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

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

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

Effect of Design Parameters and Intercalation Induced Stresses in Lithium Ion Batteries

Electrochemical power sources, especially lithium ion batteries have become major players in various industrial sectors, with applications ranging from low power/energy demands to high power/energy requirements. But there are some significant issues existing for lithium ion systems which include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. Therefore, better design, operation and control of lithium ion batteries are essential to meet the growing demands of energy storage. Physics based modeling and simulation methods provide the best and most accurate approach for addressing such issues for lithium ion battery systems. This work tries to understand and address some of these issues, by development of physics based models and efficient simulation of such models for battery design and real time control purposes. This thesis will introduce a model-based procedure for simultaneous optimization of design parameters for porous electrodes that are commonly used in lithium ion systems. The approach simultaneously optimizes the battery design variables of electrode porosities and thickness for maximization of the energy drawn for an applied current, cut-off voltage, and total time of discharge. The results show reasonable improvement in the specific energy drawn from the lithium ion battery when the design parameters are simultaneously optimized. The second part of this dissertation will develop a 2-dimensional transient numerical model used to simulate the electrochemical lithium insertion in a silicon nanowire (Si NW) electrode. The model geometry is a cylindrical Si NW electrode anchored to a copper current collector (Cu CC) substrate. The model solves for diffusion of lithium in Si NW, stress generation in the Si NW due to chemical and elastic strain, stress generation in the Cu CC due to elastic strain, and volume expansion in the Si NW and Cu CC geometries. The evolution of stress components, i.e., radial, axial and tangential stresses in different regions in the Si NW are studied in details. Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms with an appropriate discretization or approximation for the solid phase diffusion within the electrode particle. One of the major difficulties in simulating Li-ion battery models is the need for simulating solid-phase diffusion in the second radial dimension r within the particle. It increases the complexity of the model as well as the computation time/cost to a great extent. This is particularly true for the inclusion of pressure induced diffusion inside particles experiencing volume change. Therefore, to address such issues, part of the work will involve development of efficient methods for particle/solid phase reformulation - (1) parabolic profile approach and (2) a mixed order finite difference method. These models will be used for approximating/representing solid-phase concentration variations within the active material. Efficiency in simulation of particle level models can be of great advantage when these are coupled with macro-homogenous cell sandwich level battery models

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