Nitrous oxide emissions from gasohol, ethanol and CNG light duty vehicles

A sample of 21 light duty vehicles powered by Otto cycle engines were tested on a chassis dynamometer to measure the exhaust emissions of nitrous oxide (N2O). The tests were performed at the Vehicle Emission Laboratory of CETESB (Environmental Company of the State of Sao Paulo) using the US-FTP-75 (Federal Test Procedure) driving cycle. The sample tested included passenger cars running on three types of fuels used in Brazil: gasohol, ethanol and CNG. The measurement of N2O was made using two methods: Non Dispersive InfraRed (NDIR) analyzer and Fourier Transform InfraRed spectroscopy (FTIR). Measurements of regulated pollutants were also made in order to establish correlations between N2O and NOx. The average N2O emission factors obtained by the NDIR method was 78 +/- 41 mg.km(-1) for vehicles running with gasohol, 73 +/- 45 mg.km(-1) for ethanol vehicles and 171 +/- 69 mg.km(-1) for CNG vehicles. Seventeen results using the FTIR method were also obtained. For gasohol vehicles the results showed a good agreement between the two methods, with an average emission factor of 68 +/- 41 mg.km(-1). The FTIR measurement results of N2O for ethanol and CNG vehicles were much lower than those obtained by the NDIR method. The emission factors were 17 +/- 10 mg.km(-1) and 33 +/- 17 mg.km(-1), respectively, possibly because of the interference of water vapor (present at a higher concentration in the exhaust gases of these vehicles) on measurements by the NDIR method.

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.