Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle

Abstract

Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators.

Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems

“…The vehicle model, with the validated parameters of the real car, was created in CarMaker and can be thought of as a virtual car. The detailed information on the vehicle model's verification and parameters is described in [18]. Simulated data were preferred in this article because numerous fault scenarios can be reproduced in a virtual environment, and more data can be used to evaluate different fault detection techniques.…”

“…The first fault diagnostic approach, classic binary logic diagnosis, has already been discussed in [18]. This method is used to identify abnormal conditions in sensors and actuators by setting thresholds to distinguish between normal and abnormal symptoms.…”

“…Although this method is successful for state monitoring, it can only identify faulty and normal system states and cannot determine the form and severity of faults. In reference [18], the authors have already provided a thorough explanation and modeling of the classic binary logic diagnosis method for the entire vehicle. Therefore, it will not be repeated here.…”

“…As the input of the fuzzy logic system, there are eight residual values, the difference between the sensor-measured data and model-estimated data: r δ S (the residual of the steering wheel angle), r M S (the residual of the steering wheel torque), r ψ (the residual of the yaw rate), r F TieRodSum (the residual of the tie rod force), r v rl (the residual of the left rear wheel speed with geometric model), r v rr (the residual of the right rear wheel speed with geometric model), r v rl_KF (the residual of the left rear wheel speed with Kalman filter), r v rr_KF (the residual of the right rear wheel speed with kalman filter). For the specific calculation method, please refer to the authors' above-mentioned paper [18]. In the Mamdani fuzzy inference system, the first step is to transform the numerical value into a linguistic term and determine its degree of membership between 0 and 1.…”

“…By comparing the ideal driving states to the actual driving states, which are measured by sensors, a set of residuals is calculated, which are the essential symptoms for fault diagnosis. The detailed process for calculating residuals is described in paper [18]. The key discussion here is the content of the fault diagnosis after getting the fault symptoms.…”