WO2022160147A1 - 基于标准样本及双重-嵌入解耦的电池健康状态估计方法 - Google Patents
基于标准样本及双重-嵌入解耦的电池健康状态估计方法 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000036541 health Effects 0.000 title claims abstract description 15
- 230000032683 aging Effects 0.000 claims abstract description 69
- 238000012546 transfer Methods 0.000 claims abstract description 20
- 230000007246 mechanism Effects 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 3
- 230000008859 change Effects 0.000 claims description 24
- 238000010586 diagram Methods 0.000 claims description 10
- 238000010277 constant-current charging Methods 0.000 claims description 8
- 238000002474 experimental method Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 3
- 238000012886 linear function Methods 0.000 claims description 2
- 238000010168 coupling process Methods 0.000 abstract description 7
- 238000005859 coupling reaction Methods 0.000 abstract description 7
- 230000008878 coupling Effects 0.000 abstract description 6
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000007423 decrease Effects 0.000 description 8
- 239000003792 electrolyte Substances 0.000 description 8
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 7
- 230000000694 effects Effects 0.000 description 7
- 229910052744 lithium Inorganic materials 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000007600 charging Methods 0.000 description 4
- 150000002500 ions Chemical class 0.000 description 4
- 239000012528 membrane Substances 0.000 description 4
- 238000007599 discharging Methods 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 239000011149 active material Substances 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 239000007772 electrode material Substances 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 229910003002 lithium salt Inorganic materials 0.000 description 1
- 159000000002 lithium salts Chemical group 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
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- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007784 solid electrolyte Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
Definitions
- the invention relates to a battery performance estimation method, in particular to a battery health state estimation method under a wide temperature range based on "standard samples” and “double-embedded decoupling", belonging to the technical field of power batteries.
- the battery SOH estimation methods can be roughly divided into two categories: model-driven methods and data-driven methods.
- Model-driven methods all need to first establish a battery model, obtain the mathematical expression of the model, and then solve the model parameters through some intelligent algorithms. This method generally suffers from the problems of computational complexity and low practicability, and data-driven battery aging research does not require the establishment of a battery model, which can effectively overcome these shortcomings.
- Commonly used data-driven methods mainly include artificial neural network, support vector machine, Gaussian regression and so on. These methods are relatively similar, and the purpose of predicting the battery SOH can be achieved only by given the data of the sample battery usage process (such as charging and discharging current, voltage, temperature, etc.) and the corresponding aging state data. However, on the one hand, these methods are prone to overfitting, and on the other hand, the input data preprocessing process takes a lot of time.
- the battery IC curve that is, the differential curve of the battery capacity-voltage curve, converts the voltage platform with violent internal reactions during the charging and discharging process into multiple peaks.
- the mode and mechanism of aging and decay can be deduced and then judged. Battery health status.
- the working conditions of the power battery are more complicated. A large amount of heat will be released in the long-term charging and discharging state. Based on a specific temperature, the temperature will directly affect the internal chemical reaction of the battery. At different temperatures, many basic performance parameters of the battery, such as capacity and internal resistance, will change, and the IC curve will shift to varying degrees, which eventually leads to a large difference in the accuracy of the IC curve for SOH at different temperatures.
- the present invention provides a battery state of health estimation method based on standard samples and double-embedding decoupling.
- the invention realizes the decoupling of the influence of temperature and aging on IC curve characteristics by studying the influence mechanism of temperature and aging on IC curve characteristics, broadens the temperature range of IC curve to solve the battery SOH, and finally based on "standard battery” and “dual-embedded decoupling" "Enables estimation of battery SOH over a wide temperature range.
- Step 1 Extract the significant characteristic peaks of the standard sample, select batteries of the same type with different aging states to conduct constant current charging experiments at the same temperature, and then select any of the above batteries to conduct constant current charging experiments at different temperatures, and collect samples from each group of experiments. Based on the current, voltage and temperature data of the battery, according to the above data, draw the IC curve diagram of the battery in different aging states and the IC curve diagram of the battery measured at different temperatures, and select the significant characteristic peaks of the sample based on the above two IC curve diagrams;
- Step 2 standard sample mechanism parameter calibration, based on double-embedded decoupling to obtain standard sample relation function:
- Relation function 1 when the battery is not aging, the change of charge transfer resistance R ct caused by temperature produces the voltage deviation ⁇ U ct, the relationship function between T and temperature;
- Relationship function 2 the linear relationship function between the voltage deviation ⁇ U SEI, cycle, T and the aging state caused by the influence of the SEI film resistance after eliminating the influence of the charge transfer resistance R ct at the same temperature;
- Relationship function three the linear relationship coefficient at different temperatures and the relationship function between temperatures
- Step 3 Estimate the SOH of the battery to be tested online, conduct a constant current charging experiment for the battery to be tested, collect current, voltage and measured temperature T peak data at the same time, draw the IC curve of the battery to be tested, and extract the significant characteristic peaks corresponding to the significant characteristic peaks of the sample
- the measured voltage value U peak the SOH is solved according to the dual-embedded decoupling relation function based on the standard sample in step 2.
