1 |
黎冲, 王成辉, 王高, 等. 锂电池SOC估计的实现方法分析与性能对比[J]. 储能科学与技术, 2022, 11(10): 3328-3344.
|
|
LI C, WANG C H, WANG G, et al. Review on implementation method analysis and performance comparison of lithium battery state of charge estimation[J]. Energy Storage Science and Technology, 2022, 11(10): 3328-3344.
|
2 |
卢婷, 杨文强. 锂离子电池全生命周期内评估参数及评估方法综述[J]. 储能科学与技术, 2020, 9(3): 657-669.
|
|
LU T, YANG W Q. Review of evaluation parameters and methods of lithium batteries throughout its life cycle[J]. Energy Storage Science and Technology, 2020, 9(3): 657-669.
|
3 |
GUHA A, PATRA A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification, 2018, 4(1): 135-146.
|
4 |
高昕, 韩嵩. 基于分数阶模型的锂离子电池SOC与SOH协同估计[J]. 电源技术, 2021, 45(9): 1140-1143, 1208.
|
|
GAO X, HAN S. Collaborative estimation of SOC and SOH of Li-ion battery based on fractional order model[J]. Chinese Journal of Power Sources, 2021, 45(9): 1140-1143, 1208.
|
5 |
常春, 王少晋, 苏广伟, 等. 改进模型的锂离子电池健康状态估计[J]. 电池, 2022, 52(6): 646-650.
|
|
CHANG C, WANG S J, SU G W, et al. State of health estimation of Li-ion battery based on improved model[J/OL]. Battery Bimonthly, 2022, 52(6): 646-650.
|
6 |
高仁璟, 吕治强, 赵帅, 等. 基于电化学模型的锂离子电池健康状态估算[J]. 北京理工大学学报, 2022, 42(8): 791-797.
|
|
GAO R J, LÜ Z Q, ZHAO S, et al. Health state estimation of Li-ion batteries based on electrochemical model[J]. Transactions of Beijing Institute of Technology, 2022, 42(8): 791-797.
|
7 |
刘晓悦, 魏宇册. 优化神经网络的锂电池SOC估算[J]. 机械设计与制造, 2021(11): 83-86.
|
|
LIU X Y, WEI Y C. Optimization of neural network for lithium nattery SOC estimation[J]. Machinery Design & Manufacture, 2021(11): 83-86.
|
8 |
李旭东, 张向文. 基于主成分分析与WOA-Elman的锂离子电池SOH估计[J]. 储能科学与技术, 2022, 11(12): 4010-4021.
|
|
LI X D, ZHANG X W. State of health estimation method for lithium-ion batteries based on principal component analysis and whale optimization algorithm-Elman model[J]. Energy Storage Science and Technology, 2022, 11(12): 4010-4021.
|
9 |
王雪, 游国栋, 房成信, 等. 基于IMOCS-BP神经网络的锂离子电池SOH估计[J/OL]. 电源学报: 1-11[2023-01-31]. http://kns.cnki.net/kcms/detail/12.1420.TM.20210823.0917.002.html.
|
|
WANG X, YOU G D, FANG C X, et al. SOH estimation of lithium-ion battery based on IMOCS-BP neural network[J/OL]. Journal of Power Supply: 1-11[2023-01-31]. http://kns.cnki.net/kcms/detail/12.1420.TM.20210823.0917.002.html.
|
10 |
YANG B, WANG Y S, GAO H. State-of-charge estimation of lithium-ion batteries based on PSO-BP neural network[J]. International Journal of Energy and Power Engineering, 2021, 10(6): doi: 10.11648/j.ijepe.20211006.13.
|
11 |
吴铁洲, 刘思哲, 张晓星, 等. 基于FA-BP神经网络的锂离子电池SOH估算[J]. 电池, 2021, 51(1): 21-25.
|
|
WU T Z, LIU S Z, ZHANG X X, et al. SOH estimation of Li-ion battery based on FA-BP neural network[J]. Battery Bimonthly, 2021, 51(1): 21-25.
|
12 |
FISTER I, FISTER I J, YANG X S, et al. A comprehensive review of firefly algorithms[J]. Swarm and Evolutionary Computation, 2013, 13: 34-46.
|
13 |
程美英, 倪志伟, 朱旭辉. 萤火虫优化算法理论研究综述[J]. 计算机科学, 2015, 42(4): 19-24.
|
|
CHENG M Y, NI Z W, ZHU X H. Overview on glowworm swarm optimization or firefly algorithm[J]. Computer Science, 2015, 42(4): 19-24.
|
14 |
雍欣, 高岳林, 赫亚华, 等. 多策略融合的改进萤火虫算法[J]. 计算机应用, 2022, 42(12): 3847-3855.
|
|
YONG X, GAO Y L, HE Y H, et al. Improved firefly optimization algorithm based on multi-strategy fusion[J]. Journal of Computer Applications, 2022, 42(12): 3847-3855.
|
15 |
张远进, 吴华伟, 叶从进. 基于IFA-EKF的锂电池SOC估算[J]. 储能科学与技术, 2020, 9(1): 117-123.
|
|
ZHANG Y J, WU H W, YE C J. Estimation of SOC of lithium batteries based on IFA-EKF[J]. Energy Storage Science and Technology, 2020, 9(1): 117-123.
|
16 |
王艳. 改进的萤火虫算法及其应用研究[D]. 西安: 西安理工大学, 2018.
|
|
WANG Y. Research on improved firefly algorithm and its application[D]. Xi'an: Xi'an University of Technology, 2018.
|
17 |
WU J R, WANG Y G, BURRAGE K, et al. An improved firefly algorithm for global continuous optimization problems[J]. Expert Systems With Applications, 2020, 149: doi: 10.1016/j.eswa.2020.113340.
|
18 |
张吉宣. 锂离子电池剩余寿命预测方法研究[D]. 太原: 中北大学, 2018.
|
|
ZHANG J X. Research on remaining useful life prediction of lithium ion batteries[D]. Taiyuan: North University of China, 2018.
|
19 |
王震坡, 王秋诗, 刘鹏, 等. 大数据驱动的动力电池健康状态估计方法综述[J/OL]. 机械工程学报, 2022: 1-18 [2022-04-16]. https://kns.cnki.net/kcms/detail/11.2187.TH.20220414.1102.102.html.
|
|
WANG Z P, WANG Q S, LIU P, et al. Review on techniques for power battery state of health estimation driven by big data methods[J/OL]. Journal of Mechanical Engineering, 2022: 1-18 [2022-04-16]. https://kns.cnki.net/kcms/detail/11.2187.TH.20220414. 1102.102.html.
|
20 |
赵沁峰, 蔡艳平, 王新军. 基于WOA-ELM的锂离子电池剩余寿命间接预测[J]. 中国测试, 2021, 47(9): 138-145.
|
|
ZHAO Q F, CAI Y P, WANG X J. WOA-ELM based indirect prediction of remaining useful life of lithium-ion battery[J]. China Measurement & Test, 2021, 47(9): 138-145.
|