基于退役锂动力电池容量、内阻和荷电状态的建模与参数估计
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摘要
摘要：从电动汽车上退役下来的锂离子动力电池(简称退役电池)仍具有高达80%的剩余容量,研究剩余容量的再利用技术对间接降低锂动力电池成本与缓解环境污染问题有着重要的意义。为了最大限度发挥退役电池的价值,对其进行分选以及对具有利用价值的电池进行剩余寿命等方面的研究非常必要,这也是目前退役电池梯级利用及回收领域的热点课题之一。本文采用电化学检测-计算机模拟联合技术研究了电池在不同温度、放电倍率和放电深度下的电化学行为；针对电池的剩余寿命,劣化失效程度和荷电状态进行分析和估计,建立了电池寿命预测模型、劣化失效的普适模型和荷电状态估计模型,为确保退役电池安全、可靠地运行并保持在最佳工作状态提供了理论指导。通过对上述内容的深入研究,获得了以下四个方面的研究结果：
(1)采用现代物理测试技术和电化学检测手段对退役电池进行了安全性能检测,并结合外观/容量分选法、电压/内阻分选法及特征曲线法对退役的18650型磷酸铁锂动力电池进行了分选。本论文分选出了外观无破损特征,容量在1.1～1.0Ah,内阻在12～12.6mΩ,开路电压在3.2998-3.3002V,以及大倍率放电时,放电曲线一致性较好的退役电池作为研究对象。
(2)采用电化学检测-计算机模拟联合技术建立了退役电池的寿命预测模型,并提出了一种退役电池的寿命匹配检测方法。电化学检测结果表明：影响退役电池循环寿命的主要因素为循环次数(N),环境温度(T),放电倍率(C)和放电深度(DOD)。退役电池的放电容量随着循环次数的增加逐渐降低,环境温度越高,放电倍率越大,放电深度越深,退役电池的放电容量衰减越快。当循环时间足够长时,放电深度对退役电池寿命的衰减影响不大。计算机模拟结果表明：退役电池的放电容量衰减速率随循环次数,环境温度,放电倍率和放电深度的变化规律符合幂函数模型：
以此为标准模型,采用匹配检测技术可利用较少的容量保持率与循环次数的数据对,准确地预测出任意一颗同规格退役电池当前所处的工况,以及在该工况时退役电池的剩余寿命。
(3)采用内阻-交流阻抗联合检测技术建立了退役电池的劣化失效模型。结果表明：内阻可用来表征退役电池的劣化失效程度。随着内阻的增加,退役电池劣化程度加剧。欧姆内阻和极化内阻的变化与环境温度、放电倍率和放电深度有关。环境温度的升高,放电深度的加深以及放电倍率的增大均会导致内阻加快增长。三种因素对欧姆内阻和极化内阻影响的重要程度为：放电深度<放电倍率<环境温度。欧姆内阻与极化内阻之和与循环次数的关系符合幂函数模型：
(4)采用交流阻抗测定法、SOC-OCV曲线法和脉冲放电法相结合的方法建立了退役电池荷电状态估计模型。交流阻抗测试结果表明：对退役电池进行全荷电态建模时,应同时考虑欧姆内阻和极化内阻对退役电池荷电状态的影响。而对退役电池进行区间段建模时,主要考虑极化内阻对荷电状态的影响。SOC-OCV测试结果表明：退役电池的SOC-OCV特性不受环境温度,放电倍率,储存时间及电池充放电状态的影响,退役电池的OCV随着SOC的递增而单调递增,随着SOC的递减而单调递减。等荷电态多步脉冲放电法测试结果表明：通过所建立退役电池的等效电路模型,可确定退役电池OCV关于SOC的模型,从而估算SOC值。
Abstract:Lithium-ion power batteries retired from electric vehicles (or retired batteries for short) still possess high residual capacities as high as80%of the designed capacities. It is of great significance to reuse retired batteries in a suitable way which could reduce the energy cost, relieve the environmental pollution and make waste profitable. In order to make the best use of the residual capacity of the retired batteries, it is necessary to classify the retired batteries, as well as predict the cycle life, estimate the deterioration degree and calculate the state of charge (SOC) of the selected batteries. This is also one of the attractive and hot topics in the field of the recycle and gradient utilization of the retired batteries. In this thesis, the electrochemical test-computer simulation technology were employed to investigate the electrochemical behavior of the retired batteries in different temperature, discharge rate and the depth of discharge (DOD). Then the general models for the life prediction and the deterioration degree estimation of the retired batteries were established. In addition, the SOC estimation model of the retired batteries was developed to ensure the batteries in good working order. Through the in-depth study of the above contents, four main conclusions were obtained as follows:
(1) The modern physical and electrochemical test methods were used to examine the safety of the retired batteries. A classification method based on the appearance-capacity selecting method, the voltage-resistance selecting method and the discharge curve selecting method was introduced to select the required retired batteries. Results showed that the batteries with integrated pack, the capacity of1.1～1.0Ah, the resistance of12～12.6mΩ, the open circuit voltage of3.2998～3.3002V, and the good repeatability of discharge curves at large discharging rates would be considered and reused.
