1 |
BI Y J, TAO J H, WU Y Q, et al. Reversible planar gliding and microcracking in a single-crystalline Ni-rich cathode[J]. Science, 2020, 370(6522): 1313-1317.
|
2 |
范文杰, 徐广昊, 于泊宁, 等. 基于电化学阻抗谱的锂离子电池内部温度在线估计方法研究[J]. 中国电机工程学报, 2021, 41(9): 3283-3293.
|
|
FAN W J, XU G H, YU B N. On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy [J]. Proceedings of the CSEE, 2021, 41(9): 3283-3293.
|
3 |
李超然, 肖飞, 樊亚翔. 基于循环神经网络的锂电池SOC估算方法[J]. 海军工程大学学报, 2019, 31(6): 107-112.
|
|
LI C R, XIAO F, FAN Y X. Approach to lithium battery SOC estimation based on recurrent neural network[J]. Journal of Naval University of Engineering, 2019, 31(6): 107-112.
|
4 |
田冬冬, 李立伟, 杨玉新. 基于改进BP-EKF算法的SOC估算[J]. 电源技术, 2020, 44(9): 1274-1278.
|
|
TIAN D D, LI L W, YANG Y X. Research on estimation based on improved BP-EKF algorithm[J]. Chinese Journal of Power Sources, 2020, 44(9): 1274-1278.
|
5 |
HE Z C, YANG Z M, CUI X Y, et al. A method of state-of-charge estimation for EV power lithium-ion battery using a novel adaptive extended Kalman filter[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14618-14630.
|
6 |
XIONG R, ZHANG Y Z, HE H W, et al. A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1526-1538.
|
7 |
陈凯, 彭仲晗, 吴启瑞, 等. 基于LIBSVM的铅酸蓄电池荷电状态估计[J]. 电源技术, 2020, 44(4): 578-581.
|
|
CHEN K, PENG Z H, WU Q R, et al. State of charge estimation of lead-acid battery based on LIBSVM[J]. Chinese Journal of Power Sources, 2020, 44(4): 578-581.
|
8 |
WANG Q X, WU P Z, LIAN J L. SOC estimation algorithm of power lithium battery based on AFSA-BP neural network[J]. The Journal of Engineering, 2020, 2020(13): 535-539.
|
9 |
于仲安, 卢健, 王先敏. 基于GA-BP神经网络的锂离子电池SOC估计[J]. 电源技术, 2020, 44(3): 337-340,421.
|
|
YU Z, LU J, WANG X M. SOC estimation of Li-ion battery based on GA-BP neural network[J]. Chinese Journal of Power Sources, 2020, 44(3): 337-340,421.
|
10 |
周忠凯. 锂离子动力电池多状态估计及退役分选方法研究[D]. 济南:山东大学, 2020.
|
|
ZHOU Z K. Research on multi-state estimation of lithium-ion batteries and sorting method after retirement[D]. Ji'nan: Shandong University, 2020.
|
11 |
JIN L, LI S, HU B, et al. A noise-suppressing neural algorithm for solving the time-varying system of linear equations: A control-based approach[J]. IEEE Transactions on Industrial Informatics, 2019, 15(1): 234-246
|
12 |
JIANG X Y, LI S. BAS: beetle antennae search algorithm for optimization problems[EB/OL]. 2017: arXiv: 1710.10724[cs.NE]. https://arxiv.org/abs/1710.10724
|
13 |
YANG Aimin, ZHUANSUN Yunxi, SHI Yan, et al. IoT system for pellet proportioning based on BAS intelligent recommendation model[J]. IEEE Transactions on Industrial Informatics,2021,17(2): 934-942.
|
14 |
GAO S Z, ZHANG Y M, ZHANG Y M, et al. Elman neural network soft-sensor model of PVC polymerization process optimized by chaos beetle antennae search algorithm[J]. IEEE Sensors Journal, 2021, 21(3): 3544-3551.
|
15 |
XU Y, HUANG Y M, MA G W. A beetle antennae search improved BP neural network model for predicting multi-factor-based gas explosion pressures[J]. Journal of Loss Prevention in the Process Industries, 2020, 65: 104-117.
|
16 |
WANG Z Y, YAO L G, CAI Y W, et al. Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis[J]. Renewable Energy, 2020, 155: 1312-1327.
|
17 |
田冬冬, 李立伟, 杨玉新, 等. 基于IBA-PF的锂电池SOC估算[J]. 储能科学与技术, 2020, 9(5): 1585-1592.
|
|
TIAN D D, LI L W, Yang Y X, et al. SOC estimation of lithium-ion battery based on IBA-PF[J]. Energy Storage Science and Technology, 2020, 9(5): 1585-1592.
|