Online estimation of the state of charge of a lithium-ion battery based on the fusion model
-
摘要: 針對鋰離子電池荷電狀態（Stage of charge，SOC）在線估計精度不高，等效電路模型法估計精度與模型復雜度相矛盾的問題，本文對擴展卡爾曼濾波算法進行了改進，并以電池工作電壓、電流為輸入，對應等效電路模型法的SOC估計誤差為輸出，采用極限學習機算法，建立基于輸入輸出數據的SOC估計誤差預測模型，采用物理–數據融合方法，基于誤差預測模型，建立了等效電路模型法結合極限學習機的鋰離子電池SOC在線估計模型。仿真結果表明，改進擴展卡爾曼濾波算法提高了算法的估計精度，而物理–數據融合的鋰離子電池SOC在線估計模型減小了由電壓、電流測量所引入的估計誤差，克服了等效電路模型法估計精度與模型復雜度之間相矛盾的問題，進一步提高了SOC的估計精度，滿足估計誤差不超過5%的應用需求。Abstract: In the context of the global response to environmental pollution and climate change, countries have begun to pay attention to energy system reform and economic development to ensure low carbon transition. Among them, the development of low carbon transportation has become an important aspect of green transportation system construction. The development of electric vehicle technology can effectively reduce energy consumption and environmental pollution. However, with the recent reports of new energy vehicle safety accidents at home and abroad, the safety of lithium-ion batteries has attracted increasing attention from the industry. To prevent overcharging and overdischarging from affecting battery life and safety during use, a complete battery management system is required to control and manage a lithium-ion battery. The state of charge (SOC) used to reflect the remaining capacity of a battery is one of the key parameters. Therefore, an accurate SOC value is of significance to the safety of lithium-ion battery use and the safety performance of new energy vehicles. The low online estimation accuracy of the SOC of lithium-ion batteries and the estimation accuracy of the equivalent circuit model method are inconsistent with the model complexity. This study improved the extended Kalman filtering (EKF) algorithm and established a SOC estimation error prediction model based on the extreme learning machine (ELM) algorithm, which used the operating voltage and current of the battery as input and the SOC estimation error of the equivalent circuit model method as the output. On the basis of the physical data fusion method and the error prediction model, the online estimation model of the lithium-ion battery SOC based on the equivalent circuit model method combined with the ELM was established. The simulation results showed that the improved EKF algorithm enhances the estimation precision of the algorithm. Moreover, the physical data fusion model reduces the estimation error introduced by voltage and current measurements, overcomes the contradiction between the estimation accuracy and complexity of the equivalent circuit model method, improves the estimation accuracy of the SOC, and meets the application requirement that the estimation error must be less than 5%.
-
圖 3 改進EKF算法估計SOC流程圖
Figure 3. Flowchart of the improved extended Kalman filtering (EKF) algorithm used to estimate the state of charge (SOC)
圖 5 基于ELM的SOC誤差預測模型結構
Figure 5. Structure of the SOC error prediction model based on the extreme learning machine algorithm
圖 6 預測模型在測試集下的絕對誤差值
Figure 6. Absolute error of the prediction model under the test set
表 1 第一組放電試驗數據
Table 1. First set of discharge data
Current, I/A Voltage, UL/V Standard value of SOCS 4.991 4.110 1 6.827 4.089 1 6.501 4.084 0.999 $ \vdots $ $ \vdots $ $ \vdots $ 57.770 3.420 0.665 45.962 3.391 0.664 $ \vdots $ $ \vdots $ $ \vdots $ 29.699 3.109 0.156 25.537 3.072 0.155 
下載: 導出CSV
表 2 第二組放電試驗數據
Table 2. Second set of discharge data
Current,$I$/A Voltage,${U_{\rm{L}}}$/V Standard value of SOCs 4.992 4.109 1 6.826 4.087 1 6.501 4.084 0.999 $ \vdots $ $ \vdots $ $ \vdots $ 52.730 3.421 0.664 42.185 3.430 0.662 $ \vdots $ $ \vdots $ $ \vdots $ 33.122 3.083 0.156 32.583 2.979 0.155
下載: 導出CSV
表 3 一階Thevenin等效電路模型參數
Table 3. Parameters of the first-order Thevenin equivalent circuit model
