Rudolph G. Jungst

Accelerated calendar and pulse life analysis of lithium-ion cells

Abstract Sandia National Laboratories has been studying calendar and pulse discharge life of prototype high-power lithium-ion cells as part of the Advanced Technology Development (ATD) Program. One of the goals of ATD is to establish validated accelerated life test protocols for lithium-ion cells in the hybrid electric vehicle application. In order to accomplish this, aging experiments have been conducted on 18650-size cells containing a chemistry representative of these high-power designs. Loss of power and capacity are accompanied by increasing interfacial impedance at the cathode. These relationships are consistent within a given state-of-charge (SOC) over the range of storage temperatures and times. Inductive models have been used to construct detailed descriptions of the relationships between power fade and aging time and to relate power fade, capacity loss and impedance rise. These models can interpolate among the different experimental conditions and can also describe the error surface when fitting life prediction models to the data.

Lithium battery thermal models

Thermal characteristics and thermal behavior of lithium batteries are important both for the batteries meeting operating life requirements and for safety considerations. Sandia National Laboratories has a broad-based program that includes analysis, engineering and model development. We have determined thermal properties of lithium batteries using a variety of calorimetric methods for many years. We developed the capability to model temperature gradients and cooling rates of high-temperature primary lithium thermal batteries several years ago. Work is now under way to characterize the response of ambient-temperature rechargeable lithium-ion batteries to thermal abuse. Once the self-heating rates of lithium cells have been established over a range of temperatures, the thermal response can be estimated under a variety of conditions. We have extended this process to isolate the behavior of individual battery components and have begun to understand the chemical nature of the species responsible for heat evolution within the cells. This enhanced level of understanding will enable more accurate modeling of cell thermal behavior and will allow model-based design of safer, more abuse-tolerant lithium batteries for electric vehicles (EVs) and hybrid electric vehicles (HEVs) in the future. Progress toward this goal and key information still needed to reach it are discussed.

Energy and power characteristics of lithium-ion cells

Abstract We describe below the electrochemical performance characteristics of cylindrical (18 650) and prismatic (48.26×25.4×7.62) lithium-ion cells at ambient and sub-ambient temperatures. Ragone plots of power and energy data for these cells are compared, and indicate that at room temperature, the ∼500 mAh prismatic lithium-ion cells exhibit higher specific power and power density than the ∼1100 mAh cylindrical cells. Over the temperature range from 35 to −20°C, the cell impedance is almost constant for both cell types. These cells at 100% state-of-charge (s.o.c.) show very little voltage drop for current pulses up to 1 A.

Computational Fluid Dynamics Modeling of a Lithium/Thionyl Chloride Battery with Electrolyte Flow

This paper is a continuation of the recent series of work to explore computational fluid dynamics (CFD) techniques in conjunction with experimentation for fundamental battery research. The application of interest in this work is a lithium/thionyl chloride primary battery. As a power source, this battery has many desirable characteristics such as high energy and power densities, high operating cell voltage, excellent voltage stability over 95% of the discharge, and a large operating temperature range. As such, there has been a number of one-dimensional modeling studies 1-6 in the literature. Modeling efforts by Szpak et al. 1 and Cho 2 focused on the battery’s high-rate discharge and the ensuing thermal behavior. Other modeling efforts employed concentrated solution theory and porous electrode theory in the various regions of the cell ( e.g., separator, porous cathode) to develop one-dimensional models of the battery. 3-6 These models examined utilization issues at low to moderate currents, but they differ in how the excess electrolyte was treated. In the lithium/thionyl chloride cell, the solvent is also the reactant, and the volume it occupies is more than that of the reaction products. Therefore, more electrolyte is placed in the cell than can occupy the initial void volume of the separator and porous cathode. The excess electrolyte resides in the head space volume above the active regions. Due to the one-dimensional nature of their models, Tsaur and Pollard 3 and Evans et al. 4 introduced a fictitious reservoir region between the separator and the porous cathode. In these models, the electrolyte fills uniformly from this reservoir region, through the separator, and into the cathode. In contrast, Jain et al. 5,6 modified the one-dimensional equations so that the electrolyte flows from the head space directly into the porous cathode, thus replicating flow in a second, perpendicular direction. An advantage of this latter approach is that the model can predict the drying of the cell due to insufficient electrolyte loading. While these one-dimensional models are simple and efficient at predicting the discharge of a Li/SOCl 2 battery, a multidimensional model fully accounting for the electrolyte flow without making ad hoc approximations is deemed valuable to gain a more fundamental understanding of the processes that occurr in this battery system. The present paper describes such a model and a finite-volume method of CFD to simulate a Li/SOCl 2 battery in the operating regime of low to intermediate discharge rates. The model uses the physical parameters estimated from experimental data 6 to predict discharge curves accurately at various temperatures.

