81 research outputs found
Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context
Battery management systems may rely on mathematical models to provide higher
performance than standard charging protocols. Electrochemical models allow us
to capture the phenomena occurring inside a lithium-ion cell and therefore,
could be the best model choice. However, to be of practical value, they require
reliable model parameters. Uncertainty quantification and optimal experimental
design concepts are essential tools for identifying systems and estimating
parameters precisely. Approximation errors in uncertainty quantification result
in sub-optimal experimental designs and consequently, less-informative data,
and higher parameter unreliability. In this work, we propose a highly efficient
design of experiment method based on global parameter sensitivities. This novel
concept is applied to the single-particle model with electrolyte and thermal
dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The
proposed method avoids the simplifying assumption of output-parameter
linearization (i.e., local parameter sensitivities) used in conventional Fisher
information matrix-based experimental design strategies. Thus, the optimized
current input profile results in experimental data of higher information
content and in turn, in more precise parameter estimates.Comment: Accepted for 21st IFAC World Congres
Set-based state estimation and fault diagnosis of linear discrete-time descriptor systems using constrained zonotopes
This paper presents new methods for set-valued state estimation and active
fault diagnosis of linear descriptor systems. The algorithms are based on
constrained zonotopes, a generalization of zonotopes capable of describing
strongly asymmetric convex sets, while retaining the computational advantages
of zonotopes. Additionally, unlike other set representations like intervals,
zonotopes, ellipsoids, paralletopes, among others, linear static constraints on
the state variables, typical of descriptor systems, can be directly
incorporated in the mathematical description of constrained zonotopes.
Therefore, the proposed methods lead to more accurate results in state
estimation in comparison to existing methods based on the previous sets without
requiring rank assumptions on the structure of the descriptor system and with a
fair trade-off between accuracy and efficiency. These advantages are
highlighted in two numerical examples.Comment: This paper was accepted and presented in the 1st IFAC Virtual World
Congress, 202
Optimal design of experiments for a lithium-ion cell: parameters identification of an isothermal single particle model with electrolyte dynamics
Advanced battery management systems rely on mathematical models to guarantee
optimal functioning of Lithium-ion batteries. The Pseudo-Two Dimensional (P2D)
model is a very detailed electrochemical model suitable for simulations. On the
other side, its complexity prevents its usage in control and state estimation.
Therefore, it is more appropriate the use of simplified electrochemical models
such as the Single Particle Model with electrolyte dynamics (SPMe), which
exhibits good adherence to real data when suitably calibrated. This work
focuses on a Fisher-based optimal experimental design for identifying the SPMe
parameters. The proposed approach relies on a nonlinear optimization to
minimize the covariance parameters matrix. At first, the parameters are
estimated by considering the SPMe as the real plant. Subsequently, a more
realistic scenario is considered where the P2D model is used to reproduce a
real battery behavior. Results show the effectiveness of the optimal
experimental design when compared to standard strategies.Comment: Published in Ind. Eng. Chem. Res. 2019, 58, 3, 1286-129
Joint state and parameter estimation based on constrained zonotopes
This note presents a new method for set-based joint state and parameter
estimation of discrete-time systems using constrained zonotopes. This is done
by extending previous set-based state estimation methods to include parameter
identification in a unified framework. Unlike in interval-based methods, the
existing dependencies between states and model parameters are maintained from
one time step to the next, thus providing a more accurate estimation scheme. In
addition, the enclosure of states and parameters is refined using measurements
through generalized intersections, which are properly captured by constrained
zonotopes. The advantages of the new approach are highlighted in two numerical
examples
LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control
Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications of lithium-ion batteries. Their optimal design and management are important for safe and profitable operations. The use of accurate mathematical models can help in achieving the best performance. This article provides a detailed description of a finite volume method (FVM) for a pseudo-two-dimensional (P2D) Li-ion battery model suitable for the development of model-based advanced battery management systems. The objectives of this work are to provide: (i) a detailed description of the model formulation, (ii) a parametrizable Matlab framework for battery design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed numerical implementation with respect to the COMSOL MultiPhysics commercial software and the Newman’s DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, a model predictive control of state of charge, and a battery pack simulatio
Real-time MPC - Stability through robust MPC design
Recent results have suggested that online Model Predictive Control (MPC) can be computed quickly enough to control fast sampled systems. High-speed applications impose a hard real-time constraint on the solution of the MPC problem, which generally prevents the computation of the optimal controller. In current approaches guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC scheme based on robust MPC design that recovers these guarantees while allowing for extremely fast computation. We show that a simple warm-start optimization procedure providing an enhanced feasible solution guarantees feasibility and stability for arbitrary time constraints. The proposed method can be practically implemented and efficiently solved for dynamic systems of significant problem size. Implementation details for a real-time robust MPC method are provided that achieves computation times equal to those reported for methods without guarantees. A 12-dimensional problem with 3 control inputs and a prediction horizon of 10 time steps is solved in 2msec with a performance deterioration less than 1% and thereby allows for sampling rates of 500Hz
On real-time robust model predictive control
High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in most MPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC approach for linear systems that provides these guarantees for arbitrary time constraints, allowing one to trade off computation time vs. performance. Stability is guaranteed by means of a constraint, enforcing that the resulting suboptimal MPC cost is a Lyapunov function. The key is then to guarantee feasibility in real-time, which is achieved by the proposed algorithm through a warm-starting technique in combination with robust MPC design. We address both regulation and tracking of piecewise constant references. As a main contribution of this paper, a new warm-start procedure together with a Lyapunov function for real-time tracking is presented. In addition to providing strong theoretical guarantees, the proposed method can be implemented at high sampling rates. Simulation examples demonstrate the effectiveness of the real-time scheme and show that computation times in the millisecond range can be achieved
Guaranteed methods based on constrained zonotopes for set-valued state estimation of nonlinear discrete-time systems
This paper presents new methods for set-valued state estimation of nonlinear
discrete-time systems with unknown-but-bounded uncertainties. A single time
step involves propagating an enclosure of the system states through the
nonlinear dynamics (prediction), and then enclosing the intersection of this
set with a bounded-error measurement (update). When these enclosures are
represented by simple sets such as intervals, ellipsoids, parallelotopes, and
zonotopes, certain set operations can be very conservative. Yet, using general
convex polytopes is much more computationally demanding. To address this, this
paper presents two new methods, a mean value extension and a first-order Taylor
extension, for efficiently propagating constrained zonotopes through nonlinear
mappings. These extend existing methods for zonotopes in a consistent way.
Examples show that these extensions yield tighter prediction enclosures than
zonotopic estimation methods, while largely retaining the computational
benefits of zonotopes. Moreover, they enable tighter update enclosures because
constrained zonotopes can represent intersections much more accurately than
zonotopes.Comment: This includes the supplement "Supplementary material for: Guaranteed
methods based on constrained zonotopes for set-valued state estimation of
nonlinear discrete-time systems
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