Energy Storage Science and Technology ›› 2021, Vol. 10 ›› Issue (3): 1163-1176.doi: 10.19799/j.cnki.2095-4239.2021.0051
• Energy Storage Test: Methods and Evaluation • Previous Articles Next Articles
Yuanfu ZHU1(), Wenwu HE1(), Jianxing LI1, Youcai LI2, Peiqiang LI1
Received:
2021-02-02
Revised:
2021-02-06
Online:
2021-05-05
Published:
2021-04-30
Contact:
Wenwu HE
E-mail:yuanfuzhu0130@gmail.com;hwwhbb@163.com
CLC Number:
Yuanfu ZHU, Wenwu HE, Jianxing LI, Youcai LI, Peiqiang LI. SOC estimation for Li-ion batteries based on Bi-LSTM and Bi-GRU[J]. Energy Storage Science and Technology, 2021, 10(3): 1163-1176.
Table 2
SOC estimation results of involved models under three temperature conditions"
温度/℃ | 网络结构 | HWFET | LA92 | US06 | |||
---|---|---|---|---|---|---|---|
MAE/% | MAX/% | MAE/% | MAX/% | MAE/% | MAX/% | ||
0 | LSTM | 3.041 | 6.711 | 2.255 | 9.384 | 1.496 | 9.811 |
GRU | 1.991 | 3.804 | 1.086 | 2.431 | 0.861 | 5.542 | |
Bi-LSTM | 1.371 | 4.024 | 0.786 | 4.536 | 0.694 | 4.404 | |
Bi-GRU | 0.688 | 3.197 | 0.568 | 4.534 | 0.596 | 4.598 | |
10 | LSTM | 2.176 | 6.101 | 0.968 | 9.069 | 1.267 | 8.131 |
GRU | 1.659 | 5.624 | 0.791 | 7.754 | 1.169 | 6.407 | |
Bi-LSTM | 1.282 | 4.737 | 0.611 | 6.742 | 0.679 | 5.165 | |
Bi-GRU | 0.952 | 3.431 | 0.471 | 2.558 | 0.500 | 3.100 | |
25 | LSTM | 1.765 | 4.269 | 0.791 | 3.461 | 1.187 | 5.621 |
GRU | 1.149 | 3.327 | 0.431 | 3.519 | 0.614 | 3.443 | |
Bi-LSTM | 0.637 | 2.825 | 0.375 | 2.754 | 0.472 | 2.731 | |
Bi-GRU | 0.689 | 2.649 | 0.309 | 2.215 | 0.325 | 1.696 | |
Total | LSTM | 2.327 | 6.711 | 1.338 | 9.384 | 1.317 | 9.811 |
GRU | 1.600 | 5.624 | 0.769 | 7.754 | 0.881 | 6.407 | |
Bi-LSTM | 1.097 | 4.737 | 0.591 | 6.742 | 0.615 | 5.165 | |
Bi-GRU | 0.776 | 3.431 | 0.449 | 4.534 | 0.474 | 4.598 |
1 | GAO W, ZHANG X, ZHENG X, et al. Lithium carbonate recovery from cathode scrap of spent lithium-ion battery: A closed-loop process[J]. Environmental Science & Technology, 2017, 51(3): 1662-1669. |
2 | SHRIVASTAVA P, SOON T K, IDRIS M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 113: doi: 10.1016/j.rser.2019.06.040. |
3 | CHEMALI E, PREINDL M, MALYSZ P, et al. Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: State-of-the-art review and future trends[J]. IEEE Journal of Emerging & Selected Topics in Power Electronics, 2016, 4(3): 1117-1134. |
4 | 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062. |
LI C R, XIAO F, FAN Y X, et al. A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062. | |
5 | 丁镇涛, 邓涛, 李志飞, 等. 基于安时积分和无迹卡尔曼滤波的锂电池SOC估算方法研究[J]. 中国机械工程, 2020, 31(15): 1823-1830. |
DING Z T, DENG T, LI Z F, et al. SOC estimation of lithium-ion battery based on ampere hour integral and unscented Kalman filter[J]. China Mechanical Engineering, 2020, 31(15): 1823-1830. | |
6 | LIN C, YU Q Q, XIONG R, et al. A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries[J]. Applied Energy, 2017, 205: 892-902. |
7 | SKOOG S, DAVID S. Parameterization of linear equivalent circuit models over wide temperature and SOC spans for automotive lithium-ion cells using electrochemical impedance spectroscopy[J]. Journal of Energy Storage, 2017, 14: 39-48. |
8 | RAMADAN H S, BECHERIF M, CLAUDE F. Extended Kalman filter for accurate state of charge estimation of lithium-based batteries: A comparative analysis[J]. International Journal of Hydrogen Energy, 2017, 42(48): 29033-29046. |
9 | 张远进, 吴华伟, 叶从进. 基于AUKF-BP神经网络的锂电池SOC估算[J]. 储能科学与技术, 2021, 10(1): 237-241. |
ZHANG Y J, WU H W, YE C J. Estimation of the SOC of a battery based on the AUKF-BP algorithm[J]. Energy Storage Science and Technology, 2021, 10(1): 237-241. | |
10 | BACCOUCHE I, MANAI B, AMARA N E B. SOC estimation of LFP battery based on EKF observer and a full polynomial parameters-model[C]//2020 IEEE 91st Vehicular Technology Conference, 2020. |
11 | YE M, GUO H, XIONG R, et al. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries[J]. Energy, 2018, 144: 789-799. |
12 | NING B, XU J, CAO B G, et al. A sliding mode observer SOC estimation method based on parameter adaptive battery model[J]. Energy Procedia, 2016, 88: 619-626. |
13 | 成文晶, 潘庭龙. 基于分布估计算法LSSVM的锂电池SOC预测[J]. 储能科学与技术, 2020, 9(6): 1948-1953. |
CHENG W J, PAN T L. Prediction for SOC of lithium-ion batteries by estimating the distribution algorithm with LSSVM[J]. Energy Storage Science and Technology, 2020, 9(6): 1948-1953. | |
14 | GUO Y F, ZHAO Z S, HUANG L M. SOC estimation of lithium battery based on improved BP neural network[J]. Energy Procedia, 2017, 105: 4153-4158. |
15 | CHEMALI E, KOLLMEYER P J, PREINDL M, et al. Long short-term memory-networks for accurate state of charge estimation of Li-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2017, 65(8): 6730-6739. |
16 | CHEN C, XIONG R, YANG R X, et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter[J]. Journal of Cleaner Production, 2019, 234: 1153-1164. |
17 | ELMAN J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211. |
18 | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. |
19 | XU C Y, SHEN J Z, DU X, et al. An intrusion detection system using a deep neural network with gated recurrent units[J]. IEEE Access, 2018, 6: 48697-48707. |
20 | ARKHIPENKO K, KOZLOV I, TROFIMOVICH J, et al. Comparison of neural network architectures for sentiment analysis of Russian tweets[C]//Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference Dialogue, 2016. |
21 | GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5/6): 602-610. |
22 | LIU F G, ZHENG J Z, ZHENG L L, et al. Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification[J]. Neurocomputing, 2020, 371: 39-50. |
23 | CHEMALI E, KOLLMEYER P J, PREINDL M, et al. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach[J]. Journal of Power Sources, 2018, 400: 242-255. |
24 | LI C R, XIAO F, FAN Y X. An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit[J]. Energies, 2019, 12(9): doi: 10.3390/en12091592. |
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