Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

Anselma, Pier Giuseppe; Niutta, Carlo Boursier; Mainini, Laura; Belingardi, Giovanni

doi:10.1007/s00158-020-02603-6

Cited by 22 publications

(13 citation statements)

References 44 publications

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“…In contrast, oversizing the components leads to an excess of vehicle mass, poor efficiency in part-load operation, and an increase in cost [23]. Plenty of research has been invested in the optimization of the powertrain architecture as described in [24][25][26][27][28][29].…”

Section: Challenges With Regard To the Design And The Operation Of Hybrid Electric Vehiclesmentioning

confidence: 99%

Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings

et al. 2021

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The electric hybridization of vehicles with an internal combustion engine is an effective measure to reduce CO2 emissions. However, the identification of the dimension and the sufficient complexity of the powertrain parts such as the engine, electric machine, and battery is not trivial. This paper investigates the influence of the technological advancement of an internal combustion engine and the sizing of all propulsion components on the optimal degree of hybridization and the corresponding fuel consumption reduction. Thus, a turbocharged and a naturally aspirated engine are both modeled with the additional option of either a fixed camshaft or a fully variable valve train. All models are based on data obtained from measurements on engine test benches. We apply dynamic programming to find the globally optimal operating strategy for the driving cycle chosen. Depending on the engine type, a reduction in fuel consumption by up to 32% is achieved with a degree of hybridization of 45%. Depending on the degree of hybridization, a fully variable valve train reduces the fuel consumption additionally by up to 9% and advances the optimal degree of hybridization to 50%. Furthermore, a sufficiently high degree of hybridization renders the gearbox obsolete, which permits simpler vehicle concepts to be derived. A degree of hybridization of 65% is found to be fuel optimal for a vehicle with a fixed transmission ratio. Its fuel economy diverges less than 4% from the optimal fuel economy of a hybrid electric vehicle equipped with a gearbox.

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Section: Challenges With Regard To the Design And The Operation Of Hybrid Electric Vehiclesmentioning

confidence: 99%

Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings

et al. 2021

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“…Namely, multi-fidelity methods leverage on a fidelity spectrum of computational models (from low to high fidelity), with the objective of maximizing the model accuracy while minimizing the associated computational cost [9,10]. The fidelity spectrum may stem from using different physical models [11,12], spatial and/or time discretizations (e.g., grid size and time step) [13][14][15][16], multidisciplinary coupling (e.g., one-or two-way, tight or loose coupling, etc.) [17,18], degree of solution convergence [19,20], model dimensionality [21,22], and a combination of experimental and numerical data [23,24].…”

Section: Introductionmentioning

confidence: 99%

A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations

et al. 2022

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The paper presents a multi-fidelity extension of a local line-search-based derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for use in the simulation-driven design optimization (SDDO) context, where multi-fidelity computations are used to evaluate the objective function. The proposed algorithm starts using low-fidelity evaluations and automatically switches to higher-fidelity evaluations based on the line-search step length. The multi-fidelity algorithm is driven by a suitably defined threshold and initialization values for the step length, which are associated to each fidelity level. These are selected to increase the accuracy of the objective evaluations while progressing to the optimal solution. The method is demonstrated for a multi-fidelity SDDO benchmark, namely pertaining to the hull-form optimization of a destroyer-type vessel, aiming at resistance minimization in calm water at fixed speed. Numerical simulations are based on a linear potential flow solver, where seven fidelity levels are used selecting systematically refined computational grids for the hull and the free surface. The method performance is assessed varying the steplength threshold and initialization approach. Specifically, four MF-CS-DFN setups are tested, and the optimization results are compared to its single-fidelity (high-fidelity-based) counterpart (CS-DFN). The MF-CS-DFN results are promising, achieving a resistance reduction of about 12% and showing a faster convergence than CS-DFN. Specifically, the MF extension is between one and two orders of magnitude faster than the original single-fidelity algorithm. For low computational budgets, MF-CS-DFN optimized designs exhibit a resistance that is about 6% lower than that achieved by CS-DFN.

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“…This work, together with previous authors' papers Krzywobłocki 2017, 2019) completes this area. Since 2017, there has been a further increase in research dealing with the issues of optimization of crashworthy structures, for example, (Bahramian and Khalkhali 2020) related to topology optimization of square tube or (Anselma 2020) referred to the multidisciplinary optimization of the crashworthiness of hybrid electric vehicles, which are gaining in popularity in recent years. Furthermore, there is a growing interest in energy-absorbing structures made of CFRP (Li et al 2020;Liu et al 2020;Liu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimal crashworthiness design of vehicle S-frame using macro-element method and evolutionary algorithm

Pyrz

Krzywoblocki

Wolska

2022

Struct Multidisc Optim

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The paper presents the crashworthiness optimization of a thin-walled frame applied as energy-absorbing element in a car structure. Crushing parameters of S-frame are modeled using the macro-element methodology. This method is implemented in the Visual Crash Studio software that enables very fast simulation of structural behavior during the impact. The objective is to determine the optimal dimensions of the frame cross-section to achieve the maximal energy absorption. Moreover, the selection of the best angle between the frame segments is investigated. In the formulation of the optimization problem, constraints related to the progressive collapse of deformation zones, required by the macro-element modeling, have been introduced. An Evolutionary Algorithm was applied to search the best solution. A real-life example of thin-walled S-frame is investigated in numerical examples. An attempt to find the solution by solving a sequence of simpler problems with reduced number of design variables is investigated. This approach is compared to the best result obtained for the problem including all design variables at the same time. This study illustrates the potential of the optimization in early-design stages of the vehicle development process and prepares perspectives for the optimization of complex energy-absorbing systems.

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Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

Cited by 22 publications

References 44 publications

Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings

Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings

A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations

Optimal crashworthiness design of vehicle S-frame using macro-element method and evolutionary algorithm

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