(2) The battery life prediction model and match detection method were built, according to the electrochemical testing-computer simulation technology. Electrochemical testing results showed that the number of cycles (N), environmental temperature (T), discharge rate (C) and DOD were the main factors that influence the battery cycle life. With the increase of N, T, C or DOD, the capacity fading of the battery speeded up. When the cycle time was long enough, the influence of DOD could be neglected. Computer simulation analysis indicated that power function model was appropriate to describe the capacity loss for all conditions: Where, cn is the constant.
According to power function model, the working condition and the residual life of any batteries could be successfully predicted with a few electrochemical testing data.
(3) The battery degradation model was set up by combining the internal resistance measurement with the alternating-current (ac) impedance test method. The results showed that the battery internal resistance can be used to characterize the degree of the battery degradation. With the increase of the internal resistance, the batteries deteriorated seriously. The ohmic resistance and polarization resistance enhanced with the increase of T, C or DOD. T was the most important factor that affected the ohmic resistance and polarization resistance of the retired batteries. C is less important than T, and the least is DOD. The resistance model about the sum of the ohmic resistance and polarization resistance can be expressed by power function model: Where, a, b, c are the constants.
(4) Ac impedance measurement, SOC-OCV curve and pulse discharge method were combined and employed to build the SOC estimation model. Ac impedance measurement results showed that both the ohmic resistance and the polarization resistance should be considered when modeling at the full state of charge. While only the polarization resistance should be concerned when modeling at the range of SOC=20～80%. SOC-OCV test results demonstrated that SOC-OCV characteristic of the batteries has nothing to do with T, C, storage time and the charge/discharge status. The value of OCV ascended monotonically with the increasing of SOC, and declined with the decreasing of SOC. multistep pulse discharge test results indicated that the OCV-SOC model could be determined by equivalent circuit model so as to estimate the value of SOC.
(1)采用现代物理测试技术和电化学检测手段对退役电池进行了安全性能检测,并结合外观/容量分选法、电压/内阻分选法及特征曲线法对退役的18650型磷酸铁锂动力电池进行了分选。本论文分选出了外观无破损特征,容量在1.1～1.0Ah,内阻在12～12.6mΩ,开路电压在3.2998-3.3002V,以及大倍率放电时,放电曲线一致性较好的退役电池作为研究对象。
(2)采用电化学检测-计算机模拟联合技术建立了退役电池的寿命预测模型,并提出了一种退役电池的寿命匹配检测方法。电化学检测结果表明：影响退役电池循环寿命的主要因素为循环次数(N),环境温度(T),放电倍率(C)和放电深度(DOD)。退役电池的放电容量随着循环次数的增加逐渐降低,环境温度越高,放电倍率越大,放电深度越深,退役电池的放电容量衰减越快。当循环时间足够长时,放电深度对退役电池寿命的衰减影响不大。计算机模拟结果表明：退役电池的放电容量衰减速率随循环次数,环境温度,放电倍率和放电深度的变化规律符合幂函数模型：
以此为标准模型,采用匹配检测技术可利用较少的容量保持率与循环次数的数据对,准确地预测出任意一颗同规格退役电池当前所处的工况,以及在该工况时退役电池的剩余寿命。
(3)采用内阻-交流阻抗联合检测技术建立了退役电池的劣化失效模型。结果表明：内阻可用来表征退役电池的劣化失效程度。随着内阻的增加,退役电池劣化程度加剧。欧姆内阻和极化内阻的变化与环境温度、放电倍率和放电深度有关。环境温度的升高,放电深度的加深以及放电倍率的增大均会导致内阻加快增长。三种因素对欧姆内阻和极化内阻影响的重要程度为：放电深度<放电倍率<环境温度。欧姆内阻与极化内阻之和与循环次数的关系符合幂函数模型：
(4)采用交流阻抗测定法、SOC-OCV曲线法和脉冲放电法相结合的方法建立了退役电池荷电状态估计模型。交流阻抗测试结果表明：对退役电池进行全荷电态建模时,应同时考虑欧姆内阻和极化内阻对退役电池荷电状态的影响。而对退役电池进行区间段建模时,主要考虑极化内阻对荷电状态的影响。SOC-OCV测试结果表明：退役电池的SOC-OCV特性不受环境温度,放电倍率,储存时间及电池充放电状态的影响,退役电池的OCV随着SOC的递增而单调递增,随着SOC的递减而单调递减。等荷电态多步脉冲放电法测试结果表明：通过所建立退役电池的等效电路模型,可确定退役电池OCV关于SOC的模型,从而估算SOC值。
