| 引用本文: | 贺宁,钱成,李若夏.自适应模型与改进粒子滤波的电池RUL预测[J].哈尔滨工业大学学报,2022,54(9):111.DOI:10.11918/202104137 |
| HE Ning,QIAN Cheng,LI Ruoxia.RUL prediction for lithium-ion batteries via adaptive modeling and improved particle filter[J].Journal of Harbin Institute of Technology,2022,54(9):111.DOI:10.11918/202104137 |
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| 摘要: |
| 为提高锂电池运行的安全性和可靠性并维护系统稳定运行,提出一种自适应混合模型与改进粒子滤波（particle filter, PF）算法的锂电池剩余使用寿命（remaining useful life, RUL）预测方法。采用经验模型与神经网络模型结合建立自适应混合模型描述电池容量退化趋势,并使用天牛须搜索（beetle antennae search, BAS）算法优化PF重采样步骤解决粒子多样性丧失问题,从而提高估计精度进而实现RUL的精准预测。应用NASA和CALCE公开的两组不同类型锂电池作为实验对象,验证模型的有效性并通过对比PF与改进PF算法验证RUL预测的准确性。结果表明:自适应混合模型对于电池容量变化的表达能力更强,既能考虑电池内部的参数变化又能反应电池外部环境的变化,基于BAS改进的PF（BAS-PF）方法相较于传统PF算法的估计精度更高而且RUL预测更准确,对于不同的预测起点,两块测试电池的RUL预测误差分别为5.88%、3.92%、1.96%和3.75%、1.25%、0%。自适应混合模型能更加有效地描述电池容量特征,基于自适应混合模型的BAS-PF算法的电池RUL预测能力更好,可靠性更强,有助于提高RUL预测的精度和表现。 |
| 关键词: 粒子滤波 剩余寿命 优化算法 神经网络 锂电池 混合模型 |
| DOI：10.11918/202104137 |
| 分类号:TM912 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61903291)； 中国博士后科学基金(2019M660257) |
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| RUL prediction for lithium-ion batteries via adaptive modeling and improved particle filter |
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HE Ning1,2,QIAN Cheng1,LI Ruoxia3
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(1.School of Mechanical and Electrical Engineering, Xi′an University of Architecture and Technology, Xi′an 710055, China; 2.Ministry of Education Key Laboratory for Intelligent Networks and Network Security (Xi′an Jiaotong University), Xi′an 710049, China; 3.College of Information and Control Engineering, Xi′an University of Architecture and Technology, Xi′an 710055, China)
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| Abstract: |
| To improve the safety and reliability of lithium-ion battery operation and maintain the stable operation of the system, we proposed a prediction method for remaining useful life (RUL) of lithium-ion batteries based on adaptive hybrid model and improved particle filter (PF) algorithm. An adaptive hybrid model was established by combining empirical model and neural network model to describe the degradation trend of battery capacity, and beetle antennae search (BAS) algorithm was used to optimize the PF resampling step to solve the problem of loss of particle diversity, so as to improve the estimation accuracy and achieve accurate RUL prediction. Two groups of different types of lithium-ion batteries published by NASA and CALCE were selected as the research objects to verify the validity of the model and the accuracy of RUL prediction via comparing PF and improved PF algorithms. Experimental results show that the adaptive hybrid model was more expressive in terms of battery capacity change, which can reflect the variation of the internal parameters as well as the external environment of the battery. Compared with the traditional PF algorithm, the BAS-based improved PF (BAS-PF) method had higher estimation accuracy and more accurate RUL prediction results with the prediction errors of 5.88%, 3.92%, 1.96%, and 3.75%, 1.25%, 0%, respectively, for the two test batteries from different prediction points. The adaptive hybrid model can describe the characteristics of battery capacity more effectively, and the BAS-PF algorithm based on adaptive hybrid model has better prediction ability and greater reliability for battery RUL, which is helpful to improve the prediction accuracy and performance for RUL. |
| Key words: particle filter remaining useful life optimization algorithm neural network lithium-ion battery hybrid model |
