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 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

Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics

This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman model using asymptotic analysis including electrode-average terms. The Bayesian approach allows complex target distributions to be estimated, which enables a global analysis of the parameter space. The analysis focuses on the identification problem (i) locally, under a set of discrete quasi-steady states of charge, and in comparison (ii) globally with a continuous excursion of state of charge. The performance of the methodology was evaluated using synthetic data from multiple numerical simulations under diverse types of current excitation. We show that various diffusivities as well as the transference number may be estimated with small variances in the global case, but with much larger uncertainty in the local estimation case. This also has significant implications for estimation where parameters might vary as a function of state of charge or other latent variables

Reduced-order electrochemical models with shape functions for fast, accurate prediction of lithium-ion batteries under high C rates

This paper proposes physical-based, reduced-order electrochemical models that are much faster than the electrochemical pseudo 2D (P2D) model, while providing high accuracy even under the challenging conditions of high C-rate and strong polarization of lithium ion concentration and potential in a battery cell. In particular, an innovative weak form of equations are developed by using shape functions, which reduces the fully coupled electrochemical and transport equations to ordinary differential equations, and provides self-consistent solutions for the evolution of the polynomial coefficients. Results show that the models, named as revised single-particle model (RSPM) and fast-calculating P2D model (FCP2D), give highly reliable prediction of battery operations, including under dynamic driving profiles. They can calculate battery parameters, such as terminal voltage, over-potential, interfacial current density, lithium-ion concentration distribution, and electrolyte potential distribution with a relative error less than 2%. Applicable for moderately high C rates (below 2.5 C), the RSPM is up to more than 33 times faster than the P2D model. The FCP2D is applicable for high C rates (above 2.5 C) and is about 8 times faster than the P2D model. With their high speed and accuracy, these physics-based models can significantly improve the capability and performance of the battery management system and accelerate battery design optimization

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

A Review of Lithium-Ion Battery Models in Techno-economic Analyses of Power Systems

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored

Microstructure-resolved degradation simulation of lithium-ion batteries in space applications

In-orbit satellite REIMEI, developed by the Japan Aerospace Exploration Agency, has been relying on off-the-shelf Li-ion batteries since its launch in 2005. The performance and durability of Li-ion batteries is impacted by various degradation mechanisms, one of which is the growth of the solid-electrolyte interphase (SEI). In this article, we analyse the REIMEI battery and parameterize a full-cell model with electrochemical cycling data, computer tomography images, and capacity fading experiments using image processing and surrogate optimization. We integrate a recent model for SEI growth into a full-cell model and simulate the degradation of batteries during cycling. To validate our model, we use experimental and in-flight data of the satellite batteries. Our combination of SEI growth model and microstructure-resolved 3D simulation shows, for the first time, experimentally observed inhomogeneities in the SEI thickness throughout the negative electrode for the degraded cell

A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm.

As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as Rp = 16.66 mΩ and capacitance is identified as Cp = 13.71 kF. The ohm internal resistance is calculated as Ro = 68.71 mΩ and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs