This workshop will focus on the major features of battery modeling software and its application to electrical and thermal battery design and life estimation. Case studies will be presented to show how to address various problems in automotive battery design, or more generally, in large battery and system design.

Dr. Spotnitz is a leading developer of mathematical models that simulate battery operation. Dr. Spotnitz, who previously held several senior technical positions in materials and battery development, founded Battery Design in 1999 to develop custom software for battery developers and users. He is a well-known speaker on various aspects of battery engineering.

1. Material Modifications and Analytic Methods to Enhance State Estimation of Lithium-Ion Batteries
   Mark Verbrugge, Director, Chemical Sciences and Materials Systems Laboratory, General Motors
   Abstract 

   

   Materials modifications. Voltage based State of Charge (SOC) estimation is challenging for lithium ion batteries that exhibit little open circuit voltage (OCV) change over a large SOC range. We demonstrate that by using a composite negative electrode composed of disordered carbon and graphite or lithium titanate (LTO) added to graphite, we are able to introduce additional robust features to the OCV-SOC relationship that facilitate voltage-based SOC estimation. In contrast to graphite, the potential of disordered carbon is sensitive to the state of charge. For the LTO+graphite case, a distinct voltage step is manifest towards the end of discharge, providing a clear SOC marker. In both cases, we demonstrate the effectiveness of the approaches by comparing model simulations and corresponding experimental data.
   State estimation. We present a new algorithm that improves the prediction accuracy of the maximum charge and discharge power capabilities, i.e. state of power (SOP), of a battery state estimator (BSE) using an equivalent-circuit representation of a battery. For short time (high frequency) operation, lithium ion traction batteries are often dominated by ohmic and interfacial kinetic resistance, and conventional equivalent circuits employing resistors and capacitors (RC circuits) work well to characterize the system. However, for longer times, diffusion resistance becomes important and conventional BSEs based on RC elements fail to provide useful power predictions. In order to take into account diffusion in the SOP prediction, we propose to incorporate a nonlinear resistance into the power prediction formulas that are otherwise based on an RC circuit formulation; specifically, a circuit employing a resistor in series with a circuit element comprising a parallel resistor and capacitor combination (an R-RC circuit) is modified to include a new time-dependent element. Hence, diffusion resistance is addressed with this nonlinear resistance whose value is proportional to the square root of time. The new approach is implemented in a vehicle-simulation environment (a hardware-in-the-loop set up) to predict the SOP of a lithium-ion battery. Simulation results demonstrate that this revised estimator provides much more accurate power prediction without compromising the regression performance of the original BSE.
   

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2. Coupled Thermal-Electrochemical Methods to Model Battery Cells, Modules & Packs
   Steve Hartridge, Director Electric and Hybrid Vehicles, CD Adapco
   Abstract 

   

   The presentation will highlight the latest methods and findings in the detailed numerical analysis of battery cell, module and pack designs highlighting the technology used to move from lumped cell models to resolved 3D models. The points covered will include:

   • A brief history of the milestone advances in electrochemical modelling and the problems that they solved
   • An introduction to the multiple scales and required output for different types of battery modelling problems, electrode pairs, complete cells, module, packs, packs in an installation, etc. Also a discussion of the sensitive parameters within each type of analysis

   This will also present several enhancements within the field namely:

   • A validation study which used a 3D numerical electrochemistry solver. This captures the diffusion process modelled at a micro structural level. This is seen as a major new theme in simulation of battery cells providing a deeper understanding to the processes during extremes of operation.
   • A method of coupling the electrochemistry and thermal effects experienced within a spirally wound cell during charge and discharge. Moreover results will be presented which show the impact of the current collection method on both electrochemical and thermal performance of a cell and how this can impact module design

   Finally the presentation will discuss the value of having a connected simulation method from micro structural modelling of electrodes up to complete battery pack installations and how such an integrated approach enables companies to apply simulation methods.
   

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3. Computer-Aided Engineering of Batteries for Designing Better Li-Ion Batteries
   Ahmad Pesaran, Principal Engineer, National Renewable Energy Laboratory
   Abstract 

   

