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CN106291381B - Kind of joint estimation of power battery system state of charge and state of health approach - Google Patents

Kind of joint estimation of power battery system state of charge and state of health approach Download PDF

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CN106291381B
CN106291381B CN201610675853.4A CN201610675853A CN106291381B CN 106291381 B CN106291381 B CN 106291381B CN 201610675853 A CN201610675853 A CN 201610675853A CN 106291381 B CN106291381 B CN 106291381B
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system
soc
capacity
state
estimated
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CN106291381A (en
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熊瑞
陈铖
杨瑞鑫
田金鹏
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北京理工大学
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage for electromobility
    • Y02T10/7005Batteries
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage for electromobility
    • Y02T10/7038Energy storage management
    • Y02T10/7044Controlling the battery or capacitor state of charge

Abstract

本申请涉及种联合估计动力电池系统荷电状态与健康状态的方法,利用多时间尺度滤波算法,使用宏观时间尺度获得动力电池系统参数估计值、使用微观时间尺度估计系统状态,评估所述动力电池的健康状态与荷电状态。 The present application relates to species joint estimation battery power system state of charge and state of health, utilizing a multi-time scale filtering algorithm, using the macroscopic time scale parameter estimates obtained battery power system, the state estimation system using microscopic time scale, evaluating the power battery state of health and state of charge. 形成基于多时间尺度的动力电池参数和状态的联合估计方法,实现动力电池可用容量和荷电状态在不确定性应用环境中的精确联合估计。 Joint estimation method based on formation parameters and the battery status of the multi-time scale to achieve power battery state of charge and available capacity in the uncertainty of the precise application environment Joint Estimation. 不仅使得估计结果更加稳定可靠,同时降低了系统的计算成本。 Not only makes the results more reliable estimates, while reducing the computational cost of the system.

Description

一种联合估计动力电池系统荷电状态与健康状态的方法 A combined estimate of battery power system state of charge and state of health approach

[0001] [0001]

技术领域:本发明涉及动力电池管理技术领域,尤其是车载动力电池系统参数辨识与状态估计的估计方法。 Technical Field: The present invention relates to the field of battery technology management, particularly car battery state estimation system identification and parameter estimation method. 背景技术: Background technique:

[0002] 目前,基于模型的SOC算法大多需要采用卡尔曼滤波系列算法来估计电池的S0C, 虽然理论表明卡尔曼滤波是一种最优的线性状态估计算法,但是其只适用于模型精确、输入噪声统计特性已知的情况,这显然不能满足实际的需求。 [0002] Currently, the algorithm based SOC model series is often required Kalman filter algorithm to estimate S0C battery, although theory suggests Kalman filter is an optimal linear state estimation algorithm, but it applies only to model accurately, enter It is known statistical properties of the noise situation, which obviously can not meet the actual demand.

[0003] 基于此,本发明所采用的He»滤波是一种专为鲁棒性而设计的算法。 [0003] Based on this, the present invention is employed of He »is a filter designed specifically for robustness of the algorithm. 该算法主要方式参见《最优状态估计一一卡尔曼,He»及非线性滤波》,国防工业出版社。 The algorithm types see the "optimal state estimation eleven Kalman, He» and nonlinear filtering "National Defense Industry Press. 不同于卡尔曼滤波,即使在模型的存在误差、噪声的输入统计特性未知,甚至是在最坏情况下,该算法依旧能准确地完成状态估计。 Unlike Kalman filter, even when the input of the statistical properties of the model errors, noise is unknown, even in the worst case, the algorithm is still able to accurately estimate the completion status.

[0004] 现有动力电池SOC的在线估计方法,由于最大可用容量(以下简称为容量)的不确定性使得动力电池SOC的估计结果可靠性低。 Online Estimation Method [0004] SOC of the conventional battery, since the maximum available capacity (hereinafter referred to as capacity) Uncertainty in the battery SOC estimation result of low reliability. 且现有将相对稳定的SOC-OCV曲线(即开路电压曲线)作为SOC估计算法的修正曲线,由电池使用温度、老化程度等不同时,该曲线同样会发生较为明显的变化。 The conventional and relatively stable SOC-OCV curve (i.e., open circuit voltage curve) as a correction curve SOC estimation algorithm, the battery temperature, aging degree is not the same, the same curve would be more obvious changes.

