5,891 research outputs found
Real-time state of charge estimation of electrochemical model for lithium-ion battery
This paper proposes the real-time Kalman filter based observer for Lithium-ion concentration estimation for the electrochemical battery model. Since the computation limitation of real-time battery management system (BMS) micro-processor, the battery model which is utilized in observer has been further simplified. In this paper, the Kalman filter based observer is applied on a reduced order model of single particle model to reduce computational burden for real-time applications. Both solid phase surface lithium concentration and battery state of charge (SoC) can be estimated with real-time capability. Software simulation results and the availability comparison of observers in different Hardware-in- the-loop simulation setups demonstrate the performance of the proposed method in state estimation and real-time application
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
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
Efficient electrochemical model for lithium-ion cells
Lithium-ion batteries are used to store energy in electric vehicles. Physical
models based on electro-chemistry accurately predict the cell dynamics, in
particular the state of charge. However, these models are nonlinear partial
differential equations coupled to algebraic equations, and they are
computationally intensive. Furthermore, a variable solid-state diffusivity
model is recommended for cells with a lithium ion phosphate positive electrode
to provide more accuracy. This variable structure adds more complexities to the
model. However, a low-order model is required to represent the lithium-ion
cells' dynamics for real-time applications. In this paper, a simplification of
the electrochemical equations with variable solid-state diffusivity that
preserves the key cells' dynamics is derived. The simplified model is
transformed into a numerically efficient fully dynamical form. It is proved
that the simplified model is well-posed and can be approximated by a low-order
finite-dimensional model. Simulations are very quick and show good agreement
with experimental data
Control-oriented implementation and model order reduction of a lithium-ion battery electrochemical model
The use of electrochemical models makes it computationally intractable for online implementation as the model is subject to a complicated mathematical structure including partial-differential equations (PDE). This paper is based on the single particle model with electrolyte dynamics. Methods to solve the PDEs in the governing equations are given. Model order reduction techniques are applied to the electrochemical model to reduce the order from 350 to 14. The models solved by numerical solution, residue grouping method and balanced truncation method are compared with experimental data of a coin cell for validation. The results show that the reduced order model can decrease simulation time 75 times compared with the high order model. And the accuracy of the model is kept with 2.3% root mean square error comparing with the experiment results
Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter
This paper investigates the state estimation of a high-fidelity spatially
resolved thermal- electrochemical lithium-ion battery model commonly referred
to as the pseudo two-dimensional model. The partial-differential algebraic
equations (PDAEs) constituting the model are spatially discretised using
Chebyshev orthogonal collocation enabling fast and accurate simulations up to
high C-rates. This implementation of the pseudo-2D model is then used in
combination with an extended Kalman filter algorithm for differential-algebraic
equations to estimate the states of the model. The state estimation algorithm
is able to rapidly recover the model states from current, voltage and
temperature measurements. Results show that the error on the state estimate
falls below 1 % in less than 200 s despite a 30 % error on battery initial
state-of-charge and additive measurement noise with 10 mV and 0.5 K standard
deviations.Comment: Submitted to the Journal of Power Source
Identifiability and parameter estimation of the single particle lithium-ion battery model
This paper investigates the identifiability and estimation of the parameters
of the single particle model (SPM) for lithium-ion battery simulation.
Identifiability is addressed both in principle and in practice. The approach
begins by grouping parameters and partially non-dimensionalising the SPM to
determine the maximum expected degrees of freedom in the problem. We discover
that, excluding open circuit voltage, there are only six independent
parameters. We then examine the structural identifiability by considering
whether the transfer function of the linearised SPM is unique. It is found that
the model is unique provided that the electrode open circuit voltage functions
have a known non-zero gradient, the parameters are ordered, and the electrode
kinetics are lumped into a single charge transfer resistance parameter. We then
demonstrate the practical estimation of model parameters from measured
frequency-domain experimental electrochemical impedance spectroscopy (EIS)
data, and show additionally that the parametrised model provides good
predictive capabilities in the time domain, exhibiting a maximum voltage error
of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on
Control Systems Technolog
Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte
The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to
Suppression of Phase Separation in LiFePO4 Nanoparticles During Battery Discharge
Using a novel electrochemical phase-field model, we question the common
belief that LixFePO4 nanoparticles separate into Li-rich and Li-poor phases
during battery discharge. For small currents, spinodal decomposition or
nucleation leads to moving phase boundaries. Above a critical current density
(in the Tafel regime), the spinodal disappears, and particles fill
homogeneously, which may explain the superior rate capability and long cycle
life of nano-LiFePO4 cathodes.Comment: 27 pages, 8 figure
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