Sandia National Laboratories has been conducting studies on performance of laboratory and commercial lithium-ion and other types of electrochemical cells using inductive models [1]. The objectives of these investigations are: (1) To develop procedures and techniques to rapidly determine performance degradation rates while these cells undergo life tests; (2) To model cell voltage and capacity in order to simulate cell performance characteristics under variable load and temperature conditions; (3) To model rechargeable battery degradation under charge/discharge cycles and many other conditions. The inductive model and methodology are particularly useful when complicated cell performance behaviors are involved, which are often difficult to be interpreted from simple empirical approaches. We find that the inductive model can be used effectively: (1) To enable efficient predictions of battery life; (2) To characterize system behavior. Inductive models provide convenient tools to characterize system behavior using experimentally or analytically derived data in an efficient and robust framework. The approach does not require detailed phenomenological development. There are certain advantages unique to this approach. 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 used artificial neural network 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 will be presented. One of them is related to high power lithium-ion batteries tested under the U.S. Department of Energy Advanced Technology Development Program for hybrid vehicle applications. Sandia National Laboratories is involved in the development of accelerated life testing and thermal abuse tests to enhance the understanding of power and capacity fade issues and predict life of the battery under a nominal use condition. This paper will use power and capacity fade behaviors of a Ni-oxide-based lithium-ion battery system to illustrate how effective the inductive model can interpret the cell behavior and provide predictions of life. We will discuss the analysis of the fading behavior associated with the cell performance and explain how the model can predict cell performance.
Battery life is an important, yet technically challenging, issue for battery development and application. Adequately estimating battery life requires a significant amount of testing and modeling effort to validate the results. Integrated battery testing and modeling is quite feasible today to simulate battery performance, and therefore applicable to predict its life. A relatively simple equivalent-circuit model (ECM) is used in this work to show that such an integrated approach can actually lead to a high-fidelity simulation of a lithium-ion cell's performance and life. The methodology to model the cell's capacity fade during thermal aging is described to illustrate its applicability to battery calendar life prediction.
This document describes a general protocol (involving both experimental and data analytic aspects) that is designed to be a roadmap for rapidly obtaining a useful assessment of the average lifetime (at some specified use conditions) that might be expected from cells of a particular design. The proposed experimental protocol involves a series of accelerated degradation experiments. Through the acquisition of degradation data over time specified by the experimental protocol, an unambiguous assessment of the effects of accelerating factors (e.g., temperature and state of charge) on various measures of the health of a cell (e.g., power fade and capacity fade) will result. In order to assess cell lifetime, it is necessary to develop a model that accurately predicts degradation over a range of the experimental factors. In general, it is difficult to specify an appropriate model form without some preliminary analysis of the data. Nevertheless, assuming that the aging phenomenon relates to a chemical reaction with simple first-order rate kinetics, a data analysis protocol is also provided to construct a useful model that relates performance degradation to the levels of the accelerating factors. This model can then be used to make an accurate assessment of the average cell lifetime. The proposed experimental and data analysis protocols are illustrated with a case study involving the effects of accelerated aging on the power output from Gen-2 cells. For this case study, inadequacies of the simple first-order kinetics model were observed. However, a more complex model allowing for the effects of two concurrent mechanisms provided an accurate representation of the experimental data.
The technical and economic feasibility of applying used electric vehicle (EV) batteries in stationary applications was evaluated in this study. In addition to identifying possible barriers to EV battery reuse, steps needed to prepare the used EV batteries for a second application were also considered. Costs of acquiring, testing, and reconfiguring the used EV batteries were estimated. Eight potential stationary applications were identified and described in terms of power, energy, and duty cycle requirements. Costs for assembly and operation of battery energy storage systems to meet the requirements of these stationary applications were also estimated by extrapolating available data on existing systems. The calculated life cycle cost of a battery energy storage system designed for each application was then compared to the expected economic benefit to determine the economic feasibility. Four of the eight applications were found to be at least possible candidates for economically viable reuse of EV batteries. These were transmission support, light commercial load following, residential load following, and distributed node telecommunications backup power. There were no major technical barriers found, however further study is recommended to better characterize the performance and life of used EV batteries before design and testing of prototype battery systems.