[0005] 基于此,本发明考虑到不同老化程度、温度下的OCV-SOC曲线与容量均会产生一定的变化,从而建立容量、荷电状态以及开路电压的三维响应面,即容量-SOC-OCV三维响应面,以此为基础实现了不同使用环境(老化程度、温度)下的SOC与容量的联合估计。 [0005] Based on this, the present invention contemplates different degrees of aging, OCV-SOC and capacity curve at a certain temperature will change, thereby establishing a three-dimensional response surface capacity, state of charge and the open circuit voltage, i.e. the capacity -SOC- OCV dimensional response surface as a basis for estimating the SOC and combined to achieve a capacity under different environments (the degree of aging, temperature).

[0006] 本发明还针对动力电池系统状态量的快速时变特性与参数量的缓慢时变特性,采用微观时间尺度估计动力电池的S0C、采用宏观时间尺度估计动力电池的模型参数与可用容量,形成基于多时间尺度的动力电池SOC和容量的联合估计方法,实现动力电池SOC和容量在不确定性应用环境中的精确联合估计。 [0006] The present invention further slow time varying characteristic variable characteristic parameter into rapid time for the power system state amount of the battery, using the microscopic time scales estimated S0C battery power, macroscopic time scales estimated battery model parameters and the available capacity, forming combined battery SOC estimation method based on a multi-time scale and capacity, the battery SOC, and the capacity to achieve accurate estimates of uncertainty combined application environment. 不仅使得估计结果更加稳定可靠,同时降低了系统的计算成本。 Not only makes the results more reliable estimates, while reducing the computational cost of the system.

发明内容: SUMMARY:

[0007] 本发明涉及一种联合估计动力电池系统荷电状态与健康状态的方法,所述方法包括: [0007] The present invention relates to a joint estimation battery charge state and a system state of health, the method comprising:

[0008] 首先,建立容量-SOC-OCV三维响应面,所述OCV是所述系统的开路电压; [0008] Firstly, the capacity of the three-dimensional response surface -SOC-OCV, the OCV is the open circuit voltage of the system;

[0009] 其次,在线数据获取,实时采集动力电池单体和动力电池组的电压和电流; [0009] Next, line data acquisition, real-time acquisition of voltage and current power and the battery cells of battery power;

[0010] 然后,多时间尺度滤波算法,获得所述动力电池系统的当前宏观时间尺度下的系统容量预估修正值与当前微观时间尺度下的系统SOC预估修正值; [0010] Then, a multi-time scale filtering algorithm to obtain the current system battery power system capacity at a macroscopic time scale correction value prediction system in this microscopic time scale estimate SOC correction value;

[0011] 在每个SOC估计微观采样点下更新所述系统SOC预估修正值,每隔L个所述SOC估计微观采样点为容量估计宏观采样点,在该容量估计宏观采样点下更新所述系统容量预估修正值,每次更新后的所述系统容量预估修正值作为当前容量估计宏观采样点后L次的更新所述系统SOC预估修正值所用的参数; [0011] estimate to update the system micro sample point correction value SOC estimate, L SOC estimation of said micro-sample points every sampling point capacity estimating macro, macro-update estimated sampling points in each of the capacity SOC said correction value estimated system capacity, the system capacity after each update of the correction value as a current estimated capacity estimating the macro-L times the sampling point updating the system parameters estimated SOC correction value used;

[0012] 所述L是两个以上; [0012], wherein L is two or more;

[0013] 最后,在线SOC与SOH提取,利用所述多时间尺度滤波算法获得的当前所述系统SOC 预估修正值与系统容量预估修正值,估计动力电池系统荷电状态与健康状态。 [0013] Finally, the SOC and SOH extraction line, the multi-time scale by using the current filtering algorithm to obtain the correction value prediction system SOC estimate the system capacity correction value, the estimated battery state of charge system health condition.

[0014] 优选地、所述多时间尺度滤波算法包括: [0014] Preferably, the multi-time scale filtering algorithm comprises:

[0015] 步骤①:进行当前SOC估计微观采样点k下的系统SOC预估,得到系统SOC预估值; [0015] Step ①: SOC estimation for the current system in the micro sample point K estimated SOC, SOC estimate obtained system;

[0016] 步骤②:基于步骤①中的获得的系统SOC预估值和上个容量估计宏观采样点的系统容量预估修正值,利用所述容量-SOC-OCV三维响应面,更新当前系统开路电压得到第一开路电压; [0016] Step ②: ① step based on the obtained SOC estimate system capacity estimation and previous sampling point macroscopic system capacity correction value estimated by the capacity -SOC-OCV dimensional response surface, the system updates the current open open circuit voltage to obtain a first voltage;

