Adaptive Observer Design for a Li-Ion Cell Based on Coupled Electrochemical-Thermal Model

2014 ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew ◽  
Pierluigi Pisu
Keyword(s):  
Thermal Model ◽  
Design Procedure ◽  
State Parameter ◽  
Observer Design ◽  
Adaptive Observer ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Physical Parameters ◽  
Thermal Dynamics ◽  
Li Ion

Accurate real-time knowledge of battery internal states and physical parameters is of the utmost importance for intelligent battery management. Electrochemical models are arguably more accurate in capturing physical phenomena inside the cells compared to their data-driven or equivalent circuit based counterparts. Moreover, consideration of the coupling between electrochemical and thermal dynamics can be potentially beneficial for accurate estimation. In this paper, a nonlinear adaptive observer design is presented based on a coupled electrochemical-thermal model for a Li-ion cell. The proposed adaptive observer estimates distributed Li-ion concentrations, lumped temperature and some electrochemical parameters simultaneously. The observer design is split into two separate parts to simplify the design procedure and gain tuning. These separate parts are designed based on Lyapunov’s stability analysis guaranteeing the convergence of the combined state-parameter estimates. Simulation studies are provided to demonstrate the effectiveness of the scheme.

Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical–Thermal Model

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
S. Dey ◽  
B. Ayalew ◽  
P. Pisu
Keyword(s):  
Thermal Model ◽  
Adaptive Observer ◽  
Parameter Estimates ◽  
Physical Parameters ◽  
Model Parameters ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Adaptive Scheme ◽  
Li Ion ◽  
Battery Cells

Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunov's stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.

scholarly journals An adaptive observer design approach for a class of discrete-time nonlinear systems

2018 ◽  
Vol 28 (1) ◽  
pp. 55-67 ◽  
Author(s):  
Krishnan Srinivasarengan ◽  
José Ragot ◽  
Christophe Aubrun ◽  
Didier Maquin
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Linear Systems ◽  
Discrete Time ◽  
Design Procedure ◽  
Observer Design ◽  
Joint Estimation ◽  
Adaptive Observer ◽  
Waste Water Treatment Plant ◽  
Joint State

AbstractWe consider the problem of joint estimation of states and some constant parameters for a class of nonlinear discrete-time systems. This class contains systems that could be transformed into a quasi-LPV (linear parameter varying) polytopic model in the Takagi-Sugeno (T-S) form. Such systems could have unmeasured premise variables, a case usually overlooked in the observer design literature. We assert that, for such systems in discrete-time, the current literature lacks design strategies for joint state and parameter estimation. To this end, we adapt the existing literature on continuous-time linear systems for joint state and time-varying parameter estimation. We first develop the discrete-time version of this result for linear systems. A Lyapunov approach is used to illustrate stability, and bounds for the estimation error are obtained via the bounded real lemma. We use this result to achieve our objective for a design procedure for a class of nonlinear systems with constant parameters. This results in less conservative conditions and a simplified design procedure. A basic waste water treatment plant simulation example is discussed to illustrate the design procedure.

A new methodology for an adaptive state observer design for a class of nonlinear systems with unknown parameters in unmeasured state dynamics

2016 ◽  
Vol 40 (4) ◽  
pp. 1297-1308 ◽  
Author(s):  
Nabil Oucief ◽  
Mohamed Tadjine ◽  
Salim Labiod
Keyword(s):  
Nonlinear Systems ◽  
Design Procedure ◽  
State Observer ◽  
Observer Design ◽  
Adaptive Observer ◽  
Linear Matrix ◽  
Unknown Parameters ◽  
Structural Requirement ◽  
Persistent Excitation ◽  
Adaptive State Observer

An adaptive state observer is an adaptive observer that does not require the persistent excitation condition to estimate the state. The usual structural requirement for designing this kind of observers is that the unknown parameters explicitly appear in the measured state dynamics. This paper deals with the problem of adaptive state observer synthesis for a class of nonlinear systems with unknown parameters in unmeasured state dynamics. The novelty of the proposed approach is that it requires neither a canonical form nor the approximation of some of the output’s time derivatives. Firstly, we establish a new matrix equality that characterizes the structure of almost all systems found in the very small literature dealing with this problem. Then, this equality is exploited in the construction of the adaptation law. This simplifies the design procedure and makes it very similar to the conventional adaptive state observer design procedure. The problem of finding the observer gains is expressed as a linear matrix inequalities optimization problem. Two examples are given to demonstrate the validity of the proposed scheme.

Optimal reset adaptive observer design

2011 ◽  
Vol 60 (10) ◽  
pp. 877-883 ◽  
Author(s):  
D. Paesa ◽  
A. Baños ◽  
C. Sagues
Keyword(s):  
Observer Design ◽  
Adaptive Observer

Adaptive Estimation Using Multiagent Network Identifiers With Undirected and Directed Graph Topologies

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Teymur Sadikhov ◽  
Michael A. Demetriou ◽  
Wassim M. Haddad ◽  
Tansel Yucelen
Keyword(s):  
Directed Graph ◽  
Adaptive Estimation ◽  
Parameter Estimate ◽  
The State ◽  
Adaptive Observer ◽  
Parameter Estimates ◽  
Communication Architecture ◽  
Adaptive Identifier ◽  
The Mean ◽  
Additive Term

In this paper, we present an adaptive estimation framework predicated on multiagent network identifiers with undirected and directed graph topologies. Specifically, the system state and plant parameters are identified online using N agents implementing adaptive observers with an interagent communication architecture. The adaptive observer architecture includes an additive term which involves a penalty on the mismatch between the state and parameter estimates. The proposed architecture is shown to guarantee state and parameter estimate consensus. Furthermore, the proposed adaptive identifier architecture provides a measure of agreement of the state and parameter estimates that is independent of the network topology and guarantees that the deviation from the mean estimate for both the state and parameter estimates converge to zero. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

Adaptive Observer Design for Wide-Speed Sensorless IPMSM Drives via Equivalent Control Method

2021 ◽  
Author(s):  
Qilian Lin ◽  
Ling Liu ◽  
Han Song ◽  
Dongsong Jin ◽  
Deliang Liang ◽  
...  
Keyword(s):  
Control Method ◽  
Observer Design ◽  
Adaptive Observer ◽  
Speed Sensorless ◽  
Equivalent Control

scholarly journals Simplified Electrochemical and Thermal Model of LiFePO4-Graphite Li-Ion Batteries for Fast Charge Applications

2012 ◽  
Vol 159 (9) ◽  
pp. A1508-A1519 ◽  
Author(s):  
E. Prada ◽  
D. Di Domenico ◽  
Y. Creff ◽  
J. Bernard ◽  
V. Sauvant-Moynot ◽  
...  
Keyword(s):  
Thermal Model ◽  
Li Ion Batteries ◽  
Fast Charge ◽  
Li Ion

Simultaneous Bayesian calibration and engineering design with an application to a vibration isolation system

2021 ◽  
Author(s):  
Carl Ehrett ◽  
D. Andrew Brown ◽  
Christopher Kitchens ◽  
Xinyue Xu ◽  
Roland Platz ◽  
...  
Keyword(s):  
Computer Model ◽  
Adaptive Sampling ◽  
Model Calibration ◽  
Vibration Isolation ◽  
Traditional Approach ◽  
Design Procedure ◽  
Parameter Estimates ◽  
Computationally Efficient ◽  
Isolation System ◽  
Vibration Isolation System

Abstract Calibration of computer models and the use of those models for design are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach on the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized.

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