Thomas L. Paez

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.

Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty

Abstract Performance assessment of complex systems is ideally done through full system-level testing which is seldom available for high consequence systems. Further, a reality of engineering practice is that some features of system behavior are not known from experimental data, but from expert assessment, only. On the other hand, individual component data, which are part of the full system are more readily available. The lack of system level data and the complexity of the system lead to a need to build computational models of a system in a hierarchical or building block approach (from simple components to the full system). The models are then used for performance prediction in lieu of experiments, to estimate the confidence in the performance of these systems. Central to this are the need to quantify the uncertainties present in the system and to compare the system response to an expected performance measure. This is the basic idea behind Quantification of Margins and Uncertainties (QMU). QMU is applied in decision making—there are many uncertainties caused by inherent variability (aleatoric) in materials, configurations, environments, etc., and lack of information (epistemic) in models for deterministic and random variables that influence system behavior and performance. This paper proposes a methodology to quantify margins and uncertainty in the presence of both aleatoric and epistemic uncertainty. It presents a framework based on Bayes networks to use available data at multiple levels of complexity (i.e. components, subsystem, etc.) and demonstrates a method to incorporate epistemic uncertainty given in terms of intervals on a model parameter.

Epistemic Uncertainty in the Calculation of Margins

Epistemic uncertainty, characterizing lack-of-knowledge, is often prevalent in engineering applications. However, the methods we have for analyzing and propagating epistemic uncertainty are not as nearly widely used or well-understood as methods to propagate aleatory uncertainty (e.g. inherent variability characterized by probability distributions). In this paper, we examine three methods used in propagating epistemic uncertainties: interval analysis, Dempster-Shafer evidence theory, and second-order probability. We demonstrate examples of their use on a problem in structural dynamics, specifically in the assessment of margins. In terms of new approaches, we examine the use of surrogate methods in epistemic analysis, both surrogate-based optimization in interval analysis and use of polynomial chaos expansions to provide upper and lower bounding approximations. Although there are pitfalls associated with surrogates, they can be powerful and efficient in the quantification of epistemic uncertainty.

Investment decisions using genetic algorithms

We examine the performance of genetic algorithms as a method for deciding on a strategy to invest in different financial instruments. We discuss the literature, pointing out the different methods for making investment decisions. We then describe genetic algorithms, linking them to the procedure used in this study. We then report on the results obtained in our experiments.

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.

Data transformation methods for genetic-algorithm-based investment decisions

In an earlier work, we examined the performance of genetic algorithms as a method for determining a strategy to invest in different financial instruments every month (S.K. Kassicieh et al., 1997). The inputs in the earlier work were differenced time series of 10 economic indicators where the genetic algorithm used the best three of these series to make the timing (or equivalently switching) decision. We use the same genetic algorithm with different data transformation methods applied to economic data series. These methods are the singular value decomposition (SVD) and principal component artificial neural network (PCANN) with 3, 4, 5 and 10 nodes. We report the result of a large number of runs to determine which of these methods works best. We find that the non standardized SVD of economic data yields the highest terminal wealth for the time period examined. The terminal accumulation is 78.75% of the dollar accumulation given by a perfect timing strategy.

Status and Integrated Road-Map for Joints Modeling Research

The constitutive behavior of mechanical joints is largely responsible for the energy dissipation and vibration damping in weapons systems. For reasons arising from the dramatically different length scales associated with those dissipative mechanisms and the length scales characteristic of the overall structure, this physics cannot be captured adequately through direct simulation of the contact mechanics within a structural dynamics analysis. The only practical method for accommodating the nonlinear nature of joint mechanisms within structural dynamic analysis is through constitutive models employing degrees of freedom natural to the scale of structural dynamics. This document discusses a road-map for developing such constitutive models.

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.