CN103135065A - Iron phosphate lithium battery electric quantity detecting method based on feature points - Google Patents

Iron phosphate lithium battery electric quantity detecting method based on feature points Download PDF

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CN103135065A
CN103135065A CN 201310031858 CN201310031858A CN103135065A CN 103135065 A CN103135065 A CN 103135065A CN 201310031858 CN201310031858 CN 201310031858 CN 201310031858 A CN201310031858 A CN 201310031858A CN 103135065 A CN103135065 A CN 103135065A
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battery
soc
value
step
current
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CN 201310031858
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陈达腾
杨海马
杨晖
陈文良
陈木辉
郑鑫淼
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文创太阳能(福建)科技有限公司
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The invention relates to an iron phosphate lithium battery electric quantity detecting method based on feature points. Sectional type Kalman filtering is carried out through selecting the feature points. Kalman prediction is adopted at the feature points and an ampere-hour method is utilized to achieve integrating processing among the feature points, so that the problem that a system on chip (SOC) prediction error in a flat area is large due to the fact that voltage collecting accuracy and current collecting accuracy are low in iron phosphate lithium battery system on chip (SOC) measuring is solved, and the accuracy and the stability in iron phosphate lithium battery SOC estimation are improved. The iron phosphate lithium battery electric quantity detecting method based on the feature points has certain application value, and can accurately provides the cruising ability of the battery.

Description

基于特征点的磷酸铁裡电池电量检测方法技术领域[0001 ] 本发明涉及一种电池检测技术,特别涉及一种基于特征点的磷酸铁锂电池电量检测方法。 Based on the feature point in the iron phosphate battery detection TECHNICAL FIELD [0001] The present invention relates to a battery detection technology, and particularly relates to lithium iron phosphate capacity detecting method based on feature points. 背景技术[0002] 磷酸铁锂电池因其寿命长、安全性能好、成本低等优点越来越成为一种理想的动力电池。 [0002] Lithium iron phosphate because of its long life, safety, and low cost is increasingly becoming an ideal battery power. 电池电量的SOC值(State of Charge,电荷状态)作为电池主要的特征参数,是近年来电池管理系统研究的重点和难点之一。 Battery SOC value (State of Charge, state of charge) of the battery as the main characteristic parameters, one is the focus of research in recent years and battery management system difficult. 传统的电池电量检测技术主要包括:开路电压法、安时法、查表法等,但这些方法普遍存在误差较大、可靠性较低的问题。 Traditional battery detection technologies include: open circuit voltage, when the security process, look-up table method and the like, but these methods common error is large, the problem of low reliability. 针对这些不足,采用卡尔曼滤波的磷酸铁锂电池SOC估计逐渐成为该领域研究的热点。 To solve these problems, the Kalman filter estimates the battery SOC lithium iron phosphate has become a hot research in this field. 由于磷酸铁锂电池放电过程中,电压-SOC曲线中大部分区域过于平坦,电压变化皆小于0.3V,受制于电压的采集精度,传统的卡尔曼滤波算法误差依然较大。 Since the discharging process of lithium iron phosphate, -SOC voltage curve is too flat in most areas, the voltage change are less than 0.3V, the voltage of the subject acquisition accuracy, conventional Kalman filter error is still large. [0003]目前与本发明相似的技术有:1.程艳青等发表在《杭州电子科技大学学报》的论文“基于卡尔曼滤波的电动汽车剩余电量估计” ;2.林成涛等发表在《清华大学学报》的论文“用改进的安时计量法估计电动汽车动力电池SOC” ;3.杭州电子科技大学,何志伟等的发明专利《一种基于采样点卡尔曼滤波的电池剩余电量估计方法》(CN101598769B)和《一种基于组合采样点卡尔曼滤波的电池剩余电量估计方法》(CN101604005B) ;4.