[0017] 然后,基于上述第一开路电压,进行所述系统SOC预估值的修正,得到所述系统SOC 预估修正值; [0017] Then, based on the first open circuit voltage, the system corrects the estimated value of SOC, estimated SOC system to obtain the correction value;

[0018] 步骤③:k+1作为新的SOC估计微观采样点,判断k-1是否能被L整除,如果能,贝Ij进行步骤④;否则返回步骤①; [0018] Step ③: k + 1 as a new micro-SOC estimating sampling point, it can be determined whether L k-1 is divisible, if so, proceeds to step ④ Ij shellfish; otherwise, returns to step ①;

[0019] 步骤④:进行容量估计宏观采样点1+1下的系统容量预估,得到系统容量预估值; [0019] Step ④: estimating the capacity of macro sampling point system capacity estimated at 1 + 1, to obtain an estimate the system capacity;

[0020] 步骤⑤:基于步骤④中的获得的系统容量预估值和最近一次步骤②中获得的系统SOC预估修正值,利用所述容量-SOC-OCV三维响应面,再次更新当前系统开路电压得到第二开路电压; [0020] Step ⑤: system capacity based system to obtain an estimate obtained in step ④ and ② in the last step of the correction value estimated SOC, -SOC-OCV using the capacity of a three-dimensional response surface, again updating the current open system open circuit voltage to obtain a second voltage;

[0021] 基于所述第二开路电压,进行容量估计宏观采样点1+1下的系统容量预估值的修正,得到系统容量预估修正值;并返回步骤①。 [0021] Based on the second open-circuit voltage, the capacity correction system capacity estimation value estimated in macro 1 + 1 sampling point, to obtain system capacity is estimated correction value; and returns to step ①.

[0022] 优选地、步骤①中利用SOC估计微观采样点k-Ι下的系统电流值和系统SOC预估修正值,以及最近一次采样点1的系统容量预估修正值来进行系统SOC预估。 [0022] Preferably, the step ① SOC estimation system utilizing a current value and a system in micro sampling point k-Ι SOC estimated correction value, the system capacity and the most recent sample point 1 to the correction value estimated SOC estimated system .

[0023] 优选地、步骤②中进行所述系统SOC预估值的修正,基于步骤①中的获得的系统SOC预估值、所述第一开路电压、SOC估计微观采样点k下的电流值和电压值。 [0023] Preferably, the step ② is corrected SOC estimates of the system, based on obtained in step ① system SOC estimates, the first open-circuit voltage, the estimated SOC value at the current sample point k microscopic and voltage values.

[0024] 优选地、步骤⑤中进行所述系统容量预估值的修正,基于第二开路电压、容量估计宏观采样点1+1或最近一次SOC估计微观采样点下的电流值和电压值。 [0024] Preferably, the correction is performed in step ⑤ the system capacity estimates, based on second open circuit voltage, capacity estimating macroscopic samples 1 + 1 or last SOC estimation current value and the voltage value at the microscopic samples.

[0025] 优选地、利用系统容量预估修正值评估所述SOH。 [0025] Preferably, the system capacity using the estimated correction value evaluating the SOH.

[0026] 优选地、所述SOC估计微观采样点的采样频率与所述电流、电压采样频率相同。 [0026] Preferably, the sampling frequency and the estimated SOC microscopic current sampling point, the sampling frequency of the same voltage.

[0027] 优选地、建立所述容量-SOC-OCV三维响应面的方法为:对不同容量下的SOC与OCV 关系进行拟合,得到各所述不同容量下的组合模型系数,采用二次函数对各组合模型系数与容量的关系进行拟合,完成容量-SOC-OCV三维响应面的建立。 [0027] Preferably, the method of establishing the capacity of the three-dimensional response surface -SOC-OCV of: fitting relationship of SOC and OCV in different capacities, to obtain a combination of the model coefficients at each of the different capacity, quadratic function relational model coefficients were fitted to the capacity of each combination, to complete the establishment capacity -SOC-OCV dimensional response surface.

[0028] 优选地、通过电池管理系统实时采集动力电池单体和动力电池组的电压和电流。 [0028] Preferably, the battery management system by collecting cell voltage and current power and the power of the battery pack in real time.

