9,352 research outputs found
A novel equivalent modeling method combined with the splice-electrochemical polarization model and prior generalized inverse least-square parameter identification for UAV lithium-ion batteries.
The accuracy of lithium-ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high-fidelity modeling of the UAV lithium-ion battery, a splice-electrochemical polarization model (S-EPM) for UAV lithium-ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least-squares algorithm is proposed. Also, based on this algorithm, the full-parameter identification and multicondition error analysis of the S-EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full-function charge–discharge test were designed to further verify the applicability of the S-EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium-ion batteries
Capacity Fade Analysis and Model Based Optimization of Lithium-ion Batteries
Electrochemical power sources have had significant improvements in design, economy, and operating range and are expected to play a vital role in the future in a wide range of applications. The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This dissertation contributes to the efforts in the modeling, simulation and optimization of lithium-ion batteries and their use in the design of better batteries for the future. While physics-based models have been widely developed and studied for these systems, the rigorous models have not been employed for parameter estimation or dynamic optimization of operating conditions. The first chapter discusses a systems engineering based approach to illustrate different critical issues possible ways to overcome them using modeling, simulation and optimization of lithium-ion batteries. The chapters 2-5, explain some of these ways to facilitate: i) capacity fade analysis of Li-ion batteries using different approaches for modeling capacity fade in lithium-ion batteries,: ii) model based optimal design in Li-ion batteries and: iii) optimum operating conditions: current profile) for lithium-ion batteries based on dynamic optimization techniques. The major outcomes of this thesis will be,: i) comparison of different types of modeling efforts that will help predict and understand capacity fade in lithium-ion batteries that will help design better batteries for the future,: ii) a methodology for the optimal design of next-generation porous electrodes for lithium-ion batteries, with spatially graded porosity distributions with improved energy efficiency and battery lifetime and: iii) optimized operating conditions of batteries for high energy and utilization efficiency, safer operation without thermal runaway and longer life
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
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
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
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
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
Online State of Charge and Electrical Impedance Estimation for Multicell Lithium-ion Batteries
This paper proposes a hybrid battery model-based high-fidelity state of charge (SOC) and electrical impedance estimation method for multicell lithium-ion batteries. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A particle swarm optimization (PSO)-based online parameter identification algorithm is designed to estimate the electrical parameters of the cells sequentially. An SOC compensator is designed to correct the errors of the enhanced Coulomb counting SOC estimations for the cells sequentially. This leads to an accurate, robust online SOC estimation for individual cells of a battery pack. The proposed method is validated by simulation and experimental data collected from a battery tester for a four-cell polymer lithiumion battery pack. The proposed method is applicable to other types of electrochemical batteries
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