CN102930173A - SOC(state of charge) online estimation method for lithium ion battery - Google Patents

SOC(state of charge) online estimation method for lithium ion battery Download PDF

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CN102930173A
CN102930173A CN 201210464071 CN201210464071A CN102930173A CN 102930173 A CN102930173 A CN 102930173A CN 201210464071 CN201210464071 CN 201210464071 CN 201210464071 A CN201210464071 A CN 201210464071A CN 102930173 A CN102930173 A CN 102930173A
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charge
state
battery
soc
θ
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CN102930173B (en )
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郑英
姚振辉
张友群
袁昌荣
邓柯军
周安健
朱华荣
张新莹
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重庆长安汽车股份有限公司
重庆长安新能源汽车有限公司
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Abstract

The invention discloses an SOC (state of charge) online estimation method for a lithium ion battery. The SOC online estimation method comprises the following steps: recognizing characteristic parameter values of the battery online; recognizing real-time SOC of the battery online; revising the battery SOC through ampere-hour integral estimation to obtain revised SOC; and weighing the real-time SOC and the revised SOC to obtain final SOC of the battery. By the SOC online estimation method, online estimation capability of battery characteristic parameters can be improved; evaluation self-adaptive capability of the SOC is stronger, and reliability is higher.

Description

一种锂离子电池荷电状态在线估算方法 A lithium ion battery state of charge estimating method Online

技术领域 FIELD

[0001] 本发明涉及新能源汽车的锂离子电池技术领域,更具体地说,涉及一种锂离子电池荷电状态在线估算方法。 Lithium Ion Battery Field [0001] The present invention relates to new energy vehicles, and more particularly, to a lithium ion battery state of charge estimating method online.

背景技术 Background technique

[0002] 随着全球气候逐步恶化、城市大气污染加剧和石油资源过度消耗,汽车领域越来越多的人将目光投到了新能源汽车上。 [0002] With the gradual deterioration of the global climate, exacerbated by urban air pollution and excessive consumption of oil resources, more and more people will be eyeing the automotive sector on new energy vehicles. 锂离子电池具有体积小、能量密度高、储存寿命长、无记忆效应、高电压和自放电率低等优良性能,逐渐成为新能源汽车动力电池的赢家。 A lithium ion battery having a small size, high energy density, long storage life, no memory effect, high voltage and low self-discharge and other excellent properties, becoming winner new energy vehicles battery. 如何利用好电池已成为电池及其集成领域一项关键技术,其中准确监控电池荷电状态(Stateof Charge, S0C),已成为电池管理系统乃至电动汽车研究的难点与热点问题。 How to make good use of the battery and battery integration in the field has become a key technology, which accurately monitor battery state of charge (Stateof Charge, S0C), it has become a hot issue and difficult and battery management systems as well as electric vehicle research. 因此提高SOC 估算精度及SOC算法的实用性具有很高的理论价值和实际意义。 It has a very high theoretical and practical significance to improve the estimation accuracy and practicality SOC SOC algorithm.

[0003]目前常用的SOC估算方法主要有:安时积分法、开路电压法、神经网络法等。 [0003] SOC estimation methods currently used are: Ah integral, open circuit voltage method, neural network method. 其中安时积分法是最通用的方法。 Ah integration method which is the most common method. 但在应用中存在下面几个主要的问题:(I)受初始SOC值、电流采集精度、充放电效率影响大;(2)该方法在电池老化容量衰减后严重失效,因此实际应用中常与其他方法联合使用。 But there are several major problems in the following applications: (I) by initial SOC value, the accuracy of current detection, charge and discharge efficiency great impact; (2) the process after a serious failure in the battery capacity fade aging, thus often been applied to other methods used in combination. 开路电压法主要利用开路电压(0CV,0pen Circuit Voltage)与SOC的对应关系进行估算,但由于动力电池工作过程中开路电压难以测量,使得该方法使用受限。 Open circuit voltage using mainly open-circuit voltage (0CV, 0pen Circuit Voltage) estimated SOC to the correspondence relationship, but the battery during operation of the open-circuit voltage is difficult to measure, so that the method is limited. 神经网络法需要使用大量的数据样本对算法进行离线仿真,方法复杂不易实现和应用,且不能用于SOC的在线监测。 Neural network requires a large amount of data samples for off-line simulation algorithms, methods and applications complex to implement, and can not be used for online monitoring of the SOC.