   • Simulation and Computer-Aided Engineering (CAE) tools are widely used to speed up the research and development cycle and reduce the number of build and break steps in many industries including automotives.
   • Although there are a number battery models available, there is a need to have several user-friendly, 3D, fully integrated CAE software tools available for the battery community across many scales.
   • Realizing the need, U.S. Department of Energy (DOE) has initiated a project to bring together these battery models to develop Battery Computer Aided Engineering tools for industry and battery developers. This project is called Computer Aided Engineering for automotive Batteries (CAEBAT)
   • NREL is coordinating the CAEBAT project for DOE by collaborating with national labs and managing competitive subcontracts with three industry teams to develop battery CAE tools.
   • The industry CAEBAT teams (GM/ASNYS/ESim, EC Power/PSU/Ford/JCI, and CD-adapco/Battery Design/JCI/A123Systems) have stated the software tool development since June of 2011 with a 50/50 cost sharing.
   • As part of the CAEBAT project, NREL also is enhancing and further developing its existing electrochemical, thermal, abuse reaction, and internal short circuit models for use by industry and CAEBAT participants.
   • Through the multi-year effort supported by DOE, NREL has developed a modeling framework known as the Multi-Scale Multi-Dimensional (MSMD) model for predictive computer simulation of lithium ion batteries and addresses the interplay among the physics in varied scales.
   • This paper focuses on providing an overview or NREL's multi-physics MSMD framework and it various sub-models and provides some example results.
   • Here we will introduce multiple computational domains (cell, electrode sandwich, and particles) for corresponding length scale physics and explain how we decoupled cell geometries into separate computation domains.
   • Couple physics using the predefined inter-domain information exchange is used to provide opportunity to selectively resolve higher spatial resolution for smaller characteristic length scale physics (such as particles).
   • This resulted in achieving high computational efficiency and provide a flexible & expandable modularized framework.
   • Two sub-models for li-ion cells discussed Single Potential-Pair Continuum model applicable to stack prismatic cells, tab-less wound cylindrical or prismatic cells and Wound Potential-Pair Continuum applicable to spirally wound cylindrical or prismatic cells.

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4. One-Dimensional Physics-Based Reduced-Order Modeling of a Lithium-Ion Cell
   Gregory Plett, Professor, University of Colorado
   Abstract 

   

   Lithium ion battery models are needed to help gain insight into cell behavior and to be able to derive battery controls methods that are both effective and efficient. Present modeling strategies tend to fall into one of two categories: theoretically developed physics-based models, and empirically justified equivalent-circuit-based models. The former comprise coupled sets of partial-differential equations (PDEs), which require considerable computational resources to simulate, while the latter are generally computable as sets of continuous-time ordinary differential or discrete-time ordinary difference equations, which are relatively simple to simulate. Physics models provide much more predictive power and are able to apply even to unusual operating situations, while the empirical models should not be used to extrapolate beyond the original data used to generate their parameters. This presentation introduces a new approach to modeling that can automatically convert a physics-based model into an optimum approximate reduced-order model of similar complexity to an equivalent circuit model. At the heart of the method is an algorithm we call the "discrete-time realization algorithm" or DRA. In this presentation we will:

   • Introduce the problem we are trying to solve: that is, to be able to generate a one-dimensional physics-based reduced-order model of a lithium-ion cell that is of similar complexity as an equivalent-circuit based cell model but which retains the predictive power of the underlying physics.
   • Discuss the steps of the discrete-time realization algorithm and show a simple motivational example of how the DRA works.
   • Apply the DRA to the problem of solid diffusion in a spherical particle to show how the infinite-order partial differential equation can be reduced to a very low order discrete-time implementation with negligible loss of accuracy in the solution.
   • Apply the DRA to the more difficult problem of modeling one-dimensional behaviors in a lithium-ion cell, starting with the porous-electrode equations. We will show PDE solutions compared to reduced-order model solutions for solid-electrolyte potential difference, overpotential, reaction current density, and solid surface concentration.
   • Our conclusions are that the method works very robustly and provides accurate reduced-order models of cell behaviors.

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5. Hierarchical Models for Batteries: Overview with Some Case Studies
   Sreekanth Pannala, Senior Research Staff Member, Oak Ridge National Laboratory
   Abstract 

   

   Batteries are complex multiscale systems and a hierarchy of models has been employed to study different aspects of batteries at different resolutions. For the electrochemistry and charge transport, the models span from electric circuits, single-particle, pseudo 2D, detailed 3D, and microstructure resolved at the continuum scales and various techniques such as molecular dynamics and density functional theory to resolve the atomistic structure. Similar analogies exist for the thermal, mechanical, and electrical aspects of the batteries. We have been recently working on the development of a unified formulation for the continuum scales across the electrode-electrolyte-electrode system - using a rigorous volume averaging approach typical of multiphase formulation. This formulation accounts for any spatio-temporal variation of the different properties such as electrode/void volume fractions and anisotropic conductivities. In this talk the following will be presented:

   1. The background and the hierarchy of models that need to be integrated into a battery modeling framework to carry out predictive simulations,
   2. Our recent work on the unified 3D formulation addressing the missing links in the multiscale description of the batteries,
   3. Our work on microstructure resolved simulations for diffusion processes,
   4. Upscaling of quantities of interest to construct closures for the 3D continuum description,
   5. Sample results for a standard Carbon/Spinel cell will be presented and compared to experimental data,
   6. Finally, the infrastructure we are building to bring together components with different physics operating at different resolution will be presented.

   The presentation will also include details about how this generalized approach can be applied to other electrochemical storage systems such as supercapacitors, Li-Air batteries, and Lithium batteries with 3D architectures.
   

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