[0029] 本发明所提出的联合估计方法与传统方法相比具有以下优势: [0029] Estimation Method for the conventional method proposed by the present invention compared with the following advantages:

[0030] (1)采用SOC估计的期望值与误差限(以95%置信区间为例,但不限于95%)更加全面、准确地评价动力电池的荷电状态可能分布情况; [0030] (1) The expected value of the SOC estimation error limit (95% confidence interval, for example, but not limited to, 95%) as a more comprehensive and accurate evaluation of the state of charge of the battery may power distribution;

[0031] ⑵在SOC与容量初值均不准确(20%误差)的情况下均能快收敛到真值,即实现了电池最大容量未知时,SOC的准确估计,解决了传统SOC估计算法以最大可用容量已知为前提而无法成功运用到实车上的难题; [0031] ⑵ capacity at the initial value and not accurate SOC (20% error) of the case can quickly converge to the true value, i.e., to achieve the maximum capacity of the battery is unknown, an accurate estimate SOC, to solve the conventional SOC estimation algorithm the maximum available capacity is known as the premise can not be successfully applied to the problem of the real car;

[0032] (3)相比单时间尺度算法而言,不仅估计精度得到了较大的提高,而且较大地减少了算法计算量与计算时间。 [0032] (3) in terms of time as compared to a single scaling algorithm, estimation accuracy is not greatly improved, but also greatly reduces the amount of computation time algorithm.

附图说明: BRIEF DESCRIPTION OF:

[0033] 图1多时间尺度的动力电池系统SOC和SOH联合估计方法流程图; More than one time scale [0033] FIG battery power system for estimating SOC and SOH combined flowchart of a method;

[0034] 图2动力电池Thevenin等效电路模型; [0034] FIG 2 battery Thevenin equivalent circuit model;

[0035] 图3容量-SOC-OCV三维响应面; [0035] FIG capacity -SOC-OCV 3-dimensional response surface;

[0036] 图4单时间尺度双ft»滤波算法端电压、SOC与容量估计结果。 [0036] FIG. 4 double single time scale. Ft >> filtering algorithm voltage, SOC and capacity estimation result. 其中:(a)、端电压预测值与测量值对比;(b)、端电压预测误差;(c)、S0C估计值与参考值对比;⑹、SOC估计误差; (e)、容量估计值与参考值对比;(f)、容量估计误差。 Wherein: (a), the terminal voltage of the predicted value and the measured contrast values; (b), the terminal voltage of the prediction error; (c), S0C estimated comparison value and the reference value; ⑹, SOC estimation error; (e), the capacity estimation value and comparison reference value; (f), the capacity of the estimation error.

[0037] 图5多时间尺度ft»滤波算法端电压、SOC与容量估计结果。 [0037] FIG. Ft time scale over 5 »filtering algorithm voltage, SOC and capacity estimation result. 其中:(a)、端电压预测值与测量值对比;(b)、端电压预测误差;(c)、S0C估计值与参考值对比;(d)、S0C估计误差; (e)、容量估计值与参考值对比;(f)、容量估计误差。 Wherein: (a), the terminal voltage of the predicted value and the measured contrast values; (b), the terminal voltage of the prediction error; (c), S0C estimated value with a reference comparison value; (d), S0C estimation error; (e), capacity estimation comparison value with a reference value; (f), the capacity of the estimation error.

具体实施方式: detailed description:

[0038] 如本领域公知的,本发明的动力电池系统包括动力电池单体、动力电池包、或者成组后的动力电池。 [0038] As is known in the art, battery power system of the present invention includes a single battery, battery pack, or battery power to the group.

[0039] 本发明使用OCV代表开路电压,SOC代表电池荷电状态,SOH代表电池健康状态。 [0039] The present invention represents the use of the open-circuit voltage OCV, representative of the state of charge of the battery SOC, the SOH representative of the battery state of health.

[0040] 本发明所述的一种基于多时间尺度的动力电池系统荷电状态(SOC)与健康状态(SOH)的联合估计方法如附图1所示。 [0040] The present invention is based on one of said traction battery charge state of the multi system time scale (SOC) and state of health (the SOH) Estimation Method As shown in Figure 1.

[0041] 本发明的基于多时间尺度的动力电池系统荷电状态(SOC)与健康状态(SOH)的联合估计方法也同样适用于非线性系统。 Invention (SOC) and state of health (the SOH) Estimation Method is equally applicable to nonlinear systems [0041] The present battery state of charge based on a multi-time scale system.