Abstract:Lithium-ion power batteries retired from electric vehicles (or retired batteries for short) still possess high residual capacities as high as80%of the designed capacities. It is of great significance to reuse retired batteries in a suitable way which could reduce the energy cost, relieve the environmental pollution and make waste profitable. In order to make the best use of the residual capacity of the retired batteries, it is necessary to classify the retired batteries, as well as predict the cycle life, estimate the deterioration degree and calculate the state of charge (SOC) of the selected batteries. This is also one of the attractive and hot topics in the field of the recycle and gradient utilization of the retired batteries. In this thesis, the electrochemical test-computer simulation technology were employed to investigate the electrochemical behavior of the retired batteries in different temperature, discharge rate and the depth of discharge (DOD). Then the general models for the life prediction and the deterioration degree estimation of the retired batteries were established. In addition, the SOC estimation model of the retired batteries was developed to ensure the batteries in good working order. Through the in-depth study of the above contents, four main conclusions were obtained as follows:
(1) The modern physical and electrochemical test methods were used to examine the safety of the retired batteries. A classification method based on the appearance-capacity selecting method, the voltage-resistance selecting method and the discharge curve selecting method was introduced to select the required retired batteries. Results showed that the batteries with integrated pack, the capacity of1.1～1.0Ah, the resistance of12～12.6mΩ, the open circuit voltage of3.2998～3.3002V, and the good repeatability of discharge curves at large discharging rates would be considered and reused.
(2) The battery life prediction model and match detection method were built, according to the electrochemical testing-computer simulation technology. Electrochemical testing results showed that the number of cycles (N), environmental temperature (T), discharge rate (C) and DOD were the main factors that influence the battery cycle life. With the increase of N, T, C or DOD, the capacity fading of the battery speeded up. When the cycle time was long enough, the influence of DOD could be neglected. Computer simulation analysis indicated that power function model was appropriate to describe the capacity loss for all conditions: Where, cn is the constant.
According to power function model, the working condition and the residual life of any batteries could be successfully predicted with a few electrochemical testing data.
(3) The battery degradation model was set up by combining the internal resistance measurement with the alternating-current (ac) impedance test method. The results showed that the battery internal resistance can be used to characterize the degree of the battery degradation. With the increase of the internal resistance, the batteries deteriorated seriously. The ohmic resistance and polarization resistance enhanced with the increase of T, C or DOD. T was the most important factor that affected the ohmic resistance and polarization resistance of the retired batteries. C is less important than T, and the least is DOD. The resistance model about the sum of the ohmic resistance and polarization resistance can be expressed by power function model: Where, a, b, c are the constants.
(4) Ac impedance measurement, SOC-OCV curve and pulse discharge method were combined and employed to build the SOC estimation model. Ac impedance measurement results showed that both the ohmic resistance and the polarization resistance should be considered when modeling at the full state of charge. While only the polarization resistance should be concerned when modeling at the range of SOC=20～80%. SOC-OCV test results demonstrated that SOC-OCV characteristic of the batteries has nothing to do with T, C, storage time and the charge/discharge status. The value of OCV ascended monotonically with the increasing of SOC, and declined with the decreasing of SOC. multistep pulse discharge test results indicated that the OCV-SOC model could be determined by equivalent circuit model so as to estimate the value of SOC.
引文
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