发明内容 SUMMARY

[0004] 有鉴于此,本发明提供一种锂离子电池荷电状态在线估算方法,以实现提高电池特征参数的在线估算能力,使得对荷电状态的估算自适应能力更强,具有更高的可靠性。 [0004] Accordingly, the present invention provides a lithium ion battery state of charge line estimation methods to estimate the improved capability of the battery line characteristic parameters, so that the ability of the adaptive estimation of the state of charge of the stronger, higher reliability.

[0005] 为解决上述技术问题,本发明采用的技术方案为:一种锂离子电池荷电状态在线在线估算方法,包括: [0005] To solve the above problems, the present invention adopts the technical solution as follows: a lithium ion battery state of charge Online estimation method, comprising:

[0006] 在线辨识电池的特征参数值; [0006] Identification of characteristic parameter values ​​cell line;

[0007] 在线辨识电池的实时荷电状态; [0007] Real-time online identification state of charge of the battery;

[0008] 修正通过安时积分估算的电池的荷电状态,得出修正后的荷电状态; [0008] corrected by the battery state of charge estimated integral ampere, obtained state of charge after correction;

[0009] 加权所述实时荷电状态和所述经过修正后获得的荷电状态,获得电池最终的荷电状态。 [0009] The weighting in real time the state of charge and a state of charge after the correction is obtained, to obtain the final state of charge of the battery.

[0010] 优选地,所述特征参数包括:开路电压、欧姆电阻、极化电阻和极化电容。 [0010] Preferably, the characteristic parameters comprises: open-circuit voltage, the ohmic resistance, polarization resistance and polarization capacitance.

[0011] 优选地,所述在线辨识电池的特征参数值具体为: [0011] Preferably, the characteristic parameter values ​​for the particular cell line identification:

[0012] 获取电池负载的电压信号V(k)、电流信号I (k)和电池的温度信号T ; [0012] Gets battery load voltage signal V (k), the current signal I (k) and the battery temperature signal T;

[0013] 根据电池的等效电路模型建立最小二乘模型,模型数学关系式为: [0013] The equivalent circuit model of the battery is established in accordance with the least squares model, the mathematical model of the relation:

[0014] V=Voc-IR0-Vp (I) [0014] V = Voc-IR0-Vp (I)

dl dl

[0015] CpRp 十I-Ip (2)[0016] 其中,V。 [0015] CpRp ten I-Ip (2) [0016] wherein, V. . 为电池开路电压,R0为欧姆电阻,I为总放电电流,Ip为通过极化电阻上的电流,V为电池的负载电压,Rp为极化电阻,Cp为极化电容; A battery open-circuit voltage, R0 is the ohmic resistance, I is the total discharge current, Ip is the current through the polarization resistance, V is the battery voltage load, Rp is the polarization resistance, Cp is the polarization capacitance;

[0017] 对上述(I)式和(2)式进行离散化整理得到: [0017] the above formula (I) and (2) is discretized finishing obtained:

[0018] V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3) [0018] V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3)

[0019] 其中,Θ是电池特征参数的函数,k为当前时刻值,k-Ι为上一时刻值; [0019] where, Θ is a function of the characteristic parameters of the battery, k is the current time value, k-Ι value as at the previous time;

[0020] 通过改进递推最小二乘算法对所述特征参数进行迭代: [0020] wherein the parameters iteratively improved by recursive least squares algorithm:

[0021] 计算向量矩阵: [0021] The vector matrix calculation:

[0022] φ (k) = [V(k-1) I (k) I (k-1) 1]τ ; [0022] φ (k) = [V (k-1) I (k) I (k-1) 1] τ;

[0023] 增益矩阵: [0023] gain matrix:

