Abstract
Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%.
Highlights
The widespread use of fossil fuels in the automotive field has been considered in the last few decades as one of the major contributors responsible for urban pollution and climate change
The initial state of charge (SOC) is assumed to be 0.6, which needs to be respected at the end of the whole FH optimization as well as at the end of each Hp horizon for the receding horizon (RH) optimization
The optimal energy management of parallel hybrid electric vehicles (HEVs) based on the receding horiz approach has been addressed in this work
Summary
The widespread use of fossil fuels in the automotive field has been considered in the last few decades as one of the major contributors responsible for urban pollution and climate change These issues have created a strong impulse towards the development of alternative propulsion systems that could guarantee competitive features vs conventional internal combustion engine (ICE)-based powertrains in terms of operational costs, performance, and autonomy, as well as industrial feasibility. The energy management strategy (EMS) is in charge of instantaneously evaluating the optimal power ratio between ICE and electric motor-generator (EMG), in relation to the requested drive power and the available energy in the battery (i.e., its stateof-charge—SoC), with the aim of minimizing fuel consumption and exhaust emissions. To evaluate the power requested at the wheels, the optimal vehicle speed is accounted for given a certain driving path, and the wheel’s power request is computed through a vehicle longitudinal dynamics model
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