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Energy management of hybrid energy storage system with renewable energy sources for electric vehicles based on green anaconda optimization and multi-resolution sinusoidal neural network

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  • Soubache, Irissappane Dhanusu
  • Malleswara Rao, D S Naga
  • Susitra, D.
  • Saini, Sumit

Abstract

Efficient energy management (EM) in hybrid energy storage systems (HESS) using renewable energy sources (RESs) is critical for optimizing electric vehicles (EVs). However, integrating advanced storage technologies with RESs is costly and can suffer from energy losses during storage and conversion, impacting overall system performance. This paper suggests a novel hybrid approach, combining Green Anaconda Optimization (GAO) and Multiresolution Sinusoidal Neural Network (MSNN), termed GAO-MSNN, to optimize EM for HESS in EVs. GAO ensures efficient energy distribution between HESS components, balancing supply and demand, while MSNN predicts future energy demand and EV charging/discharging profiles based on driving patterns and renewable inputs. The suggested GAO-MSNN is implemented in MATLAB and benchmarked against existing techniques, such as ANN-PSO, COA-CANN, GOA-THDCNN, NBO-QNN, and SCSO-RERNN. Results show that GAO-MSNN achieves a 99 % efficiency and operational cost of 1508 cents, outperforming other methods in terms of both cost-effectiveness and energy optimization, making it an effective solution for sustainable EV EM.

Suggested Citation

  • Soubache, Irissappane Dhanusu & Malleswara Rao, D S Naga & Susitra, D. & Saini, Sumit, 2026. "Energy management of hybrid energy storage system with renewable energy sources for electric vehicles based on green anaconda optimization and multi-resolution sinusoidal neural network," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051874
    DOI: 10.1016/j.energy.2025.139545
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    References listed on IDEAS

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