重庆大学,邓力等的发明专利《磷酸铁锂动力电池剩余容量的估算方法》(CN101629992B)。 [0003] currently technique similar to the present invention are: 1 Cheng Yanqing and other papers published in the "Hangzhou University of Electronic Science and Technology" and "based on Kalman filter to estimate the remaining charge electric vehicles"; 2 Lin Chengtao et published in the "Journal of Tsinghua University. "paper" battery SOC estimation of electric vehicle with an improved safety metering method "; 3 UESTC Hangzhou, etc. He Zhiwei patent to" a sampling point of remaining battery power Kalman filter estimation method "(CN101598769B) based and "battery residual quantity estimation method based on a combination of the sampling points Kalman filter" (CN101604005B);. 4 Chongqing University, Deng Li et al. patent disclosure, "estimation of iron phosphate lithium battery remaining capacity" (CN101629992B). 上述这些技术都没有针对SOC曲线平坦区域估值的问题,提出有针对性的解决方法。 These techniques are not problem for valuation SOC curve flat zone, targeted proposed solutions. 发明内容[0004] 针对上述问题,本发明提出了一种基于特征点的磷酸铁锂电池电量检测方法,改善磷酸铁锂电池小电流放电情况下,精确检测SOC曲线平坦区方法存在较大误差的问题。 [0004] For the above-described problems, the present invention provides a lithium iron phosphate capacity detecting method based on feature points, to improve the low current discharge of lithium iron phosphate, the accurate detection of the presence of a large error in the SOC curve plateau method problem. [0005] 本发明一种基于特征点的磷酸铁锂电池电量检测方法,具体包括如下步骤:[0006] 步骤1、通过实验测定获得电池电量S0C、负载电压U以及电极温度T之间的关联数据表胚 [0005] The present invention provides a method for detecting iron phosphate lithium battery based on feature points, comprises the steps of: [0006] Step 1, the battery S0C experimentally determined correlation data between the load voltage U and the electrode temperature T table embryo

Figure CN103135065AD00041

[0008] Tk为电池电极温度表不电池电极温度Tk下的电池电量SOC和负载电压U的关联数据表;[0009] 步骤2、用电池状态模型和观测模型表示电池的各个时刻的SOC值X和电压y、电流i关系:[0011 ]观测模型:yk=EQ+g (Xk) -R (k) ik+vk (2 )[0012] 其中Xk为电池k时刻的SOC值,ik为k时刻电流,Q为电池额定容量,Ili和1^分别是与电流和温度有关的充放电系数,Δ t是测量时间间隔,Wk是状态噪声,yk是k时刻电池负载电压估计值,Etl是与电池电势有关的常数,R (ik)为电池内阻,与电流ik有关,采用美国《FreedomCAR电池测试手册》中的HPPC测试方法进行计算,Vk为测量噪声,g (xk)是与xk有关的变量,通过查表N (x,g)得到;[0013] 该N (x,g)由实验测定,测定方法是:测量k时刻的电池电极温度Tk、负载电压yk 和负载电流ik,由步骤I的关联数据表M得到该时刻电池yk对应的SOC值xk,由公式(2)的模型并忽略测 [0008] Tk thermometer is not the battery cell electrode associated data table battery SOC and load voltage U at electrode temperature Tk; [0009] Step 2, battery status and observation model are represented at each time of the SOC value of the battery X voltage and y, the relationship between the current i: [0011] observation model: yk = EQ + g (Xk) -R (k) ik + vk (2) [0012] where Xk is the value of the battery SOC at time k, ik is the time k current, Q is the battery rated capacity, Ili, and 1 ^ are related to the current and temperature of the discharge coefficient, Δ t is a measurement time interval, Wk is a state of noise, yk are k times the battery load voltage estimated value, Etl with battery potential related constant, R & lt (ik) is a battery internal resistance, the current ik related, using HPPC test method described in U.S. 'FreedomCAR battery test Manual "is calculated, Vk is the measurement noise, G (xk) is xk related variables , look-up table obtained by N (x, g); [0013] the N (x, g) measured by the experiment, assay methods are: a battery electrode temperature Tk measured at time k, yk load voltage and load current IK, in step I table M associated data obtained SOC value xk yk cell corresponding to the time, by the formula (2) model and measured ignore 量噪声vk,计算出g (xk):[0014] g (Xk) =yk+R (ik) ik_E。 The amount of noise vk, calculate g (xk): [0014] g (Xk) = yk + R (ik) ik_E. (3)[0015] 将结果记入表N (x, g) (3) [0015] The results are recorded in Table N (x, g)

