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Modelling and Optimization of Energy Range Extended Electric Bus Strategy Management System Using Dynamic Programming
Haryadi G.D.a, Pramaishella S.N.I.a, Haryanto I.a, Santosa S.P.b
a Diponegoro University, Department of Mechanical Engineering, Semarang, Indonesia
b Bandung Institute of Technology, Department of Mechanical and Aerospace Engineering, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.The number of motor vehicle increases at each year in Indonesia involve much negative impact on human life such as traffic jam. People choose to go by bus to avoid the traffic jam. Another negative impact is an increase amount of carbon dioxide (CO2) emissions in the air. Replacing motor vehicle to electric vehicle is the better way to decrease amount of carbon dioxide emissions. Range extended electric bus is a type of electric bus which use electric and fuel for energy source. On the basis of a typical Japanese driving cycle, optimal control strategy is designed according to the state of charge (SOC) consumption trend, which is optimized by the dynamic programming (DP) algorithm. The SOC value determines the mileage and fuel consumption, it will be the main goal of energy management. The result show that when REEB go through distance as long as the distance of BRT UNDIP-UNNES bus route, the amount of Japanese driving cycle are 11 cycles. The energy and fuel consumption that optimized by DP strategy can reach 121.66 MJ and 0.0143 L/Km. Compared with the conventional bus, the fuel consumption reach 0.212 L/Km.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Carbon dioxide emissions,Driving cycle,Dynamic programming algorithm,Electric bus,Energy ranges,Optimal control strategy,State of charge,Strategy management[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]and energy management,dynamic programming,fuel consumption,range extended electric bus,state of charge[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This paper is supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program-Center for Collaborative (CCR) National Center for Sustainable Transportation Technology CORES – Diponegoro University.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8994022[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]