Optimization of Electric Vehicle Concepts Based on Customer-Relevant Characteristics

Wiedemann, Elias; Meurle, Jürgen; Lienkamp, Markus

doi:10.4271/2012-01-0815

Cited by 16 publications

(22 citation statements)

References 6 publications

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“…Since most automotive powertrain design tasks involve competing objectives, e.g. energy consumption, range, acceleration performance, safety, comfort, and economy [4], [26]- [28], the use case in this study has multiple objectives (cf. MOP).…”

Section: B Multiobjective Optimization Problemmentioning

confidence: 99%

Comparative Case Study of a Metamodel-Based Electric Vehicle Powertrain Design

Helbing¹,

Uebel

Matthes

et al. 2021

IEEE Access

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In a variety of engineering applications, metamodels replace detailed computer models in order to reduce the computational effort of simulation processes. Unfortunately, metamodels also degrade the quality of the simulation results. Unfortunately, metamodels reduce not only the computational effort, but also the quality of the simulation results. However, no attention has been paid to this trade-off in previous research. Thus, we compare the computational effort and solution quality of a metamodel-based system design. As a use case for this comparison, we define a particular automotive powertrain design task and formulate it as a multi-objective optimization problem with two design objectives and three design variables. First, we solve this use case with a physics-based vehicle model, which is a typical detailed model for powertrain design. Second, we solve the same problem with metamodels. Here, we create several metamodels with different approximation accuracies to analyze the trade-off between computational effort and solution quality. The metamodel solution with the smallest deviation (<1 %) is computed about 9 times faster than the benchmark solution on the target computer used. The fastest metamodel solution is about 42 times faster and causes deviations between 5 % to 10 % compared to the benchmark. This case study can be used to develop metamodel schemes for other applications that provide fast yet accurate solutions. We also demonstrate the practicality of metamodels in real-world system design by comparing the computational effort quantitatively for the first time.

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Section: B Multiobjective Optimization Problemmentioning

confidence: 99%

Comparative Case Study of a Metamodel-Based Electric Vehicle Powertrain Design

Helbing¹,

Uebel

Matthes

et al. 2021

IEEE Access

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“…For the weight analysis, we implement a regression model that estimates the tire weight, m tire , based on the tire diameter and its section width. The regression is derived from the evaluation of the vehicles in Appendix A Tables A1-A3 and shown in Equation 11: m tire = −16.890 kg + (0.023 kg/mm) × D tire + (0.054 kg/mm) × w tire (11) The developed model has an adjusted R 2 of 85.85%, an MAE of 0.71 kg, and an nMAE of 6.63%.…”

Section: Weight Modelmentioning

confidence: 99%

“…Wiedemann et al [11,12] develop a more detailed method for estimating BEVs weight. They derive a basis weight for the vehicle using the model of Yanni and further add to the basis weight the weight of the electric powertrain, which comprises traction battery, electric machine, power electronics, and transmission.…”

Section: Introductionmentioning

confidence: 99%

Parametric Modeling of Mass and Volume Effects for Battery Electric Vehicles, with Focus on the Wheel Components

Nicoletti

Romano

König

et al. 2020

WEVJ

Self Cite

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Defining a vehicle concept during the early development phase is a challenging task, since only a limited number of design parameters are known. For battery electric vehicles (BEVs), vehicle weight is a design parameter, which needs to be estimated by using an iterative approach, thus causing weight fluctuations during the early development phase. These weight fluctuations, in turn, require other vehicle components to be redesigned and can lead to a change in their size (secondary volume change) and weight (secondary weight change). Furthermore, a change in component size can impact the available installation space and can lead to collision between components. In this paper, we focus on a component that has a high influence on the available installation space: the wheels. We model the essential components of the wheels and further quantify their secondary volume and weight changes caused by a vehicle weight fluctuation. Subsequently, we model the influence of the secondary volume changes on the available installation space at the front axle. The hereby presented approach enables an estimation of the impact of weight fluctuations on the wheels and on the available installation space, which enables a reduction in time‑consuming iterations during the development process.

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“…In today's VC development process, user and technological requirements are taken into account through a three step loop as illustrated in Figure 2. They incorporate customer-relevant properties, technical characteristics and VC (Fuchs and Lienkamp, 2013;Wiedemann et al, 2012): The customer-relevant properties specify the vehicle's behavior and the customer's requirements for the VC. They outline the customers attached values in relation to their purchase decision (Helm et al, 2017, p. 6).…”

Section: Vehicle Concept Developmentmentioning

confidence: 99%

Mobility Box: A Design Research Methodology to Examine People's Needs in Relation to Autonomous Vehicle Designs and Mobility Business Model

Wolff

Auernhammer

Schockenhoff

et al. 2020

Proc. Des. Soc.: Des. Conf.

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The future focus of research in the automotive sector will be on automation and data-driven technologies. These technologies provide new services and values to customers, owners and other stakeholders in mobility. We propose a wizard-of-oz like method to evaluate future user needs and redesign the current approach of vehicle development. The mobility box provides a modular and extendable framework to merge user centred design with vehicle data acquisition. This enables the design of services and properties for unknown mobility concepts.

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Optimization of Electric Vehicle Concepts Based on Customer-Relevant Characteristics

Cited by 16 publications

References 6 publications

Comparative Case Study of a Metamodel-Based Electric Vehicle Powertrain Design

Comparative Case Study of a Metamodel-Based Electric Vehicle Powertrain Design

Parametric Modeling of Mass and Volume Effects for Battery Electric Vehicles, with Focus on the Wheel Components

Mobility Box: A Design Research Methodology to Examine People's Needs in Relation to Autonomous Vehicle Designs and Mobility Business Model

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