- characteristic peaks are characteristic peaks with obvious relative changes and located near the voltage median point.
- the voltage deviation caused by the change of R ct caused by temperature is the voltage deviation value ⁇ at the significant characteristic peak caused by the temperature of the standard sample battery with SOH of 100%.
- U ct,T The relationship between ⁇ U ct,T and the actual temperature T of battery operation is described by Arrhenius function as:
- a ct , b ct , and cc ct are the temperature fitting parameters related to the charge transfer resistance R ct .
- the standard sample battery with an SOH of 100% is a virtual battery averaged by at least three batteries with an SOH of 100% that meet the factory requirements, and the fitting result is quantitatively evaluated by determining the system R 2 , and the specific evaluation formula is:
- V i represents the voltage measurement
- N represents the amount of data
- the voltage deviation ⁇ U SEI, cycle, T and the aging state of the voltage deviation caused by the influence of the SEI film resistance after eliminating the influence of the charge transfer resistance R ct can be described as :
- k SEI,T , b SEI,T are linear fitting parameters related to the SEI film resistance.
- relationship function 3 in the step 2 the relationship function between the linear relationship coefficient at different temperatures and the actual temperature T of the battery operation can be described as:
- a SEI , B SEI , and C SEI are the temperature fitting parameters related to the SEI film resistance.
- the measured temperature T peak is used as the input to obtain the linear relationship coefficient at the current temperature
- the present invention expounds the dual coupling relationship between the influence of temperature and aging on the characteristic peak voltage of the IC curve from the perspective of impedance characteristic mechanism analysis, and proposes to eliminate the voltage offset caused by the R ct internal resistance that is most sensitive to temperature based on the "standard sample".
- the first layer is decoupled; further, for the voltage deviation caused by the internal resistance of ⁇ R SEI, cycle, and T due to the influence of aging and temperature coupling, the linear relationship coefficient k SEI, T is only related to temperature when the overall linear relationship of aging is set.
- the method realizes the embedded decoupling, and finally realizes the battery SOH estimation based on the IC curve characteristics under a wide temperature range based on the double decoupling of the first layer decoupling and the embedded decoupling; the invention not only inherits the battery SOH estimation based on the IC curve characteristics. It has the characteristics of high efficiency, and overcomes the defect of the unclear mechanism of the data-driven method. From the perspective of mechanism analysis, it solves the problem of low accuracy of the previous IC curve to solve the battery SOH in a wide temperature range.
- Fig. 1 is the IC curve diagram of the battery of different aging states of the present invention
- Fig. 2 is a certain battery IC curve diagram under different temperatures of the present invention
- Fig. 3 is the relation diagram of the second characteristic peak voltage and temperature of the standard battery of the present invention.
- Fig. 5 is the aging linear relation coefficient and temperature relation diagram of the present invention.
- FIG. 6 is a flowchart of the estimation method of the present invention.
- the invention proposes a battery state of health estimation method under a wide temperature range based on "standard sample” and "dual-embedded decoupling", which mainly includes: impedance characteristic analysis, dual-embedded decoupling of temperature and aging, IC curve solution and Characteristic analysis, online estimation of battery SOH based on "standard sample”.
- the battery impedance mainly includes the bulk resistance (R bulk ) of the electrolyte, the separator and the electrode, the SEI (solid electrolyte interface film) membrane resistance (R SEI ) and the charge transfer resistance (R ct ) between the electrode and the electrolyte.
- R bulk bulk resistance
- SEI solid electrolyte interface film
- R ct charge transfer resistance
- R R bulk +R SEI +R ct (1)
- R is the battery impedance
- R bulk is the bulk resistance
- R SEI is the SEI film resistance
- R ct is the charge transfer resistance
- Battery aging causes impedance changes, and the aging mode mainly includes loss of lithium inventory (LLI) and loss of active material (LAM).
- the capacity loss rate of lithium-ion batteries for electric vehicles is generally less than 20%.
- the formation and growth of the SEI film between the electrode and the electrolyte in this use range lead to the loss of cyclable lithium.
- the consumption of circulating lithium is proportional. Considering that the radius of the active particles is much larger than the thickness of the SEI film produced thereon, it can be considered that the area of the SEI film covering the surface of the active particles does not change at the early stage of aging, and the consumption of cyclable lithium is proportional to the thickness of the SEI film. changes, and the increase in SEI film thickness is proportional to the increase in R SEI resistance.
- the resistance change of R SEI and the degree of aging can be approximately considered as a linear relationship, which shows that the IC curve as a whole shifts to the right at a certain temperature.
- the bulk resistance R bulk and the charge transfer resistance R ct of the battery remain basically unchanged under the condition of constant temperature. Therefore, the impedance change due to aging is as follows:
- ⁇ R cycle ⁇ R bulk,cycle + ⁇ R SEI,cycle + ⁇ R ct,cycle ⁇ R SEI,cycle (2)
- ⁇ R bulk,cycle the increase of battery bulk resistance due to aging changes
- the electrolyte of a lithium battery is a lithium salt electrolyte and an organic solvent, and the electrolyte conduction in the electrolyte mainly depends on the movement of ions.
