ABSTRACT
Aiming at the problems of low prediction accuracy and poor generalization ability of lead-acid battery performance prediction model in substation, this paper proposes a lead-acid battery performance prediction model based on meta-learning and gating network. Firstly, based on the historical operating data of the battery, the long-term internal resistance change prediction of the battery is realized by introducing meta-learning and gating network. Then, based on the variation trend of battery internal resistance, the battery voltage, internal resistance, temperature and other key factors as well as the data of battery charge and discharge curve are used to build a lead-acid battery performance prediction model, and the prediction accuracy of the model is improved by combining CNN and attention mechanism. The experimental results show that the proposed model can effectively improve the accuracy and generalization ability of battery performance prediction.
- Zheng Guilin, Tao Zhihao. Research and design of SOH and SOC detection system for large capacity battery [J]. Engineering Journal of Wuhan University, 2019, 52(1): 83-88.Google Scholar
- Xie Siyu, Wang Ping, Wang Zhishuang. Application of improved WNN in battery SOC estimation [J]. Journal of Power Supply, 2020, 18(6): 199-206.Google Scholar
- Xu Bintai, Meng Xianglu, Tian Anqi, Prediction for state of charge of lead-acid battery by particle swarm optimization with Gaussian process regression [J]. Journal of Nanjing University of Science and Technology, 2018, 42(2): 162-168.Google Scholar
- Changyang Li, Wanwan Li, Haikun Huang and Lap-Fai Yu. Interactive augmented reality storytelling guided by scene semantics[J]. ACM Transactions on Graphics (TOG), 2022, 41(4): 1-15.Google ScholarDigital Library
- Wanwan Li, Biao Xie, Yongqi Zhang, Walter Meiss, Haikun Huang, Lap-Fai Yu. Exertion-aware path generation[J]. ACM Trans. Graph., 2020: 39(4): 115.Google Scholar
- Wanwan Li. Image Synthesis and Editing with Generative Adversarial Networks (GANs): A Review. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), London, United Kingdom, 2021, 65-70.Google ScholarCross Ref
- Wanwan Li. Musical Instrument Performance in Augmented Virtuality. In Proceedings of the 6th International Conference on Digital Signal Processing, 2022: 91-97.Google Scholar
- Wanwan Li, Changyang Li, Kim, M., Haikun Huang, Lap-Fai Yu. Location-Aware Adaptation of Augmented Reality Narratives. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023: 1-15.Google Scholar
- Wanwan Li. Synthesizing 3D VR Sketch Using Generative Adversarial Neural Network. In Proceedings of the 7th International Conference on Big Data and Internet of Things, 2023.Google Scholar
- Zhou Qi, Luo Pei. Application of fuzzy clustering method in predicting batteries’ SOC [J]. Journal of Power Supply, 2014, 12(4): 99-104.Google Scholar
- LUO Ning, LI Yifei, LI Zhe, WEI Junru. Prediction Model of Automatic Charge and Discharge SOC of Lead-acid Battery Based on Compound Model [J]. Microcomputer Applications, 2021, 37(8): 71-74.Google Scholar
- SHU Zhengyu, ZHAI Erjie, LI Zhenhan, HUANG Zhipeng. Prediction of Lead-acid Battery Capacity Based on Dropout Optimization Algorithm and LSTM [J]. Journal of Power Supply, 2023, 21(5): 173-181.Google Scholar
- Deng Weifeng, Li Zhenbi. Estimation of SOH for micro-grid battery based on GA optimized BP neural network [J]. Electrical Measurement & Instrumentation, 2018, 55(21): 56-60, 85.Google Scholar
- Li Jinming. Analysis of service life of VRLA batteries for power systems [J]. Chinese Labat Man, 2019, 56(1): 38-41.Google Scholar
- Wang Zhongjie. Operation and maintenance strategy of DC system battery pack in substation [J]. China Plant Engineering, 2018(18): 50-51.Google Scholar
- Ma Wenchang. Study on the design points and detection of high rate (power) lead-acid battery [J]. Telecom Power Technology, 2017, 34(5): 194-195.Google Scholar
- FAN Xinxin, DING Hui, CHEN Xiuguo, WANG Jianbin, YAN Hongmei. On-Line Health Assessment of Substation Battery Based on Fuzzy Logic[J]. Chinese Journal of Electron Devices, 2021, 44(1):Google Scholar
- YANG Yuli, LI Peiren, LI Xuezhi, Prediction of battery status in cloud data centers based on linear SVM algorithms[J]. Journal of Xi'an Polytechnic University, 2023, 37(5): 77-82.Google Scholar
- HAIDER S N, ZHAO Q C, LI X L. Data driven battery anomaly detection based on shape based clustering for the data centers class[J]. Journal of Energy Storage, 2020, 29: 101479.Google ScholarCross Ref
- LI X H, YANG W, PANG A P, A fault diagnosis method for VRLA battery in data center[J]. Energy Reports, 2022, 8: 14220-14235.Google ScholarCross Ref
- Shen X, Dai Q, Zhu G, Dynamic ensemble pruning algorithms fusing meta-learning with heuristic parameter optimization for time series prediction[J]. Expert Systems with Application, 2023, 225: 120148.Google ScholarDigital Library
Index Terms
- Lead-acid Battery Performance Prediction Model Based on Meta Learning and Gated Networks
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