Figure CN103135065AD00051

[0017] 步骤3、运行自检程序,完成内部基准电压值比对、温度校准参数初始化、模型中参数的初始化、相关变量初始赋值的操作;[0018] 步骤4、用温度检测电路检测电池电极温度Ttl,根据温度Ttl,找到表M0,用电流检测电路测量电池负载电流L,用电压检测电路测量电池负载电压Utl,根据负载电压Utl在表MT() 中查得此时电池的SOC值,将此刻的SOCtl作为初始值即&+为电池O时刻的SOC值,负载电压Utl的检测方差尺=Var (為)作为卡尔曼滤波估计初始方差;[0019] 步骤5、采用快速卡尔曼滤波迭代递推运算,计算k时刻的电池SOCk值:[0020] (I)测量在k时刻的电池负载电压Uk,电池负载电流ik,以及温度T,k=l,2,3,…;[0021] (2)用电池状态模型和观测模型表示电池的各个时刻的SOC值和电压、电流关系:ηL Iii[0022]状态模型:¾ - ^-1 + + %U1O[0023]观测模型:yk=EQ+g (xk) -R ⑴ ik+vk[0024] 其中Xk为电 [0017] Step 3, runs self-test program, the completion of the internal reference voltage value than the initialized, temperature calibration parameter initialization, the parameters in the model, the initial operation of the relevant variables assignment; [0018] Step 4, a temperature detection circuit for detecting a battery electrode the Ttl temperature, according to the Ttl temperature, find the table M0, using the current detection circuit measures the load current of the battery L, measured by the battery voltage detecting circuit Utl load voltage, the load voltage Utl Richard SOC value of the battery case in the MT table (), a the moment SOCtl i.e. as an initial value for the SOC value of the battery & O + time, the load voltage detecting variance Utl scale = Var (as) as a Kalman filter to estimate the initial variance; [0019] step 5, using fast Kalman filtering iteration recursive, and calculates the value of time k SOCk battery: [0020] (I) at time k measured load voltage Uk battery, the battery load current IK, and the temperature T, k = l, 2,3, ...; [0021] (2) battery status and observation model are represented SOC value and battery voltage at each time, current relationship: ηL Iii [0022] state model: ¾ - ^ -1 + +% U1O [0023] observation model: yk = EQ + g (xk) -R ⑴ ik + vk [0024] where Xk is electrically 池k时刻的SOC值,Q为电池额定容量,η i和η τ分别是与电流和温度有关的充放电系数,△ t是测量时间间隔,Wk是状态噪声,yk是K时刻电池负载电压估计值,Etl是与电池电势有关的常数,g (Xk)是与Xk有关的变量,通过查表M (Xk)得到;R(i)为电池的内阻系数,并且与放电电流有关,R(i)由实验测定,vk为测量噪声;η.:ι,M[0025] (3 ) K 时刻电池SOC 预测值—.¾ = h + —7ΓX是; Hτ\Ι[0026] K时刻电池负载电压估计值yk:.>7 = Au + ) -1WVl[0027] K时刻滤波后电池SOC估计值X1::.< = xk + Lk (Uk — >v )[0028] K=I, 2,3...[0029] 其中,卡尔曼增益L = r 'Γ = ρ + }) ,p是卡尔曼滤波方差,Ck是与Xk有关的参数,通过查表M (Ck)得到,Dv和Dw分别为状态噪声方差和测量噪声方差;[0030] 步骤6、将分容仪测得的磷酸铁锂电池SOC曲线与步骤5卡尔曼滤波法计算的SOC 曲线进行比较,将两条曲线的交点处定义为特 SOC value pool at time k, Q is a battery rated capacity, η i and η τ are related to the current and temperature of the discharge coefficient, △ t is a measurement time interval, Wk is a state of noise, yk is the K time cell load voltage estimate value, Etl of cell potential is a constant related, G (Xk) is Xk-related variables, look-up table obtained by M (Xk); R (i) is the coefficient of the internal resistance of the battery, and the relevant discharge current, R ( i) determined by experiment, vk is the measurement noise; η:. ι, M [0025] (3) K battery SOC timing predicted value is -.