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The development process of electrified vehicles can benefit significantly from computer-aided engineering tools that predict the multi-physics response of batteries during abusive events. A coupled structural, electrical, electrochemical, and thermal model framework has been developed within the commercially available LS-DYNA software. The finite element model leverages a three-dimensional mesh structure that fully resolves the unit cell components. The mechanical solver predicts the distributed stress and strain response with failure thresholds leading to the onset of an internal short circuit. In this implementation, an arbitrary compressive strain criterion is applied locally to each unit cell. A spatially distributed equivalent circuit model provides an empirical representation of the electrochemical response with minimal computational complexity. The thermal model provides state information to index the electrical model parameters, while simultaneously accepting irreversible and reversible sources of heat generation. The spatially distributed models of the electrical and thermal dynamics allow for the localization of current density and corresponding temperature response. The ability to predict the distributed thermal response of the cell as its stored energy is completely discharged through the short circuit enables an engineering safety assessment. A parametric analysis of an exemplary model is used to demonstrate the simulation capabilities.

In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated.

The successful electrification of the transportation and stationary power markets requires batteries with 10-30 years of lifetime. Validations of such long time scales can take several years and require significant confidence in future life predictions. Battery life predictions across such timescales can be highly sensitive to uncertainty in the collection of primary parameters such as current, voltage and time. A methodology to quantify how these primary parameters propagate into the derived parameters of interest such as capacity and coulombic efficiency is presented. The recent development of high precision battery testing and equipment has met the need for accurate and precise assessment of measurement errors. As an example of the derived quantity uncertainty propagation methodology and approach developed in this paper, a prototype high current, high precision tester is also evaluated. The aim of this paper is to describe a standardized method for the precise quantification of battery derived parameter uncertainty, apply the method to the performance of a prototype high current, high precision tester (HPT) as a case study, and then to model the sensitivity of these precision results across a population of possible test conditions.

Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentration-dependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations.

During cycling, the temperature of a Li-Ion cell changes as a result of reversible and irreversible aspects of the internal electrochemistry, as well as concentration gradient dynamics. A method for real-time estimation of any of these components of heat generation is presented. Experiments are performed to quantify the reversible component of heat generation across a broad range of state-of-charge values for an automotive cell. The state-of-charge dependency of the reversible heat is fit using half-cell data from literature. A lumped energy balance model is validated with cycling data to demonstrate the range of applicability for the process, and a Kalman Filter is designed to utilize the energy balance for estimation of reversible heat generation from cycling data. The estimates are compared to the values obtained from experiments for multiple current rates and temperatures, and key advantages of the model-based method are discussed.

Lithium-ion battery electrodes utilizing mixtures of active materials have seen increased attention in recent years. These cells have merit for electric vehicle applications due to the potential for improved cycle life and increased performance. An aging campaign has been conducted with 15 Ah automotive pouch cells to quantify the relationship between a set of severity factors and the aging process for cells using composite electrodes, where aging is represented by capacity fade. A simplified electrochemical model which postulates that the materials within the composite electrode operate as parallel paths for current is used to interpret trends in the aging data. Using the model, loss of active material from individual components of the composite positive electrode can be simulated, as well as loss of cyclable lithium. Comparison with differential capacity data allows an assessment of the most likely degradation mechanism for the initial capacity fade.

This article examines the effects of various hypotheses for the electrical double layer on charge transfer in lithium-ion batteries. We begin by introducing the Poisson Nernst-Planck equations to describe ion transport within a typical liquid solvent and derive an expression for the potential difference in the electroneutral liquid of the separator. We discuss the structure of the electrical double layer and review two distinct hypotheses for interpreting the total potential difference across the electrical double layer. The limiting descriptions lead to different concentration and potential profiles and overpotential during discharge.

Experimental data is presented from crush testing of 1S4P battery modules that quantifies the fault currents experienced by each cell after the onset of an internal short circuit. Combined with voltage and temperature measurements, the newly proposed method for measuring fault currents provides a more complete picture of the module failure during abusive crush. Short circuit resistance trends versus time are calculated from the current measurements, indicating approximately 20 milliohms resistance values prior to thermal runaway and resistive heat generation on the order of hundreds of watts.

