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: 通过对锂离子电池内短路的在线诊断可以有效预防热失控的发生。本文利用锂离子电池模组的充电曲线提出一种基于剩余充电电量的内短路在线定量诊断算法，并对该算法在不同的电压采集精度与采样周期、温度变化、老化程度等条件下进行仿真与实验验证。结果表明所提出的算法在一定条件下能准确定量地诊断出内短路电阻：(1) 对于10 Ω级别的严重内短路，即使在10 mV的采集精度、10 s的采样周期、变温度条件下也能得到很高的诊断精度。对于100 Ω级别的早期内短路，所诊断的内短路阻值比实际值偏小，诊断时间变长。为了提高早期内短路诊断的精度与时效性，电压采集精度与采样频率应该分别在1 mV 与 1 Hz 以上；(2) 电池老化会降低内短路的诊断精度，但是对于10 Ω级别的内短路影响很小。极端温度变化同样会影响内短路定量诊断精度，极端高温下的诊断误差比极端低温下的诊断误差要大，在极限低温(–20 ℃)下的内短路内阻的诊断误差在6%以内。研究结论为提高锂离子内短路的定量诊断精度具有重要意义。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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图 2 基于RCC变化的内短路诊断原理
Figure 2. Principle of internal short circuit diagnosis based on the RCC change
图 3 内短路模组建模.(a)电池组模型，(b)单体模型，(c)一阶RC模型
Figure 3. Internal short-circuit module modeling: (a) module model; (b) cell model; (c) first order RC model
图 4 5 mV数据精度对算法的影响.(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
图 5 10 s采样周期对算法的影响.(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
图 7 电池老化对算法精度的影响.(a) Exp01：全新模组(RISC=100 Ω); (b) Exp08：老化模组（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)
图 8 极限温度下内短路诊断实验结果.(a) Exp03:极限高温为55 ℃; (b) Exp05:极限低温为−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 ℃
图 9 变温度下的内短路诊断实验结果.(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 各种仿真场景下的内短路诊断结果
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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表 2 内短路阻值定量诊断实验结果
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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