Electrical and chemical measurements have been made on 18650-size lithium-ion cells that have been exposed to calendar and cycle life aging at temperatures up to 70 C. Aging times ranged from 2 weeks at the highest temperature to several months under more moderate conditions. After aging, the impedance behavior of the cells was reversed from that found originally, with lower impedance at low state of charge and the total impedance was significantly increased. Investigations using a reference electrode showed that these changes are primarily due to the behavior of the cathode. Measurements of cell impedance as a function of cell voltage reveal a pronounced minimum in the total impedance at approximately 40--50% state-of-charge (SOC). Chemical analysis data are presented to support the SOC assignments for aged and unaged cells. Electrochemical impedance spectroscopy (EIS) data have been recorded at several intermediate states of charge to construct the impedance vs. open circuit voltage curve for the cell. This information has not previously been available for the LiNi{sub 0.85}Co{sub 0.15}O{sub 2} cathode material. Structural and chemical analysis information obtained from cell components removed during postmortems will also be discussed in order to reveal the true state of charge of the cathode and to develop a more complete lithium inventory for the cell.
The authors have developed a model for the probabilistic behavior of a rechargeable battery acting as the energy storage component in a photovoltaic power supply system. Stochastic and deterministic models are created to simulate the behavior of the system components. The components are the solar resource, the photovoltaic power supply system, the rechargeable battery, and a load. One focus of this research is to model battery state of charge and battery capacity as a function of time. The capacity damage effect that occurs during deep discharge is introduced via a non-positive function of duration and depth of deep discharge events. Because the form of this function is unknown and varies with battery type, the authors model it with an artificial neural network (ANN) whose parameters are to be trained with experimental data. The battery capacity loss model will be described and a numerical example will be presented showing the predicted battery life under different PV system use scenarios.
Valve-Regulated Lead-Acid (VRLA) batteries continue to be employed in a wide variety of applications for telecommunications and Uninterruptible Power Supply (UPS). With the rapidly growing penetration of Internet services, the requirements for standby power systems appear to be changing. For example, at last year's INTELEC, high voltage standby power systems up to 300-vdc were discussed as alternatives to the traditional 48-volt power plant. At the same time, battery reliability and the sensitivity of VRLAs to charging conditions (e.g., in-rush current, float voltage and temperature), continue to be argued extensively. Charge regimes which provide 'off-line' charging or intermittent charge to the battery have been proposed. Some of these techniques go against the widely accepted rules of operation for batteries to achieve optimum lifetime. Experience in the telecom industry with high voltage systems and these charging scenarios is limited. However, GNB has several years of experience in the installation and operation of large VRLA battery systems that embody many of the power management philosophies being proposed. Early results show that positive grid corrosion is not accelerated and battery performance is mantained even when the battery is operated at a partial state-of-charge for long periods of time.
The pace of development and fielding of electric vehicles is briefly described and the principal advanced battery chemistries expected to be used in the EV application are identified as Ni/MH in the near term and Li-ion/Li-polymer in the intermediate to long term. The status of recycling process development is reviewed for each of the two chemistries and future research needs are discussed.
The desire to reduce the time and cost of design engineering on new components or to validate existing designs in new applications is stimulating the development of modeling and simulation tools. The authors are applying a model-based design approach to low and moderate rate versions of the Li/SOCl{sub 2} D-size cell with success. Three types of models are being constructed and integrated to achieve maximum capability and flexibility in the final simulation tool. A phenomenology based electrochemical model links performance and the cell design, chemical processes, and material properties. An artificial neural network model improves computational efficiency and fills gaps in the simulation capability when fundamental cell parameters are too difficult to measure or the forms of the physical relationships are not understood. Finally, a PSpice-based model provides a simple way to test the cell under realistic electrical circuit conditions. Integration of these three parts allows a complete link to be made between fundamental battery design characteristics and the performance of the rest of the electrical subsystem.
Maximizing the reclamation/recycle of electric-vehicle (EV) batteries is considered to be essential for the successful commercialization of this technology. Since the early 1990s, the US Department of Energy has sponsored the ad hoc advanced battery readiness working group to review this and other possible barriers to the widespread use of EVs, such as battery shipping and in-vehicle safety. Regulation is currently the main force for growth in EV numbers and projections for the states that have zero-emission vehicle (ZEV) programs indicate about 200,000 of these vehicles would be offered to the public in 2003 to meet those requirements. The ad hoc Advanced Battery Readiness Working Group has identified a matrix of battery technologies that could see use in EVs and has been tracking the state of readiness of recycling processes for each of them. Lead-acid, nickel/metal hydride, and lithium-ion are the three EV battery technologies proposed by the major automotive manufacturers affected by ZEV requirements. Recycling approaches for the two advanced battery systems on this list are partly defined, but could be modified to recover more value from end-of-life batteries. The processes being used or planned to treat these batteries are reviewed, as well as those being considered for other longer-term technologies in the battery recycling readiness matrix. Development efforts needed to prepare for recycling the batteries from a much larger EV population than exists today are identified.
