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

Hysteresis and phase transition in many-particle storage systems

We study the behavior of systems consisting of ensembles of interconnected storage particles. Our examples concern the storage of lithium in many-particle electrodes of rechargeable lithium-ion batteries and the storage of air in a system of interconnected rubber balloons. We are particularly interested in those storage systems whose constituents exhibit non-monotone material behavior leading to transitions between two coexisting phases and to hysteresis. In the current study we consider the case that the time to approach equilibrium of a single storage particle is much smaller than the time for full charging of the ensemble. In this regime the evolution of the probability to find a particle of the ensemble in a certain state, may be described by a nonlocal conservation law of Fokker-Planck type. Two constant parameter control whether the ensemble transits the 2-phase region along a Maxwell line or along a hysteresis path or if the ensemble shows the same non-monotone behavior as its constituents

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

The final publication is available at Elsevier via http://doi.org/10.1016/j.jpowsour.2014.01.057 © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and MapleSoft

Hybrid nonlinear observer for battery state- of- charge estimation using nonmonotonic force measurements

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/4/adc238.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/3/adc238-sup-0001-supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/2/adc238_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162783/1/adc238-sup-0002-supinfo.pd

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

Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time

Modeling of Lithium-ion Battery Considering Temperature and Aging Uncertainties

This dissertation provides a systematic methodology for analyzing and solving the temperature and aging uncertainties in Li-ion battery modeling and states estimation in the electric vehicle applications. This topic is motivated by the needs of enhancing the performance and adaptability of battery management systems. In particular, temperature and aging are the most crucial factors that influence battery performance, modeling, and control. First, the basic theoretical knowledge of Li-ion battery modeling and State of Charge (SoC) estimation are introduced. The thesis presents an equivalent circuit battery model based SoC estimation using Adaptive Extended Kalman Filter (AEKF) algorithm to solve the initial SoC problem and provide good estimation result. Second, the thesis focuses on the understanding of the temperature-dependent performance of Li-ion battery. The temperature influence is investigated through Electrochemical Impedance Spectroscopy (EIS) tests to enhance the theoretical basis understanding and to derive model compensation functions for better model adaptability at different temperatures. Third, the battery aging mechanisms are revisited first and then a series of aging tests are conducted to understand the degradation path of Lithium-ion battery. Moreover, the incremental capacity analysis (ICA) based State of Health (SoH) estimation method xiv are applied to track battery aging level and develop the bias correction modeling method for aged battery. In the final phase, the study of parallel-connected battery packs is presented. The inconsistency problem due to different battery aging levels and its influence to parallel-connected packs are discussed. Based on simulation and experimental test results, it shows that the current difference in parallel connected cells is increased significantly at low SoC, despite the battery aging levels and the number of cells in parallel. In total, this dissertation utilizes physics-based battery modeling and states estimation method to optimize battery management under temperature and aging uncertainties in electric vehicle applications. The unique contributions include developing analytical compensation functions to improve equivalent circuit battery model adaptability under temperature uncertainty and developing ICA based SoH estimation and battery modeling method to overcome aging uncertainty.Ph.D.CECS Automotive Systems EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/134041/1/Gong Dissertation Final.pdfDescription of Gong Dissertation Final.pdf : Dissertatio