Figure CN102930173AD00051

[0025] 估算误差: [0025] estimation error:

[0026] a =V (k) - θ τ φ (k); [0026] a = V (k) - θ τ φ (k);

[0027]估算 θ : [0027] Estimation θ:

[0028] Θ (k)= θ (k-1)+Ka ; [0028] Θ (k) = θ (k-1) + Ka;

[0029] 估计误差协方差矩阵: [0029] estimation error covariance matrix:

[0030] P (k) =P (k-1) -K ΦT (k) P (k-1) +f (a ); [0030] P (k) = P (k-1) -K ΦT (k) P (k-1) + f (a);

[0031] 其中,f(a)是估算残差a的函数; [0032] 所述 [0031] where, f (a) is a function of the estimated residuals; [0032] The

Figure CN102930173AD00052

通过离线仿真确定m及An的值; An emulation and the value of m is determined by off-line;

[0033] 得出电池特征参数值: [0033] The battery obtained characteristic parameters:

Figure CN102930173AD00053

[0035] 优选地,所述在线辨识电池的实时荷电状态具体为: [0035] Preferably, the real-time identification of the state of charge of the cell line specifically:

[0036] 根据所述开路电压V。 [0036] The open-circuit voltage of the V. . 、温度T与荷电状态曲线,查表得出实时荷电状态SOCristj , The temperature T and state of charge curve, look-up table derived in real time the state of charge SOCristj

[0037] 优选地,所述修正通过安时积分估算的电池的荷电状态,得出修正后的荷电状态具体为: [0037] Preferably, the corrected state of charge by integrating the estimated Ah battery state of charge after the correction is derived specifically:

[0038] 将前一时刻通过安时积分估算的荷电状态SOC (k_ I)加上电流积分 [0038] The integrated safety estimated by the previous time when the state of charge SOC (k_ I) plus current integration

Figure CN102930173AD00054

获得修正后的荷电状态SOCah (k): After obtaining corrected state of charge SOCah (k):

[0039] [0039]

Figure CN102930173AD00055

[0040] 其中,K。 [0040] where, K. 、Kd为充放电电流修正因子,Kt为温度修正因子。 , Kd is the charge and discharge current correction factor, Kt is the temperature correction factor.

[0041] 优选地,所述加权所述实时荷电状态和所述经过修正后获得的荷电状态,获得电池最终的荷电状态具体为: [0041] Preferably, the weighting of the real state of charge and a state of charge of the obtained corrected, to obtain the final state of charge of the battery is specifically:

[0042] 将安时积分法估算SOCah与辨识法估算SOCrts加权获得最终SOC估算值: [0042] When the estimated integration method SOCah An identification method for estimating and obtaining a final SOC SOCrts weighted estimates:

[0043] SOC=WSOCah+ (I -w) SOCrls ; [0043] SOC = WSOCah + (I -w) SOCrls;

[0044] 其中,w动态可调且O≤w≤I。 [0044] wherein, w and dynamically adjustable O≤w≤I.

[0045] 从上述的技术方案可以看出,本发明公开的一种锂离子电池荷电状态在线估算方法,通过在线辨识电池的特征参数值,估算电池的实时荷电状态,通过修正通过安时积分估算的电池的荷电状态,最后将实时荷电状态与修正后的荷电状态进行加权,获得电池最终的荷电状态,提高了电池特征参数的在线估算能力,使得对荷电状态的估算自适应能力更强,具有了更高的可靠性。 When [0045] As can be seen from the above technical solution, a lithium ion battery of the present invention, the state of charge estimation method disclosed in-line, by identification of characteristic parameter values ​​cell line, real-time estimated state of charge of the battery, through safety corrected by state of charge of the integral estimate of the battery, and finally the real-time state of charge and state of charge of the revised weighted to obtain battery final state of charge, to improve the online ability to estimate battery characteristic parameters, making the estimate of state of charge greater adaptability, with a higher reliability.

