首页|储能用质子交换膜燃料电池长期老化预测

储能用质子交换膜燃料电池长期老化预测

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质子交换膜燃料电池(PEMFC)的长期老化预测有助于缩短耐久性测试时间,降低成本,为维护策略提供依据.针对超参数问题,提出一种将优化算法和储备池计算相结合的数据驱动预测方法.基于耐久性测试数据集,以电堆输出电压为老化指标,利用麻雀搜索算法(SSA)优化回声状态网络(ESN)的储备池尺寸、泄漏率和正则化系数,以构建预测模型.分别利用原始数据的前30％、40％、50％和60％作为训练集训练模型,验证模型在各训练集比例下的长期老化预测性能.训练集比例为30％时,所提方法在静态工况下的长期预测均方根误差(RMSE)达到0.008 3,准动态工况下可达到0.035 9.
Long-term degradation prediction of proton exchange membrane fuel cell for energy storage
Long-term degradation prediction for proton exchange membrane fuel cell(PEMFC)contributes to the reduction of durability test time or cost and provides a basis for maintenance strategy.Aiming at the super-parameter issue,a data-driven method combining optimization algorithm and reservoir computing is proposed.Using available durability test datasets,with stack voltage as the degradation indicator,the sparrow search algorithm(SSA)is employed to optimize the reservoir size,leakage rate and regularization coefficient of the echo state network(ESN)to construct the prediction model.Training sets comprising 30％,40％,50％and 60％of original data are used to train the model,the long-term degradation prediction performance of the model is validated under different training set proportions.When the training set proportion is 30％,the long-term prediction root mean squared error(RMSE)of the proposed method can reach 0.008 3 under the static condition and 0.035 9 under the quasi-dynamic condition.

proton exchange membrane fuel cell(PEMFC)echo state network(ESN)sparrow search algorithm(SSA)performance degradationlong-term prediction

柏帆、王路达、左红群、谢长君

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宁海县雁苍山电力建设有限公司,浙江宁波 315600

武汉理工大学自动化学院,湖北武汉 430070

质子交换膜燃料电池(PEMFC) 回声状态网络(ESN) 麻雀搜索算法(SSA) 性能退化 长期预测

国家自然科学基金面上项目宁波永耀电力投资集团有限公司科技项目

51977164KJXM2022046

2024

10.19535/j.1001-1579.2024.02.004

电池
全国电池工业信息中心 湖南轻工研究院

电池

CSTPCD北大核心
影响因子：0.336
ISSN：1001-1579
年,卷(期)：2024.54(2)
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