Online quantitative diagnosis algorithm for the internal short circuit of a lithium-ion battery module based on the remaining charge capacity
-
摘要: 通過對鋰離子電池內短路的在線診斷可以有效預防熱失控的發生。本文利用鋰離子電池模組的充電曲線提出一種基于剩余充電電量的內短路在線定量診斷算法，并對該算法在不同的電壓采集精度與采樣周期、溫度變化、老化程度等條件下進行仿真與實驗驗證。結果表明所提出的算法在一定條件下能準確定量地診斷出內短路電阻：(1) 對于10 Ω級別的嚴重內短路，即使在10 mV的采集精度、10 s的采樣周期、變溫度條件下也能得到很高的診斷精度。對于100 Ω級別的早期內短路，所診斷的內短路阻值比實際值偏小，診斷時間變長。為了提高早期內短路診斷的精度與時效性，電壓采集精度與采樣頻率應該分別在1 mV 與 1 Hz 以上；(2) 電池老化會降低內短路的診斷精度，但是對于10 Ω級別的內短路影響很小。極端溫度變化同樣會影響內短路定量診斷精度，極端高溫下的診斷誤差比極端低溫下的診斷誤差要大，在極限低溫(–20 ℃)下的內短路內阻的診斷誤差在6%以內。研究結論為提高鋰離子內短路的定量診斷精度具有重要意義。Abstract: Lithium-ion batteries are widely used in energy storage and new energy electric vehicles due to their superior performance, but the internal short circuit problem of lithium-ion batteries is a safety hazard during usage for energy storage and vehicle battery packs. If it cannot be detected in time, the deepening of the internal short circuit will be accompanied by an increase in heat, which will cause thermal runaway and lead to safety accidents. Diagnosing whether the battery pack has an internal short circuit and quantitatively estimating the short circuit resistance of the battery cell that has the internal short circuit can effectively prevent the occurrence of thermal runaway. This study proposes a quantitative diagnosis algorithm of Internal short circuit (ISC) based on the remaining charge capacity based on the charging curve of the lithium-ion battery module. The simulation and experimental verification of the algorithm are carried out under the conditions of different voltage acquisition accuracies, sampling periods, temperatures, and aging degrees. The results show that the proposed algorithm can accurately and quantitatively diagnose the ISC under certain conditions: (1) For serious ISC of 10 Ω level, high diagnosis accuracy can be obtained even under the conditions of 10 mV acquisition accuracy, 10 s sampling period, and variable temperature. For early ISC of 100 Ω level, the ISC resistance is smaller than the actual value and the diagnosis time is longer. To improve the accuracy and timeliness of early ISC diagnosis, the voltage acquisition accuracy, and sampling frequency should be higher than 1 mV and 1 Hz, respectively. (2) Battery aging will reduce the accuracy of ISC diagnosis, but it has little effect on the 10 Ω level ISC, and the diagnostic error of the ISC resistance is less than 6% even at an extremely low temperature (?20 ℃). The conclusions are of great significance to improve the accuracy of quantitative diagnosis of ISC for lithium-ion batteries.
-
圖 2 基于RCC變化的內短路診斷原理
Figure 2. Principle of internal short circuit diagnosis based on the RCC change
圖 3 內短路模組建模.(a)電池組模型，(b)單體模型，(c)一階RC模型
Figure 3. Internal short-circuit module modeling: (a) module model; (b) cell model; (c) first order RC model
圖 4 5 mV數據精度對算法的影響.(a) Sim03: RISC=100 Ω; (b) Sim04: RISC=10 Ω
Figure 4. Impact of the 5 mV data accuracy on the algorithm: (a) Sim03: RISC=100 Ω, (b) Sim04: RISC=10 Ω
圖 5 10 s采樣周期對算法的影響.(a) Sim07: RISC=100 Ω; (b) Sim08: RISC=10 Ω
Figure 5. Impact of the 10 s sampling period on the algorithm: (a) Sim07: RISC=100 Ω; (b) Sim08: RISC=10 Ω
圖 7 電池老化對算法精度的影響.(a) Exp01：全新模組(RISC=100 Ω); (b) Exp08：老化模組（RISC=100 Ω）
Figure 7. Impact of battery aging on algorithm accuracy: (a) Exp01: new module (RISC=100 Ω); (b) Exp08: aging module (RISC=100 Ω)
圖 8 極限溫度下內短路診斷實驗結果.(a) Exp03:極限高溫為55 ℃; (b) Exp05:極限低溫為?20 ℃
Figure 8. Test results of internal short circuit diagnosis under extreme temperatures: (a) Exp03: extreme high temperature of 55 ℃; (b) Exp05: extreme high temperature of ?20 ℃
