[1] |
周钰, 郝为瀚. 面向数据中心的储能系统应用研究[J]. 南方能源建设, 2021, 8(3):58-62.
|
[2] |
张一迪. 数据中心: 向绿色节能过渡[N]. 中国电子报, 2021-12-21(6).
|
[3] |
唐建华, 方兴. 浅谈UPS在数据机房中的节能与维护[J]. 技术与市场, 2021, 28(7): 131+133.
|
[4] |
袁世魁. 阀控式铅酸蓄电池性能在线诊断方法的研究[D]. 南京:东南大学, 2018.
|
[5] |
Yang Y, Mo Y, Wang Q. Research on dynamic impedance characteristics of hybrid vehicle battery?. IEEE, 2014.
|
[6] |
臧鑫善. 蓄电池健康状况多参数监测系统研究[D]. 南京:南京邮电大学, 2019.
|
[7] |
魏东涛, 黄之杰, 孔华, 等. 蓄电池SOC的研究及预测方法[J]. 电源技术, 2016, 40(6):1321-1323.
|
[8] |
李涛, 梅成林, 刘波峰, 等. 基于粒子群的模糊神经网络铅酸蓄电池SOC估计[J]. 电源技术, 2017, 41(1):64-67.
|
[9] |
Chang WY . The state of charge estimating methods for battery: a review[J]. Isrn Applied Mathematics, 2015, 2013:203-209.
|
[10] |
Zhuang HM, Xiao J. VRLA battery SOH estimation based on WCPSO-LVSVM[J]. Applied Mechanics and Materials, 2014, 628:396-400.
doi: 10.4028/www.scientific.net/AMM.628
URL
|
[11] |
张文圳. VRLA电池的SOC估计与其模型参数辨识研究[D]. 北京:北京工业大学, 2016.
|
[12] |
王君瑞, 单祥, 贾思宁, 等. 基于扩展卡尔曼滤波的蓄电池组SOC估算[J]. 电源技术, 2020, 44(8):1168-1172.
|
[13] |
刘兴涛, 李坤, 武骥, 等. 基于EKF-SVM算法的动力电池SOC估计[J]. 汽车工程, 2020, 42(11): 1522-1528+1544.
|
[14] |
周奇, 罗培. 基于聚类算法的蓄电池SOC模糊预测[J]. 电源技术, 2017(1):71-74.
|
[15] |
Han J S H S T, Zhou B. Neuro-symbolic program search for autonomous driving decision module design[J], 2020.
|
[16] |
袁世魁, 程力. 基于Coup de fouet现象的蓄电池SOH估测[J]. 蓄电池, 2018, 55(2):4.
|
[17] |
Sun H, Guo J, Kim EJ, et al. Unsupervised star galaxy classification with cascade variational auto-encoder[J]. CoRR, 2019.
|
[18] |
Costa D, Nunes M, Vieira J, et al. Decision tree-based security dispatch application in integrated electric power and natural-gas networks[J]. Electric Power Systems Research, 2016, 141:442-449.
doi: 10.1016/j.epsr.2016.08.027
URL
|
[19] |
Shi chao, Zhang, Xuelong, et al. Efficient kNN classification with different numbers of nearest neighbors.[J]. IEEE transactions on neural networks and learning systems, 2017.
|
[20] |
González C, Mira-McWilliams, José, Juárez I. Important variable assessment and electricity price forecasting based on regression tree models: classification and regression trees, Bagging and Random Forests[J]. Generation Transmission & Distribution Iet, 2015, 9(11):1120-1128.
doi: 10.1049/gtd2.v9.11
URL
|
[21] |
胡晨, 金翼, 崔邴晗, 等. 基于深度学习的铅酸电池健康状态估计[J]. 电池, 2021, 51(1):63-67.
|
[22] |
Xu Z, Yu C, Sun H, et al. The response of sediment phosphorus retention and release to reservoir operations: Numerical simulation and surrogate model development[J]. Journal of Cleaner Production, 2020, 271:122688.
doi: 10.1016/j.jclepro.2020.122688
URL
|
[23] |
雒宁, 李一非, 李哲, 等. 基于复合模型的铅酸蓄电池自动充放电SOC预估模型[J]. 微型电脑应用, 2021, 37(8):71-74.
|
[24] |
徐帅, 刘雨辰, 周飞. 基于RNN的锂离子电池SOC估算研究进展[J]. 电源技术, 2021, 45(2):263-269.
|
[25] |
程一伟, 朱海平, 吴军, 等. 基于嵌套长短期记忆网络的机械装备剩余使用寿命预测方法[J]. 中国科学: 技术科学, 2022, 52(1):76-87.
|
[26] |
张少宇, 伍春晖, 熊文渊. 采用门控循环神经网络估计锂离子电池健康状态[J]. 红外与激光工程, 2021, 50(2):236-243.
|
[27] |
Sun H, Xu Z, Song Y, et al. Zeroth-order supervised policy improvement[J], 2020.
|
[28] |
Sun H, Peng Z, Dai B, et al. Novel policy seeking with constrained optimization[J], 2020.
|
[29] |
倪水平, 李慧芳. 基于一维卷积神经网络与长短期记忆网络结合的电池荷电状态预测方法[J]. 计算机应用, 2021, 41(5):1514-1521.
|