[0042] 本发明系统的状态指代时时变化的系统指标,包括电池SOC、极化电压,如SOC—次完全充放电过程中即完成了一个完整周期的变化。 [0042] The system of the present invention refers to the state of the system changes from time to time the generation of indicators, including the battery the SOC, polarization voltage, as SOC- complete charge and discharge to complete the process of change in a complete cycle. 而系统的参数指代相对于状态变化较慢的系统指标,如电池容量和电池模型参数,其在一次完全充放电过程中几乎没有变化。 Refer to the parameters of the system with respect to slow changes in the system status indicators, such as battery capacity and the model parameters, which is almost no change in a complete charge-discharge process. 本发明下述系统非特殊说明皆指代动力电池系统,系统状态对应电池系统状态,优选地对应电池系统的SOC。 Non-specific system of the present invention are described below referring to battery power system, the battery system state corresponding to the state of the system, preferably corresponds to the SOC of the battery system. 系统参数对应电池系统参数,优选地对应电池系统SOH或最大可用容量。 System parameters corresponding to parameters of the battery system, the battery system preferably corresponds to SOH or the maximum available capacity.

[0043] 该联合估计方法包括:容量-SOC-OCV三维响应面的建立、在线数据获取、多时间尺度Η~滤波算法、以及在线SOC与SOH提取四个方面。 [0043] The joint estimation method comprising: establishing capacity -SOC-OCV dimensional response surface, on-line data acquisition, multiple time scales Η ~ filtering algorithm, and the SOC and SOH extraction line four. 下面分别对上述四个方面就行详细叙述: Below each of the above four aspects described in detail on the line:

[0044] 算法准备工作:容量-SOC-OCV三维响应面的建立 [0044] Preparation method: establishing capacity -SOC-OCV dimensional response surface

[0045] 相对稳定的SOC-OCV曲线,即开路电压曲线,常常作为SOC估计算法的修正曲线,但电池使用温度、老化程度等变化时,该曲线同样会发生较为明显的变化。 When the [0045] relatively stable SOC-OCV curve, i.e., open circuit voltage curve, usually a correction curve SOC estimation algorithm, the battery temperature, aging degree of variation, the more obvious the same curve will change. 本发明将温度、老化程度等因素对该曲线的影响直接反映到电池容量的差异之上,利用容量、SOC与OCV三者的关系作为容量与SOC联合估计算法的修正曲面。 The present invention is the effect of temperature, aging and other factors of the curve is directly reflected on the difference in the battery capacity, the capacity utilization, the relationship between SOC and OCV and the SOC as the capacity of the three joint estimation correction algorithm surface. 具体过程如下: Specific process is as follows:

[0046] 在不同电池容量(即温度、老化程度变化时)下进行开路电压试验,以获取不同电池容量下的SOC与OCV对应关系,采用组合模型(如式(1)所示)分别对不同容量下的SOC与OCV关系进行拟合,从而得到各个不同容量下的α〇,αι,…,CI6参数值,最后采用二次函数(如式(2)所示)对参数α〇,αι,…,Ct6与容量的关系进行拟合,至此完成容量-SOC-OCV三维响应面的建立。 [0046] carried out at different battery capacity (i.e., when the change in temperature, the degree of aging) the open circuit voltage test, to obtain a correspondence relationship between SOC and OCV under different battery capacity, using a combination of models (e.g., of formula (1)) are different SOC and OCV fitting relationship in capacity, resulting in various capacities α〇, αι, ..., CI6 parameter value of the last quadratic function (e.g. formula (2)) of the parameters α〇, αι, ..., and the relationship between the capacity Ct6 fitting, thereby completing dimensional response surface capacity -SOC-OCV.

[0047] Uoc (Ca,ζ) =α〇+αιζ+α2Ζ2+α3Ζ3+α4/z+asln (z) +〇6ln (l~z) (I) [0047] Uoc (Ca, ζ) = α〇 + αιζ + α2Ζ2 + α3Ζ3 + α4 / z + asln (z) + 〇6ln (l ~ z) (I)

Figure CN106291381BD00061

(2) (2)

[0049] Ca为电池容量; [0049] Ca battery capacity;

[0050] z 为电池SOC; [0050] z is the SOC of the battery;

[0051] Ucic(C^Z)表示开路电压(OCV),其为电池容量与SOC的函数; [0051] Ucic (C ^ Z) represents the open circuit voltage (OCV), which is a function of the SOC of the battery capacity;

[0052] αο,αν,ΰ^为组合模型的系数; [0052] αο, αν, ΰ ^ as a combination of model coefficients;

[0053] 上标T表示矩阵的转置; [0053] superscript T denotes the transpose of a matrix;

[0054] Λ为7 X 3常数矩阵。 [0054] Λ 7 X 3 is a constant matrix.

