Joint vehicle state and parameters estimation via Twin-in-the-Loop observers

Vehicular control systems are required to be both extremely reliable and robust to different environmental conditions, e.g. load or tire-road friction. In this paper, we extend a new paradigm for state estimation, called Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters describing the vehicle operating conditions. In such an approach, a digital-twin of the vehicle (usually already available to the car manufacturer) is employed on-board as a plant replica within a closed-loop scheme, and the observer gains are tuned purely from experimental data. The proposed approach is validated against experimental data, showing to significantly outperform the state-of-the-art solutions.Comment: Preprint under review at Vehicle Systems Dynamic

The Twin-in-the-Loop approach for vehicle dynamics control

In vehicle dynamics control, engineering a suitable regulator is a long and costly process. The starting point is usually the design of a nominal controller based on a simple control-oriented model and its testing on a full-fledged simulator. Then, many driving hours are required during the End-of-Line (EoL) tuning phase to calibrate the controller for the physical vehicle. Given the recent technological advances, in this paper we consider the pioneering perspective where the simulator can be run on-board in the electronic control unit, so as to calculate the nominal control action in real-time. In this way, it can be shown that, in the EoL phase, we only need to tune a simple compensator of the mismatch between the expected and the measured outputs. The resulting approach not only exploits the already available simulator and nominal controller and significantly simplifies the design process, but also outperforms the state-of-the-art in terms of tracking accuracy and robustness within a challenging active braking control case study

Advantages of rear steer in LTI and LPV vehicle stability control

International audienceIn this paper, the advantages of the rear wheel steer in robust yaw stability control of four wheeled vehicles are shown. A MIMO vehicle dynamic stability controller (VDSC) involving front steer, rear steer and rear braking torques is synthesized. The comparison between a vehicle with and without rear steer is done on avoidance maneuver using both LTI and gain-scheduling LPV controller. Both robust Hinf controllers are built by the solution of an LMI problem. To better evaluate the influence of the rear steer on the performance time domain indexes are introduced. The simulation results show that active rear steer enhances vehicle handling on a low friction surface

Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study

Given the urgent need of simplifying the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run on-board to compute a nominal control action in run-time and an additional block C_delta is used to compensate for the mismatch between the simulator and the real vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of C_delta. More specifically, we compare the originally proposed Bayesian Optimization (BO) approach with the recently developed Set Membership Global Optimization (SMGO) and Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study -- the yaw-rate tracking problem -- so as to further prove the TiL-C effctiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to significantly reduce the computational effort required by BO.Comment: Preprint submitted to European Journal of Contro

Automatic dimensionality reduction of Twin-in-the-Loop Observers

State-of-the-art vehicle dynamics estimation techniques usually share one common drawback: each variable to estimate is computed with an independent, simplified filtering module. These modules run in parallel and need to be calibrated separately. To solve this issue, a unified Twin-in-the-Loop (TiL) Observer architecture has recently been proposed: the classical simplified control-oriented vehicle model in the estimators is replaced by a full-fledged vehicle simulator, or digital twin (DT). The states of the DT are corrected in real time with a linear time invariant output error law. Since the simulator is a black-box, no explicit analytical formulation is available, hence classical filter tuning techniques cannot be used. Due to this reason, Bayesian Optimization will be used to solve a data-driven optimization problem to tune the filter. Due to the complexity of the DT, the optimization problem is high-dimensional. This paper aims to find a procedure to tune the high-complexity observer by lowering its dimensionality. In particular, in this work we will analyze both a supervised and an unsupervised learning approach. The strategies have been validated for speed and yaw-rate estimation on real-world data

Non-Invasive Experimental Identification of a Single Particle Model for LiFePO4 Cells

The rapid spread of Lithium-ions batteries (LiBs) for electric vehicles calls for the development of accurate physical models for Battery Management Systems (BMSs). In this work, the electrochemical Single Particle Model (SPM) for a high-power LiFePO4 cell is experimentally identified through a set of non-invasive tests (based on voltage-current measurements only). The SPM is identified through a two-step procedure in which the equilibrium potentials and the kinetics parameters are characterized sequentially. The proposed identification procedure is specifically tuned for LiFePO4 chemistry, which is particularly challenging to model due to the non-linearity of its open circuit voltage (OCV) characteristic. The identified SPM is compared with a second-order Equivalent Circuit Model (ECM) with State of Charge dependency. Models performance is compared on dynamic current profiles. They exhibit similar performance when discharge currents peak up to 1C (RMSE between simulation and measures smaller than 20 mV) while, increasing the discharge peaks up to 3C, ECM's performance significantly deteriorates while SPM maintains acceptable RMSE (< 50 mV).Comment: Accepted for publication at the IFAC World Congress 202

Unsupervised Learning Techniques for an Intrusion Detection System

With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, however, is made di#cult by the sheer size of the input; this is usually avoided by discarding the network packet contents. In this paper, we introduce a two-tier architecture to overcome this problem: the first tier is an unsupervised clustering algorithm which reduces the network packets payload to a tractable size. The second tier is a traditional anomaly detection algorithm, whose e#ciency is improved by the availability of data on the packet payload content

A Diffusive Electro-Equivalent Li-ion Battery Model

Lithium ion (Li-ion) batteries are the standard choice for many applications; but their behavior is complex. In order to safely and efficiently exploit their advantages, advanced model-based Battery Management Systems (BMS) are required. This paper introduces a computationally efficient, control-oriented model for a Li-ion cell. The model, by augmenting the classical Randle model with diffusive dynamics, is capable of describing both the high and low frequency behavior of the cell. The identification of the proposed model is detailed and an identification protocol proposed. The model is validated on a commercial lithium-ion cell. The proposed model yields an efficient simulation tool that can be employed for BMS design

Friction-curve peak detection by wheel-deceleration measurements

Tire-road friction characteristics are deeply interlaced with all vehicle safety oriented control systems, as road conditions strongly affect the control schemes behavior. In this work we focus on estimating the peak–value of the tire-road friction curve, as this is the stability boundary for the wheel braking dynamics. Moreover, we show how such algorithm can be employed as a supervisory control to enhance safety properties and performance of current Anti-lock Braking Systems. The proposed strategy is analyzed and tested for different sensors’ configurations, i.e., with and without longitudinal wheel slip measurement available and its validity is assessed on experimental data collected on a test vehicle