锂电池健康状态(state of health,SOH)的在线估计是锂电池管理系统中必不可少的一部分。大部分基于数据驱动的锂电池SOH估计方法由于计算量较大,难以在锂电池管理系统微控制器中在线使用。因此,文中提出基于新型健康特征的锂电池SOH快...锂电池健康状态(state of health,SOH)的在线估计是锂电池管理系统中必不可少的一部分。大部分基于数据驱动的锂电池SOH估计方法由于计算量较大,难以在锂电池管理系统微控制器中在线使用。因此,文中提出基于新型健康特征的锂电池SOH快速估计方法。首先,分析锂电池的充电数据,基于已有的锂电池恒流充电过程的等压升时间(time interval of an equal charging voltage difference,TIECVD)健康特征,构建一个同充电电压起点、同充电时间间隔的健康特征。其次,文中提出基于新型健康特征和多元线性回归(multiple linear regression,MLR)的锂电池SOH快速估计方法。然后,通过对牛津锂电池老化数据集和美国国家航空航天局锂电池随机使用数据集进行分析,以0.01 V步长遍历恒流充电电压区间,以皮尔逊相关系数最大为目标,确定锂电池最优的起始电压。最后,考虑不同充电时间间隔,利用最小二乘(ordinary least squares,OLS)回归分析方法,确定锂电池最优充电时间间隔参数。使用2个数据集划分的训练集建立MLR模型,使用2个数据集划分的验证集对文中方法进行验证。实验结果表明,文中基于新型健康特征方法可极大缩减计算量,并且可以在保障预测精度的前提下实现锂电池SOH的快速估计。展开更多
From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the...From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the Langmuir isotherm model were selected as the research objects.An optimal quantitative structure-activity relationship(QSAR)model was built by using the multiple linear regression(MLR)method,with the saturation adsorption capacity Q_(m) from the Langmuir adsorption isotherm as the response variable and the structural parameters of 50 organic compounds as independent variables.The results show that the optimal model exhibits good stability,reliability and robustness,with a regression coefficient R^(2)of 0.88,an adjusted regression coefficient R_(adj)^(2) of 0.87,an internal validation coefficient q^(2) of 0.81,and an external validation coefficient Q_(ext)^(2) of 0.68.The variables included in the optimal model indicate that the polarity of the molecule,the molecular potential energy,and the stability and bonding strength of the organic compound are the main factors affecting the adsorption on activated carbon.The results provide key information for predicting the adsorption capacity of organic compounds on activated carbon and offer a theoretical reference for adsorption treatment in water environments.展开更多
文摘锂电池健康状态(state of health,SOH)的在线估计是锂电池管理系统中必不可少的一部分。大部分基于数据驱动的锂电池SOH估计方法由于计算量较大,难以在锂电池管理系统微控制器中在线使用。因此,文中提出基于新型健康特征的锂电池SOH快速估计方法。首先,分析锂电池的充电数据,基于已有的锂电池恒流充电过程的等压升时间(time interval of an equal charging voltage difference,TIECVD)健康特征,构建一个同充电电压起点、同充电时间间隔的健康特征。其次,文中提出基于新型健康特征和多元线性回归(multiple linear regression,MLR)的锂电池SOH快速估计方法。然后,通过对牛津锂电池老化数据集和美国国家航空航天局锂电池随机使用数据集进行分析,以0.01 V步长遍历恒流充电电压区间,以皮尔逊相关系数最大为目标,确定锂电池最优的起始电压。最后,考虑不同充电时间间隔,利用最小二乘(ordinary least squares,OLS)回归分析方法,确定锂电池最优充电时间间隔参数。使用2个数据集划分的训练集建立MLR模型,使用2个数据集划分的验证集对文中方法进行验证。实验结果表明,文中基于新型健康特征方法可极大缩减计算量,并且可以在保障预测精度的前提下实现锂电池SOH的快速估计。
基金National Natural Science Foundation of China(No.21876025)National Key R&D Program of China(No.2023YFC3207204)Shanghai Municipal Education Commission Artificial Intelligence-Enabled Scientific Research Plan,China(No.SMEC-AI-DHUZ-07)。
文摘From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the Langmuir isotherm model were selected as the research objects.An optimal quantitative structure-activity relationship(QSAR)model was built by using the multiple linear regression(MLR)method,with the saturation adsorption capacity Q_(m) from the Langmuir adsorption isotherm as the response variable and the structural parameters of 50 organic compounds as independent variables.The results show that the optimal model exhibits good stability,reliability and robustness,with a regression coefficient R^(2)of 0.88,an adjusted regression coefficient R_(adj)^(2) of 0.87,an internal validation coefficient q^(2) of 0.81,and an external validation coefficient Q_(ext)^(2) of 0.68.The variables included in the optimal model indicate that the polarity of the molecule,the molecular potential energy,and the stability and bonding strength of the organic compound are the main factors affecting the adsorption on activated carbon.The results provide key information for predicting the adsorption capacity of organic compounds on activated carbon and offer a theoretical reference for adsorption treatment in water environments.
