1 |
明彤彤, 王凯, 田冬冬, 等. 基于LSTM神经网络的锂离子电池荷电状态估算[J]. 广东电力, 2020, 33(3): 26-33.MING T T, WANG K, TIAN D D, etal. Estimation on state of charge of lithium battery based on LSTM neural network[J]. Guangdong Electric Power, 2020, 33(3): 26-33.
|
2 |
张明军. 电池管理系统研究[J]. 通信电源技术, 2020, 37(1): 103-104.ZHANG M J. Research on battery management system[J]. Telecom Power Technology, 2020, 37(1): 103-104.
|
3 |
张博远, 罗羽, 杨玉新, 等. 基于扩展卡尔曼滤波德尔SOC估算与仿真[J]. 舰船电子工程, 2020, 40(1): 99-102.ZHANG B Y, LUO Y, YANG Y X, et al. Estimation and SOC simulation based on extended Kalman filter[J]. Ship Electronic Engineering, 2020, 40(1): 99-102.
|
4 |
张易航, 王鼎, 肖围, 等. 锂离子电池SOC估算方法概况及难点分析[J]. 电源技术, 2019, 43(11): 1894-1896.ZHANG Y H, WANG D, XIAO W, et al. Review of SOC estimation and difficulties in Li-ion battery[J]. Chinese Journal of Power Sources, 2019, 43(11): 1894-1896.
|
5 |
贾海峰, 李聪. 基于BP神经网络的锂电池组SOC估算[J]. 农业装备与车辆工程, 2020, 58(1): 105-107.JIA H F, LI C. SOC estimation of lithium battery pack based on BP neural network[J]. Agricultural Equipment and Vehicle Engineering, 2020, 58(1): 105-107.
|
6 |
李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(3): 321-327.LI W, LIU W W, DENG Y L. SOC estimation for lithium-ion batteries based on EKF[J]. China Mechanical Engineering, 2020, 31(3): 321-327.
|
7 |
汪永志, 贝绍轶, 汪伟, 等. 基于粒子滤波算法的动力电池SOC估计[J]. 机械设计与制造工程, 2014, 43(10): 69-73.WANG Y Z, BEI S Y, WANG W, et al. Battery state of charge estimation based on particle filter algorithm[J]. Machine Design and Manufacturing Engineering, 2014, 43(10): 69-73.
|
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 |
吴兰花, 杨秀芝, 郑明魁, 等. 一种基于优化粒子滤波的锂电池SOC估计算法[J]. 福州大学学报(自然科学版), 2018, 46(2): 186-191.WU L H, YANG X Z, ZHENG M K, et al. An improved particle filter algorithm for Li-ion batteries SOC estimation[J]. Journal of Fuzhou University (Natural Science Edition), 2018, 46(2): 186-191.
|
10 |
宫轶松, 归庆明, 孙付平, 等. 智能优化方法与粒子滤波技术融合的分析与展望[J]. 海洋测绘, 2009, 29(2): 74-77.GONG Y S, GUI Q M, SUN F P, etal. Survey and prospect of the fusion of intelligent computational approaches and particle filtering technique[J]. Hydrographic Surveying and Charting, 2009, 29(2): 74-77.
|
11 |
曹义亲, 钟涛, 黄晓生. 一种改进的基于蚁群优化的粒子滤波算法[J]. 计算机应用研究, 2013, 30(8): 2402-2404.CAO Y Q, ZHONG T, HUANG X S. Improved particle filter algorithm based on ant colony optimization[J]. Application Research of Computers, 2013, 30(8): 2402-2404.
|
12 |
夏飞, 王志成, 郝硕涛, 等. 基于卡尔曼粒子滤波算法的锂电池SOC估计[J]. 系统仿真学报, 2020, 32(1): 44-53.XIA F, WANG Z C, HAO S T, et al. State of charge estimation of the lithium-ion battery based on improved extended Kalman particle filter algorithm[J]. Journal of System Simulation, 2020, 32(1): 44-53.
|
13 |
赵又群, 周晓凤, 刘英杰. 基于扩展卡尔曼粒子滤波算法的锂电池SOC估计[J]. 中国机械工程, 2015, 26(3): 394-397.ZHAO Y Q, ZHOU X F, LIU Y J. SOC estimation for Li-ion battery based on extended Kalman particle filter[J]. China Mechanical Engineering, 2015, 26(3): 394-397.
|
14 |
刘泽, 金世俊, 王庆. 基于改进蚁群算法的移动机器人二维路径规划[J]. 传感器与微系统, 2020, 39(10): 149-152.LIU Z, JIN S J, WANG Q. 2D path planning of mobile robots based on improved ant colony algorithm[J]. Transducer and Microsystem Technologies, 2020, 39(10): 149-152.
|
15 |
WANG F S, LIN B W, LI X C. An ant particle filter for visual tracking[C]//2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan: IEEE, 2017.
|
16 |
ZHONG J P, FUNG Y F. Case study and proofs of ant colony optimisation improved particle filter algorithm[J]. IET Control Theory & Applications, 2012, 6(5): 689-697.
|
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 of estimation of lithium battery based on IBA-PF[J]. Energy Storage Science and Technology, 2020, 9(5): 1585-1592.
|