¾ = h + -7ΓX; Hτ \ Ι [0026] K time cell load voltage estimated value yk:> 7 = Au +) -1WVl [0027] K time the battery SOC estimation value filtered X1 :: <= xk + Lk (Uk -> v) [0028] K = I, 2,3.. .. [0029] wherein the Kalman gain L = r 'Γ = ρ +}), p is a Kalman filter variance, Xk Ck is related parameters, obtained by look-up table M (Ck), Dv and Dw are noise covariance and measurement noise variance; [0030] step 6, the calculated SOC curve lithium iron phosphate of step battery SOC curve measured points receiving apparatus 5 Kalman filtering method is compared, is defined at the intersection of two curves special 点;[0031] 步骤7、判断此时的电池SOCk是否处于特征点附近,如果不在特征点附近,则利用步骤5的卡尔曼滤波法计算SOC值,然后回到步骤7 ;[0032] 步骤8、如果此时的电池SOCk处于特征点附近,则开始采用电流积分法计算SOC 值,并判断是否到达下一个特征点;[0033] 步骤9、如果未到达下一个特征点,则继续采用电流积分法计算SOC值,并判断是否到达特征点;[0034] 步骤10、如果到达下一个特征点,则利用特征值对电流积分值进行修正,并回到步骤8。 Point; [0031] Step 7, it is judged whether the case is in the vicinity of the battery SOCk feature points to calculate the SOC value if not near the feature point, using the Kalman filter method step 5, and then returns to step 7; [0032] Step 8 , if the battery case in the vicinity of the feature point SOCk, calculated SOC value is started using the current integration method, and determines whether to reach the next feature point; [0033] step 9, if the next feature point is not reached, continue to use the current integration SOC value calculation, and determines whether or not the feature point reaches; [0034] step 10, if the arrival of the next feature point, the feature-value pairs using the current integral value is corrected, and returns to step 8. [0035] 本发明通过选取特征点进行分段式卡尔曼滤波,特征点处采用卡尔曼预测、特征点间采用安时法积分处理,弥补了磷酸铁锂电池SOC测量中电压、电流采集精度低造成的平坦区SOC预测误差较大的问题,提高了磷酸铁锂电池SOC估计中的精度和稳定性,具有一定的应用价值,能够更加准确的给出电池的续航能力。 [0035] The present invention is segmented by selecting the feature point Kalman filter, the Kalman prediction feature points between a feature point using the security method when the integration process, the lithium iron phosphate to make up a battery SOC measurement voltage, low current acquisition accuracy SOC estimation caused flat region large problem that an error, to improve the accuracy and stability of the estimated SOC of the lithium iron phosphate battery, has a certain value, more accurate analysis is possible battery life. 附图说明[0036] 图1为电压-SOC曲线特征点示意图;[0037] 图2为本发明中电压-SOC曲线特征点选取方法示意图;[0038] 图3为本发明方法流程示意图。 BRIEF DESCRIPTION [0036] FIG. 1 is a schematic diagram of a voltage -SOC feature point curve; 2 a schematic view of the present invention -SOC voltage curve characteristic points [0037] FIG selection method; flowchart of a method [0038] FIG. 3 of the present invention. [0039] 以下结合具体实施例对本发明作进一步详述。 [0039] The following embodiments in conjunction with specific embodiments of the present invention will be described in further detail. 具体实施方式[0040] 本发明的基本原理是:根据磷酸铁锂电池放电的SOC曲线特征,如图1所示的一款2000mAH的磷酸铁锂电池的充放电曲线,把平坦区划分成几段,将每段的起点和终点定义为特征点。 DETAILED DESCRIPTION [0040] The basic principle of the present invention is: a graph wherein the lithium iron phosphate SOC discharge, charge and discharge curves of a lithium iron phosphate as shown in FIG. 1 2000mAH, the flat area is divided into several sections, the start and end of each segment is defined as a feature point. 在特征点附近的SOC值采用卡尔曼滤波估计,在特征点之间的SOC值,采用电流安时积分法估计。 When the estimated SOC value near the feature point is estimated using the Kalman filter, the SOC value between the feature points using the current integration method Ann. [0041] 如图3所示,本发明一种基于特征点的磷酸铁锂电池电量检测方法,具体包括如下步骤:[0042] 步骤1、通过实验测定获得电池电量S0C、负载电压U以及电极温度T之间的关联数据表M: [0043]其中 [0041] 3, the present invention provides a method for detecting iron phosphate lithium battery based on feature points, comprises the steps of: [0042] Step 1, the battery S0C determined experimentally, and the electrode temperature of the load voltage U M between the correlation data table T: [0043] wherein

