ASTERICS – Advanced Simulation Models and Accelerated Testing for the Development of Electric Vehicles

Pfluegl, Horst; Diwoky, Franz; Brunnsteiner, Bernhard; Schlemmer, E.; Olofsson, Ylva; Groot, Jens; Piu, Alessandro; Magnin, Pacôme; Sellier, Franck; Sarrazin, Mathieu; Berzi, Lorenzo; Delogu, Massimo; Katrašnik, Tomaž; Kaufmann, André

doi:10.1016/j.trpro.2016.05.432

Cited by 7 publications

(3 citation statements)

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“…In another study [45], the authors presented an objective to develop better models at the early-phase design to aid component-level optimisation and system-level global optimisation in EV design. The similar study carried out in [46] argues that e-component models need to be fully representative of steady-state, dynamic and degradation behaviour. Nevertheless, the full representative models require component characterization and model parameterization efforts to deliver the level of accuracy they are claiming.…”

Section: Current Early-phase Modelling Practices For Electric Vehiclesmentioning

confidence: 89%

“…As previously discussed, motorcycles are more sensitive to mass and volume constraints than four-wheelers. In the high-fidelity tools for early-stage design proposed in the literature [36,43,45,46,64], the complexity of the modelling aims to answer component-level topology questions rather than high-level vehicle system questions. This approach in vehicle development might have been embedded due to traditional requirement-based design approaches.…”

Section: Electric Motorcycle Development Point Of Viewmentioning

confidence: 99%

“…There are certain control features, such as gear shifting, ideal traction control, and regenerative braking based on ideal traction, which are included in the model to achieve the most from this level of fidelity. Moreover, most of the methodologies proposed in the literature are high-fidelity [36,43,45,46,64] and suitable for second, or later, development projects, where fundamental data for model parameterisation are available after the development of the first project. However, it is costly, time consuming and, most of the time, infeasible to invest in the characterisation of candidate components at the detail required for model parameterisation.…”

Section: Modelling Point Of Viewmentioning

confidence: 99%

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Rapid Decision-Making Tool for Electric Powertrain Sizing for Motorcycles during New Product Development

Kirca,

McGordon,

Dinh

2024

Energies

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As part of the intergovernmental and public interventions to reduce carbon dioxide emissions, there are no existing regulations to ban the sale of petrol motorcycles (PM), but it is expected that motorcycle regulations will follow car regulations with several years of delay. There is an emerging trend in motorcycle uptake, which will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfies design challenges specific to electric motorcycles (EM) and electric powertrains. There is significant importance in motorcycle design to quantify the vehicle-level performance indicators and specifications, which are not limited to total vehicle mass, range, acceleration performance, and top speed. Those performance indicators should be quantified for different powertrain configurations and component selections to identify the most suitable configuration for the specific motorcycle development. In this paper, an innovative powertrain sizing approach is proposed to provide solutions for EMs against the design challenges specific to electric motorcycles. The innovative approach is to apply the practice of design space exploration (DSE) in resilient system design (RSD) to EM development. As a proof of concept, a case study of battery sizing is presented, in which a powertrain sizing tool is used to identify battery pack sizing requirements using requirement-based design (RBD), sensitivity analysis and DSE. The case study shows that the RBD approach allows EM product developers to identify a single solution, while DSE clearly demonstrates the trade-off between different configurations, taking multiple design variables into account. The tool prioritises high accessibility and high confidence with limited information at the early phases of electric motorcycle powertrain component sizing and selection.

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Section: Current Early-phase Modelling Practices For Electric Vehiclesmentioning

confidence: 89%