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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Energy optimization in autonomous driving using deep reinforced learning and stochastic system modelling |
| Authors Name: | Akash Ladha , Divya Priyadharshini Mohan |
| Unique Id: | IJSDR2109021 |
| Published In: | Volume 6 Issue 9, September-2021 |
| Abstract: | In the development of future low-emission vehicles, Machine Learning is playing a significant role, since manufacturers progressively hit constraints with existing technology. In addition to independent driving, new improvements in reinforcement learning are also quite good at handling complicated parameterisation challenges. Deep reinforced training is utilised in this research to derive efficient electric hybrid vehicle operating methods. A wide range of possible driving and traffic conditions should be predicted, so that fuel-efficient solutions may be achieved in order to achieve intelligent and adaptable processes. This study demonstrates a reinforced learning agent's capacity to learn almost optimum operational strategies without previous route knowledge and gives a large potential for more factors to be included in the optimization procedure. This paper includes (1) a deep learning context that will enable discovering virtually optimum operating strategies. (2) The use of stochastic driver models to increase public generalisation and prevent overfitting of the approach. (3) Inclusion of the optimization process of battery modelling with extra power restrictions. The results are simulated and comparison graphs are plotted for the derived model. |
| Keywords: | Electric vehicles, Fuel economy, Exhaust emissions, Battery state of charge, Deep reinforced learning |
| Cite Article: | "Energy optimization in autonomous driving using deep reinforced learning and stochastic system modelling", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.6, Issue 9, page no.132 - 140, September-2021, Available :http://www.ijsdr.org/papers/IJSDR2109021.pdf |
| Downloads: | 000223213 |
| Publication Details: | Published Paper ID: IJSDR2109021 Registration ID:193669 Published In: Volume 6 Issue 9, September-2021 DOI (Digital Object Identifier): Page No: 132 - 140 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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