Vehicle sideslip estimation for four-wheel-steering vehicles using a particle filter

The availability of the most relevant vehicle states is crucial for the development of advanced vehicle control systems and driver assistance systems. Specifically the vehicle sideslip angle plays a key role, yet this state is unpractical to measure and still not straightforward to estimate. This paper investigates a particle filter approach to estimate the chassis sideslip angle of road vehicles. The filter relies on a physical model of the vehicle and on measurements available from cheap and widespread sensors including inertial measurement unit and steering wheel angle sensor(s). The approach is validated using experimental data collected with the research platform RoboMobil (RoMo), a by-wire electric vehicle with wheel-individual traction and steering actuators. Results show that the performance of the proposed particle filter is satisfactory, and indicate directions for further improvement

A new knock event definition for knock detection and control optimization

[EN] In this paper, the knock phenomenon is studied and characterized in the time-frequency domain. From the analysis results, a new knock event definition is proposed, which compares the excitation of the cylinder resonance produced by the autoignition of the end gas to that associated with the combustion. The new definition permits a more consistent differentiation between knocking and not knocking cycles than the classical approach in the literature, thus allowing the improvement of the knock control strategies. The new knock index proposed analyses the frequency spectrum of the pressure signal in two locations, i.e. near the maximum heat release and near the end of combustion, by using the fast Fourier transform and a window function, and it is compared with the classical MAPO definition, which consists on finding the maximum pressure oscillation in the time domain. Both indices have been implemented online in a four-stroke SI engine and its performance is illustrated by using a classical knock control strategy. Results obtained under different operating conditions demonstrate that the improved knock index definition can substantially reduce the variability of the spark advance angle control, avoiding strong knocking events and reducing engine vibration.Bares-Moreno, P.; Selmanaj, D.; Guardiola, C.; Onder, C. (2018). A new knock event definition for knock detection and control optimization. Applied Thermal Engineering. 131:80-88. https://doi.org/10.1016/j.applthermaleng.2017.11.138S808813

Knock probability estimation through an in-cylinder temperature model with exogenous noise

[EN] This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online. In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model. A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1% were found. (C) 2017 Elsevier Ltd. All rights reserved.Bares-Moreno, P.; Selmanaj, D.; Guardiola, C.; Onder, C. (2018). Knock probability estimation through an in-cylinder temperature model with exogenous noise. Mechanical Systems and Signal Processing. 98:756-769. https://doi.org/10.1016/j.ymssp.2017.05.033S7567699

Vehicle sideslip angle estimation using Kalman filters: modelling and validation

The knowledge of the vehicle sideslip angle provides useful information about the state of the vehicle and it is often considered to increase the performance of the car as well as to develop safety systems, especially in the vehicle equipped with Torque Vectoring control systems. This paper describes two methods, based on the use of Kalman filters, to estimate the vehicle sideslip angle and the tire forces of a vehicle starting from the longitudinal and yaw velocity data. In particular, these data refer to on-track testing of a Range Rover Evoque performing ramp steer maneuvers at constant speed. The results of the sideslip estimation method are compared with the actual vehicle sideslip measured by a Datron sensor and are also used to estimate the tire lateral forces

On vehicle pitch estimation via solid-state LIDAR

Solid-state LIDAR technology has recently emerged, allowing for smaller and more affordable devices. In the present work, we investigate the possibility of using a vehicle mounted solid-state LIDAR to estimate the vehicle pitch and heave dynamics. We present and compare two approaches: a model-based estimation and a data driven algorithm. The algorithms are tested on an instrumented vehicle. The experimental results show that the data-driven approach outperforms the model-based estimation in estimating pitch caused both by accelerations and braking and by road disturbances

Accelerometer-based data-driven hazard detection and classification for motorcycles

This article deals with collision and hazard detection for motorcycles via accelerometer measures. A machine learning approach is proposed. A two-phase method is developed that is capable of first detecting non critical anomalies (unusually high accelerations) and critical hazards for which an airbag deployment could be needed. The method is based on Self Organizing Maps and has two may advantages over the classical approach: 1) the machine learning approach easily scales with the number of sensors. 2) It is tuned using normal driving and does not require expensive crash-tests for tuning. In the paper the system is designed starting from data from an instrumented vehicle and validated in simulatio

Robust Vehicle Sideslip Estimation Based on Kinematic Considerations

- This paper proposes an inertial measurement based sideslip angle estimation for automotive applications. Measuring sideslip angle is costly; in most cases, in most case estimation is a preferred choice. Most estimation solutions are based on dynamical models of the car. To properly work, these solutions need to also estimate the road friction. This increases their complexity and affects their robustness. The present study proposes an inertial based approach that does not require the knowledge nor the estimation of road friction. This considerably simplifies the design, tuning and validation of the approach. Focus is devoted to the study of the effect of measurement errors. An extensive experimental validation confirms that the estimate is accurate and robust to a wide range of driving scenarios