用混合模型与贝叶斯优化提升电池健康状态预测精度。
State of Health Estimation using Convolutional and Bidirectional LSTM Neural Networks tuned by Bayesian Optimization
- CNN与双向LSTM融合，中间加全连接层提升特征提取能力。
- 在三个数据集上MAE最低达0.68%，RMSE为1.23%。
- 适合电池寿命预测、智能运维等工业场景使用。
本研究提出一种新型框架用于电池健康状态（SOH）估计，采用卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）的串联结构，并结合贝叶斯优化进行超参数调优。对比了三种架构：纯循环模型、CNN-RNN结构及加入中间全连接层的CNN-RNN组合。结果显示，加入中间全连接层的模型预测精度最高。通过系统性地整合容量（Q）、电压（V）、增量容量分析（ICA）与微分电压分析（DVA）四种特征，评估多种输入组合以确定最优表示。采用三个公开数据集验证方法，其中两个来自外部来源，一个由作者基于独特实验设计构建，确保结果可复现。评估指标包括平均绝对误差（MAE）、均方根误差（RMSE）和浮点运算量（FLOPs）。
原文摘要 · Abstract (English)
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.
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