圖 9 變溫度下的內短路診斷實驗結果.(a) Exp06: 25 ℃→15 ℃; (b) Exp07: 45 ℃→35 ℃
Figure 9. Test results of internal short circuit diagnosis under variable temperature: (a) Exp06: 25 ℃→15 ℃ (b) Exp07: 45 ℃→35 ℃
表 1 各種仿真場景下的內短路診斷結果
Table 1. Diagnosis results of the internal short circuit in various simulation scenarios
Simulation number Voltage accuracy / mV Sampling period / s RISC / Ω Diagnostic value / Ω Error / % Detection time / h Sim01 0.5 1 100 95.47 4.53 18.1 Sim02 0.5 1 10 9.53 4.7 18.1 Sim03 1 1 100 71.90 28.1 18.1 Sim04 1 1 10 9.57 4.3 18.1 Sim05 5 1 100 27.03 72.9 65.3 Sim06 5 1 10 8.87 11.3 18.2 Sim07 10 1 100 17.89 83.1 77.1 Sim08 10 1 10 8.86 11.4 18.1 Sim09 1 10 100 126.66 26.6 18.1 Sim10 1 10 10 9.61 3.5 18.1 Sim11 1 20 100 196.2 96.2 18.2 Sim12 1 20 10 9.65 3.9 18.1 
下載: 導出CSV
表 2 內短路阻值定量診斷實驗結果
Table 2. Quantitative diagnosis test results of the internal short-circuit resistance
Experiment number Aging degree Temperature / ℃ RISC / Ω Diagnostic value / Ω Diagnostic
error / %Exp01 New module 25 100 105 5 Exp02 New module 25 10 10.6 6 Exp03 New module 55 100 146 46 Exp04 New module 5 100 97 3 Exp05 New module ?20 100 94 6 Exp06 New module 25→15 100 138 38 Exp07 New module 45→35 100 102 2 Exp08 Aging module 25 100 132 32 Exp09 Aging module 25 10 11.2 12
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] Pan F W, Gong D L, Gao Y, et al. Lithium-ion battery state of charge estimation based on a robust H∞ filter. Chin J Eng, 2021, 43(5): 693潘鳳文, 弓棟梁, 高瑩, 等. 基于魯棒H∞濾波的鋰離子電池SOC估計. 工程科學學報, 2021, 43(5):693 [2] Lai X, Zheng Y J, Zhou L, et al. Electrical behavior of overdischarge-induced internal short circuit in lithium-ion cells. Electrochimica Acta, 2018, 278: 245 doi: 10.1016/j.electacta.2018.05.048 [3] An F Q, Zhao H L, Cheng Z, et al. Development status and research progress of power battery for pure electric vehicles. Chin J Eng, 2019, 41(1): 22安富強, 趙洪量, 程志, 等. 純電動車用鋰離子電池發展現狀與研究進展. 工程科學學報, 2019, 41(1):22 [4] Song Y H, Yang Y X, Hu Z C. Present status and development trend of batteries for electric vehicles. Power Syst Technol, 2011, 35(4): 1 doi: 10.13335/j.1000-3673.pst.2011.04.009宋永華, 陽岳希, 胡澤春. 電動汽車電池的現狀及發展趨勢. 電網技術, 2011, 35(4):1 doi: 10.13335/j.1000-3673.pst.2011.04.009 [5] Feng X N, Ouyang M G, Liu X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Mater, 2018, 10: 246 doi: 10.1016/j.ensm.2017.05.013 [6] Chen Z Y, Xiong R, Sun F C. Research status and analysis for battery safety accidents in electric vehicles. J Mech Eng, 2019, 55(24): 93 doi: 10.3901/JME.2019.24.093陳澤宇, 熊瑞, 孫逢春. 電動汽車電池安全事故分析與研究現狀. 機械工程學報, 2019, 55(24):93 doi: 10.3901/JME.2019.24.093 [7] Su W, Zhong G B, Shen J N, et al. The progress in fault diagnosis techniques for lithium-ion batteries. Energy Storage Sci Technol, 2019, 8(2): 225 doi: 10.12028/j.issn.2095-4239.2018.0195蘇偉, 鐘國彬, 沈佳妮, 等. 鋰離子電池故障診斷技術進展. 儲能科學與技術, 2019, 8(2):225 doi: 10.12028/j.issn.2095-4239.2018.0195 [8] Zheng Y F. Study on safety of lithium-ion battery under overuse conditions. Mar Electr Electron Eng, 2021, 41(2): 44 doi: 10.3969/j.issn.1003-4862.2021.02.011鄭蕓菲. 鋰離子電池在濫用條件下的安全性研究. 船電技術, 2021, 41(2):44 doi: 10.3969/j.issn.1003-4862.2021.02.011 [9] Gan W, Han X Y. A lithium ion battery internal short circuit fault diagnosis method based on wavelet noise reduction and curve similarity. Mach Des Manuf Eng, 2021, 50(5): 57 doi: 10.3969/j.issn.2095-509X.2021.05.012甘偉, 韓孝耀. 基于小波降噪-曲線相似程度的鋰離子電池內短路故障診斷方法. 機械設計與制造工程, 2021, 50(5):57 doi: 10.3969/j.issn.2095-509X.2021.05.012 [10] Chen M B, Bai F F, Song W J, et al. Multi-point internal short circuit and physical field variation of Li-ion battery. Battery Bimon, 2021, 51(2): 131 doi: 10.19535/j.1001-1579.2021.02.006陳明彪, 白帆飛, 宋文吉, 等. 