Abstract
This paper presents a new methodology for optimal sizing of the energy storage system ( E S S ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( R C V ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( L i – P o ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.
Highlights
In cities with high density of population, refuse collector vehicles (RCVs) are of vital importance due to the need to maintain a healthy city
A new methodology is developed for the optimal design of the energy storage system for an hybrid electric (HE)–RCV, which has been validated using real routes from an Iveco Stralis GNC 270 RCV
A model of a nonlinear multi-objective optimisation problem with constraints is achieved, which allows for defining the characteristics of the cell that makes up the energy storage system (ESS)
Summary
In cities with high density of population, refuse collector vehicles (RCVs) are of vital importance due to the need to maintain a healthy city. Several layers are defined, the components sizing and the control strategy can fulfil the requirements to achieve the HEV optimal design, either for series, parallel or hybrid topology. In [19], the optimal component sizing based on the weight sum method is achieved for a hybrid refuse collector vehicle. It should be noted that, in the HEV design, multi-objective optimisation is required due to the nature of the system, i.e., energy storage system and control strategy. In [25,26], the optimal powertrain component sizing with a multi-objective genetic algorithm is achieved. Each item from the optimal set allows for sizing the energy storage system, based on the real drive cycle of a conventional refuse collector vehicle.
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