${R_0}$/Ω ${R_1}$/Ω ${C_1}$/F 0.0056 0.0072 5631.8
下載: 導出CSV
表 4 傳統EKF算法與改進EKF算法均方誤差對比
Table 4. Comparison of the mean squared error between the traditional and improved extended Kalman filtering (EKF) algorithms
Algorithm Mean squared error Traditional EKF algorithm 2.188 × 10–3 Improved EKF algorithm 9.899 × 10–4
下載: 導出CSV
表 5 SOC估計絕對誤差
Table 5. Absolute error of the state of charge estimation
First group Second group 0 0 1.052 × 10–4 1.053 × 10–4 9.685 × 10–5 9.680 × 10–5 $ \vdots $ $ \vdots $ 0.027 0.032 0.027 0.032 $ \vdots $ $ \vdots $ 0.042 0.047 0.043 0.047
下載: 導出CSV
表 6 基于ELM的誤差預測模型性能
Table 6. Error prediction of model line performance based on the extreme learning machine algorithm
Decisive factor Mean square error Training time/s 0.48 3 × 10–5 5.05
下載: 導出CSV
表 7 二階Thevenin等效電路參數
Table 7. Parameters of the second-order Thevenin equivalent circuit model
R0/Ω R1/Ω R2/Ω C1/F C2/F 0.0055 0.0041 0.0017 21797 3634
下載: 導出CSV
表 8 不同模型估計結果對比
Table 8. Comparison of the estimation results of different models
Model Mean square error Maximum absolute error Maximum percent error/% First-order Thevenin model 9.89 × 10–4 0.05 0.3 Second-order Thevenin model 4.98 × 10–4 0.03 0.10 Fusion model 3.01 × 10–5 0.02 0.09
下載: 導出CSV
www.77susu.com<span id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> <span id="fpn9h"><noframes id="fpn9h"> <th id="fpn9h"></th> <strike id="fpn9h"><noframes id="fpn9h"><strike id="fpn9h"></strike> <th id="fpn9h"><noframes id="fpn9h"> <span id="fpn9h"><video id="fpn9h"></video></span> <ruby id="fpn9h"></ruby> <strike id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> -
參考文獻
[1] Feng Z H, Wang X C, Zhang H Y, et al. Path and policy of green transportation development from low carbon perspective. <italic>Transp Res</italic>, 2019, 5(4): 37鳳振華, 王雪成, 張海穎, 等. 低碳視角下綠色交通發展路徑與政策研究. 交通運輸研究, 2019, 5(4):37 [2] Zhang M D. Current status and development trend of electric vehicle batteries. <italic>Internal Combust Engine Parts</italic>, 2019(15): 230張美迪. 電動汽車電池的現狀及發展趨勢. 內燃機與配件, 2019(15):230 [3] Jiang J C, Gao Y, Zhang C P, et al. Online diagnostic method for health status of lithium-ion battery in electric vehicle. <italic>J Mech Eng</italic>, 2019, 55(20): 60姜久春, 高洋, 張彩萍, 等. 電動汽車鋰離子動力電池健康狀態在線診斷方法. 機械工程學報, 2019, 55(20):60 [4] Fu X L, Shang Y L, Cui N X. Research and development trend on battery management system for EV. <italic>Power Electron</italic>, 2011, 45(12): 27符曉玲, 商云龍, 崔納新. 電動汽車電池管理系統研究現狀及發展趨勢. 電力電子技術, 2011, 45(12):27 [5] Tan Z F, Sun R L, Yang R, et al. Overview of battery management system. <italic>J Chongqing Univ Technol Nat Sci</italic>, 2019, 33(9): 40譚澤富, 孫榮利, 楊芮, 等. 電池管理系統發展綜述. 重慶理工大學學報: 自然科學, 2019, 33(9):40 [6] Zhang C J, Chen H. Review of state of charge estimation methods for lithium battery. <italic>Chin J Power Sources</italic>, 2016, 40(6): 1318張持健, 陳航. 鋰電池SOC預測方法綜述. 電源技術, 2016, 40(6):1318 [7] Yan X W, Deng H R, Guo Q, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization. <italic>Trans China Electrotech Soc</italic>, 2019, 34(18): 3937顏湘武, 鄧浩然, 郭琪, 等. 基于自適應無跡卡爾曼濾波的動力電池健康狀態檢測及梯次利用研究. 電工技術學報, 2019, 34(18):3937 [8] Lu L G, Li J Q, Hua J F, et al. A review on the key issues of the lithium-ion battery management. <italic>Sci Technol Rev</italic>, 2016, 34(6): 39盧蘭光, 李建秋, 華劍鋒, 等. 電動汽車鋰離子電池管理系統的關鍵技術. 科技導報, 2016, 34(6):39 [9] Xia C Y, Zhang S, Sun H T. A strategy of estimating state of charge based on extended Kalman filter. <italic>Chin J Power Sources</italic>, 2007, 31(5): 414夏超英, 張術, 孫宏濤. 基于推廣卡爾曼濾波算法的SOC估算策略. 電源技術, 2007, 31(5):414 [10] Li Z Y, Li Z Q, Lv F. State of charge estimation of lithium-ion battery based on UKF method. <italic>J J Guangxi Univ Sci Technol</italic>, 2019, 30(3): 41李澤洋, 李振強, 呂豐. 基于UKF方法的鋰離子電池荷電狀態估計研究. 