An energy management strategy for plug-in hybrid electric vehicles
ColaboratorLux, Gerhard; Romeral Martínez, José Luis; Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
Document typeDoctoral thesis
PublisherUniversitat Politècnica de Catalunya
Rights accessOpen Access
This dissertation formulates a proposal for a real time implementable energy management strategy (EMS) for plug-in hybrid electric vehicles. The EMS is developed to minimize vehicle fuel consumption through the utilisation of stored electric energy and high-efficiency operation of powertrain components. This objective is achieved through the development of a predictive EMS, which, in addition to fuel efficiency, is optimized in terms of computational cost and drivability. The requirement for an EMS in hybrid powertrain vehicles stems from the integration of two energy stores and converters in the powertrain; in the case of hybrid electric vehicles (HEVs) usually a combustion engine and one or more electric machines powered by a battery. During operation of the vehicle the EMS controls power distribution between engine and electric traction motor. Power distribution is optimized according to the operating point dependent efficiencies of the components, energy level of the battery and trip foreknowledge. Drivability considerations, e.g. frequency of engine starts, can also be considered. Due to high oil prices and legislative requirements caused by the environmental impact of greenhouse emissions, fuel economy has gained importance in recent years. In addition to increased fuel economy, powertrain hybridization permits the substituton of fuel for electrical energy by implementing an external recharging option for the battery. This vehicle class, incorporating a battery rechargeable via the electrical grid, is known as a plug-in HEV (PHEV). PHEV share characteristics of both HEVs and all-electric vehicles combining several advantages of both technologies. The rechargeable battery feature of the PHEVs makes their EMS development espe-cially challenging. For minimal fuel consumption, the battery is discharged optimally over the whole trip length, prioritising electrical energy when driving conditions are such that its use maximises the fuel saving that can be achieved. Therefore, an EMS for a PHEV depends heavily on the availability of a priori knowledge about the trip, i.e. the knowledge about future vehicle speed and road grade. This requires the driver to indi-cate the route before trip start. The route knowledge in combination with GPS or Galileo based next generation navigation systems using information from a geographic in-formation system (GIS) about terrain height profile, road type (e.g. motorway or country road), and legal speed limits can be evaluated by a speed prediction algorithm including information about the driver's behaviour for a detailed prediction of the trip. These navigation systems and algorithms in combination with expected future advances and the deployment of technologies such as intelligent transport systems (ITS) and vehicle-to-vehicle communication (V2V), will make more exact traffic information available to further improve prediction. Despite expected advances in prediction quality, inaccuracy of prediction data has to be considered and is therefore regarded in this work. The EMS proposed in this dissertation combines different approaches which are exe-cuted step by step. A first approximation of the energy distribution during the trip is based on a mixed integer linear program (MILP), which gives the optimal energy state of the battery during the trip. This is especially important for trips with long uphill, downhill or urban phases, i.e. sections with a particularly high or lower power requirement. The results from MILP are then used by a dynamic programming (DP) algorithm to calculate optimal torque and gear using a receding prediction horizon. Using a receding prediction horizon, an important reduction of computational cost is achieved. Lastly, from the DP results a rule-based strategy is extracted using a support vector machine (SVM). This last step is necessary to ensure the drivability of the vehicle also for inaccurate prediction data.
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