State and Federal regulations have been implemented that are intended to encourage more widespread use of low-emission vehicles. These regulations include requirements of the California Air Resources Board (CARB) and regulations pursuant to the Clean Air Act Amendments of 1990 and the Energy Policy Act. If the market share of electric vehicles increases in response to these initiatives, corresponding growth will occur in quantities of spent electric vehicle batteries for disposal. Electric vehicle battery recycling infrastructure must be adequate to support collection, transportation, recovery, and disposal stages of waste battery handling. For some battery types, such as lead-acid, a recycling infrastructure is well established; for others, little exists. This paper examines implications of increasing electric vehicle use for lead recovery infrastructure. Secondary lead recovery facilities can be expected to have adequate capacity to accommodate lead-acid electric vehicle battery recycling. However, they face stringent environmental constraints that may curtail capacity use or new capacity installation. Advanced technologies help address these environmental constraints. For example, this paper describes using backup power to avoid air emissions that could occur if electric utility power outages disable emissions control equipment. This approach has been implemented by GNB Technologies, a major manufacturer and recycler of lead-acid batteries. Secondary lead recovery facilities appear to have adequate capacity to accommodate lead waste from electric vehicles, but growth in that capacity could be constrained by environmental regulations. Advances in lead recovery technologies may alleviate possible environmental constraints on capacity growth.
A method of determining the dynamic operating cost benefits of energy storage systems for utility applications is presented. The production costing program DYNASTORE is used to analyze economic benefits for ``utility B,`` an isolated island utility, using heuristic unit commitment algorithms. The unit commitment is done using chronologic load data and a detailed model of the utility characteristics. Several unit commitment scenarios are run for utility B, and the results are presented. Comparisons between various Battery Energy Storage System (BESS) applications, as well as cases with and without battery storage, are shown. Results show that for utility B, a BESS of 300 MW size used for either load leveling or spinning reserve provides the greatest economic benefit.
Disposition of electric vehicle (EV) batteries after they have reached the end of their useful life is an issue that could impede the widespread acceptance of EVs in the commercial market. This is especially true for advanced battery systems where working recycling processes have not as yet been established. The DOE sponsors an Ad Hoc Electric Vehicle Battery Readiness Working Group to identify barriers to the introduction of commercial EVs and to advise them of specific issues related to battery reclamation/recycling, in-vehicle battery safety, and battery shipping. A Sub-Working Group on the reclamation/recycle topic has been reviewing the status of recycling process development for the principal battery technologies that are candidates for EV use from the near-term to the long-term. Recycling of near-term battery technologies, such as lead-acid and nickel/cadmium, is occurring today and it is believed that sufficient processing capacity can be maintained to keep up with the large number of units that could result from extensive EV use. Reclamation/recycle processes for midterm batteries are partially developed. Good progress has been made in identifying processes to recycle sodium/sulfur batteries at a reasonable cost and pilot scale facilities are being tested or planned. A pre-feasibility cost study on the nickel/metal hydride battery also indicates favorable economics for some of the proposed reclamation processes. Long-term battery technologies, including lithium-polymer and lithium/iron disulfide, are still being designed and developed for EVs, so descriptions for prototype recycling processes are rather general at this point. Due to the long time required to set up new, full-scale recycling facilities, it is important to develop a reclamation/recycling process in parallel with the battery technologies themselves.
Technical advances in lead-acid battery design have created new opportunities for battery systems in telecommunications, computer backup power and vehicle propulsion power. Now the lead-acid battery has the opportunity to become a major element in the mix of technologies used by electric utilities for several power quality and energy and resource management functions within the network. Since their introduction into industrial applications, Valve Regulated Lead-Acid (VRLA) batteries have received widespread acceptance and use in critical telecommunications and computer installations, and have developed over 10 years of reliable operational history. As further enhancements in performance, reliability and manufacturing processes are made, these VRLA batteries are expanding the role of battery-based energy storage systems within utility companies portfolios. This paper discusses the rationale and process of designing, optimizing and testing VRLA batteries for specific utility application requirements.