附图说明[0046] 为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。 BRIEF DESCRIPTION [0046] In order to more clearly illustrate the technical solutions in the embodiments or the prior art embodiment of the present invention, the accompanying drawings for describing the embodiments or the prior art described in the introduction required simply Apparently, the following the drawings are merely described some embodiments of the present invention, those of ordinary skill in the art is concerned, without creative efforts, we can derive from these drawings other drawings.

[0047] 图I为本发明实施例公开的一种锂离子电池荷电状态在线估算方法的流程图; A lithium ion battery state of charge of the disclosed embodiments [0047] Figure I-line estimation of the present invention, a flow chart of a method;

[0048] 图2为本发明应用的电池等效电路图; [0048] FIG 2 cell equivalent circuit diagram of the present invention is applied;

[0049] 图3为本发明电池特征参数值的辨识结果图; [0049] FIG 3 battery characteristic parameter values ​​of the present invention. FIG identification result;

[0050] 图4为本发明实车测试结果图。 [0050] FIG. 4 of the actual vehicle test result of the present invention FIG.

具体实施方式 Detailed ways

[0051] 下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明的一部分实施例,而不是全部的实施例。 [0051] below in conjunction with the present invention in the accompanying drawings, technical solutions in the embodiments will be apparent to the present invention, completely described, obviously, the described embodiments are merely part of embodiments of the present invention, but not all Example. 基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。 Based on the embodiments of the present invention, those of ordinary skill in the art to make all other embodiments without creative work obtained by, it falls within the scope of the present invention.

[0052] 本发明实施例公开了一种锂离子电池荷电状态在线估算方法,以实现提高电池特征参数的在线估算能力,使得对荷电状态的估算自适应能力更强,具有更高的可靠性。 [0052] The embodiments of the present invention disclose a lithium ion battery state of charge line estimating method for improved battery capacity to estimate characteristic parameters online, such estimation adaptive to the state of charge of the stronger, more reliable with sex.

[0053] 如图I所示,一种锂离子电池荷电状态在线估算方法,包括: [0053] As shown in FIG. I, a lithium ion battery state of charge line estimation method, comprising:

[0054] SI、在线辨识电池的特征参数值; [0054] The characteristic parameter values ​​SI, online identification cell;

[0055] 具体的,电池的特征参数包括开路电压、欧姆电阻、极化电阻和极化电容; [0055] Specifically, the characteristic parameters including the open circuit voltage of the battery, ohmic resistance, polarization resistance and polarization capacitance;

[0056] 首先,通过电池管理系统实时获取电池负载的电压信号V(k)、电流信号I(k)和电池的温度信号T ; [0056] First, a battery management system by the real-time voltage signal V (k) of the battery load current signal I (k) and the battery temperature signal T;

[0057] 然后,再根据如图2所示的电池的等效电路建立最小二乘模型,其中,模型数学关系式为: [0057] Then, the establishment of the equivalent circuit model according to the least square cell shown in FIG. 2, wherein the model is a mathematical relationship:

[0058] V=Voc-IR0-Vp (I) [0058] V = Voc-IR0-Vp (I)

[0059] CpRp^L^I-Ip {2) [0059] CpRp ^ L ^ I-Ip {2)

[0060] 其中,V。 [0060] wherein, V. . 为电池开路电压,R0为欧姆电阻,I为总放电电流,Ip为通过极化电阻上的电流,V为电池的负载电压,Rp为极化电阻,Cp为极化电容; A battery open-circuit voltage, R0 is the ohmic resistance, I is the total discharge current, Ip is the current through the polarization resistance, V is the battery voltage load, Rp is the polarization resistance, Cp is the polarization capacitance;

[0061] 对上述(I)式和(2)式进行离散化整理得到: [0061] the above formula (I) and (2) is discretized finishing obtained:

[0062] V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3) [0062] V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3)

[0063] 其中,Θ是电池特征参数的函数,k为当前时刻值,k-Ι为上一时刻值; [0063] where, Θ is a function of the characteristic parameters of the battery, k is the current time value, k-Ι value as at the previous time;

[0064] 通过改进递推最小二乘算法对所述特征参数进行迭代: [0064] wherein the parameters iteratively improved by recursive least squares algorithm:

