发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合贝叶斯优化调优的CNN-BiLSTM混合架构，经特征工程处理后在三个公开数据集上以MAE、RMSE、FLOPs为指标，实现了高精度的电池SOH估计。
AI 中文摘要
本研究提出了一种用于健康状态（SOH）估计的新型框架，该框架采用卷积神经网络（CNN）与双向长短期记忆神经网络（BiLSTM）级联的混合深度学习架构，并结合基于贝叶斯优化的超参数调优技术对该网络进行优化。研究评估了三种不同的深度学习架构：独立循环模型、CNN-循环神经网络（RNN）架构，以及添加了中间全连接（FC）层的CNN-RNN组合架构。在这三种架构中，带有中间FC层的模型表现出最高的预测精度。综合特征工程方法结合了容量（Q）、电压（V）、增量容量分析（ICA）和差分电压分析（DVA），并对多种组合进行系统评估以确定最优输入表示。为验证所提方法，研究使用了三个公开数据集以确保结果的可复现性，其中两个来自外部来源，另一个由本研究作者采用独特实验设置开发。对比研究采用平均绝对误差（MAE）、均方根误差（RMSE）和浮点运算次数（FLOPs）作为评估指标。
英文摘要
In this research, a novel framework is proposed for the SOH estimation, which employs a hybrid deep learning architecture of a concatenation of a Convolution Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) Neural Network (NN) with the integration of Bayesian Optimization-based hyperparameter tuning for the network. Three different deep learning architectures are being evaluated: standalone recurrent models, CNN-RNN architectures and CNN-RNN combinations enhanced with intermediate Fully Connected (FC) layers. Among the three, the model with the intermediate FC layers demonstrated the highest predictive accuracy. A comprehensive feature engineering approach combines capacity (Q), voltage (V), Incremental Capacity Analysis (ICA), and Differential Voltage Analysis (DVA), with systematic evaluation of multiple combinations to identify the optimal input representation. To validate the proposed method, three publicly available datasets were utilized, ensuring reproducibility of the results, two from external sources and one developed by the author of this study using a unique experimental setup. The comparison study was performed using the Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE) and the FLoating-point OPerations (FLOPs) as evaluation metrics.
