Batteries are a key enabling technology for the decarbonization of transport
and energy sectors. The safe and reliable operation of batteries is crucial for
battery-powered systems. In this direction, the development of accurate and
robust battery state-of-health prognostics models can unlock the potential of
autonomous systems for complex, remote and reliable operations. The combination
of Neural Networks, Bayesian modelling concepts and ensemble learning
strategies, form a valuable prognostics framework to combine uncertainty in a
robust and accurate manner. Accordingly, this paper introduces a Bayesian
ensemble learning approach to predict the capacity depletion of lithium-ion
batteries. The approach accurately predicts the capacity fade and quantifies
the uncertainty associated with battery design and degradation processes. The
proposed Bayesian ensemble methodology employs a stacking technique,
integrating multiple Bayesian neural networks (BNNs) as base learners, which
have been trained on data diversity. The proposed method has been validated
using a battery aging dataset collected by the NASA Ames Prognostics Center of
Excellence. Obtained results demonstrate the improved accuracy and robustness
of the proposed probabilistic fusion approach with respect to (i) a single BNN
model and (ii) a classical stacking strategy based on different BNNs.

本研究采用贝叶斯集成学习方法，结合神经网络和贝叶斯建模概念，预测锂离子电池容量衰减，同时量化电池设计和老化过程所带来的不确定性，实现了对复杂、遥远和可靠操作自主系统潜力的准确预测。

稳健电池预测的概率融合方法探索

Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

Batteries are an essential component in a deeply decarbonized future.
Understanding battery performance and "useful life" as a function of design and
use is of paramount importance to accelerating adoption. Historically, battery
state of health (SOH) was summarized by a single parameter, the fraction of a
battery's capacity relative to its initial state. A more useful approach,
however, is a comprehensive characterization of its state and complexities,
using an interrelated set of descriptors including capacity, energy, ionic and
electronic impedances, open circuit voltages, and microstructure metrics.
Indeed, predicting across an extensive suite of properties as a function of
battery use is a "holy grail" of battery science; it can provide unprecedented
insights toward the design of better batteries with reduced experimental
effort, and de-risking energy storage investments that are necessary to meet
CO2 reduction targets. In this work, we present a first step in that direction
via deep transformer networks for the prediction of 28 battery state of health
descriptors using two cycling datasets representing six lithium-ion cathode
chemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multiple
electrolyte/anode compositions, and different charge-discharge scenarios. The
accuracy of these predictions versus battery life (with an unprecedented mean
absolute error of 19 cycles in predicting end of life for an LFP fast-charging
dataset) illustrates the promise of deep learning towards providing deeper
understanding and control of battery health.

通过使用深度转换网络，我们展示了对 28 个电池健康描述符进行预测的第一步，使用两组循环数据集代表了六种锂离子阴极化学组成（LFP，NMC111，NMC532，NMC622，HE5050 和 5Vspinel），多种电解质 / 阳极组成和不同的充放电方案。这些预测与电池寿命相比的准确性（对于一个 LFP 快充数据集预测终点寿命的绝对平均误差为 19 个循环）展示了深度学习在提供更深入理解和控制电池健康方面的潜力。

用变压器预测多变量电池性能和健康状态

Prognosis of Multivariate Battery State of Performance and Health via  Transformers

The properties of lithium metal are key parameters in the design of lithium
ion and lithium metal batteries. They are difficult to probe experimentally due
to the high reactivity and low melting point of lithium as well as the
microscopic scales at which lithium exists in batteries where it is found to
have enhanced strength, with implications for dendrite suppression strategies.
Computationally, there is a lack of empirical potentials that are consistently
quantitatively accurate across all properties and ab-initio calculations are
too costly. In this work, we train Machine Learning Interaction Potentials
(MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy in
reproducing experimental and ab-initio results across a wide range of
simulations at large length and time scales. We accurately predict
thermodynamic properties, phonon spectra, temperature dependence of elastic
constants and various surface properties inaccessible using DFT. We establish
that there exists a Bell-Evans-Polanyi relation correlating the self-adsorption
energy and the minimum surface diffusion barrier for high Miller index facets.

使用机器学习相互作用势 (M-LIP) 对密度泛函理论 (DFT) 数据进行预测，以实现对热力学性质、表面性质等多个方面的准确预测。研究部分关注锂金属电池和电解液的问题。