ISSN: 2641-3086
Trends in Computer Science and Information Technology
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Optimal E-Powertrain Solutions for Future Electric Vehicles

Erfan Mohagheghi1*, Joan Gubianes Gasso1 and Pu Li2

1MicroFuzzy GmbH, Taunusstraße 38, 80807 Munich, Germany
2Department of Process Optimization, Ilmenau University of Technology, Ilmenau, Germany
*Corresponding author: Dr. Erfan Mohagheghi, MicroFuzzy GmbH, Taunusstraße 38, 80807 Munich, Germany, E-mail:
Received: 29 June, 2020 | Accepted: 28 August, 2020 | Published: 29 August, 2020
Keywords: Adversarial examples; Adversarial robustness; Computer vision; Machine learning; Neural networks

Cite this as

Mohagheghi E, Gasso JG, Li P (2020) Optimal E-Powertrain Solutions for Future Electric Vehicles. Trends Comput Sci Inf Technol 5(1): 042-043. DOI: 10.17352/tcsit.000018

Owing to increasing emission specification, decreasing price of energy storage systems and power electronic devices, in addition to fast-developing technology, Electric Vehicles (EVs) will become a significant share of automotive market in the near future [1]. Therefore, there is a huge competition among car manufacturers to produce EVs. The final price and driving range are known as vital factors to win the competition. For this purpose, e-powertrain of EVs should be efficiently designed and managed to maximize the driving range [2,3] while the total costs (including implementation, operation, maintenance and replacements) be minimized [4]. A complex multi-objective dynamic Mixed-Integer Nonlinear Programming (MINLP) optimization problem [5-11] needs to be solved to achieve this goal. The major reason of the complexity lies in the hybridization of EVs with different types of power sources [12-17] e.g., Battery Storage Systems (BSSs), Supercapacitors (SCs), Fuel cells (FCs), Photovoltaic (PV) modules, and flywheels (FWs) (Figure 1). BSSs are used for their high energy density, while FWs and SCs are utilized mostly due to their high power density supplying transient active-reactive power demand. FCs and PV modules work to generate power without exposing pressure to energy grid. As shown in Figure 1, these sources are connected to the Electric Machine (EM) through different DC/DC and DC/AC converters enabling the EM to consume/generate active-reactive power. The efficiency of all these components varies based on their size and load profiles. Therefore, the optimization problem aims to minimize the operation costs (probabilistic) during the lifetime of EV (e.g., 15-year prediction horizon) and the initial costs (deterministic) by finding both the size and the number of all components while satisfying operation and design constraints.

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© 2020 Mohagheghi E, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.