鋰離子電池多點內短路及物理場變化. 電池, 2021, 51(2):131 doi: 10.19535/j.1001-1579.2021.02.006 [11] Zheng Y J, Ouyang M G, Lu L G, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 1. Equalization based on remaining charging capacity estimation. J Power Sources, 2014, 247: 676 [12] Wang H B, Li Y, Wang Q Z, et al. Experimental study on the thermal runaway and its propagation of a lithium-ion traction battery with NCM cathode under thermal abuse. Chin J Eng, 2021, 43(5): 663王淮斌, 李陽, 王欽正, 等. 三元鋰離子動力電池熱失控及蔓延特性實驗研究. 工程科學學報, 2021, 43(5):663 [13] Wang Z P, Yuan C G, Li X Y. An analysis on challenge and development trend of safety management technologies for traction battery in new energy vehicles. Automot Eng, 2020, 42(12): 1606 doi: 10.19562/j.chinasae.qcgc.2020.12.002王震坡, 袁昌貴, 李曉宇. 新能源汽車動力電池安全管理技術挑戰與發展趨勢分析. 汽車工程, 2020, 42(12):1606 doi: 10.19562/j.chinasae.qcgc.2020.12.002 [14] Zhang Y J, Wang H W, Feng X N, et al. Research progress on thermal runaway combustion characteristics of power lithiumion batteries. J Mech Eng, 2019, 55(20): 17張亞軍, 王賀武, 馮旭寧, 等. 動力鋰離子電池熱失控燃燒特性研究進展. 機械工程學報, 2019, 55(20):17 [15] Feng X N, Pan Y, He X M, et al. Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm. J Energy Storage, 2018, 18: 26 doi: 10.1016/j.est.2018.04.020 [16] Kong X D, Zheng Y J, Ouyang M G, et al. Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs. J Power Sources, 2018, 395: 358 doi: 10.1016/j.jpowsour.2018.05.097 [17] Kenney B, Darcovich K, MacNeil D D, et al. Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules. J Power Sources, 2012, 213: 391 doi: 10.1016/j.jpowsour.2012.03.065 [18] Dubarry M, Truchot C, Cugnet M, et al. Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part I: Initial characterizations. J Power Sources, 2011, 196(23): 10328 [19] Guo Z Q, Xiong Q, Liang B H, et al. Consistency detection approach for lithium-ion battery pack based on current characteristics of bridging capacitors. High Voltage Engineering, 2022, 48(5): 1933郭自清, 熊慶, 梁博航, 等. 基于橋接電容電流特性的鋰離子電池組一致性檢測方法. 高電壓技術, 2022, 48(5):1933 [20] Chen C, Zhu R Y. Research on consistency simulation of lithium ion battery applied to special equipment. Electron Test, 2020(24): 43 doi: 10.3969/j.issn.1000-8519.2020.24.015陳晨, 朱瑞銀. 應用于特種設備的鋰離子電池一致性仿真研究. 電子測試, 2020(24):43 doi: 10.3969/j.issn.1000-8519.2020.24.015 [21] Lai X, Qin C, Zheng Y J, et al. An adaptive capacity estimation scheme for lithium-ion battery based on voltage characteristic points in constant-current charging curve. Automot Eng, 2019, 41(1): 1 doi: 10.19562/j.chinasae.qcgc.2019.01.001來鑫, 秦超, 鄭岳久, 等. 基于恒流充電曲線電壓特征點的鋰離子電池自適應容量估計方法. 汽車工程, 2019, 41(1):1 doi: 10.19562/j.chinasae.qcgc.2019.01.001 [22] Zheng Y J, Lu L G, Han X B, et al. LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation. J Power Sources, 2013, 226: 33 doi: 10.1016/j.jpowsour.2012.10.057 [23] Hu X S, Tang X L. Review of modeling techniques for lithium-ion traction batteries in electric vehicles. J Mech Eng, 2017, 53(16): 20 doi: 10.3901/JME.2017.16.020胡曉松, 唐小林. 電動車輛鋰離子動力電池建模方法綜述. 機械工程學報, 2017, 53(16):20 doi: 10.3901/JME.2017.16.020 [24] Wang J, Liu P, Hicks-Garner J, et al. Cycle-life model for graphite-LiFePO4 cells. J Power Sources, 2011, 196(8): 3942 doi: 10.1016/j.jpowsour.2010.11.134 [25] Zhou L, Zheng Y J, Ouyang M G, et al. A study on parameter variation effects on battery packs for electric vehicles. J Power Sources, 2017, 364: 242 doi: 10.1016/j.jpowsour.2017.08.033 -
點擊查看大圖
計量
- 文章訪問數: 598
- HTML全文瀏覽量: 223
- PDF下載量: 95
- 被引次數: 0
