Battery health monitoring and prediction are critically important in the era
of electric mobility with a huge impact on safety, sustainability, and economic
aspects. Existing research often focuses on prediction accuracy but tends to
neglect practical factors that may hinder the technology's deployment in
real-world applications. In this paper, we address these practical
considerations and develop models based on the Bayesian neural network for
predicting battery end-of-life. Our models use sensor data related to battery
health and apply distributions, rather than single-point, for each parameter of
the models. This allows the models to capture the inherent randomness and
uncertainty of battery health, which leads to not only accurate predictions but
also quantifiable uncertainty. We conducted an experimental study and
demonstrated the effectiveness of our proposed models, with a prediction error
rate averaging 13.9%, and as low as 2.9% for certain tested batteries.
Additionally, all predictions include quantifiable certainty, which improved by
66% from the initial to the mid-life stage of the battery. This research has
practical values for battery technologies and contributes to accelerating the
technology adoption in the industry.

基于贝叶斯神经网络和传感器数据，本研究针对电池健康监测和终端寿命预测进行建模，在实验中验证了建议模型的有效性，预测误差率平均值为 13.9%，对特定测试电池可降至 2.9%；同时，预测结果包含可量化的确定性信息，从电池初始到中期使用阶段提高了 66%，提高了电池技术的实际应用价值。

基于不确定性感知的贝叶斯神经网络实用电池健康监测

Practical Battery Health Monitoring using Uncertainty-Aware Bayesian  Neural Network

An accurate estimation of the state of health (SOH) of batteries is critical
to ensuring the safe and reliable operation of electric vehicles (EVs).
Feature-based machine learning methods have exhibited enormous potential for
rapidly and precisely monitoring battery health status. However, simultaneously
using various health indicators (HIs) may weaken estimation performance due to
feature redundancy. Furthermore, ignoring real-world driving behaviors can lead
to inaccurate estimation results as some features are rarely accessible in
practical scenarios. To address these issues, we proposed a feature-based
machine learning pipeline for reliable battery health monitoring, enabled by
evaluating the acquisition probability of features under real-world driving
conditions. We first summarized and analyzed various individual HIs with
mechanism-related interpretations, which provide insightful guidance on how
these features relate to battery degradation modes. Moreover, all features were
carefully evaluated and screened based on estimation accuracy and correlation
analysis on three public battery degradation datasets. Finally, the
scenario-based feature fusion and acquisition probability-based practicality
evaluation method construct a useful tool for feature extraction with
consideration of driving behaviors. This work highlights the importance of
balancing the performance and practicality of HIs during the development of
feature-based battery health monitoring algorithms.

开发了一种考虑驾驶行为的特征提取方法，通过评估在真实驾驶情况下特征的获取概率，实现了可靠的电池健康监测，并突出了在开发面向特征的电池健康监测算法时平衡性能和实用性的重要性。