[0055] 1、在线数据获取 [0055] 1, online data acquisition

[0056] 当电动汽车运行时,动力电池系统中的电池管理系统(BMS)能够通过数据采集器实时采集动力电池单体和动力电池组的电压、电流等信息,并储存在相应的存储器,为下面的多时间尺度Η~滤波算法提供可靠地实时信息输入,所述信息输入包括tk时刻系统的测量值yk=Ut,k,tk时刻系统的输入信息uk=iL,k。 [0056] When the electric vehicle is running, the battery power system battery management system (BMS) can be acquired by data acquisition in real time voltage and current information of the power cell and the power of the battery pack, and stored in a corresponding memory for the following multi-time scale Η ~ filter algorithm to provide reliable real-time information input, the input information comprises information input measured values ​​uk yk = Ut, k, tk tk system time system time = iL, k.

[0057] 控制电流; [0057] a control current;

[0058] Ut,k为端电压。 [0058] Ut, k is a terminal voltage.

[0059] 2、多时间尺度Ε»滤波算法 [0059] 2, a multi-time scale Epsilon »Filtering Algorithm

[0060] 本发明使用多时间尺度ft»滤波算法来实现动力电池参数与状态联合精确估计。 [0060] The present invention accurately estimate the combined use of multiple time scale. Ft >> filtering algorithms to achieve dynamic parameters and the battery status. 该算法适用于如下非线性离散系统: The algorithm is intended for non-linear discrete system:

Figure CN106291381BD00071

(3) (3)

[0062] X表示系统的状态; [0062] X represents the state of the system;

[0063] Θ表示系统的参数; [0063] Θ represent the parameters of the system;

[0064] 下标k表示tk时刻系统采样时间点(外界输入),同时也代表了状态估计的时间尺度,即在每个采样时间点下均进行一次状态估计; [0064] The subscript k indicates the sampling time points tk timing system (external input), but also represents the state estimate time scale, i.e., a state estimation are performed at each sampling time points;

[0065] 下标1表示参数估计的时间尺度,其数值上等于k除以L的商(L为尺度转换限值), 即每隔L个采样时间点进行一次参数辨识,且每次参数辨识结果被用来估计tlxL时刻之后的L个时刻下的状态值; [0065] The subscript 1 indicates the estimated time scale parameter, its value is equal to k divided by the quotient of L (L is the scaling value), i.e. once every L samples parameter identification point in time, and each parameter identification the results were used to estimate the state after the time L tlxL time value;

[0066] 微观时间尺度,即上述状态估计的时间尺度; [0066] The microscopic time scale, i.e., the above-described state estimation time scale;

[0067] 宏观时间尺度,即上述参数估计的时间尺度; [0067] The macroscopic time scale, i.e., the time scale of said parameter estimation;

[0068] f (xk-i,Q1 ,Uk-O表示模型的状态函数; [0068] f (xk-i, Q1, Uk-O represents a state function model;

[0069] g (XkJiuk)表示模型的观测函数; [0069] g (XkJiuk) represents a function of the observation models;

[0070] yk为tk时刻系统的测量值,yk = Ut, k; [0070] yk is the system measurement at tk, yk = Ut, k;

[0071] Uk为tk时刻系统的输入信息,Uk=iL,k; [0071] Uk as the input timing information tk system, Uk = iL, k;

[0072] wk-i和Pi-i分别为系统状态噪声和参数噪声,Vk为测量噪声,在Η~滤波算法之中,所述系统状态噪声、参数噪声和测量噪声被设计为随机且未知的,突破了传统滤波算法状态噪声、参数噪声和测量噪声为白噪声这一假设,因而与实际生产结合更加紧密。 [0072] wk-i, and Pi-i are noise and system state parameters of the noise, Vk is the measurement noise in Η ~ filtering algorithm, the system state noise, the parameters and measurement noises is designed to be random and unknown , breaking the traditional state noise filtering algorithm, and the parameters measured noise assumption that the noise is white, and thus more closely with the actual production.

[0073] 对于电池系统这一非线性离散系统而言,将上述参数具体定义如下: [0073] For cell system of this nonlinear discrete system, the above parameters defined as follows:

[0074] 本发明使用The ven i η动力电池等效电路模型为例来阐述该动力电池SOC与SOH联合估计方法。 [0074] The present invention uses The ven i η battery equivalent circuit model to illustrate an example of the battery SOC Estimation Method and SOH. 图2为Thevenin动力电池等效电路模型,该模型由电压源、欧姆内阻、以及RC网络三部分组成。 FIG 2 is a Thevenin equivalent circuit model of a battery power, this model voltage source consists of three parts, an ohmic resistance and an RC network. 根据各元器件特性以及电学基本定律建立相应数学模型,如式⑷所示。 To establish the corresponding mathematical model according to the characteristics of each component and the basic laws of electricity, as shown in formula ⑷.

