skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on January 31, 2026

Title: In‐device Battery Failure Analysis
Abstract Lithium‐ion batteries are indispensable power sources for a wide range of modern electronic devices. However, battery lifespan remains a critical limitation, directly affecting the sustainability and user experience. Conventional battery failure analysis in controlled lab settings may not capture the complex interactions and environmental factors encountered in real‐world, in‐device operating conditions. This study analyzes the failure of commercial wireless earbud batteries as a model system within their intended usage context. Through multiscale and multimodal characterizations, the degradations from the material level to the device level are correlated, elucidating a failure pattern that is closely tied to the specific device configuration and operating conditions. The findings indicate that the ultimate failure mode is determined by the interplay of battery materials, cell structural design, and the in‐device microenvironment, such as temperature gradients and their fluctuations. This holistic, in‐device perspective on environmental influences provides critical insights for battery integration design, enhancing the reliability of modern electronics.  more » « less
Award ID(s):
2349665 2349666
PAR ID:
10569546
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  more » ;   « less
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Advanced Materials
Volume:
37
Issue:
10
ISSN:
0935-9648
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Wang, Dong (Ed.)
    Lithium-ion batteries have been extensively used to power portable electronics, electric vehicles, and unmanned aerial vehicles over the past decade. Aging decreases the capacity of Lithium-ion batteries. Therefore, accurate remaining useful life (RUL) prediction is critical to the reliability, safety, and efficiency of the Lithium-ion battery-powered systems. However, battery aging is a complex electrochemical process affected by internal aging mechanisms and operating conditions (e.g., cycle time, environmental temperature, and loading condition). In this paper, a physics-informed machine learning method is proposed to model the degradation trend and predict the RUL of Lithium-ion batteries while accounting for battery health and operating conditions. The proposed physics-informed long short-term memory (PI-LSTM) model combines a physics-based calendar and cycle aging (CCA) model with an LSTM layer. The CCA model measures the aging effect of Lithium-ion batteries by combining five operating stress factor models. The PI-LSTM uses an LSTM layer to learn the relationship between the degradation trend determined by the CCA model and the online monitoring data of different cycles (i.e., voltage, current, and cell temperature). After the degradation pattern of a battery is estimated by the PI-LSTM model, another LSTM model is then used to predict the future degradation and remaining useful life (RUL) of the battery by learning the degradation trend estimated by the PI-LSTM model. Monitoring data of eleven Lithium-ion batteries under different operating conditions was used to demonstrate the proposed method. Experimental results have shown that the proposed method can accurately model the degradation behavior as well as predict the RUL of Lithium-ion batteries under different operating conditions. 
    more » « less
  2. Abstract Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions. 
    more » « less
  3. AbstractGreenhouse gas emission reduction is often cited as a reason for high energy density, next-generation battery development. As lithium-O2battery research has progressed, researchers have examined the potential of many novel materials in the drive to reduce parasitic reactions and increase capacity. While the field has made great strides towards producing more reliable batteries, there has been little verification that lithium-O2batteries will reduce net environmental impacts. This paper examines how material selection ultimately impacts lithium-O2battery environmental impacts. Given that researchers should not wait until lithium-O2batteries reach commercialization to assess their environmental impact, this paper describes how to incorporate LCA as an integral part of the battery design process. Furthermore, it provides impact factors of many relevant materials to increase the ease of LCA for the field. <bold>Graphic abstract</bold> 
    more » « less
  4. The ever-increasing demand for high-capacity rechargeable batteries highlights the need for sensitive and accurate diagnostic technology for determining the state of a cell, for identifying and localizing defects, and for sensing capacity loss mechanisms. Here, we leverage atomic magnetometry to map the weak induced magnetic fields around Li-ion battery cells in a magnetically shielded environment. The ability to rapidly measure cells nondestructively allows testing even commercial cells in their actual operating conditions, as a function of state of charge. These measurements provide maps of the magnetic susceptibility of the cell, which follow trends characteristic for the battery materials under study upon discharge. In particular, hot spots of charge storage are identified. In addition, the measurements reveal the capability to measure transient internal current effects, at a level of μA, which are shown to be dependent upon the state of charge. These effects highlight noncontact battery characterization opportunities. The diagnostic power of this technique could be used for the assessment of cells in research, quality control, or during operation, and could help uncover details of charge storage and failure processes in cells. 
    more » « less
  5. null (Ed.)
    The ability to sense ambient temperature pervasively, albeit crucial for many applications, is not yet available, causing problems such as degraded indoor thermal comfort and unexpected/premature shutoffs of mobile devices. To enable pervasive sensing of ambient temperature, we propose use of mobile device batteries as thermometers based on (i) the fact that people always carry their battery-powered smart phones, and (ii) our empirical finding that the temperature of mobile devices' batteries is highly correlated with that of their operating environment. Specifically, we design and implement Batteries-as-Thermometers (BaT), a temperature sensing service based on the information of mobile device batteries, expanding the ability to sense the device's ambient temperature without requiring additional sensors or taking up the limited on-device space. We have evaluated BaT on 6 Android smartphones using 19 laboratory experiments and 36 real-life field-tests, showing an average of 1.25°C error in sensing the ambient temperature. 
    more » « less