- the ion activity in the battery decreases, the ion migration speed decreases, and the charge transfer resistance Rct increases, and the impedance is more sensitive to low temperature.
- the IC curve it can be seen that the lateral shift of the curve is smaller at high temperature, and the curve shift is larger at low temperature.
- temperature not only affects the ionic conductivity in the electrolyte, but also affects the ionic conductivity in the SEI membrane.
- the ionic conductivity in the SEI membrane also increases, which is manifested as a decrease in the internal impedance of the SEI membrane. It can be seen that the effects of temperature and aging on the impedance of the SEI film are coupled with each other. Relatively speaking, the bulk resistance is related to the battery body and is not sensitive to temperature. Therefore, the total resistance change of the battery due to temperature change can be expressed as:
- ⁇ R T ⁇ R bulk,T + ⁇ R SEI,T + ⁇ R ct,T ⁇ R SEI,T + ⁇ R ct,T (3)
- ⁇ RT The increment of total battery impedance due to temperature change
- ⁇ R ⁇ R SEI,T + ⁇ R SEI,cycle + ⁇ R ct,T (4)
- ⁇ R SEI,cycle,T The increase in resistance caused by the coupling of temperature and aging changes.
- the temperature-induced charge transfer resistance R ct can be described by the Arrhenius function.
- the resistance change of RSEI and the degree of aging can be approximately considered to be linear; while at different temperatures, RSEI is related to the temperature change, but because the SEI film resistance thickness is about 20 ⁇ 120nm, the relative ion migration path is shorter, which is more sensitive to aging. Therefore, when the ⁇ R SEI,cycle,T caused by the coupling of aging and temperature is in line with the overall linear relationship of aging, its linear relationship coefficient k SEI,T can be described by the Arrhenius function. Therefore, formula (5) can be further described as:
- T The actual temperature of battery operation
- a ct , b ct , c ct temperature fitting parameters related to charge transfer resistance
- a SEI ,B SEI ,C SEI Temporal fitting parameters related to SEI film resistance.
- the IC curve solution in the above-mentioned IC curve solution and characteristic analysis can be solved by conventional numerical differentiation, or by polynomial fitting and probability density function method, and can also be solved by referring to the method in the invention patent (CN 109632138 A).
- the present invention only provides an example, and does not specifically limit the solution method.
- Figure 1 is the IC curve of a battery in different aging states
- Figure 2 is the IC curve of a battery at different temperatures. It can be seen from Figure 1 that with the intensification of aging, the characteristic peak of the IC curve shifts laterally to the right, and the peak height decreases; it can be seen from Figure 2 that as the temperature increases, the characteristic peak of the IC curve shifts laterally to the left shift, and the peak height rises.
- the voltage shift of the battery IC curve can be approximately attributed to the effect of impedance. The decrease in temperature and the aggravation of aging cause the increase of impedance and the right shift of the lateral voltage.
- the second characteristic peak has a relatively obvious change, and it is located near the voltage median point. Therefore, the present invention selects the second characteristic peak as a significant characteristic peak to study the correlation between its characteristic change and aging/temperature sex.
- the above-mentioned online estimation of battery SOH based on the "standard sample” is mainly divided into a mechanism parameter calibration stage and an online estimation stage.
- the above-mentioned mechanism parameter calibration stage first, carry out different temperature and aging tests, and measure the battery voltage, current and temperature data; then obtain the IC curve; finally, use the second characteristic peak voltage value of the IC curve to calculate the mechanism parameters by formula (7).
- Calibration mainly calibrate three relationships: 1) When the battery is not aging, the voltage offset caused by the change of R ct caused by temperature; 2) At the same temperature, the linear relationship between the voltage deviation and the aging state after eliminating the influence of R ct ; 3) Different temperatures The relationship between the linear relationship coefficient and temperature below.
- V i represents the voltage measurement
- V fitting value represents the average value of the measured voltage
- N represents the amount of data.
- R2 represents the closer R2 is to 1 , the better the fit.
- R 2 0.996, it is obvious that the Arrhenius equation can describe the voltage shift caused by temperature well.
- the measured temperature value is brought into the fitting function to obtain the voltage offset value of the IC curve caused by the temperature change, and then the voltage offset caused by the corresponding temperature is subtracted from the characteristic peak measured voltage value. value, the effect of R ct impedance caused by temperature changes can be eliminated.
- the above calibration relationship 2) the linear relationship between the voltage deviation and the aging state after eliminating the influence of Rct : the present invention selects 3 single cells with SOH of 100%, 88% and 82% respectively to analyze the linear relationship between the voltage deviation and the aging state.
- SOH the linear relationship between the voltage deviation and the aging state after eliminating the influence of Rct
- the present invention selects 3 single cells with SOH of 100%, 88% and 82% respectively to analyze the linear relationship between the voltage deviation and the aging state.
- SOH the number of batteries and SOH selection is not limited to this.
- the voltage deviation is the result of eliminating the effect of the Rct impedance change caused by the temperature change, as shown in Figure 4.
