Drive cycle identification and future energy-demand prediction are advantageous when developing hybrid propulsion systems. They are applicable to vehicles that are driven along the same route everyday such as buses, refuse-collecting vehicles (RCVs) or delivery vehicles.Drive cycle identification can be used to identify what power transients can be expected to prepare the power train to operate under these conditions. If the energy management algorithm of a hybrid vehicle can account for future energy demand, then it can be arranged in such a way that the non-fossil-fuel energy sources are fully depleted at the end of the drive cycle.Given that RCVs always drive in similar drive cycles, drive cycle has been modeled and its main characteristics parameterized. The model is separated into different drive cycles which are related to different power-consumption modes.In this paper, a new method to identify drive cycles and the energy left to finish a route is proposed. The drive cycle identification is based on artificial intelligence algorithms, which have been trained and tested with real data with an average efficiency in drive cycle identification of over 90%.The energy necessary to finish the route is based on vehicle energy models and statistical analysis. This method can be used in the daily management of fleet vehicles to replace fossil fuel by electric energy, as is demonstrated in the proposed examples.Index Terms-Engines, hybrid power systems, energy management optimization, drive cycle prediction.
ABSTRACT− In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14%.
ABSTRACT-In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this second part, three different hybrid electric powertrain architectures are proposed and modeled. These architectures are based on the use of fuel cells, ultracapacitors, and batteries. A calculation engine, which is specifically designed to estimate energy consumption, respecting the original performance as the original internal combustion engine (ICE), is presented and used for simulations and component sizing. Finally, the overall performance of the different architectures (hybrid hydraulic, taken from the first paper part, and hybrid electric, estimated in this second part) and control strategies are summarized in a fuel and energy consumption table. Based on this table, an analysis of the different architecture performance results is carried out. From this analysis, a technological evolution of these vehicles in the medium-and long terms is proposed.
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.
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