基于健康指数提取和QHDBO-BiTCN-BiGRU的锂离子电池SOH估计混合数据驱动方法

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Rui Quan, Yulong Zhou, Wen Li, Hang Wan
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引用次数: 0

摘要

为了进一步提高锂离子电池健康状态(SOH)的预测精度,提出了一种将双向时间卷积网络(BiTCN)与双向门控循环单元(BiGRU)相结合的混合数据驱动预测模型,并利用量子计算和多策略混合蜣螂优化算法(QHDBO)对其超参数进行优化。首先,采用等放电电压差时间间隔(TIEDVD)方法提取电池健康指标(HI)作为输入序列,并采用自适应噪声全系综经验模态分解(CEEMDAN)对健康指标序列进行软阈值重构和去噪;接下来,BiTCN学习序列中的隐藏信息,提取时间序列特征,输入BiGRU进行SOH预测。最后,使用来自NASA和CALCE电池老化数据集的8个不同的初始soh电池与其他8个数据驱动模型进行实验比较。结果表明,新的QHDBO-BiTCN-BiGRU方法具有优异的预测性能,平均绝对百分比误差(MAPE)最低为0.4%,均方根误差(RMSE)最低为0.64%,决策系数(R2)高达99.8%。该方法为锂离子电池提供了更准确的SOH估计指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid data-driven approach for SOH estimation of lithium-ion batteries based on health index extraction and QHDBO-BiTCN-BiGRU
To further enhance the forecasting exactness of the state of health (SOH) regarding lithium-ion batteries, a hybrid data-driven prediction model that integrated a bidirectional temporal convolution network (BiTCN) with the bidirectional gated recurrent unit (BiGRU) was presented, and its hyperparameters were optimized using quantum computing and a hybrid dung beetle optimization algorithm with multiple strategies (QHDBO). Firstly, the time interval of an equal discharging voltage difference (TIEDVD) method was used to extract the health indicator (HI) of batteries as the input sequence, and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to perform soft thresholding reconstruction and denoising on the health indicator sequence. Next, BiTCN learned the hidden information in the sequence, extracted the time series characteristics, and input them to BiGRU for SOH prediction. Finally, eight different initial SOH-based batteries from NASA and CALCE battery aging datasets were used for experimentation comparison, with eight other data-driven models. Results demonstrate that the novel QHDBO-BiTCN-BiGRU method achieves superior prediction performance, evidenced by the lowest mean absolute percentage error (MAPE) of 0.4 % and root mean square error (RMSE) of 0.64 %, alongside an exceptionally high decision coefficient (R2) of 99.8 %. The proposed approach offers a more accurate SOH estimation guide for lithium-ion batteries.
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来源期刊
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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