廣西科技大學學報, 2019, 30(3):41 [11] Johnson V H. Battery performance models in ADVISOR. <italic>J Power Sources</italic>, 2002, 110(2): 321 doi: 10.1016/S0378-7753(02)00194-5 [12] Salameh Z M, Casacca M A, Lynch W A. A mathematical model for lead-acid batteries. <italic>IEEE Trans Energy Convers</italic>, 1992, 7(1): 93 doi: 10.1109/60.124547 [13] Zhang L, Zhang Q, Chang C, et al. Research on equivalent circuit model for state of charge estimation of electric vehicle. <italic>J Electron Meas Instrum</italic>, 2014, 28(10): 1161張利, 張慶, 常成, 等. 用于電動汽車SOC估計的等效電路模型研究. 電子測量與儀器學報, 2014, 28(10):1161 [14] Ren Y H. The Research on the On-Line SOC Estimation Method for Power Battery[Dissertation]. Tianjin: Hebei University of Technology, 2017任育涵. 動力電池SoC在線估計方法研究[學位論文]. 天津: 河北工業大學, 2017 [15] Zhang W P, Lei G Y, Zhang X Q. A simplified Li-ion battery SOC estimation method. <italic>Chin J Power Sources</italic>, 2016, 40(7): 1359張衛平, 雷歌陽, 張曉強. 一種簡化的鋰離子電池SOC估計方法. 電源技術, 2016, 40(7):1359 [16] Gu M, Xia C Y, Tian C Y. Li-ion battery state of charge estimation based on comprehensive Kalman filter. <italic>Trans China Electrotech Soc</italic>, 2019, 34(2): 419谷苗, 夏超英, 田聰穎. 基于綜合型卡爾曼濾波的鋰離子電池荷電狀態估算. 電工技術學報, 2019, 34(2):419 [17] Ren J, Wang K, Ren B S. State of charge estimation of lithium-ion battery based on improved model and unscented Kalman filter. <italic>Electr Energy Manage Technol</italic>, 2019(4): 64任軍, 王凱, 任寶森. 基于改進模型和無跡卡爾曼濾波的鋰離子電池荷電狀態估計. 電器與能效管理技術, 2019(4):64 [18] Qian N, Yan Y B, Li W J, et al. Improving of Thevenin equivalent model for lithium iron phosphate Li-ion battery. <italic>Battery Bimonthly</italic>, 2018, 48(4): 257錢能, 嚴運兵, 李文杰, 等. 磷酸鐵鋰鋰離子電池Thevenin等效模型的改進. 電池, 2018, 48(4):257 [19] Cai X, Li B, Wang H H, et al. Estimation of state-of-charge for electric vehicle power battery with neural network method. <italic>Mech Electr Eng Mag</italic>, 2015, 32(1): 128蔡信, 李波, 汪宏華, 等. 基于神經網絡模型的動力電池SOC估計研究. 機電工程, 2015, 32(1):128 [20] Lei X, Chen Q Q, Liu K P, et al. Battery state of charge estimation based on neural-network for electric vehicles. <italic>Trans China Electrotech Soc</italic>, 2007, 22(8): 155雷肖, 陳清泉, 劉開培, 等. 電動車蓄電池荷電狀態估計的神經網絡方法. 電工技術學報, 2007, 22(8):155 [21] Fan X M, Wang C, Zhang X, et al. A prediction method of Li-ion batteries SOC based on incremental learning relevance vector machine. <italic>Trans China Electrotech Soc</italic>, 2019, 34(13): 2700范興明, 王超, 張鑫, 等. 基于增量學習相關向量機的鋰離子電池SOC預測方法. 電工技術學報, 2019, 34(13):2700 [22] Song S J, Wang Z H, Lin X F. Research on SOC estimation of LiFePO4 batteries based on ELM. <italic>Chin J Power Sources</italic>, 2018, 42(6): 806宋紹劍, 王志浩, 林小峰. 基于極限學習機的磷酸鐵鋰電池SOC估算研究. 電源技術, 2018, 42(6):806 [23] Zhang Y H, Wang D, Xiao W, et al. Review of SOC estimation and difficulties in Li-ion battery. <italic>Chin J Power Sources</italic>, 2019, 43(11): 1894張易航, 王鼎, 肖圍, 等. 鋰離子電池SOC估算方法概況及難點分析. 電源技術, 2019, 43(11):1894 [24] Zhu Z. Research on SOC Estimation of LifeP04 Battery[Dissertation]. Harbin: Harbin Institute of Technology, 2013朱政. 磷酸鐵鋰電池荷電狀態估計方法的研究[學位論文]. 哈爾濱: 哈爾濱工業大學, 2013 [25] Zhang Y X. Parameter Identification and SOC Estimation of Power Battery for Electric Vehicle[Dissertation]. Changchun: Jilin University, 2014張禹軒. 電動汽車動力電池模型參數在線辨識及SOC估計[學位論文]. 長春: 吉林大學, 2014 [26] Wang X B, Xu J H, Zhang Z. On analysis and application approach for Kalman filter parameters. <italic>Comput Appl Software</italic>, 2012, 29(6): 212王學斌, 徐建宏, 張章. 卡爾曼濾波器參數分析與應用方法研究. 計算機應用與軟件, 2012, 29(6):212 [27] Feng G R, Huang G B, Lin Q P, et al. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. <italic>IEEE Trans Neural Networks</italic>, 2009, 20(8): 1352 doi: 10.1109/TNN.2009.2024147 [28] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks//Proceedings of 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). Budapest, Hungary, 2004: 985 -
點擊查看大圖
計量
- 文章訪問數: 1502
- HTML全文瀏覽量: 951
- PDF下載量: 77
- 被引次數: 0