- the battery is in the early stage of aging, its aging state and impedance are approximately linear, but the slight difference in practice may mainly come from the simplified treatment of the complex composition of the SEI film.
- the corresponding linear fitting curve is also given in Figure 4.
- the battery management system (BMS) is used to collect the voltage, current and temperature data during the charging process, and the battery IC curve is solved to find the second characteristic peak voltage.
- BMS battery management system
- the temperature is used as the input to obtain the voltage deviation caused by R ct
- the standard voltage obtained by subtracting the temperature and voltage deviation from the measured voltage of the second characteristic peak is obtained.
- the estimation formula is as follows:
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Abstract
本发明公开了一种基于标准样本及双重-嵌入解耦的电池健康状态估计方法，包括提取标准样本显著特征峰，标准样本机理参数标定，待测电池SOH在线估计等步骤。有益效果：本发明从阻抗特征机理分析角度阐述了温度和老化对于IC曲线特征峰电压影响双重耦合关系，提出了基于"标准样本"消除对温度最为敏感的电荷转移电阻引起电压偏移，实现首层解耦，进一步，设定受老化和温度耦合影响的SEI膜电阻整体符合老化线性关系下，该线性关系系数只与温度相关的方式实现嵌入解耦；本发明不仅承袭了基于IC曲线特征估计电池SOH高效率的特征，并从机理分析角度解决了以往IC曲线求解电池SOH在宽温度范围内的精度不高的问题。
Description
本发明涉及一种电池性能的估计方法，特别涉及一种基于“标准样本”及“双重-嵌入解耦”的宽温度范围下电池健康状态的估计方法，属于动力电池技术领域。
近年来电动汽车发展迅猛，作为电动汽车主要动力源的动力电池越来越受到人们关注。其中，锂离子电池以其较高的比能量和比功率被普遍应用于电动汽车中。然而，随着循环次数增加，电池电极活性材料会逐渐消耗，最终导致电池老化及健康状态(State of Health,SOH)降低。随着电池不断老化，内阻升高与容量降低会造成电池性能下降，电动汽车故障发生率也会随之增加。因此，准确估计电池SOH能够保证电池使用安全性，提高电动汽车使用性能。
电池SOH估计方法大体上可以分为两大类：基于模型驱动的方法和基于数据驱动的方法。基于模型驱动的方法都需要首先建立电池模型，得到模型数学表达式，然后通过一些智能算法求解模型参数。该方法普遍存在计算复杂，实用性低的问题，而基于数据驱动的电池老化研究不需要建立电池模型，可以有效克服这些不足。常用的数据驱动方法主要有人工神经网络，支持向量机，高斯回归等。这些方法都比较类似，只需要给定样本电池使用过程的数据(如充放电电流、电压和温度等)及对应的老化状态数据，便可达到预测电池SOH的目的。但是一方面这些方法容易陷入过拟合状况，另一方面在输入数据预处理环节需要耗费大量时间。
近年来，研究者倾向于基于容量增量法(Incremental Capacity Analysis,ICA)的电池健康状态估计。电池IC曲线，即电池容量-电压曲线的微分曲线，将充放电过程中内部反应剧烈的电压平台转化为多个峰，通过对曲线特征的分析，可推导得到老化衰退的模式和机理，进而判断电池健康状态。实际使用过程中动力电池工况较为复杂，长时间的充放电状态会释放出大量热量，积聚的热量难以快速散发到外界，最终造成电池温度远高于外界温度，但以往的研究大部分都是基于某一特定温度下进行，而温度会直接影响到电池内部化学反应。不同温度下电池许多基本性能参数如容量和内阻等都会发生变化，IC曲线也会发生不同程度偏移，最终导致IC曲线求SOH在不同温度下准确度相差较大。
发明内容
