The challenge and opportunity of battery lifetime prediction from field data

Sulzer, Valentin; Mohtat, Peyman; Aitio, Antti; Lee, Suhak; Yeh, Yen T.; Steinbacher, Frank; Khan, Muhammad Umer; Lee, Jang Woo; Siegel, Jason B.; Stefanopoulou, Anna G.; Howey, David A.

doi:10.1016/j.joule.2021.06.005

Cited by 193 publications

(90 citation statements)

References 125 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, quantifying the values of bulk internal states from field data remains challenging. 40,151,171 Furthermore, data-driven models trained on cycling data will naturally be poorly suited for knee pathways with signals that are challenging to measure via electrochemistry. To this end, two promising research directions are to develop input perturbations that magnify the response of hard-to-detect signals and to introduce additional characterization techniques into cell cycling tests.…”

Section: Modeling and Prediction Outlookmentioning

confidence: 99%

"Knees" in lithium-ion battery aging trajectories

Attia,

Bills,

Planella

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of "internal state trajectories" (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee "pathways", including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation-some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

show abstract

Section: Modeling and Prediction Outlookmentioning

confidence: 99%

"Knees" in lithium-ion battery aging trajectories

Attia,

Bills,

Planella

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…At the early stage of design, the data flow of partial differential equation calculation needs to be carefully analysed, the process is designed according to the relevant scientific specifications, and the system structure and related interfaces are divided from the system perspective. e user only needs to perform relevant interaction operations within the browser, and the local computing resources are called during the calculation [16].…”

Section: Partial Differential Equation Combined With Artificialmentioning

confidence: 99%

Partial Differential Equation-Assisted Accounting Professional Education and Training Artificial Intelligence Collaborative Course System Construction

Hu¹

2022

Scientific Programming

View full text Add to dashboard Cite

In this paper, we use partial differential equations combined with artificial intelligence methods to conduct an in-depth study and analysis of the curriculum system of accounting and finance professional education and training. Under the condition of high-performance advanced computers, neural network technology has been developed rapidly and started to adapt to the requirements of computational accuracy and speed in the field of PDEs. Neural networks have good self-learning as well as self-adaptive capabilities, and the use of deep neural networks to solve the numerical solution of partial differential equations has become a popular application that has emerged recently. The system provides a variety of interfaces for the output of calculation results, which can be directly or indirectly read into the calculation results by various common drawing software programs. In addition, the system can also directly plot the results using its plotting plug-in. This paper presents a case study to illustrate the overview of the practical work of the group before the application of accounting AI, i.e., the human manual model, reveals the problems faced by the accounting practice in this model and then discusses the specific application process and the effect it brings, emphasizing the superiority of accounting AI. Intra-group heterogeneity is to fully consider the learning ability, interest, personality, gender, and other factors of individual students when grouping, to ensure the differences between the members of the group. In terms of accounting personnel and accounting credit system, which are indirectly affected by AI in accounting, this paper discusses the overview before the application of AI in accounting and the impact and challenges after the application and then comes up with countermeasures for the development of accounting. Through the study of specific enterprises to elaborate the current situation of the application of accounting AI in enterprises and its impact, it exposes that the application of accounting AI has a great impact on the accounting process, accounting information quality, accounting personnel, accounting information security, and other aspects of enterprises and this paper proposes solutions in four aspects: first, machine learning for adversarial AI needs to be promoted; second, AI-related laws need to be improved; third, management accounting talent transformation needs to be realized; and fourth, the integration of artificial intelligence and accounting needs to be deepened.

show abstract

“…It is generally considered that when the discharge capacity decays by 20% or the internal resistance increases by 100%, the battery reaches its end of life. 10,95 In recent years, the remaining useful life (RUL) prediction of batteries has been a hot topic. The existing prediction methods are mainly categorized into two types: the physical failure model and the data-driven method.…”

Section: Prediction Of Electrochemical Propertiesmentioning

confidence: 99%

Multiscale computations and artificial intelligent models of electrochemical performance in Li‐ion battery materials

Qiu

Wang

Liu

2021

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Li-ion battery (LIB) is widely used as one of renewable energy resources for powerful electronics and electric vehicles. The main challenge in developing next-generation LIB is to further improve the energy density, rate capability, and cycling stability of electrode and electrolyte materials. With the rapid development of computational science, the material design has changed from the traditional trial-and-error approach to integrated database-based computation. Multiscale computational methods and machine learning (ML) not only speed up the development of new materials, but also provide insight into the intrinsic relationship between the microscopic composition and macroscopic performance of materials. In this review, we revealed the correlation of electrochemical activity of LIB materials with the response of energy to variable parameters such as charge-transfer number and Li-ion diffusion coordinates.Based on the structure-property relationship, we reviewed multiscale calculation and ML methods for electrochemical properties in LIB materials. A comparison for various applied methods was outlined to provide a methodselecting reference in LIB material design. It is expected that a deep combination of multiscale computations, experimental data, ML should be more powerful methods for discovering new LIB materials.

show abstract

The challenge and opportunity of battery lifetime prediction from field data

Cited by 193 publications

References 125 publications

"Knees" in lithium-ion battery aging trajectories

"Knees" in lithium-ion battery aging trajectories

Partial Differential Equation-Assisted Accounting Professional Education and Training Artificial Intelligence Collaborative Course System Construction

Multiscale computations and artificial intelligent models of electrochemical performance in Li‐ion battery materials

Contact Info

Product

Resources

About