More Like this

1. Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

   https://doi.org/10.3390/pr7010038  

   Son, Jeongeun; Du, Yuncheng (January 2019, Processes)

   The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations. 

   more » « less
2. A Review of Modeling, Management, and Applications of Grid Connected Li ion Battery Storage Systems

   https://doi.org/10.1109/TSG.2022.3188598  

   Rouholamini, Mahdi; Wang, Caisheng; Nehrir, Hashem; Hu, Xiaosong; Hu, Zechun; Aki, Hirohisa; Zhao, Bo; Miao, Zhixin; Strunz, Kai (July 2022, IEEE Transactions on Smart Grid)

   The intermittency of renewable energy sources makes the use of energy storage systems (ESSs) indispensable in modern power grids for supply-demand balancing and reliability enhancement. Besides pumped-storage hydroelectric power stations, energy storage deployment worldwide is still quite low. However, the status quo might rapidly change as the energy storage technologies are growing and facilitating market regulations are being ratified. Battery energy storage systems (BESSs), Li-ion batteries in particular, possess attractive properties and are taking over other types of storage technologies. Thus, in this article, we review and evaluate the current state of the art in managing grid-connected Li-ion BESSs and their participation in electricity markets. The review mainly includes battery modeling, the architecture of battery management systems (BMSs), the incorporation of BESSs for electricity market services, global utility-scale battery storage facilities, and challenges in implementing and managing grid-connected BESSs. 

   more » « less
3. Advancing electric mobility with lithium-ion batteries: A materials and sustainability perspective

   https://doi.org/10.1557/s43577-024-00749-y  

   Promi, Anika; Meyer, Katelyn; Ghosh, Rupayan; Lin, Feng (July 2024, MRS Bulletin)

   Abstract In the last three decades, lithium-ion batteries (LIBs) have become one of the most influential technologies in the world, allowing the widespread adoption of consumer electronics and now electric vehicles (EVs), a key technology for tackling climate change. Decades of research in both academia and industry have led to the development of diverse chemistries for LIB components, aligning these technological advancements with global carbon neutrality goals. In this article, we discuss the fundamental materials chemistries employed in LIBs for EVs, focusing on how materials-level properties influence the electrochemical performance of the battery. We elaborate on factors such as supply-chain sustainability, raw materials availability, and geopolitical influences that shape the market dynamics of these battery materials. Additionally, we delve into current innovative materials design strategies aimed at enhancing the performance of LIBs, with a focus on improving energy density, safety, stability, and fast-charging capabilities. Finally, we offer our insights into the future trajectory of EV batteries, considering the ongoing research trends and evolving landscape of EVs in the context of global efforts toward a more sustainable and environmentally friendly transportation system. Graphical abstract 

   more » « less
4. Utilizing Machine Learning to Predict the Charge Storage Capability of Lithium-Ion Battery Materials

   https://doi.org/10.18321/ectj1651  

   Chhetri, Manoj; Martirosyan, Karen S (April 2025, Eurasian Chemico-Technological Journal)

   With the increasing demand for high-performance batteries in applications such as electric vehicles and portable electronics, accurately predicting the charge storage capacity of battery materials is crucial for developing more efficient and reliable energy storage systems. Machine Learning (ML) and data-driven approaches, plays a vital role in enhancing our understanding of Li-ion battery performance, guiding materials design, optimizing system efficiency, and accelerating innovation in energy storage technologies. In this study, an ML-based approach was applied to a dataset of 2345 rechargeable Li-ion battery materials, obtained from the Materials Project online portal, to predict gravimetric charge storage capacity ─ a key parameter for energy storage capability. To model this relationship, three key independent features were selected: average operating voltage, gravimetric energy density, and charging stability. Given the nonlinear dependencies between these features and the target variable, an ensemble learning algorithm, Gradient Boosting Regression (GBR), was employed. The model exhibited high predictive accuracy, achieving an R² value of 0.99 on the test dataset with a Mean Squared Error (MSE) of 20.08 for target feature values. These results confirm the model’s effectiveness in capturing complex relationships within the battery materials dataset, demonstrating its reliability in predicting charge storage capacity with minimal error. The feature selection strategy emphasizes practical electrochemical properties, enhancing the model’s interpretability and relevance for battery material screening. Its low error metrics indicate strong generalizability, positioning it as a valuable tool for accelerating battery material discovery and optimizing performance. This study distinguishes itself by focusing on gravimetric charge storage capacity prediction using domain-relevant features and an ensemble learning approach, leveraging a large open-source dataset to achieve high predictive accuracy. This is crucial for energy storage capabilities, but it has been less frequently modeled directly in ML-driven battery studies. 

   more » « less
5. Challenge-driven printing strategies toward high-performance solid-state lithium batteries

   https://doi.org/10.1039/D1TA09322C  

   Wang, Jing; Huang, Xingkang; Chen, Junhong (February 2022, Journal of Materials Chemistry A)

   Solid-state lithium batteries (SSLBs) are promising candidates for replacing traditional liquid-based Li-ion batteries and revolutionizing battery systems for electric vehicles and portable devices. However, longstanding issues such as form factors, interfacial contact resistance, balance between ion conductivity and mechanical strength, and manufacturing processability limit their applications. In this review we present how advanced printing technologies can help to mitigate typical problems in main components of SSLBs and improve device performance. We first introduce the common printing techniques for energy storage devices, then focus on the issues and corresponding printing strategies for anodes, cathodes, and solid-state electrolytes to guide the construction of energy-dense, free-form SSLBs. The features and effects of the printed structures are emphasized, as well. We conclude by discussing the problems associated with printing technologies and the potential research directions for printed solid-state batteries. 

   more » « less