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Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs

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  • Singh, Krishna Veer
  • Bansal, Hari Om
  • Singh, Dheerendra

Abstract

This paper focuses on optimal energy sharing between the two sources i.e., the internal combustion engine and the battery-powered electric motor in a hybrid electric vehicle (HEV). It is necessary that these sources operate in their efficient operating region while fulfilling the energy demanded by the vehicle to obtain the maximum fuel economy. As both of these sources have different operating characteristic and vehicle running conditions, the situation requires a smart controller to address this problem appropriately. In this work, fuzzy logic and Elman neural network-based adaptive energy management strategies (EMS) in an HEV are designed and implemented. The input parameters to these EMS are torque demand, battery state of charge, and regenerative braking. The proposed strategy aims to maximise the fuel economy while maintaining the battery health. A power-split HEV along with EMS is designed, modelled and simulated in MATLAB/Simulink first and then the whole system is validated in real-time using controller hardware in the loop testing platform (CHIL). The FPGA based MicroLabBox CHIL has been employed to test the system behaviour in real-time. The proposed EMS have been compared with conventional strategies and the comparison reveals that the Elman neural network-based method results in higher fuel economy, faster response, and minimal mismatch between desired and attained vehicle speeds.

Suggested Citation

  • Singh, Krishna Veer & Bansal, Hari Om & Singh, Dheerendra, 2021. "Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221004011
    DOI: 10.1016/j.energy.2021.120152
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    References listed on IDEAS

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    2. Yuguo Xu & Enyong Xu & Weiguang Zheng & Qibai Huang, 2023. "Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    3. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
    4. Chengqun, Qiu & Wan, Xinshan & Wang, Na & Cao, Sunjia & Ji, Xinchen & Wu, Kun & Hu, Yaoyu & Meng, Mingyu, 2023. "A novel regenerative braking energy recuperation system for electric vehicles based on driving style," Energy, Elsevier, vol. 283(C).
    5. Ahmad Esmaeilzadeh & Brian Deal & Aghil Yousefi-Koma & Mohammad Reza Zakerzadeh, 2022. "How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading," Energies, MDPI, vol. 15(22), pages 1-27, November.
    6. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    7. Saksham Consul & Krishna Veer Singh & Hari Om Bansal & Katherine A. Kim, 2023. "Intelligent switching mechanism for power distribution in photovoltaic-fed battery electric vehicles," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8259-8278, August.

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