[ [

Figure CN106291381BD00081

[0076] Up为极化电压, [0076] Up to the polarization voltage,

Figure CN106291381BD00082

为其导数; Its derivative;

[0077] Cp为极化电容; [0077] Cp is the polarization capacitance;

[0078] Rp为极化电阻; [0078] Rp is the polarization resistance;

[0079] iL为控制电流; [0079] iL control current;

[0080] Ut为端电压; [0080] Ut is the terminal voltage;

[0081] U。 [0081] U. . 为开路电压; Open circuit voltage;

[0082] Rq为欧姆内阻。 [0082] Rq ohms resistance.

[0083] 动力电池SOC的计算方程为: [0083] battery SOC calculation equation is:

[0084] [0084]

Figure CN106291381BD00083

[0085] ZQ表示SOC的初值; [0085] ZQ represents the initial value of the SOC;

[0086] Ca为动力电池最大可用容量(下文简称为容量),即指在一定的使用条件下,电池充满电后能放出的最大电量,同时电池最大可用容量是表征电池健康状态(SOH)的重要参数,即相同使用条件下,电池最大可用容量越小,电池衰退越明显,电池健康状态(SOH)越差。 [0086] Ca is the maximum available power capacity of the battery (hereinafter simply referred to as capacity), refers to the maximum amount under certain conditions of use, the battery is fully charged can be discharged, while the battery is characterized by the maximum available capacity of the battery state of health (the SOH) of the important parameter, i.e., using the same conditions, the maximum available capacity of the battery is smaller, the apparent decline battery, the battery state of health (the SOH) worse.

[0087] 鉴于电压电流的采样是离散的,S卩SOC估计算法的输入也是离散的,因而有必要将式⑷和⑸线性离散化,同时改写成如式⑹所不的包含隐含状态Xk和隐含参数θι的多时间尺度动力电池系统,即: [0087] In view of the current sampled voltage is discrete, input S Jie SOC estimation algorithm is discrete, it is necessary and formula ⑷ ⑸ discrete linear, while rewritten as formula ⑹ do not contain the implicit state Xk and implicit parameter θι multiple time scale battery systems, namely:

Figure CN106291381BD00084

[0089] Δ t表示时间尺度k的单位时间间隔; [0089] Δ t k represents a time scale of a unit time interval;

[0090] n (iLX)表示充放电效率。 [0090] n (iLX) indicates the charge-discharge efficiency.

[0091] 基于上式(1)、(2)与(6),得到双ft»滤波算法中状态函数与观测函数关于状态或参数的雅克比矩阵: [0091] Based on the formula (1), (2) and (6), to give bis. Ft »filter algorithm and the observed state functions on a state or function parameter Jacques ratio of matrix:

Figure CN106291381BD00091

[0095] 状态函数关于状态的雅克比矩阵; [0095] The state function of the state of the Jacobian matrix;

[0096] [0096]

Figure CN106291381BD00092

^观测函数关于状态的雅克比矩阵; Jacques ^ observation function of the ratio of the state matrix;

[0097] [0097]

Figure CN106291381BD00093

.为观测函数关于参数的雅克比矩阵。 For the observation function of the parameters of the Jacobian matrix.

[0098] 这里,对上式(7-9)中各函数求导结果进行整理,即: [0098] Here, in the above formula (7-9) each of the derivative function results are arranged, namely:

Figure CN106291381BD00094

[0102]其中,初值dxo/de在未能得到可信的经验值的情况下,常常被初始化为零。 In the case [0102] where, the initial value dxo / de not been trusted in the experience, and are often initialized to zero.

Figure CN106291381BD00095

[0105] 至此,已完成动力电池非线性离线系统中各相关参数的定义,如式(6)-(9)所示。 [0105] At this point, we have completed the definition of each parameter related to the offline nonlinear power battery systems, such as the formula (6) - (9). 下面对该算法具体过程进行描述: The following specific description of the process of the algorithm:

[0106] 算法的初始化:分别设置宏观参数观测器H~Fe和微观状态观测器H~FX的初始参数值。 [0106] The initialization algorithm: Set the initial parameter values ​​are parameter observation macro and micro H ~ Fe state observer of H ~ FX. 包括: include:

Figure CN106291381BD00096

(15) (15)

[0109] [0109]

Figure CN106291381BD00097

_为参数观测器H~Fe中系统参数的初始值; The initial value for the parameter _ H ~ observer in the system parameters of Fe;