In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated.

The successful electrification of the transportation and stationary power markets requires batteries with 10–30 years of lifetime. Validations of such long time scales can take several years and require significant confidence in future life predictions. Battery life predictions across such timescales can be highly sensitive to uncertainty in the collection of primary parameters such as current, voltage and time. A methodology to quantify how these primary parameters propagate into the derived parameters of interest such as capacity and coulombic efficiency is presented. The recent development of high precision battery testing and equipment has met the need for accurate and precise assessment of measurement errors. As an example of the derived quantity uncertainty propagation methodology and approach developed in this paper, a prototype high current, high precision tester is also evaluated. The aim of this paper is to describe a standardized method for the precise quantification of battery derived parameter uncertainty, apply the method to the performance of a prototype high current, high precision tester (HPT) as a case study, and then to model the sensitivity of these precision results across a population of possible test conditions.

The development process of electrified vehicles can benefit significantly from computer-aided engineering tools that predict the multi-physics response of batteries during abusive events. A coupled structural, electrical, electrochemical, and thermal model framework has been developed within the commercially available LS-DYNA software. The finite element model leverages a three-dimensional mesh structure that fully resolves the unit cell components. The mechanical solver predicts the distributed stress and strain response with failure thresholds leading to the onset of an internal short circuit. In this implementation, an arbitrary compressive strain criterion is applied locally to each unit cell. A spatially distributed equivalent circuit model provides an empirical representation of the electrochemical response with minimal computational complexity. The thermal model provides state information to index the electrical model parameters, while simultaneously accepting irreversible and reversible sources of heat generation. The spatially distributed models of the electrical and thermal dynamics allow for the localization of current density and corresponding temperature response. The ability to predict the distributed thermal response of the cell as its stored energy is completely discharged through the short circuit enables an engineering safety assessment. A parametric analysis of an exemplary model is used to demonstrate the simulation capabilities.

Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentration-dependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations.

Experimental data is presented from crush testing of 1S4P battery modules that quantifies the fault currents experienced by each cell after the onset of an internal short circuit. Combined with voltage and temperature measurements, the newly proposed method for measuring fault currents provides a more complete picture of the module failure during abusive crush. Short circuit resistance trends versus time are calculated from the current measurements, indicating approximately 20 milliohms resistance values prior to thermal runaway and resistive heat generation on the order of hundreds of watts.

During cycling, the temperature of a Li-Ion cell changes as a result of reversible and irreversible aspects of the internal electrochemistry, as well as concentration gradient dynamics. A method for real-time estimation of any of these components of heat generation is presented. Experiments are performed to quantify the reversible component of heat generation across a broad range of state-of-charge values for an automotive cell. The state-of-charge dependency of the reversible heat is fit using half-cell data from literature. A lumped energy balance model is validated with cycling data to demonstrate the range of applicability for the process, and a Kalman Filter is designed to utilize the energy balance for estimation of reversible heat generation from cycling data. The estimates are compared to the values obtained from experiments for multiple current rates and temperatures, and key advantages of the model-based method are discussed.

Lithium-ion battery electrodes utilizing mixtures of active materials have seen increased attention in recent years. These cells have merit for electric vehicle applications due to the potential for improved cycle life and increased performance. An aging campaign has been conducted with 15 Ah automotive pouch cells to quantify the relationship between a set of severity factors and the aging process for cells using composite electrodes, where aging is represented by capacity fade. A simplified electrochemical model which postulates that the materials within the composite electrode operate as parallel paths for current is used to interpret trends in the aging data. Using the model, loss of active material from individual components of the composite positive electrode can be simulated, as well as loss of cyclable lithium. Comparison with differential capacity data allows an assessment of the most likely degradation mechanism for the initial capacity fade.

This article examines the effects of various hypotheses for the electrical double layer on charge transfer in lithium-ion batteries. We begin by introducing the Poisson Nernst-Planck equations to describe ion transport within a typical liquid solvent and derive an expression for the potential difference in the electroneutral liquid of the separator. We discuss the structure of the electrical double layer and review two distinct hypotheses for interpreting the total potential difference across the electrical double layer. The limiting descriptions lead to different concentration and potential profiles and overpotential during discharge.