Artificial neural network simulation of battery performance

Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery, such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, we have begun development of non-phenomenological models for battery systems based on artificial neural networks. The paper describes initial feasibility studies as well as current models and makes comparisons between predicted and actual performance.

Inductive modeling of lithium-ion cells

Abstract Sandia National Laboratories has conducted a sequence of studies on the performance of lithium ion and other types of electrochemical cells using inductive models. The objectives of some of these investigations are: (1) to develop procedures to rapidly determine performance degradation rates while these cells undergo life tests; (2) to model cell voltage and capacity in order to simulate cell output under variable load and temperature conditions; (3) to model rechargeable battery degradation under conditions of cyclic charge/discharge, and many others. Among the uses for the models are: (1) to enable efficient predictions of battery life; (2) to characterize system behavior. Inductive models seek to characterize system behavior using experimentally or analytically obtained data in an efficient and robust framework that does not require phenomenological development. There are certain advantages to this. Among these advantages is the ability to avoid making measurements of hard to determine physical parameters or having to understand cell processes sufficiently to write mathematical functions describing their behavior. We have used artificial neural networks (ANNs) for inductive modeling, along with ancillary mathematical tools to improve their accuracy. This paper summarizes efforts to use inductive tools for cell and battery modeling. Examples of numerical results are presented.

Analysis of a lithium/thionyl chloride battery under moderate-rate discharge

The lithium/thionyl chloride battery (Li/SOCl 2) has received considerable attention as a primary energy source due to its high energy density, high operating cell voltage, voltage stability over 95% of the discharge, large operating temperature range ( 255 to 708C), long storage life, and low cost of materials. 1,2 However, a loss in performance may occur after periods of prolonged storage at high and low temperatures or when exposed to intermittent use. This loss in performance may result in reduced capacity or even worse, catastrophic failure, especially when operated at high discharge rates. High discharge rates and high temperatures promote thermal runaway, which can result in the venting of toxic gases and explosion. 2 Mathematical models can be used to tailor a battery design to a specific application, perform accelerated testing, and reduce the amount of experimental data required to yield efficient, yet safe cells. Models can also be used in conjunction with the experimental data for parameter estimation and to obtain insights into the fundamental processes occurring in the battery. Previous investigators 3,4 presented a one-dimensional mathematical model of the Li/SOCl2 battery. They used porous electrode theory 5 to model the porous cathode and concentrated solution theory 6 for the electrolyte solution to study the effect of various design and operational parameters on the discharge curves. The model equations were written under the assumption that the excess electrolyte was in a reservoir between the separator and the porous cathode. The result was that the electrolyte replenished the porous cathode through the front face of the electrode. The theoretical results showed similar qualitative trends to those observed experimentally. However, a lack of experimental data and unknown values for many of the kinetic and transport parameters as a function of temperature prevented quantitative comparisons. Evans and White 7 presented a parameter estimation technique and used it in conjunction with the one-dimensional mathematical model presented earlier. 4 However, the comparison between simulated and experimental discharge curves was done only for partially discharged cells at ambient temperature. This paper presents a one-dimensional mathematical model for the Li/SOCl2 cell, with model equations similar to those presented previously.3,4 The exception is the modification to the material balance in the porous cathode that accounts for electrolyte replenishment through the top rather than the front of the porous cathode. 8 The model is used to predict discharge curves at low-to-moderate discharge rates (discharge loads #10 V, corresponding to current densities less than 2 mA/cm 2 for a D-size cell). Previous thermal models of Li/SOCl2 cells have shown that under these operating conditions thermal runaway is not a problem. 9,10 Therefore, it is also assumed here that the temperature of the cell is uniform throughout but allowed to change during discharge. 3,4 The model is then used in conjunction with experimental data to obtain estimates for the transference number, diffusion coefficient, and kinetic parameters for the reactions at the anode and cathode as a function of temperature. Using the estimated parameters, the model predictions show good agreement with the experimental data over a wide temperature (255 to 498C) and load range (10 to 250 V). Finally, the model is used to study the effect of cathode thickness on cell performance as a function of operating temperature and load to illustrate the application in optimization studies. Experimental

