摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的新研究结果已经发表。根据来自意大利米兰的新闻，NewsRx记者称，“在这项研究中，提出了一种新的基于机器学习的锂电池荷电状态和健康状态联合估计方法，该方法解决了实际应用和贝叶斯超参数优化问题。”这项研究的财政支持者包括国家钢琴协会（Pnrr）？导弹4组分2，调查1.4.我们的新闻编辑从米兰理工大学的研究中获得了一句话:“考虑到电池的退化，将估计的健康状态作为充电状态评估的输入。将所提方法的精度和计算成本与其他最先进的机器学习模型进行了比较。对于最有前途的解决方案，我们的新闻编辑们说：”对影响估计精度的因素进行了深入分析。为了便于进一步的研究，使用扩展的动态驾驶循环创建了一个新的电池数据集，涵盖了广泛的温度条件和老化阶段。该数据集可在网上公开获得，以支持科学界的模型开发和比较测试。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from Milan, Italy, by NewsRx correspondents, research stated, "In this study, a novel Machine learning -based method for the joint State of Charge and State of Health estimation of Li thium Batteries that tackle real-world applications and with Bayesian Hyperparam eter optimization is proposed." Financial supporters for this research include Piano Nazionale Di Ripresa E Resi lienza (Pnrr) ? Missione 4 Componente 2, Investimento 1.4. Our news editors obtained a quote from the research from Polytechnic University Milan: "The estimated State of Health is used as an input for State of Charge es timation, considering battery degradation. The accuracy and computational cost o f the proposed method are compared with the other state-of-the-art Machine Learn ing models. For the most promising solutions, an in-depth analysis on factors af fecting the estimation accuracy is performed. To facilitate further research, a new battery dataset was created using extended dynamic driving cycles, encompass ing a wide range of temperature conditions and aging stages. This dataset is pub licly available online to support model development and comparative testing by t he scientific community."
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