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Issue title: Special Section: Ambient advancements in intelligent computational sciences
Guest editors: Shailesh Tiwari, Munesh Trivedi and Mohan L. Kohle
Article type: Research Article
Authors: Gujarathi, Pritam K.a; * | Shah, Varsha A.a | Lokhande, Makarand M.b
Affiliations: [a] Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India | [b] Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharastra, India
Correspondence: [*] Corresponding author. Pritam K. Gujarathi, Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat-395007, India. E-mail: pritamgujarathi@rediffmail.com.
Abstract: Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms.
Keywords: Artificial bee colony, emission, optimization, plug-in hybrid electric vehicle
DOI: 10.3233/JIFS-169710
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1743-1753, 2018
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