发明目的：针对现有技术中存在的问题，本发明提供了一种基于标准样本及双重-嵌入解耦的电池健康状态估计方法。本发明通过研究温度及老化对IC曲线特征影响机制，实现温度及老化对IC曲线特征影响的解耦，拓宽IC曲线求解电池SOH的温度范围，最终基于“标准 电池”及“双重-嵌入解耦”实现宽温度范围下电池SOH的估计。
技术方案：一种基于标准样本及双重-嵌入解耦的电池健康状态估计方法，包括以下步骤：
步骤一、提取标准样本显著特征峰，选取同一类型不同老化状态的电池在同一温度下进行恒流充电实验，再选择上述任意一块电池在不同温度下进行恒流充电实验，分别采集各组实验中电池的电流、电压和温度数据，根据上述数据分别绘制不同老化状态电池的IC曲线图和不同温度下所测电池的IC曲线图，结合上述两张IC曲线图选定样本显著特征峰；
步骤二、标准样本机理参数标定，基于双重-嵌入解耦得到标准样本关系函数：
关系函数一，电池未老化时，由温度引起电荷转移电阻R
ct变化产生电压偏差△U
ct,T与温度的关系函数；
关系函数二，同一温度下，消除电荷转移电阻R
ct影响后由SEI膜电阻影响引起的电压偏差△U
SEI,cycle,T与老化状态的线性关系函数；
关系函数三，不同温度下的线性关系系数和温度间的关系函数；
步骤三、待测电池SOH在线估计，对待测电池进行恒流充电实验，同时采集电流、电压和实测温度T
peak数据，绘制待测电池的IC曲线，提取与样本显著特征峰对应的显著特征峰的实测电压值U
peak，根据步骤二中基于标准样本的双重-嵌入解耦的关系函数求解SOH。
进一步，所述显著特征峰是相对变化明显且位于电压中值点附近的特征峰。
进一步，所述步骤二中的关系函数一，电池未老化时，由温度引起R
ct变化产生的电压偏差为SOH为100％的标准样本电池由温度引起的在显著特征峰处电压偏差值△U
ct,T；△U
ct,T与电池工作的实际温度T的关系用Arrhenius函数描述为：
△U
ct,T＝a
ctexp(b
ct/T)+c
ct
式中a
ct，b
ct，c
ct为与电荷转移电阻R
ct有关的温度拟合参数。
进一步，所述SOH为100％的标准样本电池为至少三块满足出厂要求的SOH为100％电池平均后的虚拟电池，且通过决定系统R
2定量评估拟合结果，具体评估公式为：
其中，V
i代表电压测量值，
代表电压拟合值，
代表测量电压平均值，N代表数据量，R
2越接近1，表明拟合度越优。
进一步，所述步骤二中关系函数二，同一温度下，消除电荷转移电阻R
ct影响后由SEI膜电阻影响引起的电压偏差△U
SEI,cycle,T与老化状态的线性关系函数可以描述为：
△U
SEI,cycle,T＝k
SEI,TSOH+b
SEI,T
式中k
SEI,T,b
SEI,T为与SEI膜电阻有关的线性拟合参数。
进一步，获得所述消除R
ct影响后的电压偏差与老化状态的线性关系函数的方法为通过选取至少三块SOH为100％-80％之间的已知SOH值的单体电池样本，再根据所选取的单体电池样本拟合出电压偏差△U
SEI,cycle,T与老化状态SOH线性函数关系；得出线性拟合参数k
SEI,T与b
SEI,T的关系；所述k
SEI,T＝-0.01b
SEI,T，则△U
SEI,clcye,T＝-0.01b
SEI,TSOH+b
SEI,T。
进一步，所述步骤二中关系函数三，不同温度下的线性关系系数和电池工作的实际温度T间的关系函数可以描述为：
k
SEI,T＝kb
SEI,T＝A
SEIexp(B
SEI/T)+C
SEI
式中A
SEI,B
SEI,C
SEI为与SEI膜电阻有关的温度拟合参数。
进一步，所述步骤三中SOH的求解方法为：
首先、以实测温度T
peak作为输入并且根据步骤二中△U
ct,T与温度的关系得到△U
ct,T；
其次、根据△U
SEI,cycle,T＝U
peak-△U
ct,T，以实测电压值U
peak作为输入得到△U
SEI,cycle,T，
然后、根据步骤二中不同温度下的线性关系系数和温度间的关系以实测温度T
peak作为输入获得当前温度下线性关系系数，
最后、根据△U
SEI,cycle,T与老化状态的线性关系函数以△U
SEI,cycle,T和当前温度下线性关系系数获得所测电池的SOH估计结果。
有益效果：本发明从阻抗特征机理分析角度阐述了温度和老化对于IC曲线特征峰电压影响双重耦合关系，提出了基于“标准样本”消除对温度最为敏感的R
ct内阻引起电压偏移，实现首层解耦；进一步，针对受老化和温度耦合影响△R
SEI,cycle,T内阻引起电压偏差，设定整体符合老化线性关系的情况下，该线性关系系数k
SEI,T只与温度相关的方式实现嵌入解耦，最终基于首层解耦与嵌入解耦相结合的双重解耦实现了宽温度范围下基于IC曲线特征的电池SOH估计；本发明不仅承袭了基于IC曲线特征估计电池SOH高效率的特征，并克服了数据驱动型方法机理不明确的缺陷，从机理分析角度解决了以往IC曲线求解电池SOH在宽温度范围内的精度不高的问题。
