用充电曲线建模锂电池健康度，提升跨数据集泛化能力
A novel Neural-ODE model for the state of health estimation of lithium-ion battery using charging curve
- 将注意力、CNN与LSTM融入神经微分方程，构建混合预测模型
- 在TJU和HUST数据集上SOH估计RMSE分别低至1.01%和2.24%
- 适合电池健康监控场景，尤其适用于多源数据泛化需求
锂离子电池（LIBs）的健康状态（SOH）对电动汽车安全可靠运行至关重要。然而，现有SOH估计方法普遍存在泛化能力不足的问题。本文提出一种数据驱动的SOH估计方法，旨在提升模型泛化性。构建了一种名为ACLA的混合模型，将注意力机制、卷积神经网络（CNN）与长短期记忆网络（LSTM）集成到增强型神经常微分方程（ANODE）框架中。该模型以恒流充电阶段特定电压对应的归一化充电时间为输入，输出SOH及剩余使用寿命。模型在NASA和Oxford数据集上训练，并在TJU和HUST数据集上验证。相比基准模型NODE和ANODE，ACLA在TJU和HUST数据集上的SOH估计均方根误差（RMSE）分别低至1.01%和2.24%。
原文摘要 · Abstract (English)
The state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safe and reliable operation of electric vehicles. Nevertheless, the prevailing SOH estimation methods often have limited generalizability. This paper introduces a data-driven approach for estimating the SOH of LIBs, which is designed to improve generalization. We construct a hybrid model named ACLA, which integrates the attention mechanism, convolutional neural network (CNN), and long short-term memory network (LSTM) into the augmented neural ordinary differential equation (ANODE) framework. This model employs normalized charging time corresponding to specific voltages in the constant current charging phase as input and outputs the SOH as well as remaining useful of life. The model is trained on NASA and Oxford datasets and validated on the TJU and HUST datasets. Compared to the benchmark models NODE and ANODE, ACLA exhibits higher accuracy with root mean square errors (RMSE) for SOH estimation as low as 1.01% and 2.24% on the TJU and HUST datasets, respectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。