A Data-Driven Approach for Electric Vehicle Powertrain Modeling
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918250037641216 |
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| author | Ipek, Eymen Hirz, Mario |
| author_facet | Ipek, Eymen Hirz, Mario |
| contents | Electrification in the automotive industry and increasing powertrain complexity demand accelerated, cost-effective development cycles. While data-driven models are recently investigated at component level, a gap exists in systematically integrating them into cohesive, system-level simulations for virtual validation. This paper addresses this gap by presenting a modular framework for developing powertrain simulations. By defining standardized interfaces for key components-the battery, inverter, and electric motor-our methodology enables independently developed models, whether data-driven, physics-based, or empirical, to be easily integrated. This approach facilitates scalable system-level modeling, aims to shorten development timelines and to meet the agile demands of the modern automotive industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Data-Driven Approach for Electric Vehicle Powertrain Modeling Ipek, Eymen Hirz, Mario Systems and Control Electrification in the automotive industry and increasing powertrain complexity demand accelerated, cost-effective development cycles. While data-driven models are recently investigated at component level, a gap exists in systematically integrating them into cohesive, system-level simulations for virtual validation. This paper addresses this gap by presenting a modular framework for developing powertrain simulations. By defining standardized interfaces for key components-the battery, inverter, and electric motor-our methodology enables independently developed models, whether data-driven, physics-based, or empirical, to be easily integrated. This approach facilitates scalable system-level modeling, aims to shorten development timelines and to meet the agile demands of the modern automotive industry. |
| title | A Data-Driven Approach for Electric Vehicle Powertrain Modeling |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.14344 |