CLC number: U469.72; TP391.4
On-line Access: 2023-02-27
Received: 2022-05-16
Revision Accepted: 2023-02-27
Crosschecked: 2022-08-23
Cited: 0
Clicked: 1288
Citations: Bibtex RefMan EndNote GB/T7714
Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE. Dynamic time prediction for electric vehicle charging based on charging pattern recognition[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200212 @article{title="Dynamic time prediction for electric vehicle charging based on charging pattern recognition", %0 Journal Article TY - JOUR
一种基于充电模式识别的电动汽车充电时间预测方法1上海海事大学物流工程学院,中国上海市,200135 2北京交通大学经济管理学院,中国北京市,102603 3南京信息工程大学自动化学院,中国南京市,210044 4南京信息工程大学江苏省大气环境与装备技术协同创新中心,中国南京市,210044 5南京信息工程大学江苏省大数据分析技术重点实验室,中国南京市,210044 摘要:电动汽车动力电池过度充电容易导致电池加速老化和严重的安全事故。因此,准确预测车辆充电时间对充电安全防护意义重大。由于电池组结构复杂,充电方式多样,传统方法因缺乏充电模式识别而预测精度不高。本文应用数据驱动和机器学习理论,提出一种新的基于充电模式识别的充电时间预测方法。首先,基于动态加权密度峰值聚类(DWDPC)和随机森林融合的智能算法对车辆充电模式进行分类;然后,采用改进的简化粒子群优化算法(ISPSO)和强跟踪滤波器(STF),对LSTM神经网络进行优化,构建了一种高性能的充电时间预测方法;最后,通过实际工程数据对所提出的ISPSO-LSTM-STF方法进行了验证。实验结果表明,该方法能够有效区分充电模式,提高了充电时间预测精度,具有实际工程意义。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Altché F, de la Fortelle A, 2017. An LSTM network for highway trajectory prediction. Proc IEEE 20th Int Conf on Intelligent Transportation Systems, p.353-359. [2]Chang XQ, Song ZX, Wang JH, 2020. Electric vehicle charging load prediction and system development based on Monte Carlo algorithm. High Volt Appar, 56(8):1-5 (in Chinese). [3]Cheng SY, Lin PC, Lin PJ, 2019. New method of predict remaining charging time for lithium-ion batteries. Chin J Power Sources, 43(1):99-102, 135 (in Chinese). [4]Du MJ, Ding SF, Jia HJ, 2016. Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl-Based Syst, 99:135-145. [5]Frendo O, Graf J, Gaertner N, et al., 2020. Data-driven smart charging for heterogeneous electric vehicle fleets. Energy AI, 1:100007. [6]Ge QB, Guo C, Jiang HY, et al., 2022. Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM. IEEE Trans Cybern, 52(2):1112-1124. [7]Han T, Xu M, Lu YF, 2014. Charging Remaining Time Estimation Method, Device and Mobile Equipment. CN Patent 201210297383.4 (in Chinese). [8]Hu W, Li ZS, 2007. A simpler and more effective particle swarm optimization algorithm. J Softw, 18(4):861-868 (in Chinese). [9]Huang TS, Wu SY, 2005. Enhanced particle swarm optimization algorithm based on Kalman filter principle. Comput Eng Appl, 41(35):56-58 (in Chinese). [10]Li B, Han R, He YG, et al., 2020. Applications of the improved random forest algorithm in fault diagnosis of motor bearings. Proc CSEE, 40(4):1310-1319 (in Chinese). doi: [11]Li CX, Fu YY, Zhang JM, et al., 2020. Evaluation methods on charging safety for EV power battery. Proc 35th Youth Academic Annual Conf of Chinese Association of Automation, p.318-323. [12]Li YY, 2020. Engineering Intelligent Kalman Filtering Method. MS Thesis, Hangzhou Dianzi University, Hangzhou, China (in Chinese). doi: [13]Lin PC, 2018. Tri-section SVR model for predicting charging time of lithium-ion batteries. Chin J Power Sources, 42(8):1155-1157, 1232 (in Chinese). [14]Liu B, 2020. Research of Short-Term Power Load Forecasting Based on PSO-LSTM Algorithm. MS Thesis, Jilin University, Jilin, China (in Chinese). doi: [15]Liu WW, Sang SB, Zhang HP, 2022. Study on improved heart sound classification model based on CNN+LSTM. Electron Des Eng, 30(2):38-42 (in Chinese). [16]Liu X, 2020. Prediction of Lithium Battery's Remaining Charging Time Based on IndyLSTM. MS Thesis, Xi’an University of Science and Technology, Xi’an, China (in Chinese). doi: [17]Liu YY, 2018. Research on modeling and simulation of detailed model of photovoltaic power generation system. Comput Knowl Technol, 14(10):221-224, 228 (in Chinese). [18]Rodriguez A, Laio A, 2014. Clustering by fast search and find of density peaks. Science, 344(6191):1492-1496. [19]Roondiwala M, Patel H, Varma S, 2017. Predicting stock prices using LSTM. Int J Sci Res, 6(4):1754-1756. [20]Sun J, Chen SB, You PC, et al., 2021. Battery-assisted online operation of distributed data centers with uncertain workload and electricity prices. IEEE Trans Cloud Comput, early access. [21]Wang ZL, Li J, Song YF, 2020. Improved K-means algorithm based on distance and weight. Comput Eng Appl, 56(23):87-94 (in Chinese). [22]Xu ZY, Kang Y, Cao Y, et al., 2019. Man-machine verification of mouse trajectory based on the random forest model. Front Inform Technol Electron Eng, 20(7):925-929. [23]Yang QM, Liu GL, Bao YN, et al., 2022. Fault detection of wind turbine generator bearing using attention-based neural networks and voting-based strategy. IEEE/ASME Trans Mechatron, 27(5):3008-3018. [24]Zhang QS, Zhao QC, 2020. Effects of overcharge cycling on the aging and safety of lithium ion batteries. High Volt Eng, 46(10):3390-3397 (in Chinese). [25]Zhang YF, Zhao ZD, Deng YJ, et al., 2021. ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model. Front Inform Technol Electron Eng, 22(12):1641-1654. [26]Zhou D, Song XH, Lu WB, et al., 2019. Real-time SOH estimation algorithm for lithium-ion batteries based on daily segment charging data. Proc CSEE, 39(1):105-111 (in Chinese). doi: [27]Zhu XQ, Wang ZP, Wang C, et al., 2019. An experimental study on overcharge behaviors of lithium-ion power battery with LiNi0.6Co0.2Mn0.2O2 cathode. Automot Eng, 41(5):582-589 (in Chinese). [28]Zhu ZC, Zheng YJ, 2017. Prediction method of rechargeable electricity of vehicle battery at different temperature. Agric Equip Veh Eng, 55(8):1-5. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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