English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Two of the most important aspects of electric vehicles are their efficiencyor achievable range. In order to achieve high efficiency and thus a long range,it is essential to avoid over-dimensioning the drive train. Therefore, thedrive train has to be kept as lightweight as possible while at the same timebeing utilized to the best possible extent. This can only be achieved if thedynamic behavior of the drive train is accurately known by the controller. Thetask of the controller is to achieve a desired torque at the wheels of the carby controlling the currents of the electric motor. With machine learningmodeling techniques, accurate models describing the behavior can be extractedfrom measurement data and then used by the controller. For the comparison ofthe different modeling approaches, a data set consisting of about 40 milliondata points was recorded at a test bench for electric drive trains. The dataset is published on Kaggle, an online community of data scientists.

Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)