锂电池健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)的准确估计对保证电池的安全稳定运行至关重要,然而两者都难以被直接测量。该文提出一种基于高斯过程回归(gaussian process regression,GPR)的SOH和RUL...锂电池健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)的准确估计对保证电池的安全稳定运行至关重要,然而两者都难以被直接测量。该文提出一种基于高斯过程回归(gaussian process regression,GPR)的SOH和RUL联合估计方法。该方法从充电曲线中提取健康特征(health factor,HF),并通过主成分分析(principle component analysis,PCA)进行降维处理得到间接健康特征(indirect health factor,IHF),然后利用GPR建立电池老化模型进行SOH估计。在此基础上,采用最小二乘支持向量机(least squares support vector machine,LS-SVM)对IHF随循环次数增加的变化趋势进行预测,将其结果与所建立的电池老化模型结合,实现RUL估计。2组不同温度下的电池数据被用来验证算法的准确性和适应性,实验结果表明所提出的算法具有较高的精度和可靠性。展开更多
The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity fr...The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.展开更多
文摘锂电池健康状态(state of health,SOH)和剩余使用寿命(remaining useful life,RUL)的准确估计对保证电池的安全稳定运行至关重要,然而两者都难以被直接测量。该文提出一种基于高斯过程回归(gaussian process regression,GPR)的SOH和RUL联合估计方法。该方法从充电曲线中提取健康特征(health factor,HF),并通过主成分分析(principle component analysis,PCA)进行降维处理得到间接健康特征(indirect health factor,IHF),然后利用GPR建立电池老化模型进行SOH估计。在此基础上,采用最小二乘支持向量机(least squares support vector machine,LS-SVM)对IHF随循环次数增加的变化趋势进行预测,将其结果与所建立的电池老化模型结合,实现RUL估计。2组不同温度下的电池数据被用来验证算法的准确性和适应性,实验结果表明所提出的算法具有较高的精度和可靠性。
基金supported by the National Natural Science Foundation of China under project 41275013the National High-Tech Research and development program of China under project 2013AA09A506-4the National Basic Research Program under project 2009CB723903
文摘The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
