Online quantitative diagnosis algorithm for the internal short circuit of a lithium-ion battery module based on the remaining charge capacity
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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.
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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 
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中文字幕在线观看表 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
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