[0065] 计算向量矩阵:[0066] Φ (k) = [V(k-1) I (k) I (k-1) 1]τ ; [0065] The calculated vector matrix: [0066] Φ (k) = [V (k-1) I (k) I (k-1) 1] τ;

[0067] 增益矩阵: [0067] gain matrix:

[0068] [0068]

Figure CN102930173AD00071

[0069] 估算误差: [0069] estimation error:

[0070] a =V (k) - θ τ φ (k); [0070] a = V (k) - θ τ φ (k);

[0071]估算 θ : [0071] Estimation θ:

[0072] Θ (k)= θ (k-1)+Ka ; [0072] Θ (k) = θ (k-1) + Ka;

[0073] 估计误差协方差矩阵: [0073] estimation error covariance matrix:

[0074] P (k) =P (k-1) -K ΦT (k) P (k-1) +f (a ); [0074] P (k) = P (k-1) -K ΦT (k) P (k-1) + f (a);

[0075] 其中,f (a )是估算残差a的函数; [0075] where, f (a) is a function of the estimated residuals;

[0076] 所述 The [0076]

Figure CN102930173AD00072

次V通过离线仿真确定m及An的值; Times the value of V is determined by m and An off-line simulation;

[0077] 得出电池特征参数值: [0077] The battery obtained characteristic parameters:

[0078] [0078]

Figure CN102930173AD00073

[0079] S2、在线辨识电池的实时荷电状态; [0079] S2, real-time online identification state of charge of the battery;

[0080] 具体的,通过系统辨识算法估算出的电池开路电压V。 [0080] Specifically, by the estimated battery open circuit voltage system identification algorithm V. . 、温度T与荷电状态SOC曲线查表得出电池的实时荷电状态SOCris ; , The temperature T and state of charge SOC look-up table curves obtained in real time the state of charge of the battery SOCris;

[0081] S3、修正通过安时积分估算的电池的荷电状态,得出修正后的荷电状态; [0081] S3, the state of charge by integrating the estimated correction Ah battery state of charge after the correction is derived;

[0082] 具体的,将前一时刻通过安时积分估算的荷电状态SOC(kl)加上电流积分 [0082] Specifically, the integrator estimated through a safety time before a state of charge SOC (kl) plus current integration

Figure CN102930173AD00074

获得修正后的荷电状态SOCahGO : After obtaining corrected state of charge SOCahGO:

[0083] [0083]

Figure CN102930173AD00075

[0084] 其中,K。 [0084] where, K. 、Kd为充放电电流修正因子,Kt为温度修正因子; , Kd is the charge and discharge current correction factor, Kt is the temperature correction factor;

[0085] S4、加权所述实时荷电状态和所述经过修正后获得的荷电状态,获得电池最终的荷电状态; [0085] S4, the weighting in real time the state of charge and a state of charge of the obtained corrected, to obtain the final state of charge of the battery;

[0086] 具体的,将安时积分法估算SOCah与辨识法估算SOCrts加权获得最终SOC [0086] Specifically, when the estimated SOCrts An estimated weighted integration method and identification method SOCah final SOC

[0087]估算值:S0C=wS0Cah+ (1-w) SOCrls ; [0087] Estimates: S0C = wS0Cah + (1-w) SOCrls;

[0088] 其中,w动态可调且O≤w≤I。 [0088] wherein, w and dynamically adjustable O≤w≤I.

