Scalable Dimension-, Weight- and Cost-Modeling for Components of Electric Vehicle Powertrains

Angerer, Christian; Felgenhauer, Matthias; Eroglu, Isaak; Zähringer, Maximilian; Kalt, Svenja; Lienkamp, Markus

doi:10.23919/icems.2018.8549460

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…Based on the chosen layout, volumetric modeling is employed to estimate the SVCs of the gearbox (Figure 3). The model structure follows the approach presented by (Angerer et al, 2018(Angerer et al, -2018. Given the model inputs (Section 3.1), it is possible to roughly estimate the wheel dimensions (Naunheimer et al, 2019, p. 228).…”

Section: Volumetric Modelingmentioning

confidence: 99%

Parametric Modelling of Weight and Volume Effects in Battery Electric Vehicles, With Focus on the Gearbox

et al. 2021

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The modeling of battery electric vehicles (BEVs) still represents a challenge for vehicle manufacturers. The installation of the new types of components needed for BEVs gives rise to uncertainties in the quantification of parameters like the vehicle's weight. Indeed, vehicle weight plays a key role, since it has a drastic effect on the vehicle's range, which is an important selling point for BEVs. Uncertainties in weight estimation create weight fluctuations during the early development phase and the need to resize components like the electric machine or battery. This in turn affects the components' volume and weight. However, such resizing can also lead to component collision and unfeasibility of the vehicle architecture. To solve this problem and to support concept engineers during the early development phase, an iterative approach is required that is capable of estimating weight and volume fluctuations in the relevant components. The approach should also consider the geometrical interdependencies of the components, to ensure that no collisions occur between them. Taking the gearbox as an example application, this paper presents a novel approach that satisfies these requirements.

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Section: Volumetric Modelingmentioning

confidence: 99%

Parametric Modelling of Weight and Volume Effects in Battery Electric Vehicles, With Focus on the Gearbox

et al. 2021

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“…The term 'Performance' mentioned here precisely referred to vehicle operating performance like vehicle maximum speed, maximum acceleration, speed error, acceleration error and the capability in covering the target distance as well as performance related to fuel economy and emission attributes under ecodesign category. The use of powertrain cost in PSP can be found in Pourabdollah, et al, 2018 and Angerer et al,2018 23,24 . The studies show a reduction in vehicle weight along with improvement in FE.…”

Section: Introductionmentioning

confidence: 99%

“…The term ‘Performance’ mentioned here precisely referred to vehicle operating performance like vehicle maximum speed, maximum acceleration, speed error, acceleration error and the capability in covering the target distance as well as performance related to fuel economy and emission attributes under ecodesign category. The use of powertrain cost in PSP can be found in Pourabdollah et al 23 and Angerer et al 24 The studies show a reduction in vehicle weight along with improvement in FE. The component size has effect on the total cost of ownership (TCO), which is an indicator of long-term PHEV benefits, and this can be found in the study of Luján et al 25 Song et al 26 researchers applied Pontryagin minimum principle to optimise the battery pack in a PHEV for improving FE.…”

Section: Introductionmentioning

confidence: 99%

Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain

Bhattacharjee

Ghosh

Bhola

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This work presents an ecodesigning and operating performance improvement methodology in series-parallel Plugin hybrid electric vehicle (PHEV) in passenger car category, through optimisation of powertrain, considering gradeability overreaching rolling terrain. Designing involves consideration for power of prime movers and the geometric specification governing gear ratio, which is the teeth number. PHEV performance is measured in terms of various output characteristics, such as, fuel economy, emissions, vehicle weight, battery charge, maximum velocity and maximum acceleration etc. and such output indicators comprising both ecodesign and vehicle operating performance attributes, eleven in all, are considered. For optimisation, the design space is generated using NREL, ADVISOR simulator in accordance with Taguchi’s method. Multi-criteria optimisation is used to converge the aforesaid output indicators into a single one using TOPSIS, MTOPSIS, Grey Relational Analysis and their surrogate assisted evolutionary algorithm (SAEA) based solutions to select the best from. Such design solutions are tested with UDDS driving cycle for performance analysis; reflecting superiority of SAEA based results. However, best values of output indicators are not from a single solution but are spread over these SAEAs. While, gradability is embedded in the model, its variation as supplemental factor, together with total ownership cost, are included, for extended modelling to ascertain the suitability amongst SAEAs. To extend the test for suitability beyond one driving cycle, also a combined one is formed by integrating two other, namely NEDC and 1015Prius with UDDS. The simulation experiment results from combined driving cycle also indicate preference in favour of MTOPSIS-SAEA model, complying upto 25% gradability for rolling terrain, substantially better than the reference model while also ensuring savings in fuel cost by about 60% over the entire ownership period besides reduction in greenhouse gas emissions ranging between 18% and 21%. This solution also helps in lightweighting the vehicle by over 6%.

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“…Three different methods are applied to obtain the solution points, namely Second Order Cone Program, Linear Program, and Energy Based methods for a plugin HEV. Another study presents a specialized model for determining the powertrain component sizing, weight and product cost estimation [20]. The authors have used this model for further component selection for powertrain of HEV.…”

Section: Introductionmentioning

confidence: 99%

Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance

Bhattacharjee

Ghosh

Bhola

et al. 2019

Energy

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This article presents an optimized series-parallel hybrid powertrain transformed from a conventional vehicle using an analytical approach without changing the original chassis. The proposed approach is based on a many-objective hybrid powertrain model, which aims to optimize the vehicle weight, fuel consumptions, emissions, and performances. Three different Surrogate Assisted Evolutionary Algorithms (SAEAs) are introduced based on the improved Non-dominated Sorting Genetic Algorithm (NSGA III), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Genetic Algorithm (MOGA) for optimization of powertrain components. Initially, the powertrain optimization is performed for Urban Dynamometer Driving Schedule (UDDS) driving cycle with 20% road grade. Thereafter, all the obtained Pareto solutions are combined, screened, and 78 best feasible design points are considered depending on the constraints imposed. Subsequently, 15 design points are randomly selected for validation in Federal Test Procedure (FTP) driving cycle without road grade. It is observed that NSGA III reduces the vehicle weight and fuel consumptions by 4.39% and 46.47% respectively. The powertrain energy efficiency is improved by 57.49%, and the engine is downsized by 40%. The contribution of this article is twofold. First, the many-objective simulation model for hybrid powertrain is developed. Second, SAEAs are implemented as optimization techniques for optimal component sizing and promising results are obtained.

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Scalable Dimension-, Weight- and Cost-Modeling for Components of Electric Vehicle Powertrains

Cited by 7 publications

References 14 publications

Parametric Modelling of Weight and Volume Effects in Battery Electric Vehicles, With Focus on the Gearbox

Parametric Modelling of Weight and Volume Effects in Battery Electric Vehicles, With Focus on the Gearbox

Ecodesigning and improving performance of plugin hybrid electric vehicle in rolling terrain through multi-criteria optimisation of powertrain

Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance

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