Figure CN103135065AD00061

[0044] Tk为电池电极温度,表不电池电极温度Tk下的电池电量SOC和负载电压U的关联数据表;[0045] 步骤2、用电池状态模型和观测模型表示电池的各个时刻的SOC值X和电压y、电流i关系:[0048] 其中xk为电池k时刻的SOC值,ik为k时刻电流,Q为电池额定容量,η i和η τ分别是与电流和温度有关的充放电系数,Δ t是测量时间间隔,Wk是状态噪声,yk是k时刻电池负载电压估计值,Etl是与电池电势有关的常数,R (ik)为电池内阻,与电流ik有关,采用美国《FreedomCAR电池测试手册》中的HPPC测试方法进行计算,Vk为测量噪声,g (xk)是与xk有关的变量,通过查表N (x,g)得到;[0049] 该N (X,g)由实验测定,测定方法是:测量k时刻的电池电极温度Tk、负载电压yk 和负载电流ik,由步骤I的关联数据表M得到该时刻电池yk对应的SOC值xk,由公式(2)的模型并忽略测量噪声vk,计算出g (xk):[0050] g (xk) [0044] Tk battery electrode temperature, table battery SOC and load voltage U in no battery electrode temperature Tk associated data table; [0045] Step 2, and observation model are represented at each time of the battery by the battery state SOC value X and voltage y, the current i the relationship: [0048] where xk is the SOC value of the battery at time k, ik is k times the current, Q is the battery rated capacity, η i and η τ are related to the current and temperature of the charge-discharge coefficient , Δ t is a measurement time interval, Wk is a state of noise, yk are k times the battery load voltage estimated value, Etl of a battery potential related constant, R & lt (ik) is a battery internal resistance, the current ik is related by the United States "FreedomCAR battery test test method HPPC Manual "calculated variable Vk measurement noise, G (xk) is associated with xk, look-up table obtained by N (x, g); [0049] the N (X, g) the experimental determination, assay methods are: a battery electrode temperature Tk measured at time k, the load voltage yk and load current IK from step I related data tables M obtained SOC value xk of the timing cell yk corresponds, by equation (2) model and ignore the measurement noise vk, calculate g (xk): [0050] g (xk) =yk+R (k) i k-E0 (3)[0051] 将结果记入表N (X,g)8(χο) = Yk + R (k) i k-E0 (3) [0051] The results are recorded in Table N (X, g) 8 (χο)

Figure CN103135065AD00071

[0053] 步骤3、运行自检程序,完成内部基准电压值比对、温度校准参数初始化、模型中参数的初始化、相关变量初始赋值的操作;[0054] 步骤4、用温度检测电路检测电池电极温度Ttl,根据温度Ttl,找到表%,用电流检测电路测量电池负载电流L,用电压检测电路测量电池负载电压Utl,根据负载电压Utl在表Μτ。 [0053] Step 3, runs self-test program, the completion of the internal reference voltage value than the initialized, temperature calibration parameter initialization, the parameters in the model, the initial operation of the relevant variables assignment; [0054] Step 4, a temperature detection circuit for detecting a battery electrode the Ttl temperature, according to the Ttl temperature,% find the table, by the current detection circuit measures the load current of the battery L, measured by the battery voltage detecting circuit Utl load voltage, the load voltage Utl table Μτ. 中查得此时电池的SOC值,将此刻的SOCtl作为初始值A1I即为电池O时刻的SOC值,负载电压U0的检测方差=Vdr ( Z )作为卡尔曼滤波估计初始方差;[0055] 步骤5、采用快速卡尔曼滤波迭代递推运算,计算k时刻的电池SOCk值:[0056] (I)测量在k时刻的电池负载电压Uk,电池负载电流ik,以及温度T,k=l,2,3,…;[0057] (2)用电池状态模型和观测模型表示电池的各个时刻的SOC值和电压、电流关系:[0058]状态模型:¾ = Xn + + W,IrQ[0059]观测模型:yk=EQ+g (xk) -R ⑴ ik+vk[0060] 其中Xk为电池k时刻的SOC值,Q为电池额定容量,η i和η τ分别是与电流和温度有关的充放电系数,△ t是测量时间间隔,Wk是状态噪声,yk是K时刻电池负载电压估计值,Etl是与电池电势有关的常数,g (Xk)是与Xk有关的变量,通过查表M (Xk)得到;R(i)为电池的内阻系数,并且与放电电流有关,R(i)由实验测定,vk为测量噪声; In this case Richard SOC value of the battery, the initial value at the moment SOCtl A1I O is the value of the battery SOC timing, detecting load voltage U0 variance = Vdr (Z) as a Kalman filter to estimate the initial variance; [0055] Step 5, using fast recursive Kalman filter iteration, calculates the value of time k SOCk