1 |
CHEN J L, YANG L, WANG Q, et al. Helix-sense-selective and enantiomer-selective living polymerization of phenyl isocyanide induced by reusable chiral lactide using achiral palladium initiator[J]. Macromolecules, 2015, 48(21): 7737-7746.
|
2 |
DU J, LIU Z, WANG Y, et al. An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles[J]. Control Engineering Practice, 2016, 54: 81-90.
|
3 |
马华, 从长杰, 王驰伟. 储能用锂离子动力电池研究进展[J]. 化学工业与工程, 2014, 31(3): 26-33.
|
|
MA H, CONG C J, WANG C W. Research progress on lithium-ion batteries for energy storage application[J]. Chemical Industry and Engineering, 2014, 31(3): 26-33.
|
4 |
AFSHAR S, MORRIS K, KHAJEPOUR A. State-of-charge estimationusing an EKF-based adaptive observer[J]. IEEE, Transactions on Control Systems Technology, 2019, 27(5): 1907-1923.
|
5 |
陈毅, 黄妙华, 王树坤. 基于数据驱动的锂电池剩余容量估计[J]. 自动化与仪表, 2017, 32(8): 69-73.
|
|
CHEN Y, HUANG M H, WANG S K. Lithium battery residual capacity estimation based on the data-driven[J]. Automation & Instrumentation, 2017, 32(8): 69-73.
|
6 |
陈颖,黄凯,丁恒, 等. 基于子种群自适应思维进化-BP神经网络的锂离子电池SOC估计[J/OL].电源学报:1-15[2021-09-23].http://kns.cnki.net/kcms/detail/12.1420.TM.20210125.0922.002.html.
|
|
CHEN Y, HUANG K, DING H, et al. SOC estimation of lithium ion battery based on sub population adaptive thought evolution BP neural network[J/OL]. Acta Power Supply: 1-15[2021-09-23].http://kns.cnki.net/kcms/detail/12.1420.TM.20210125.0922.002.html.
|
7 |
HONG J C, WANG Z P, CHEN W, et al. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles[J]. The Journal of Energy Storage, 2020, 30: 101459.
|
8 |
高建树, 刘浩, 王明强, 等. 改进粒子滤波算法对电动汽车电池SOC的估计[J]. 机械科学与技术, 2017, 36(9): 1428-1433.
|
|
GAO J S, LIU H, WANG M Q, et al. An improved particle filter algorithm for SOC estimation of electric vehicle battery[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(9): 1428-1433.
|
9 |
徐秋雨. 基于无迹卡尔曼滤波的磷酸铁锂电池SOC估计算法[D]. 北京: 清华大学, 2014.
|
|
Xu Q Y. Estimation algorithm of LiFePO4 battery SOC based on Unscented Kalman filter[D]. Beijing: Tsinghua University, 2014.
|
10 |
PLETT G L. Kalman-filter SOC estimation for LiPb HEV cells[C]// Proceedings of the 19th International Battery Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition. Busan, Korea, 2002: 527-538.
|
11 |
王宇鹏. 基于内阻与开路电压差联合法的铅酸蓄电池SOC估计[J]. 科技与企业, 2015(8): 186-186.
|
|
WANG Y P. SOC estimation of lead-acid battery based on combined method of internal resistance and open circuit voltage difference[J]. Science and Technology and Enterprise, 2015(8): 186-186.
|
12 |
DU Jiani, WANG Youyi, WEN Changyun. Li-ion battery SOC estimation using particle filter based on an equivalent circuit model[C]// IEEE International Conference on Control and Automation. IEEE, 2013: 580-585.
|
13 |
缪平, 姚祯, LEMMON John, 等. 电池储能技术研究进展及展望[J]. 储能科学与技术, 2020, 9(3): 670-678.
|
|
MIAO P, YAO Z, LEMMON J, et al. Current situations and prospects of energy storage batteries[J]. Energy Storage Science and Technology, 2020, 9(3): 670-678.
|
14 |
HUA Y, CORDOBA-ARENAS A, WARNER N, et al. A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control[J]. Journal of Power Sources, 2015, 280: 293-312.
|
15 |
张洁. 基于粒子滤波算法的电动汽车剩余电量动态估计研究[D]. 北京: 北京交通大学, 2012.
|
|
ZHANG J. Research on dynamic estimation of electric vehicle residual power based on particle filter algorithm[D]. Beijing: Beijing Jiaotong University, 2010.
|
16 |
胡小军. 基于无迹卡尔曼滤波的动力锂电池SOC估计与实现[D]. 长沙: 中南大学, 2014.
|
|
HU X J. Estimation and implementation of power lithium battery SOC based on unscented Kalman filter[D]. Changsha: Central South University, 2014.
|
17 |
HAVANGI R. Intelligent adaptive unscented particle filter with application in target tracking[J]. Signal, Image and Video Processing, 2020, 14(7): 1487-1495.
|
18 |
杜振新. 基于无迹卡尔曼滤波算法的动力电池剩余电量估算[D]. 西安: 长安大学, 2016.
|
|
DU Z X. Estimation of residual power of power battery based on unscented Kalman filter algorithm[D]. Xi'an: Chang'an University, 2016.
|
19 |
PASCHERO M, STORTI G L, RIZZI A, et al. A novel mechanicalanalogy based battery model for SOC estimation using amulti-cell EKF[J]. IEEE Transactions on Sustainable Energy, 2016, 7(4): 1695-1702.
|
20 |
EL-MEJDOUBI A, GUALOUS H, CHAOUI H, et al. Lithium-ionbatteries health prognosis considering aging conditions[J]. IEEE Transactions on Power Electronics, 2019, 34(7): 6834-6844.
|
21 |
LAWSRI K, PONGAM S. DC electronics load for AH batterytesting[C]//2017 International Electrical Engineering Congress (I EECON). Pattaya: IEEE, 2017: 1-4.
|
22 |
万亚坤, 李阳春, 马浩天, 等. 基于扩展卡尔曼滤波算法的锂电池SOC估计[J]. 蓄电池, 2020, 57(5): 243-246,250.
|
|
WAN Y K, LI Y C, MA H T, et al. Estimation of lithium battery SOC based on extended Kalman filter algorithm[J]. Chinese Labat Man, 2020, 57(5): 243-246,250.
|
23 |
WASSILIADIS N, ADERMANN J, FRERICKS A, et al. Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis[J]. The Journal of Energy Storage, 2018, 19: 73-87.
|