Engineering topics

Kim, Sangwook

Publications and source records attributed to Kim, Sangwook.

5 records

DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗

DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗

DOE OSTI2023

Calendar life of lithium metal batteries: Accelerated aging and failure analysis

Lithium metal batteries (LMBs) are prime candidates for next-generation energy storage devices characterized by their remarkable energy storage capabilities. Despite the importance of understating the calendar aging impacts in LMBs, cycle life and calendar life have received inconsistent attention across various research and development stages. For acceptance into an application, especially electric vehicles, batteries are required to have sufficient calendar life to ensure performance over the life of vehicles which often experience extended periods of inactivity. In this study, an in-depth exploration into the calendar aging of LMB (Li/ Li[Ni 0.8 Mn 0.1 Co 0.1 ]O 2 in pouch cell format) is conducted. Specifically, the impact of calendar aging factors, state-of-charge (SOC), temperature, pressure, and operating conditions (open-circuit voltage, OCV and constant voltage, CV) on the surface morphology and thickness change rate in lithium metal anode is investigated. Notably, the results show that storing LMB pouch cells at an OCV, 80 % SOC, 25 °C, and 10 psi external pressure leads to a <1% reduction in capacity over 18 months. The results also suggest routes which can be utilized in the future for accelerated aging without altering the primary degradation modes. The results also show an accelerated calendar aging at high temperatures (45 °C), 100 % SOC, and CV suggesting they may serve as key accelerated aging methods for future studies. During calendar aging, pressure mitigates dendrite growth and extends calendar life. In conclusion, the insights from this study reinforce the viability of LMBs as a compelling solution for addressing increased energy storage needs in real-world applications.

25 ENERGY STORAGE↗

DOE OSTI2023

Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology

There are numerous factors that can have an impact on the degradation behavior of batteries, such as the number of recharge cycles or the charge rate. Here, we investigate the influence of operating temperature on the structural degradation of the microstructure in lithium-ion positive electrodes. For that purpose, the microstructure is characterized for cathodes which have been cycled for 200 cycles under 6C (10-minute) charging at different operating temperatures, namely, 20°C, 30°C, 40°C, and 50°C. For each operating condition scanning electron microscopy (SEM) images of cross-sectioned Li x Ni 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) electrodes have been analyzed, to determine structural descriptors such as global particle porosity, crack size/length/width distribution, and porosity and specific surface area distribution of individual particles. Additionally, a stereological method has been deployed to investigate the local particle porosity as a function of distance to the particle center. Results show that particle porosity increases with increasing cycling temperature. Particle porosity is greatest at the particle center and decreases along the particle radius to the exterior. Particle surface area is similar across the four cycling-temperature aging conditions.

25 ENERGY STORAGE↗

DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