battery: [0056] (I) a load cell measuring the voltage Uk at time k, the battery load current IK, and the temperature T, k = l, 2 , 3, ...; [0057] (2) battery state model and observation model showing SOC and voltage at each time of the battery, current relationship: [0058] state model: ¾ = Xn + + W, IrQ [0059] observation model: yk = EQ + g (xk) -R ⑴ ik + vk [0060] where Xk is the value of the battery SOC at time k, Q is a battery rated capacity, η i and η τ are temperature dependent current and the charge and discharge coefficient, △ t is a measurement time interval, Wk is a state of noise, yk is the K time cell load voltage estimated value, Etl of a battery potential related constants, G (Xk) is Xk-related variables, look-up table M (Xk ) obtained; resistance coefficient R (i) of the battery, and the relevant discharge current, R (i) determined by experiment, VK measurement noise; > H1I1A/.[0061 ] ( 3 ) K 时刻电池SOC 预测值—Xk = xL -1 + ~TT:HrQ[0062] K时刻电池负载电压估计值yk 匕=厂。 > H1I1A / [0061] (3) K battery SOC timing predicted value -Xk = xL -1 + ~ TT:. HrQ [0062] K time cell load voltage estimated value yk = dagger plant. +狀)—lUJ)iL[0063] K时刻滤波后电池SOC估计值Xi = XsT + Lk (Uk - )[0064] K=I, 2,3...[0065] 其中,卡尔曼增益4 μ I"! " ,p — p-丄np—是卡尔曼滤波方差,Ck是rk L k + ljV rk — rk-l 十ljW,r1-与xk有关的参数,通过查表M (Ck)得到,Dv和Dw分别为状态噪声方差和测量噪声方差;[0066] 步骤6、将分容仪测得的磷酸铁锂电池SOC曲线与步骤5卡尔曼滤波法计算的SOC 曲线进行比较,如图2所示,将两条曲线的交点处定义为特征点;[0067] 步骤7、判断此时的电池SOCk是否处于特征点附近,如果不在特征点附近,则利用步骤5的卡尔曼滤波法计算SOC值,然后回到步骤7 ;[0068] 步骤8、如果此时的电池SOCk处于特征点附近,则开始采用电流积分法计算SOC 值,并判断是否到达下一个特征点;[0069] 步骤9、如果未到达下一个特征点,则继续采用电流积分法计算SOC值,并判断是否到达特征点;[0070] 步骤10 + Form) -lUJ) iL [0063] K time the battery SOC estimation value filtered Xi = XsT + Lk (Uk -) [0064] K = I, 2,3 ... [0065] wherein the Kalman gain 4 μ I, p "!" - p- Shang np- Kalman filter is the variance, Ck is rk k + ljV rk L - rk-l ten ljW, r1- with xk parameter related to the look-up table obtained by M (Ck), Dv and Dw are noise covariance and measurement noise variance; [0066] step 6, the calculated SOC curve lithium iron phosphate of step battery SOC curve measured points receiving apparatus 5 Kalman filtering method are compared, in FIG. 2 shown, at the intersection of the two curves define feature points; [0067] step 7, it is determined at this time whether the battery is in the vicinity of the feature point SOCk, if not in the vicinity of the feature point, the SOC value calculated using the Kalman filter method step 5 and then returns to step 7; [0068] step 8, if the battery case in the vicinity of the feature point SOCk, starts calculating a current integration method using the SOC value, and determines whether to reach the next feature point; [0069] step 9, if a feature point has not reached, continue using the current integration method is calculated SOC value, and determines whether or not the feature point reaches; [0070] step 10 、如果到达下一个特征点,则利用特征值对电流积分值进行修正,并回到步骤8。 , If reaching the next feature point, the feature-value pairs using the current integral value is corrected, and returns to step 8. [0071] 以上所述,仅是本发明较佳实施例而已,并非对本发明的技术范围作任何限制,故凡是依据本发明的技术实质对以上实施例所作的任何细微修改、等同变化与修饰,均仍属于本发明技术方案的范围内。 [0071] The above is only preferred embodiments of the present invention is only, not any limit to the technical scope of the present invention, it is usually based on the technical essence any slight modification of the above embodiment of the present invention is made of embodiments, modifications and equivalents, It falls within the scope of the present invention.

