Abstract
In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.
Highlights
Motion control is a sub-field of automation, in which the position and/or velocity of machines are controlled using some type of device such as a hydraulic pump, linear actuator, or an electric motor, generally a servo
variable reluctance (VR) sensor is well-suited for a variety of other industrial applications, such as conveyer belts, truck, construction equipment, railroad and marine transmissions, automatic transmission in vehicles, All Terrain Vehicles (ATV) tachometer sensors, and ABS brake systems for wheel slip and traction control
A simplified block diagram of the Parallel Hybrid Electric Vehicle (HEV) system used in the experiment is shown in Figure 9 shows the picture of the proposed experimental system to illustrate the main components of its structure
Summary
The chi-square test is one of the most commonly used methods for comparing frequencies, distributions, or proportions. The chi-square test is a statistical method used to determine if observed data deviate from those expected under a particular hypothesis. The distribution of the test statistic under the null hypothesis fits the theoretical chi-square distribution. This means that once we know the chi-square test statistic, we can calculate the probability of getting that value of the chi-square statistic [15]. The chi-square analysis is used to test the null hypothesis (H0), which is the hypothesis that states there is no significant difference between expected and observed data. H0 is either accepted or rejected after the value of chi-square is compared to a probability distribution. Chi-square values with low probability lead to the rejection of H0 and it is assumed that a factor other than chance creates a large deviation between expected and observed results. As with all nonparametric tests (that do not require normal distribution curves), chi-square tests only evaluate a single variable, they do not take into account the interaction among more than one variable upon the outcome [15]
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