Learning Based Energy Management Strategy Offline Trainers Comparison for Plug-In Hybrid Electrical Buses
| Autor: | Yancho Todorov, Mikko Pihlatie, Haizea Gaztanaga, Jon Ander Lopez-Ibarra |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Energy management
Computer science neural network 020209 energy neo-fuzzy neuron Automotive industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Bridge (nautical) 0202 electrical engineering electronic engineering information engineering dynamic optimization Plug-in SDG 7 - Affordable and Clean Energy 0105 earth and related environmental sciences Adaptive neuro fuzzy inference system Artificial neural network business.industry Control engineering Fuzzy control system fuzzy logic business computer Efficient energy use backpropagation |
| Zdroj: | Lopez-Ibarra, J A, Gaztanaga, H, Todorov, Y & Pihlatie, M 2020, Learning Based Energy Management Strategy Offline Trainers Comparison for Plug-In Hybrid Electrical Buses . in 2020 IEEE Vehicle Power and Propulsion Conference (VPPC) ., 9330855, IEEE Institute of Electrical and Electronic Engineers, 17th IEEE Vehicular Power and Propulsion Conference, VPPC 2020, 18/11/20 . https://doi.org/10.1109/VPPC49601.2020.9330855 |
| DOI: | 10.1109/VPPC49601.2020.9330855 |
| Popis: | The automotive industry is facing a transformation towards the massive digitalization and data-acquisition of the vehicles operation. The exploitation of operational data opens up new opportunities in the energy efficiency improvement of the vehicles. In this regard, the combination of optimization techniques with neural networks and fuzzy systems in one unified framework, known as learning-based energy management strategies, have been identified as promising methods. These learning-based techniques combine the optimized operation with the IF-THEN human-type reasoning simplicity of a fuzzy system through neural-type of learning. Therefore, fuzzy-neural networks are the bridge that allows to learn offline from the optimal operation and design energy management strategy for real time implementation. In this regard, the main contribution of this paper lies on the comparison of a previously developed ANFIS approach with a simpler Neo-Fuzzy neuron based, with the aim to evaluate the tradeoff between accuracy and computational and structural efficiency. The proposed approach represents a fuzzy-neural structure with less parameters for training that is expected to facilitate its future real time application for energy management strategies for each bus from a fleet operating on a predefined route. |
| Databáze: | OpenAIRE |
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