which go beyond the results achieved withsolely optimal control for a given topology. Due to the large number of variables foroptimization, their diversity, the nonlinear and multi-objective nature of the problem,various design methodologies have been developed, yet none has proven to be widelyaccepted. Moreover, current design methods lack generality and a systematic analysisof the vehicle. In this thesis, defining such a design methodology is discussed, from thegeneral problem definition to how to solve different design layers.

The first contribution of this work is a framework on how to automatically generatetopologies for HEVs. The first HEVs design area, the topology, has the largest designspace, yet, so far in literature, the topology design is limited investigated due to its highcomplexity. Having more than 1045 possible topo