Abstract-Current automotive electromagnetic compatibility (EMC) standards do not discuss the effect of the driving profile on real traffic vehicular radiated emissions. This paper describes a modeling methodology to evaluate the radiated electromagnetic emissions of electric motorcycles in terms of the driving profile signals such as the vehicle velocity remotely controlled by means of a CAN bus. A time domain EMI measurement system has been used to measure the temporal evolution of the radiated emissions in a semi-anechoic chamber. The CAN bus noise has been reduced by means of adaptive frequency domain cancellation techniques. Experimental results demonstrate that there is a temporal relationship between the motorcycle velocity and the radiated emission power in some specific frequency ranges. A Multilayer Perceptron (MLP) neural model has been developed to estimate the radiated emissions power in terms of the motorcycle velocity. Details of the training and testing of the developed neural estimator are described.
It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch) reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration) that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.
With the increase of electrical/electronic equipment integration complexity, the electromagnetic compatibility (EMC) becomes one of the key points to be respected in order to meet the constructor standard conformity. Electrical drives are known sources of electromagnetic interferences due to the motor as well as the related power electronics. They are the principal radiated emissions source in automotive applications. This paper shows that there is a direct relationship between the input control voltage and the corresponding level of radiated emissions. It also introduces a novel model using artificial intelligence techniques for estimating the radiated emissions of a DC-motor-based electrical drive in terms of its input voltage. Details of the training and testing of the developed extreme learning machine (ELM) are described. Good agreement between the electrical drive behavior and the developed model is observed.
The polluting emissions (gases and particles) produced by the traffic of automobiles are directly related to the activity of vehicle, but they are also affected by the route conditions and moreover, by the driver's behavior. However, PAMS commercial systems do not usually include elements to register this last component. This article presents an electronic system, specially designed to evaluate, ad-hoc, the effect of driver's activity on polluting emissions. This electronic application integrates a hardware and a software component, both designed concerning MIVECO research project. From the hardware component the sensorial part stands out, formed by potentiometers connected to the pedals that control the vehicle and the inertial device, which allows one to evaluate the instantaneous accelerations in the x-y-z-axes as well as the turns with respect to these axes. Once the signals are conditioned and acquired, the software component processes them for on-line monitoring in a GUI and stores them in a database to facilitate its evaluation off-line. This electronic application has two important properties: it can be incorporated in any vehicle of the market (light or heavy, diesel or gasoline, pre or post-eobd) and it allows the capture and registration of information about the driver's activity synchronously with PEMS (gases and/or particles) systems. The work includes experimental results obtained in an urban circuit in the city of Madrid.
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