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

The rapid development of new software and algorithms, fueled by the immense
amount of data available, has made the shelf life of software products a lot
shorter. With a rough estimate of more than 40,000 new software projects
developed every day, it is becoming quicker and cheaper to abandon old software
and acquire new software that meets rapidly changing needs and demands. What
happens to software that is abandoned and what consequences may arise from
'throwaway' culture (Cooper, 2005) are still open questions. This paper will
explore the systems engineering concept of end-of-life for software, it will
highlight the gaps in existing software engineering practices, it will bring
forward examples of software that has been abandoned in an attempt to
decommission and it will explore the repercussions of abandoned software
artefacts. A proposed way forward for addressing the identified research gaps
is also detailed.

探讨软件工程中软件寿命周期结束的概念，强调现有软件工程实践中存在的差距，提出废弃软件的例子并探讨其后果，详细介绍解决已识别研究差距的可行方法。