[0089] 在上述实施例中,本发明使用系统辨识算法估算电池开路电压,并通过开路电压、温度与SOC曲线(OCV,T)-S0C查表估算S0C,自适应能力更强;且本发明提出一种改进递推最小二乘算法,协方差矩阵P的计算在传统方法的基础上引入动态的修正项,根据辨识残差自动调整修正项,大大提高了电池特征参数在线估算能力;同时本发明与传统安时积分相比,引入初始值实时修正、充放电电流、温度修正,减小了安时积分的累积误差;采用加权算法充分考虑了传统安时积分算法与递推最小二乘辨识算法各自的优缺点,具有更高的可靠性。 [0089] In the above embodiment, the present invention using the system identification algorithm to estimate the open circuit voltage of the battery, and open circuit voltage, temperature profile and the SOC (OCV, T) -S0C lookup estimated S0C, adaptive stronger; and the present invention an improved recursive least squares algorithm to calculate the covariance matrix P introduced into the dynamic correction term on the basis of the conventional method, according to the identification residual automatically adjust the correction term, greatly improving the cell capacity characteristic line parameter Estimation; present at the same time compared with integral safety when conventional, real-time correction introduced into the initial value, the charge and discharge currents, temperature correction is reduced when the accumulated error integral safety; weighting algorithm using fully conventional safety consideration when integration algorithm and recursive Least Squares identification the advantages and disadvantages of each algorithm has higher reliability.

[0090] 如图3和图4所示,为电池容量出现衰减时时实车测试情况,可以看出由于电流采集精度或老化等影响,传统安时积分法最终估算误差为33%,本发明估算的SOC避免了传统安时积分法受初始SOC值和电流采集精度、老化等影响,能迅速的收敛到真实值附近,自适应能力强,具有很好的估算效果。 [0090] As shown in FIG. 3 and FIG. 4, the attenuation always occurs where the actual vehicle test battery capacity, it can be seen due to the current detection precision or aging effects upon the traditional safety integration method final estimation error is 33%, estimation of the present invention when the SOC avoid the traditional safety law integration SOC initial value and a current acquisition accuracy, aging effects can quickly converges to the true value, adaptability strong, has a good estimation results.

[0091] 本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似部分互相参见即可。 [0091] In the present specification, the various embodiments described in a progressive manner, differences from the embodiment and the other embodiments each of which emphasizes embodiment, the same or similar portions between the various embodiments refer to each other.

[0092] 对所公开的实施例的上述说明,使本领域专业技术人员能够实现或使用本发明。 [0092] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. 对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下,在其它实施例中实现。 Various modifications to these professionals skilled in the art of the present embodiments will be apparent, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. 因此,本发明将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。 Accordingly, the present invention will not be limited to the embodiments shown herein but is to be accorded herein consistent with the principles and novel features disclosed widest scope.

Claims (6)

