锂离子电池广泛应用于电动汽车、移动电子、储能系统等领域。作为核心储能部件，锂离子电池老化会影响整个系统的正常运行，甚至造成严重的安全事故和经济损失。锂离子电池的健康状态估计、高效聚类和容量估计可以有效地评估电池老化程度，有助于实现锂离子电池的安全使用和二次利用。本论文结合机器学习方法，研究锂离子电池健康状态、快速聚类及剩余容量估算的新方法。本论文研究内容包括:（1）通过X射线计算机断层扫描(CT)技术，结合基于结构相似度(SSIM)计算方法，对锂离子电池的结构一致性进行量化计算得到对应CT评分值。结果表明，锂电池的CT评分值与其健康状态特征量如内阻、容量密切相关。本方法能定量无损的反映锂离子电池的健康状态。（2）提出了一种锂离子电池快速聚类模型，本模型结合短时脉冲测试和改进等分K均值算法，可以有效地对全生命周期的锂离子电池进行分类。通过对所选聚类变量的相关性进行了严格验证，与传统的完全充放电测试聚类相比，本模型准确率高达88%。这种基于数据驱动的快速脉冲测试聚类模型是一种很有前景的方法，通过自制的高通量自动聚类机验证了其应用潜力。（3）通过结合锂离子电池脉冲测试与高斯过程回归算法,研究并验证了从50%到100%健康状态的锂离子电池剩余容量预测方法,准确率超过95%。通过对不同电压阶段和荷电状态(SOC)的脉冲研究，找到了最合适的特征参数并深入揭示了其反映锂电池老化的机理。此外，探讨了五种不同的机器学习方法对容量估计的有效性，其中Matern核的高斯过程回归方法的容量预测准确率最高。

Lithium-ion batteries are widely used in electric vehicles, mobile electronics, energy storage systems, and a variety of other applications. As a core component, the aging of lithium-ion battery will affect the normal operation of the entire system, and may even result in serious safety accidents and economic losses. The estimation of lithium-ion battery state of health, efficient clustering and capacity prediction can effectively evaluate their aging degree, which is useful to realize the safe use and secondary utilization of lithium ion batteries. This thesis proposes new methods for estimating the state of health, fast clustering, and capacity prediction of lithium-ion batteries using machine leaning methods.The research highlights include: The structural consistency of the lithium-ion battery was quantified by X-ray computed tomography (CT) technology, combined with the calculation method based on structural similarity measure (SSIM), and the corresponding CT score was obtained. The results show CT score of lithium ion battery is closely related to its state of health characteristics, such as internal resistance and capacity. This method can reflect the state of health of lithium ion battery quantitatively and nondestructively; To classify lithium ion batteries over the entire lifetime, a fast clustering model for lithium ion batteries was proposed, which combined a short-time pulse test and an improved bisecting K-means algorithm. The correlation of the selected cluster variables was rigorously verified, and the accuracy of this model was over 88% when compared with the traditional full charge and discharge test parameters. This data-driven fast pulse test clustering model is a promising method, and its application potential is verified by a lab-made high throughput automatic clustering machine; By combining pulse test and Gaussian process regression algorithm, the capacity prediction method of lithium ion battery from 100% to 50% state of health was successfully developed and verified, with an accuracy of more than 95%. By studying the pulse of different voltage stages and state-of-charges, the most suitable characteristic parameters were discovered, and the corresponding mechanism of reflecting the aging of lithium ion batteries was revealed. Moreover, we investigated the validity of five different machine learning methods for capacity estimation, finding that the Gaussian process regression method with the Matern kernel has the highest accuracy for capacity estimation.