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Kim, Bill
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Combined Sizing and EMS Optimization of Fuel-Cell Hybrid Powertrains for Commercial Vehicles

AVL Powertrain UK Ltd.-Tommi Jokela, Athanasios Iraklis, Bill Kim, Bo Gao
Published 2019-04-02 by SAE International in United States
During the last years, fuel-cell-based powertrains have been attracting a lot of attention from commercial vehicle manufacturers for reducing vehicle-related Greenhouse Gas (GHG) emissions. Compared to Battery-Electric Vehicles (BEV), fuel-cell-based powertrains has the strong advantage of dealing with range-anxiety, which is crucial for commercial vehicle with high duty-cycle energy requirements. Amongst the different fuel-cell types, Proton Exchange Membrane Fuel-Cells (PEMFC) have the greatest potential for utilization in automotive applications, due to their relatively high technical readiness, market availability and utilization of hydrogen (H2) fuel. In addition, Solid Oxide Fuel-Cells (SOFC) show good potential due to existing re-fueling infrastructure for light hydrocarbon fuels or heavier hydrocarbon fuels (e.g. diesel). This study focuses on the application of both PEMFCs and diesel-fueled SOFCs in Fuel-Cell Hybrid Electric Vehicle (FCHEV) architectures for commercial vehicles. Delivery vans in the 2.5 t-3.5 t weight range, coach buses and 3-axle tractor-type long-haul trucks are considered energy-driven types and highly suitable for fuel-cell systems, which offer high energy density values. Due to the high number of vehicle application types and system configurations, and…
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Driving Style Identification Algorithm with Real-World Data Based on Statistical Approach

AVL Powertrain-Tarek Ouali, Nirav Shah, Bill Kim, David Fuente, Bo Gao
Published 2016-04-05 by SAE International in United States
This paper introduces a new method for driving style identification based on vehicle communication signals. The purpose of this method is to classify a trip, driven in a vehicle, into three driving style categories: calm, normal or aggressive. The trip is classified based on the vehicle class, the type of road it was driven on (urban, rural or motorway) and different types of driving events (launch, accelerating and braking). A representative set of parameters, selected to take into consideration every part of the driver-vehicle interaction, is associated to each of these events. Due to the usage of communication signals, influence factors, other than vehicle speed and acceleration (e.g. steering angle or pedals position), can be considered to determine the level of aggressiveness on the trip. The conversion of the parameters from physical values to dimensionless score is based on conversion maps that consider the road and vehicle types. These maps have been defined from a representative set of subjectively-rated test trips. The method used to define these maps is described as well. The correlation between…
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