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Recurrent Neural Network-Based Energy Management System in Electric Vehicle Application with Hybrid Energy Sources

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ICT: Innovation and Computing (ICTCS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 879))

  • 39 Accesses

Abstract

In recent years, electric vehicle charging stations have improved as EVs have grown incredibly popular and attracted a lot of attention. By providing efficient platforms for the exchange of energy between power grids and EVs, EVCSs are unquestionably essential for the emergence of energy. Additionally, the temporary and geographic distribution of electric charges is influenced by the requirements of EV charging. The effective management of energy among EVCSs, the power grid and EVs depends on EV charging. The primary objective of this study that is being proposed is to create and modify an artificial intelligence-based energy management system for electric vehicles. In this proposed work, fuel cells, ultracapacitors, batteries and EVs are considered. The ultracapacitor may provide peak power and recover braking energy by connecting to the DC bus in parallel via a bidirectional DC-DC converter, which reduces the load on the fuel cell system and battery. This lengthens battery life by requiring less energy to charge and discharge the battery. The RNN model, which uses the fuel cell, UC, battery and EV factors, works in tandem with energy management. The appropriate speed, battery power, UC's SOC, UC current, UC voltage and fuel cell power are taken by altering the FTP 75 driving cycle. The suggested RNN model uses these regulating factors as input and uses them to predict the phase delay for better energy management. The experimental evaluation and analysis of the proposed model are carried out in MATLAB. Lastly, the superiority of the suggested technique is demonstrated with respect to error measures.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

BFS:

Breadth first search

DP:

Dynamic programming

ECMS:

Equivalent consumption minimization strategy

EMS:

Energy management system

EVs:

Electric vehicles

FCs:

Fuel cells

FSM:

Finite state machine

GVs:

Gasoline-powered vehicles

HEV:

Hybrid electric system

ICE:

Internal combustion engine

OER:

Oxygen excess ratio

PEMFC:

Proton-exchange membrane fuel cell

PMS:

Power management scheme

PSO:

Particle swarm optimization

RNN:

Recurrent neural network

SOC:

State of charge

UC:

Ultracapacitor

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Authors and Affiliations

  1. DATU, Lonere, Maharashtra, 421601, India

    Harsh Jondhle, Anil B. Nandgaonkar, Sanjay Nalbalwar & Brijesh R. Iyer

  2. AI & ML, Mumbai, India

    Sneha Jondhle

Authors
  1. Harsh Jondhle
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  2. Anil B. Nandgaonkar
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  3. Sanjay Nalbalwar
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  4. Sneha Jondhle
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  5. Brijesh R. Iyer
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Corresponding author

Correspondence to Harsh Jondhle .

Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

  2. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  3. University of Peradeniya, Kandy, Sri Lanka

    Roshan G. Ragel

  4. Department of Comp Sci & Engg, SNS College of Technology, Coimbatore, Tamil Nadu, India

    S. Karthik

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jondhle, H., Nandgaonkar, A.B., Nalbalwar, S., Jondhle, S., Iyer, B.R. (2024). Recurrent Neural Network-Based Energy Management System in Electric Vehicle Application with Hybrid Energy Sources. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_4

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  • DOI: https://doi.org/10.1007/978-981-99-9486-1_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9485-4

  • Online ISBN: 978-981-99-9486-1

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