A Creative Tool for the Musician Combining LSTM and Markov Chains in Max/MSP

Privato, Nicola; Rampado, Omar; Novello, Alberto

doi:10.1007/978-3-031-03789-4_15

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…A flat ground is considered, neglecting the effect of gravitational force on vehicle motion. The corresponding model is built using components from the Modelica Standard Library 4.0.0 [17] by extending the main idea of the drivetrain model from the Electric and Hybrid Power Trains (EHPT) library [18], as presented in Figure 5.…”

Section: Drivetrain Subsystemmentioning

confidence: 99%

Dynamic Battery Modeling for Electric Vehicle Applications

Rotas,

Iliadis,

Nikolopoulos

et al. 2024

Batteries

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The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica.

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Section: Drivetrain Subsystemmentioning

confidence: 99%

Dynamic Battery Modeling for Electric Vehicle Applications

Rotas,

Iliadis,

Nikolopoulos

et al. 2024

Batteries

View full text Add to dashboard Cite

show abstract

“…A flat ground is considered, neglecting the effect of gravitational force on vehicle motion. The corresponding model is built with the use of components of the Modelica Standard Library 4.0.0 [16] by extending the main idea of the drivetrain model of the EHPT Modelica library [17] and is presented in Figure 5.…”

Section: Drivetrain Subsystemmentioning

confidence: 99%

Dynamic Battery Modeling for Electric Vehicle Applications

Rotas,

Iliadis,

Nikolopoulos

et al. 2024

Preprint

View full text Add to dashboard Cite

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

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Mouja: Experiencing AI through Magnetic Interactions

Privato

2024

Proceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction

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A Creative Tool for the Musician Combining LSTM and Markov Chains in Max/MSP

Cited by 3 publications

References 11 publications

Dynamic Battery Modeling for Electric Vehicle Applications

Dynamic Battery Modeling for Electric Vehicle Applications

Dynamic Battery Modeling for Electric Vehicle Applications

Mouja: Experiencing AI through Magnetic Interactions

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