图1为本发明不同老化状态电池IC曲线图；
图2为本发明不同温度下某电池IC曲线图；
图3为本发明标准电池第二特征峰电压与温度关系图；
图4为本发明不同温度下老化状态与电压偏移关系图；
图5为本发明老化线性关系系数与温度关系图；
图6为本发明的估计方法的流程图。
下面结合附图以及具体实施例对本发明作进一步的说明，但本发明的保护范围并不限于此。
本发明提出了一种基于“标准样本”及“双重-嵌入解耦”的宽温度范围下电池健康状态估计方法，主要包括：阻抗特征分析，温度与老化双重-嵌入解耦，IC曲线求解及特征分析，基于“标准样本”的电池SOH在线估计。
上述阻抗特性分析中电池阻抗主要包含电解质、隔膜、电极的体电阻(R
bulk)，电极与电解质间的SEI(solid electrolyte interface film)膜电阻(R
SEI)和电荷转移电阻(R
ct)，具体表达式如下：
R＝R
bulk+R
SEI+R
ct (1)
其中，R为电池阻抗，R
bulk表示体电阻，R
SEI为SEI膜电阻，R
ct表示电荷转移电阻。
电池老化引起阻抗变化，老化模式主要包含可循环锂损失(loss of lithium inventory,LLI)与活性材料损失(loss of active material,LAM)。电动汽车用锂离子电池容量损失率一般小于20％，在此使用区间内电极和电解液间的SEI膜的形成、生长导致可循环锂损失占主导，SEI膜生成过程中其体积的增加与可循环锂的消耗量成正比关系。考虑到活性粒子的半径远远大于其上所生产的SEI膜的厚度，可认为在老化初期覆盖在活性粒子表面的SEI膜面积并不发生改变，可循环锂的消耗量正比于SEI膜厚度的变化，且SEI膜厚度增加正比于R
SEI阻值增加。由此可知，R
SEI阻值变化和老化程度(可循环锂损失)可近似认为呈线性关系，表现为在某一温度下IC曲线整体向右偏移。同时，在老化的初期，温度恒定的情况下电池体电阻R
bulk和电荷转移电阻R
ct基本保持不变。因此，因老化引起的阻抗变化如下：
△R
cycle＝△R
bulk,cycle+△R
SEI,cycle+△R
ct,cycle≈△R
SEI,cycle (2)
△R
cycle——由于老化变化引起电池阻抗的增量；
△R
bulk,cycle——由于老化变化引起电池体电阻的增量；
△R
SEI,cycle——由于老化变化引起电池SEI膜电阻的增量；
△R
ct,cycle——由于老化变化引起电荷转移电阻的增量。
锂电池的电解液为锂盐电解质和有机溶剂，该电解液中电解质导电主要依靠离子运动。在一定的温度范围内，随着温度的降低，电池内离子活性较低，离子迁移速度降低，电荷转移电阻R
ct增大，且该阻抗对低温更为敏感。在IC曲线中直观表现为高温下曲线横向偏移较小，且低温下曲线偏移较大。另一方面，温度不仅影响电解液内离子电导率，而且也会影响SEI膜内离子电导率，随着温度升高，SEI膜内离子电导率同样升高，表现为SEI膜内阻抗的减少。由此可知，温度和老化对SEI膜的阻抗影响互相耦合。相对而言，体电阻与电池本体有关，对温度并不敏感。因此，温度变化引起的电池总电阻变化可表示为：
△R
T＝△R
bulk,T+△R
SEI,T+△R
ct,T≈△R
SEI,T+△R
ct,T (3)
△R
T——由于温度变化引起电池总阻抗的增量；
△R
bulk,T——由于温度变化引起电池体电阻的增量；
△R
SEI,T——由温度变化引起SEI膜电阻的增量；
△R
ct,T——由于温度变化引起电荷转移电阻的增量。
结合式(2)和式(3)可知，由老化及温度变化引起的电池总阻抗的增加可描述为：
△R＝△R
SEI,T+△R
SEI,cycle+△R
ct,T (4)
从上述分析可知，电池电荷转移电阻R
ct仅受温度影响而对老化并不敏感；但SEI膜电阻R
SEI既受老化影响又与温度变化关联，因此，式(4)可进一步表示为：
△R＝△R
SEI,cycle,T+△R
ct,T (5)
△R
SEI,cycle,T——由温度和老化变化耦合引起电阻的增量。
上述温度与老化双重-嵌入解耦中认为由温度引起的电荷转移内阻R
ct可用Arrhenius函数描述。同时，同一温度下，R
SEI阻值变化和老化程度(可循环锂损失)可近似认为呈线性关系；而在不同温度下，R
SEI又与温度变化关联，但由于SEI膜电阻厚度约20～120nm，相对离子迁移路径较短，其对老化更为敏感。因此，由老化和温度耦合引起的△R
SEI,cycle,T在整体符合老化线性关系的情况下，其线性关系系数k
SEI,T又可以采用Arrhenius函数描述。因此，式(5)可进一步描述成：
T——电池工作的实际温度；
k
SEI,T,b
SEI,T——与SEI膜电阻有关的线性拟合参数；
a
ct，b
ct，c
ct——与电荷转移电阻有关的温度拟合参数；
A
SEI,B
SEI,C
SEI——与SEI膜电阻有关的温度拟合参数.
