357 research outputs found
Least costly energy management for series hybrid electric vehicles
Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different
challenges from non-plug-in HEVs, due to bigger batteries and grid recharging.
Instead of tackling it to pursue energetic efficiency, an approach minimizing
the driving cost incurred by the user - the combined costs of fuel, grid energy
and battery degradation - is here proposed. A real-time approximation of the
resulting optimal policy is then provided, as well as some analytic insight
into its dependence on the system parameters. The advantages of the proposed
formulation and the effectiveness of the real-time strategy are shown by means
of a thorough simulation campaign
Robust Narrow-Band Disturbances Rejection Using Overparametrized Pole-Assignment Control
In this work the problem of designing feedback controllers for the rejection of narrow-band disturbances is considered. The design technique proposed herein is based upon a well-suited overparameterization of a standard model-based minimum-variance controller; the extra-degrees-of-freedom so introduced are used to improve the performence of the basic controller. In particular, the problem of improving the robustness of the system, when the time delay of the system is subject to uncertainties, is considered, and an innovative solution proposed
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
Virtual-bike emulation in a series-parallel human-powered electric bike
Combining the advantages of standard bicycles and electrified vehicles,
electric bikes (e-Bikes) are promising vehicles to reduce emission and traffic.
The current literature on e-Bikes ranges from works on the energy management to
the vehicle control to properly govern the human-vehicle interaction. This last
point is fundamental in chain-less series bikes, where the link between the
human and the vehicle behavior is only given by a control law. In this work, we
address this problem in a series-parallel bike. In particular, we provide an
extension of the virtual-chain concept, born for series bikes, and then we
improve it developing a virtual-bike framework. Experimental results are used
to validate the effectiveness of the solutions, when the cyclist is actually
riding the bike.Comment: Accepted for publication at the IFAC World Congress 202
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
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
Direct learning ofLPVcontrollers from data
In many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV) plants by linear parametervarying (LPV) models and design controllers based on such representations to regulate the behaviour of the system. The LPV system class offers the representation of NL and TV phenomena as a linear dynamic relationship between input and output signals, which relationship is dependent on some measurable signals, e.g., operating conditions, often called as scheduling variables. For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research. Therefore, it is attractive to synthesize the controller directly from data without the need of modeling the plant and addressing the underlying difficulties. Hence, in this paper, a novel data-driven synthesis scheme is proposed in a stochastic framework to provide a practically applicable solution for synthesizing LPV controllers directly from data. Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided. The effectiveness of the proposed methods is also illustrated by means of an academic example and a real application based simulation case study
Direct data-driven control of linear parameter-varying systems
In many control applications, nonlinear plants can be modeled as linear parameter-varying (LPV) systems, by which the dynamic behavior is assumed to be linear, but also dependent on some measurable signals, e.g., operating conditions. When a measured data set is available, LPV model identification can provide low complexity linear models that can embed the underlying nonlinear dynamic behavior of the plant. For such models, powerful control synthesis tools are available, but the way the modeling error and the conservativeness of the embedding affect the control performance is still largely unknown. Therefore, it appears to be attractive to directly synthesize the controller from data without modeling the plant. In this paper, a novel data-driven synthesis scheme is proposed to lay the basic foundations of future research on this challenging problem. The effectiveness of the proposed approach is illustrated by a numerical example
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