准确预测锂离子电池早期循环寿命对于确保安全性和可靠性、加快电池开发周期至关重要。然而，由于非线性电池容量衰减且早期循环中的变化可以忽略不计，大多数现有研究对早期预测的预测结果较差。在本文中，为了实现电池寿命的准确早期周期预测，提出了一种基于机器学习（ML）的综合框架，包含特征提取、特征选择和基于机器学习的预测三个模块。首先，通过分析各种信息参数的演化模式，根据前100个循环的充放电原始数据手工制作42个特征。其次，为了管理特征的不相关性和冗余性，采用四种典型的特征选择方法来生成最佳的低维特征子集。最后，将选定的特征输入到六个代表性的机器学习模型中，以有效预测电池寿命。进行数值实验和配对测试来统计评估所提出框架的性能。结果表明，基于wrapper的特征选择方法优于其他方法，并显着提高了后续ML模型的预测性能。在包装器特征选择之前和之后，弹性网络、高斯过程回归和支持向量机都比其他复杂的 ML 预测模型表现出更好的性能。支持向量机模型与包装器特征选择相结合，在统计上呈现出电池寿命预测的最佳结果，均方根误差为 115 个周期，a 为 0.90。 最后，与现有工作相比，通过使用所提出的框架，均方根误差从 173 个周期大幅减少到 115 个周期。
"点击查看英文标题和摘要"
Early prediction of battery lifetime via a machine learning based framework
Accurately predicting the lifetime of lithium-ion batteries in early cycles is crucial for ensuring the safety and reliability, and accelerating the battery development cycle. However, most of existing studies presented poor prediction results for early prediction, due to the nonlinear battery capacity fade with negligible variation in early cycles. In this paper, to achieve an accurate early-cycle prediction of battery lifetime, a comprehensive machine learning (ML) based framework containing three modules, the feature extraction, feature selection, and machine learning based prediction, is proposed. First, by analysing the evolution pattern of various informative parameters, forty-two features are manually crafted based on the first-100-cycle charge-discharge raw data. Second, to manage feature irrelevancy and redundancy, four typical feature selection methods are adopted to generate an optimal lower-dimensional feature subset. Finally, the selected features are fed into six representative ML models to effectively predict the battery lifetime. Numerical experiments and paired -test are conducted to statistically evaluate the performance of the proposed framework. Results show that the wrapper-based feature selection method outperforms other methods, and significantly improves the prediction performance of subsequent ML models. Both before and after wrapper feature selection, the elastic net, Gaussian process regression, and support vector machine present better performance than other complex ML prediction models. The support vector machine model combined with wrapper feature selection statistically presents the best result for battery lifetime prediction, with a root-of-mean-square-error of 115 cycles, and a of 0.90. Finally, when compared with an existing work, the root-of-mean-square-error is substantially decreased from 173 to 115 cycles, by using the proposed framework.
京公网安备 11010802027423号