Claims (1)

  1. 1.一种基于特征点的磷酸铁锂电池电量检测方法,其特征在于包括如下步骤: 步骤1、通过实验测定获得电池电量SOC、负载电压U以及电极温度T之间的关联数据表M: A lithium iron phosphate capacity detecting method based on feature points, comprising the following steps: Step 1, the battery charge SOC experimentally determined correlation data table between the load voltage U M and the electrode temperature T:
    Figure CN103135065AC00021
    Tk为电池电极温度,1¾表示电池电极温度Tk下的电池电量SOC和负载电压U的关联数据表; 步骤2、用电池状态模型和观测模型表示电池的各个时刻的SOC值X和电压1、电流i关系: 状态模型: Tk battery electrode temperature, 1¾ indicates that the battery SOC and a load voltage U at the cell electrode temperature Tk associated data table; Step 2, battery state model and observation model represents SOC values ​​of X and the voltage at each time of the battery 1, the current i relations: state model:
    Figure CN103135065AC00022
    'h'lrQ 观测模型:yk=EQ+g (xk) -R (k) ik+vk (2) 其中xk为电池k时刻的SOC值,ik为k时刻电流,Q为电池额定容量,η i和η τ分别是与电流和温度有关的充放电系数,Λ t是测量时间间隔,Wk是状态噪声,yk是k时刻电池负载电压估计值,Etl是与电池电势有关的常数,R (ik)为电池内阻,与电流ik有关,采用美国《FreedomCAR电池测试手册》中的HPPC测试方法进行计算,Vk为测量噪声,g (xk)是与xk有关的变量,通过查表N (X,g)得到; 该N (x,g)由实验测定,测定方法是:测量k时刻的电池电极温度Tk、负载电压yk和负载电流ik,由步骤I的关联数据表M得到该时刻电池yk对应的SOC值xk,由公式(2)的模型并忽略测量噪声vk,计算出g (xk):g(xk) =yk+R(ik) ik-E0 (3) 将结果记入表N (X, g) 'H'lrQ observation model: yk = EQ + g (xk) -R (k) ik + vk (2) where xk is the value of the battery SOC at time k, ik is the current time k, Q is a battery rated capacity, η i and η τ are related to the current and temperature of the discharge coefficient, Λ t is a measurement time interval, Wk is a state of noise, yk are k times the battery load voltage estimated value, Etl with cell potential related constant, R (ik) the internal resistance of the battery, the current ik related, using the test method described in U.S. HPPC "FreedomCAR battery test Manual" is calculated, Vk is the measurement noise, G (xk) is associated with the variable xk, look-up table N (X, g ) to obtain; the N (x, g) measured experimentally measuring method is: cell electrode temperature Tk measured at time k, the load voltage yk and load current IK, by the associated data tables M step I obtained this time battery yk corresponding SOC value xk, from equation (2) model and ignore the measurement noise vk, calculate g (xk): g (xk) = yk + R (ik) ik-E0 (3) the results are recorded in table N (X, g)
    Figure CN103135065AC00023
    步骤3、运行自检程序,完成内部基准电压值比对、温度校准参数初始化、模型中参数的初始化、相关变量初始赋值的操作; 步骤4、用温度检测电路检测电池电极温度Ttl,根据温度Ttl,找到表M0,用电流检测电路测量电池负载电流L,用电压检测电路测量电池负载电压U。 Step 3, run self-tests, completion of the internal reference voltage value than the initialized, temperature calibration parameter initialization, the parameters in the model, the initial assignment of the relevant variables operation; step 4, a temperature detection circuit for detecting the temperature Ttl battery electrode, according to the temperature Ttl , find the table M0, using the current detection circuit measures the load current of the battery L, battery voltage detection circuit for measuring the load voltage U. ,根据负载电压Utl在表Mi中查得此时电池的SOC值,将此刻的SOCtl作为初始值^即2„力电池O时刻的SOC值,负载电压U0的检测方差i3/ =var (為)作为卡尔曼滤波估计初始方差; 步骤5、采用快速卡尔曼滤波迭代递推运算,计算k时刻的电池SOCk值: (1)测量在1^时刻的电池负载电压队,电池负载电流4,以及温度1',1^1,2,3,…; (2)用电池状态模型和观测模型表示电池的各个时刻的SOC值和电压、电流关系:状态模型S The load voltage Utl SOC value at this point in the look up