  1. 1. 一种锂离子电池荷电状态在线估算方法,其特征在于,包括: 在线辨识电池的特征参数值; 在线辨识电池的实时荷电状态; 修正通过安时积分估算的电池的荷电状态,得出修正后的荷电状态; 加权所述实时荷电状态和所述经过修正后获得的荷电状态,获得电池最终的荷电状态。 1. A lithium ion battery state of charge estimation line, characterized in that, comprising: a characteristic parameter values ​​online identification cell; real-time online identification of the state of charge of the battery; state of charge by integrating the estimated correction Ah battery, the state of charge after the correction is derived; weighting the real state of charge and a state of charge after the correction is obtained, to obtain the final state of charge of the battery.
  2. 2.根据权利要求I所述的方法,其特征在于,所述特征参数包括:开路电压、欧姆电阻、极化电阻和极化电容。 2. The method as claimed in claim I, characterized in that said characteristic parameters include: open circuit voltage, the ohmic resistance, polarization resistance and polarization capacitance.
  3. 3.根据权利要求2所述的方法,其特征在于,所述在线辨识电池的特征参数值具体为: 获取电池负载的电压信号V(k)、电流信号I (k)和电池的温度信号T ; 根据电池的等效电路模型建立最小二乘模型,模型数学关系式为: V=Voc-IR0-Vp (I) 3. The method according to claim 2, wherein said characteristic parameter values ​​for the online identification of specific cell: temperature acquiring battery load voltage signal V (k), the current signal I (k) and the battery signals T ; equivalent circuit model of the battery is established in accordance with the least squares model, the model for the mathematical relationship: V = Voc-IR0-Vp (I)
    Figure CN102930173AC00021
    其中,V。 Where, V. . 为电池开路电压,R0为欧姆电阻,I为总放电电流,Ip为通过极化电阻上的电流,V为电池的负载电压,Rp为极化电阻,Cp为极化电容; 对上述(I)式和(2)式进行离散化整理得到: V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3) 其中,Θ是电池特征参数的函数,k为当前时刻值,k-Ι为上一时刻值; 通过改进递推最小二乘算法对所述特征参数进行迭代: 计算向量矩阵: Φ (k) = [V(kl)I(k)I(kl)l]T ; 增益矩阵: A battery open-circuit voltage, R0 is the ohmic resistance, I is the total discharge current, Ip is the current through the polarization resistance, V is the battery voltage load, Rp is the polarization resistance, Cp is the polarization capacitance; above (I) and (2) is discretized finishing to give: V (k) = Θ J (k-Ι) + Θ 2I (k) + Θ 3I (k-1) + Θ 4V0CU (k) (3) where, Θ characteristic parameter is a function of the battery, k is the current time value, k-Ι value as at the previous time; iteration by modifying the characteristic parameters of recursive least squares algorithm: calculated vector matrix: Φ (k) = [V ( kl) I (k) I (kl) l] T; gain matrix:
    Figure CN102930173AC00022
    估算误差: a =V (k) - θ τ φ (k); 估算Θ : Θ (k)= Θ (k-1)+Ka ; 估计误差协方差矩阵: P (k) =P (k-1) -K ΦT (k) P (k-1) +f (a ); 其中,fU)是估算残差a的函数; 所述 Estimation error: a = V (k) - θ τ φ (k); Estimation Θ: Θ (k) = Θ (k-1) + Ka; estimation error covariance matrix: P (k) = P (k-1 ) -K ΦT (k) P (k-1) + f (a); wherein, fU) is a function of the estimated residuals; the
    Figure CN102930173AC00023
    通过离线仿真确定m及An的值; 得出电池特征参数值: An emulation and the value of m is determined by off-line; battery characteristic parameter values ​​obtained:
    Figure CN102930173AC00024
  4. 4.根据权利要求3所述的方法,其特征在于,所述在线辨识电池的实时荷电状态具体为: 根据所述开路电压V。 4. The method according to claim 3, wherein the real-time identification of the state of charge of the cell line is specifically: the open circuit voltage according to V. . 、温度T与荷电状态曲线,查表得出实时荷电状态SOCrts。 , The temperature T and state of charge curve, look-up table derived in real time the state of charge SOCrts.
  5. 5.根据权利要求4所述的方法,其特征在于,所述修正通过安时积分估算的电池的荷电状态,得出修正后的荷电状态具体为: 将前一时刻通过安时积分估算的荷电状态soc(kl)加上电流积分κ^κτ*^·获得修正后的荷电状态SOCah (k): SOCah(k)=SOC(kl 卜K, Kd- V 爷: 其中,K。、Kd为充放电电流修正因子,Kt为温度修正因子。 5. The method as claimed in claim 4, wherein, when the corrected state of charge by integrating the estimated safe battery state of charge after the correction is derived specifically: An integral through the previous time estimation the state of charge soc (kl) plus the current integration κ state of charge after ^ κτ * ^ · get corrected SOCah (k): SOCah (k) = SOC (kl BU K, Kd- V Lord: where, K. , Kd is the charge and discharge current correction factor, Kt is the temperature correction factor.
  6. 6.根据权利要求5所述的方法,其特征在于,所述加权所述实时荷电状态和所述经过修正后获得的荷电状态,获得电池最终的荷电状态具体为: 将安时积分法估算SOCah与辨识法估算SOCrls加权获得最终SOC估算值: SOC=WSOCah+(IW) SOCrls ; 其中,w动态可调且O < w < I0 6. The method according to claim 5, characterized in that the weighting of the real state of charge and a state of charge of the obtained corrected, to obtain the final state of charge of the battery is specifically: the integration ampere Estimation of the identification method for estimating SOCah SOCrls weighting to obtain the final estimate SOC: SOC = WSOCah + (IW) SOCrls; wherein, w dynamically adjustable and O <w <I0
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