Recycling of electric vehicle batteries

Publisher Summary This chapter reviews the present state of recycling technologies for electric vehicle (EV) and hybrid electric vehicle (HEV) battery power sources. The term recycling is used to include materials that are reclaimed for use in different products as well as the materials that are reclaimed and transformed into new batteries. A practical method for recycling batteries and other energy storage components from EVs is viewed as essential for the successful implementation of this transportation technology. Toxic materials are found in many battery technologies. Disposal of EV batteries may be allowable by regulations in some cases, but is likely to be costly and detracts from the environmental benefits of a zero-emission vehicle. The battery is a major cost component for EVs, and therefore disposal is doubly expensive, especially if the waste contains valuable materials. Recycling provides an opportunity to reduce life cycle costs through recovery of high-value materials and avoidance of the cost of hazardous waste disposal. Most developers of power sources for EVs, therefore, have a goal of recycling as much material as possible at the end of life. Less demanding, secondary uses for the energy storage device may extend its term of operation, or in some cases refurbishment could also be considered. Eventually, however, the battery must be processed in such a way that all the valuable and/or hazardous components and materials can be recycled.

Stochastic modeling of rechargeable battery life in a photovoltaic power system

The authors have developed a stochastic model for the power generated by a photovoltaic (PV) power supply system that includes a rechargeable energy storage device. The ultimate objective of this work is to integrate this photovoltaic generator along with other generation sources to perform power flow calculations to estimate the reliability of different electricity grid configurations. For this reason, the photovoltaic power supply model must provide robust, efficient realizations of the photovoltaic electricity output under a variety of conditions and at different geographical locations. This has been achieved by use of a Karhunen-Loeve framework to model the solar insolation data. The capacity of the energy storage device, in this case a lead-acid battery, is represented by a deterministic model that uses an artificial neural network to estimate the reduction in capacity that occurs over time. When combined with an appropriate stochastic load model, all three elements yield a stochastic model for the photovoltaic power system. This model has been operated on the Monte Carlo principle in stand-alone mode to infer the probabilistic behavior of the system. In particular, numerical examples are shown to illustrate the use of the model to estimate battery life. By the end of one year of operation, there is a 50% probability for the test case shown that the battery will be at or below 95% of initial capacity.

Reliability of rechargeable batteries in a photovoltaic power supply system

We investigated the reliability of a rechargeable battery acting as the energy storage component in a photovoltaic power supply system. A model system was constructed for this that includes the solar resource, the photovoltaic power supply system, the rechargeable battery and a load. The solar resource and the system load are modelled as stochastic processes. The photovoltaic system and the rechargeable battery are modelled deterministically, and an artificial neural network is incorporated into the model of the rechargeable battery to simulate damage that occurs during deep discharge cycles. The equations governing system behaviour are solved simultaneously in the Monte Carlo framework, and a first passage problem is solved to assess system reliability.