Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

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Data-driven prediction of battery cycle life before capacity degradation

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Lithium-ion battery fast charging: A review

The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed

A review on the key issues of the lithium ion battery degradation among the whole life cycle

A review of lithium ion battery failure mechanisms and fire prevention strategies

Fast charging technique for high power LiFePO4 batteries: A mechanistic analysis of aging

Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule

Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis

State of health battery estimator enabling degradation diagnosis: Model and algorithm description

https://robots.iopscience.iop.org/article/10.1149/2.0421805jes/pdf

Arnaud Devie

Papers

Fast charging technique for high power LiFePO4 batteries: A mechanistic analysis of aging

Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule

Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis

State of health battery estimator enabling degradation diagnosis: Model and algorithm description

Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path Dependence of Battery Degradation