Comparison of different approaches for lifetime prediction of electrochemical systems—Using lead-acid batteries as example (original) (raw)

Electrochemistry-based Battery Modeling for Prognostics

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

Lifetime Modelling of Lead Acid Batteries

2005

Risø-R-1515(EN) 1 5.5.1 Performance 45 5.5.2 Lifetime, degradation and aging 48 6 UMass Model 54 6.1 Model description 54 6.2 Improvements in the model undertaken during the Benchmarking Project 55 6.3 Parameter estimation 55 6.3.1 Capacity Model 55 6.3.2 Voltage Model 56 6.3.3 Lifetime Model 57 6.3.4 Modified Rainflow Cycle Counter 57 6.3.5 Determination of Constants 59 6.4 Simulations 67 6.5 Comparison with test results for validation before and after model improvements 70 7 Discussion of findings 72 8 Status at the end of the project 74 8.1 Common Status 74 8.2 The FhG/Risø Model 74 8.3 The UMASS Model 75 8.4 Throughput Model 76 9 Recommendations for future work 77 References 78 2 Risø-R-1515(EN) Risø-R-1515(EN) 3 Risø-R-1515(EN) 9 Risø-R-1515(EN) 14 Risø-R-1515(EN)

Dynamic model development for lead acid storage battery

Indonesian Journal of Electrical Engineering and Computer Science, 2019

It is widely accepted that electrochemical batteries ensure superior energy storage and reliability of power supply. This paper proposes to discuss the dynamic performance of the Lead Acid Storage battery and to develop an Electrical Equivalent circuit and study its response to sudden changes in the output. A detailed explanation of the discharging process for lead-acid storage batteries and the factors affecting the rate of chemical reactions is provided. The objective of the study is to find the reduction in terminal voltage due to the change in reaction rate and to evolve a simple dynamic model for discharge of the battery.

Modeling discharge characteristics for predicting battery remaining life

2017 IEEE Transportation Electrification Conference and Expo (ITEC), 2017

Due to the global energy crisis and air pollution, the demand for electric vehicles (EVs) and battery storage systems grows at a gallop. To support this growth, it is important to have an effective exploitation of electrochemical based energy storage system with a reliable battery management system (BMS). The remaining useful life (RUL) prediction and estimation of different age batteries are necessary for BMS design. Terminal voltage, current and surface temperature are three main types of data that have significant impacts on predicting the battery's RUL. In this paper, a mathematical model based on regression analysis is formulated to estimate the batterys RUL. Additionally, the corresponding relationship between discharge curve and battery's age is analyzed base on the battery's capacity variety with using time. Finally, the proposed model is validated with experiments on valve-regulated lead acid (VRLA) batteries.

Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries

Journal of The Electrochemical Society, 2009

An isothermal, multimodal, physics-based aging model for life prediction of Li-ion batteries is developed, for which a solventdecomposition reaction leading to the growth of a solid electrolyte interphase ͑SEI͒ at the carbonaceous anode material is considered as the source of capacity fade. The rate of SEI film growth depends on both solvent diffusion through the SEI film and solvent-reduction kinetics at the carbon surface. The model is able to simulate a wide variety of battery aging profiles, e.g., open-circuit and constant-voltage storage, charge/discharge cycling, etc. An analysis of capacity-fade data from the literature reveals that the same set of aging parameters may be used for predicting cycling and constant-voltage storage. The use of this set of parameters for predicting storage under open-circuit voltage points out that part of the self-discharge is reversible.

From accelerated aging tests to a lifetime prediction model: Analyzing lithium-ion batteries

2013 World Electric Vehicle Symposium and Exhibition (EVS27), 2013

As lithium-ion batteries play an important role for the electrification of mobility due to their high power and energy density, battery lifetime prediction is a fundamental aspect for successful market introduction. This work shows the development of a lifetime prediction model based on accelerated aging tests. To investigate the impact of different voltages and temperatures on capacity loss and resistance increase, calendar life tests were performed. Additionally, several cycle aging tests were performed using different cycle depths and mean SOC. Both the calendar and the cycle test data were analyzed to find mathematical equations that describe the aging dependence on the varied parameters. Using these functions an aging model coupled to an impedance-based electrical-thermal model was built. The lifetime prognosis model allows analyzing and optimizing different drive cycles and battery management strategies. The cells modeled in this work were thoroughly tested taking into account a wide range of influence factors. As validation tests on realistic driving profiles show, a robust foundation for simulation results is granted. Together with the option of using temperature profiles changing over the seasons, this tool is able to simulate battery aging in various applications.

A review of battery life-cycle analysis : state of knowledge and critical needs

2010

A literature review and evaluation has been conducted on cradle-to-gate life-cycle inventory studies of lead-acid, nickel-cadmium, nickel-metal hydride, sodium-sulfur, and lithium-ion battery technologies. Data were sought that represent the production of battery constituent materials and battery manufacture and assembly. Life-cycle production data for many battery materials are available and usable, though some need updating. For the remaining battery materials, lifecycle data either are nonexistent or, in some cases, in need of updating. Although battery manufacturing processes have occasionally been well described, detailed quantitative information on energy and material flows is missing. For all but the lithium-ion batteries, enough constituent material production energy data are available to approximate material production energies for the batteries, though improved input data for some materials are needed. Due to the potential benefit of battery recycling and a scarcity of associated data, there is a critical need for lifecycle data on battery material recycling. Either on a per kilogram or per watt-hour capacity basis, lead-acid batteries have the lowest production energy, carbon dioxide emissions, and criteria pollutant emissions. Some process-related emissions are also reviewed in this report.