Optimal Control Strategy for the Next Generation Range Extended Electric Bus

Electric and Hybrid-Electric buses have become a major vehicle platform for demonstrating the advantages and capabilities of electrification in heavy duty vehicles. This type of vehicle can be powered from several different sources that each have several unique operating characteristics and performa...

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Bibliographic Details
Main Authors: Zhang, Yuanjian, Murtagh, Martin, Early, Juliana, Cunningham, Geoff, Steele, David, Best, Robert
Format: Report
Language:English
Online Access:Request full text
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Summary:Electric and Hybrid-Electric buses have become a major vehicle platform for demonstrating the advantages and capabilities of electrification in heavy duty vehicles. This type of vehicle can be powered from several different sources that each have several unique operating characteristics and performance requirements that necessitate novel solutions. In this paper, a novel optimal control strategy based on the next generation range-extended electric bus (REEB) has been developed. Control strategies play an essential role in realizing the full potential of electric buses and through careful implementation can increase their effectiveness at displacing conventional internal-combustion powered buses and thus, reducing global fuel consumption and emissions. Initially, a control-oriented powertrain model was developed in Matlab/Simulink. A new control strategy was devised for a series-hybrid baseline vehicle based on an equivalent consumption minimization strategy (ECMS) to govern the powertrain when operating in different modes. Due to the high impact the drive cycle has on the equivalent factor (EF) in an ECMS, an offline optimization process is performed to further increase the effect of the ECMS on various driving routes. Particle swarm optimization (PSO) was applied to the offline control model, generating the most appropriate EF values for different driving routes. The optimization of the EF values, the implementation of the new control strategy and the control execution process are described here in detail. Finally, the proposed control strategy is demonstrated in the simulation environment by analysing its performance with those of a simple rule-based strategy and a dynamic programming based global optimisation strategy. Comparison of results suggests the superior performance of the proposed method in fuel economy optimization versus the rule-based control method and the similarities to the dynamic programming based control method.
ISSN:0148-7191
2688-3627
DOI:10.4271/2020-01-0844