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Optimal design and power management control of hybrid biofuel–electric powertrain

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  • Eckert, Jony Javorski
  • Silva, Fabrício L.
  • da Silva, Samuel Filgueira
  • Bueno, André Valente
  • de Oliveira, Mona Lisa Moura
  • Silva, Ludmila C.A.

Abstract

This paper presents a multi-objective optimization procedure for a hybrid biofuel–electric vehicle (HBEV) powertrain design and fuzzy logic control. The vehicle concept is based on an electric drivetrain, while the battery is recharged by an onboard generator coupled to an ethanol-powered engine. The optimization procedure is done by the interactive adaptive-weight genetic algorithm method, aiming for the minimization of the fuel consumption, tailpipe emissions, powertrain size, and charging time, while the battery lifespan is improved. The powertrain parameters are defined as design variables, along with the membership functions, rules, and weights of the fuzzy logic controllers, responsible for the power split among the four in-wheel electric motors, the engine and generator operation. The optimization is done under the combination of four standard driving cycles combined in a 4 times loop, resulting in a 281.8 km path. As compared to a conventional vehicle with the same engine data, the best HBEV solution was able to save 17.78% fuel, while decreasing the emissions by 52.38% HC, 22.85% CO, and 28.57% NOx, considering a battery life expectation of 23,907 h. Moreover, this optimum HBEV was also evaluated under two combinations of real-world driving cycles, reaching fuel savings up to 30% and lower emissions as compared to the conventional vehicle in all analyzed scenarios. Finally, a cost analysis is performed to compare the proposed HBEV powertrain concept with other hybrid vehicle configurations presented in the literature.

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  • Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922011643
    DOI: 10.1016/j.apenergy.2022.119903
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    2. Hong, Jichao & Zhang, Tiezhu & Zhang, Zhen & Zhang, Hongxin, 2023. "Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio," Energy, Elsevier, vol. 278(C).
    3. Anwar, Hamza & Vishwanath, Aashrith & Ahmed, Qadeer & Chunodkar, Apurva, 2023. "Comprehensive energy footprint benchmarking of commercial electrified powertrains," Applied Energy, Elsevier, vol. 345(C).
    4. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).
    5. Zhou, Xingyu & Sun, Chao & Sun, Fengchun & Zhang, Chuntao, 2023. "Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency," Applied Energy, Elsevier, vol. 344(C).
    6. Ireneusz Pielecha & Wojciech Cieslik & Filip Szwajca, 2023. "Energy Flow and Electric Drive Mode Efficiency Evaluation of Different Generations of Hybrid Vehicles under Diversified Urban Traffic Conditions," Energies, MDPI, vol. 16(2), pages 1-17, January.

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