结合式(6)，恒流充电阶段电池IC曲线电压偏移可描述为：
△U
ct,T——由温度影响转移电阻引起的电压偏差；
△U
SEI,cycle,T——由SEI膜电阻影响引起的电压偏差。
由式(7)可知，针对不同温度下电池，可尝试先消除对温度最为敏感的R
ct内阻引起电压偏移，进而得到受老化和温度耦合影响△R
SEI,cycle,T内阻引起电压偏差。利用线性关系描述该电压偏差，并采用Arrhenius方程描述相关线性关系系数，进一步得到单独因△R
SEI,cycle内阻引起的电压偏差，进而实现温度及老化对IC曲线特征的双重-嵌入解耦。
上述IC曲线求解及特征分析中IC曲线求解可以通过常规数值微分求解，也可以通过多项式拟合及概率密度函数法求解，也可参考发明专利(CN 109632138 A)中方法进行求解。本发明仅给出示例，不对求解方法进行具体限定。
上述IC曲线特征分析：图1为不同老化状态电池IC曲线，图2为不同温度下某电池IC曲线。从图1中可看出随着老化程度的加剧，IC曲线特征峰横向向右偏移，且峰值高度下降；从图2中可以看出随着温度升高，IC曲线特征峰横向向左偏移，且峰值高度上升。在恒流充电阶段，电池IC曲线电压偏移可近似归因于阻抗的影响，温度降低及老化程度加剧引起阻抗增大进而导致横向电压的右移。在IC曲线三个主要特征峰中，第二特征峰相对变化明显，且其位于电压中值点附近，因此，本发明选取第二特征峰作为显著特征峰来研究其特征变化与老化/温度关联性。
上述基于“标准样本”的电池SOH在线估计主要分为机理参数标定阶段及在线估计阶段。
上述机理参数标定阶段：首先，进行不同温度和老化试验，实测电池电压、电流及温度数据；然后求取IC曲线；最后，利用IC曲线第二特征峰电压值，通过式(7)进行机理参数标定，主要标定三个关系1)电池未老化时，温度引起R
ct变化产生的电压偏移；2)同一温 度下，消除R
ct影响后的电压偏差与老化状态的线性关系；3)不同温度下的线性关系系数和温度间的关系。
上述标定关系1)电池未老化时，温度引起R
ct变化产生的电压偏移需基于“标准样本”实现，“标准样本”为至少三块满足出厂要求的电池(SOH为100％)平均后的虚拟电池，本发明所用电池基本参数如表1所示，当然这里只是为了阐述相关理论，针对电池数量及电池基本参数并不仅仅局限于此。不同温度下标准样本电池的SOH为100％，即电池并未老化，此时温度引起第二特征峰电压的偏移理论上是由电荷转移电阻R
ct阻抗变化影响。图3中统计了不同温度下标准样本电池第二特征峰电压，同时还给出了基于Arrhenius函数拟合结果。表2给出标准样本电池由温度引起电压偏移Arrhenius方程拟合参数。为了定量的评估拟合结果的优劣，本发明引入决定系数R
2，具体可表示为：
其中，V
i代表电压测量值，
代表电压拟合值，
代表测量电压平均值，N代表数据量。R
2越接近1，表明拟合度越优。当然这不是发明的重点，只是引入通用指标阐述拟合的效果。从表中可以看R
2＝0.996，显然Arrhenius方程能够较好描述了由温度引起的电压偏移。
针对不同老化状态的电池，将实测的温度值带入该拟合函数便可得到因温度变化引起的IC曲线电压偏移值，然后将特征峰测量电压值减去对应的温度引起的电压偏移值，便可消除温度变化导致的R
ct阻抗的影响。
表1 电池基本参数
表2 由温度引起电压偏移Arrhenius方程拟合参数
上述标定关系2)消除R
ct影响后的电压偏差与老化状态的线性关系：本发明选取SOH分别是100％，88％和82％的3块单体电池来分析电压偏差与老化状态线性关系。当然这里只是为了阐述相关理论，有关电池数量及SOH选择并不仅仅局限于此。其中电压偏差是消除温度变化导致的R
ct阻抗变化的影响后的结果，如图4所示。电池处于老化初期时，其老化状态与阻抗近似呈线性关系，但实际中存在微小的差别主要可能来自于对于SEI膜复杂成分的简化处理。图4中还给出了相应的线性拟合曲线。表3中给出了不同温度下的线性拟合系数。 从表可以看出，k
SEI,T与b
SEI,T具有一定的关系，k
SEI,T＝-0.01b
SEI,T，从而可以对上述线性拟合函数进一步简化如下：
△U
SEI,clcye,T＝-0.01b
SEI,TSOH+b
SEI,T (9)
表3 不同温度下的线性拟合系数
上述标定关系3)不同温度下的线性关系系数和温度间的关系：从上述温度与老化双重-嵌入解耦分析中可知线性关系系数k
SEI,T，b
SEI,T与温度符合Arrhenius方程，为此，对表3中的系数b
SEI,T进行拟合，具体结果如图5所示，其中拟合参数如表4所示，决定系数R
2＝0.994，接近1，表明该曲线具有较好的拟合度。
表4 线性关系系数b
SEI,T拟合参数
上述在线估计阶段：利用电池管理系统(Battery Management System,BMS)采集充电过程中的电压、电流和温度数据，求解电池IC曲线寻求第二特征峰电压。基于采集的第二特征峰电压及机理参数标定阶段得到的离线标定函数，首先，将温度作为输入求取由R
ct引起电压偏差，得到第二特征峰实测电压减去温度电压偏差后的标准电压值；接着，将温度带入计算线性关系系数k
SEI,T和b
SEI,T；最终基于线性关系系数和标准电压值间标定的线性方程求解SOH，实现宽温度范围下电池SOH估计，具体流程如图6所示。这里为了阐述方面，举例说明一下。
将第二特征峰的实测电压值U
peak和实测温度T
peak作为输入，将电池SOH作为输出，估计式如下：