table Mi battery, the initial value at the moment SOCtl i.e., 2 ^ SOC value "O battery power time, load voltage U0 of the variance detection i3 / = var (as) as a Kalman filter to estimate the initial variance; step 5, using fast recursive Kalman filter iteration, calculates the value of time k SOCk battery: (1) measured at a load voltage of the battery ^ team time, the load current of the battery 4, and the temperature 1 ', 1 ^ 1,2,3, ...; (2) the battery status and observation model are represented SOC value and battery voltage at each time, current relationship: S state model
    Figure CN103135065AC00031
    观测模型 Observation model
    Figure CN103135065AC00032
    其中xk为电池k时刻的SOC值,Q为电池额定容量,η i和η τ分别是与电流和温度有关的充放电系数,Δ t是测量时间间隔,Wk是状态噪声,yk是K时刻电池负载电压估计值,Etl是与电池电势有关的常数,g (Xk)是与Xk有关的变量,通过查表M (Xk)得到;R(i)为电池的内阻系数,并且与放电电流有关,R(i)由实验测定,vk为测量噪声; (3 ) K时刻电池SOC预测值 Where xk is the SOC value of the battery at time k, Q is a battery rated capacity, η i and η τ are related to the current and temperature of the discharge coefficient, Δ t is a measurement time interval, Wk is a state of noise, yk is the K time battery load voltage estimated value, Etl of cell potential is a constant related, G (Xk) is Xk-related variables, look-up table obtained by M (Xk); R (i) is the coefficient of the internal resistance of the battery, and a discharge current associated with the , R (i) determined by experiment, vk is the measurement noise; (3) K battery SOC timing predicted value
    Figure CN103135065AC00033
    K时刻电池负载电压估计值 K time cell load voltage estimate
    Figure CN103135065AC00034
    K时刻滤波后电池SOC估计值4: After filtering the battery SOC estimation time value K 4:
    Figure CN103135065AC00035
    K=I, 2,3… 其中,卡尔曼增益 K = I, 2,3 ... where the Kalman gain
    Figure CN103135065AC00036
    是卡尔曼滤波方差,Ck是与xk 有关的参数,通过查表M (Ck)得到,Dv和Dw分别为状态噪声方差和测量噪声方差; 步骤6、将分容仪测得的磷酸铁锂电池SOC曲线与步骤5卡尔曼滤波法计算的SOC曲线进行比较,将两条曲线的交点处定义为特征点; 步骤7、判断此时的电池SOCk是否处于特征点附近,如果不在特征点附近,则利用步骤5的卡尔曼滤波法计算SOC值,然后回到步骤7 ; 步骤8、如果此时的电池SOCk处于特征点附近,则开始采用电流积分法计算SOC值,并判断是否到达下一个特征点; 步骤9、如果未到达下一个特征点,则继续采用电流积分法计算SOC值,并判断是否到达特征点; 步骤10、如果到达下一个特征点,则利用特征值对电流积分值进行修正,并回到步骤8。 The variance is the Kalman filter, xk with Ck-related parameters, look-up table M (Ck) to give, Dv and Dw are noise covariance and measurement noise variance; Step 6, the sub-capacity meter measured battery lithium iron phosphate SOC SOC curve curve calculated in step 5 Kalman filter method is compared, it is defined at the intersection of the two curves as a feature point; step 7, it is judged whether the case is in the vicinity of the battery SOCk feature point near the feature point if not, then calculated using the Kalman filter method step 5 SOC value, and then returns to step 7; step 8, if the battery case in the vicinity of the feature point SOCk, starts calculating a current integration method using the SOC value, and determines whether the feature point reaches the next ; step 9, if the next feature point is not reached, continue to use the calculated SOC value the current integration method, and determines whether the arrival of feature points; step 10, if the arrival of the next feature point, using the feature value of the current integral value is corrected, and returns to step 8.
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