求取不同温度下的1～3号电池SOH，估计SOH结果如表5所示。从表中可以看出求取SOH的最大绝对误差为4.03％，估计SOH为88％的2号电池平均绝对误差为2.41％，估计SOH为82％的3号电池平均绝对误差为1.21％，表明所提方法可实现宽温度范围下的电池SOH估计。
表5 不同老化电池估计SOH
所述实施例为本发明的优选的实施方式，但本发明并不限于上述实施方式，在不背离本发明的实质内容的情况下，本领域技术人员能够做出的任何显而易见的改进、替换或变型均属于本发明的保护范围。
Claims (8)
- 一种基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于，包括以下步骤：步骤一、提取标准样本显著特征峰，选取同一类型不同老化状态的电池在同一温度下进行恒流充电实验，再选择上述任意一块电池在不同温度下进行恒流充电实验，分别采集各组实验中电池的电流、电压和温度数据，根据上述数据分别绘制不同老化状态电池的IC曲线图和不同温度下所测电池的IC曲线图，结合上述两张IC曲线图选定样本显著特征峰；步骤二、标准样本机理参数标定，基于双重-嵌入解耦得到标准样本关系函数：关系函数一，电池未老化时，由温度引起电荷转移电阻R ct变化产生电压偏差△U ct,T与温度的关系函数；关系函数二，同一温度下，消除电荷转移电阻R ct影响后由SEI膜电阻影响引起的电压偏差△U SEI,cycle,T与老化状态的线性关系函数；关系函数三，不同温度下的线性关系系数和温度间的关系函数；步骤三、待测电池SOH在线估计，对待测电池进行恒流充电实验，同时采集电流、电压和实测温度T peak数据，绘制待测电池的IC曲线，提取与样本显著特征峰对应的显著特征峰的实测电压值U peak，根据步骤二中基于标准样本的双重-嵌入解耦的关系函数求解SOH。
- 根据权利要求1所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：所述显著特征峰是相对变化明显且位于电压中值点附近的特征峰。
- 根据权利要求1所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：所述步骤二中的关系函数一，电池未老化时，由温度引起R ct变化产生的电压偏差为SOH为100％的标准样本电池由温度引起的在显著特征峰处电压偏差值△U ct,T；△U ct,T与电池工作的实际温度T的关系用Arrhenius函数描述为：△U ct,T＝a ctexp(b ct/T)+c ct式中a ct，b ct，c ct为与电荷转移电阻R ct有关的温度拟合参数。
- 根据权利要求3所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：所述SOH为100％的标准样本电池为至少三块满足出厂要求的SOH为100％电池平均后的虚拟电池，且通过决定系统R 2定量评估拟合结果，具体评估公式为：
其中，V i代表电压测量值，代表电压拟合值，
代表测量电压平均值，N代表数据量，R 2越接近1，表明拟合度越优。
- 根据权利要求1所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：所述步骤二中关系函数二，同一温度下，消除电荷转移电阻R ct影响后由SEI膜电阻影响引起的电压偏差△U SEI,cycle,T与老化状态的线性关系函数可以描述为：△U SEI,cycle,T＝k SEI,TSOH+b SEI,T式中k SEI,T,b SEI,T为与SEI膜电阻有关的线性拟合参数。
- 根据权利要求5所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：获得所述消除R ct影响后的电压偏差与老化状态的线性关系函数的方法为通过选取至少三块SOH为100％-80％之间的已知SOH值的单体电池样本，再根据所选取的单体电池样本拟合出电压偏差△U SEI,cycle,T与老化状态SOH线性函数关系；得出线性拟合参数k SEI,T与b SEI,T的关系；所述k SEI,T＝-0.01b SEI,T，则△U SEI,clcye,T＝-0.01b SEI,TSOH+b SEI,T。
- 根据权利要求1所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于：所述步骤二中关系函数三，不同温度下的线性关系系数和电池工作的实际温度T间的关系函数可以描述为：k SEI,T＝kb SEI,T＝A SEIexp(B SEI/T)+C SEI式中A SEI,B SEI,C SEI为与SEI膜电阻有关的温度拟合参数。
- 根据权利要求1所述的基于标准样本及双重-嵌入解耦的电池健康状态估计方法，其特征在于，所述步骤三中SOH的求解方法为：首先、以实测温度T peak作为输入并且根据步骤二中△U ct,T与温度的关系得到△U ct,T；其次、根据△U SEI,cycle,T＝U peak-△U ct,T，以实测电压值U peak作为输入得到△U SEI,cycle,T，然后、根据步骤二中不同温度下的线性关系系数和温度间的关系以实测温度T peak作为输入获得当前温度下线性关系系数，最后、根据△U SEI,cycle,T与老化状态的线性关系函数以△U SEI,cycle,T和当前温度下线性关系系数获得所测电池的SOH估计结果。
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