Figure CN106291381BD00098

[0110] [0110]

Figure CN106291381BD00099

..表示参数的估计值或期望值,即 A desired value or an estimated value of a parameter indicating .., i.e.,

Figure CN106291381BD000910

[0111] [0111]

Figure CN106291381BD000911

;为参数观测器Hc„Fe中矩阵的初始值; ; Is a parameter observation Hc "and Fe matrix initial value;

Figure CN106291381BD000912

[0112] [0112]

Figure CN106291381BD00101

为设计者基于系统噪声〇1所设计的对称正定阵,如假如我们提前知道了Pk的第三个元素很大(如比其它元素大几个数量级)时,那么 Based on symmetric positive definite matrix system noise 〇1 designed, as if we knew in advance (such as other elements than several orders of magnitude) when the third element Pk designer of large, then

Figure CN106291381BD00102

)应该大于/中其它元素; ) Should be greater than / other elements;

Figure CN106291381BD00103

[0113] [0113]

Figure CN106291381BD00104

..为参数观测器Hc„Fe中矩阵 .. observer parameters Hc "Fe Matrix

Figure CN106291381BD00105

/的初始值; The initial value of / in;

[0114] [0114]

Figure CN106291381BD00106

/为设计者基于测量噪声Vk所设计的对称正定阵,如假如我们提前知道了Vk的第二个元素很大时,那么 / Designers based on a symmetric positive definite matrix Vk designed measurement noise, as if we know in advance when the second element Vk is large, then

Figure CN106291381BD00107

I应该大于」 I should be more than "

Figure CN106291381BD00108

中其它元素; Other elements;

[0115] Se为参数观测器H~Fe中设计者基于参数Θ中各分量的关心程度而设定的对称正定阵,如当我们对状态向量9k的第3个元素非常感兴趣时,那么可以设计50(3,3)使得其远远大于&中其它元素; [0115] Se is a parameter observation H ~ Fe based on the degree of interest in the designer of each component of the parameter Θ set of symmetric positive definite matrix, as when we are very interested in the third element of the state vector 9k, it can be design 50 (3,3) such that it is much larger than & amp; other elements;

[0116] [0116]

Figure CN106291381BD00109

+为参数观测器Hc„Fe中设计者基于系统估计误差所设计的对称正定阵,如当我们对参数向量初值%的第3个元素一无所知时,那么可以设计·^ + Symmetric positive definite matrix system estimates based on the error to the designed parameter observation Hc "the designer Fe, such as when the initial value of the parameter vector We know nothing about 3% of elements, it can be designed ^ ·

Figure CN106291381BD001010

丨使得其远远大于 Shu making it much larger than

Figure CN106291381BD001011

中其它元素; Other elements;

[0117] λθ为参数观测器H~Fe选定的性能边界,选定的性能边界值越大说明算法鲁棒性越强,即能更好地适应外界的干扰(如噪声等),且当性能边界设置为〇(最小值)时,算法退化为卡尔曼滤波算法,但大的性能边界值往往依赖于矩阵 [0117] λθ is a parameter observation performance H ~ Fe selected boundaries, the performance of the selected limit value the greater the robustness of the algorithm, that is better adapted to the external disturbance (e.g., noise, etc.), and when when the performance of the boundary set square (minimum value), the algorithm is the Kalman filter algorithm degraded, but the performance is often dependent on the boundary value matrix

Figure CN106291381BD001012

;的充分合理设计,因而使得算法的调试难度较大; ; Fully rational design, thus making it more difficult to debug algorithms greater;

[0118] [01]

Figure CN106291381BD001013

为状态观测器H~FX中的系统状态的初始值; State observer system state H ~ FX in the initial value;

Figure CN106291381BD001014

[0119] [0119]

Figure CN106291381BD001015

即为状态Xk的估计值或期望值,即 Xk is the state estimation value or desired value, i.e.,

Figure CN106291381BD001016

[0120] [0120]

Figure CN106291381BD001017

/为状态观测器E„FX中的系统状态估计误差协方差矩阵棼的初始值; / E "FX system state estimation error covariance matrix initial value is confused state observer;

[0121] [0121]

Figure CN106291381BD001018

„为状态估计协方差矩阵,不同于以往仅从期望值毛评价估计结果,本专利从期望值 "Is the covariance matrix of the estimated state, only a desired value different from the previous gross evaluation results of estimation, the expected value from the present patent

Figure CN106291381BD001019

和方差S两个方面更全面地评价系统估计结果,并引入估计误差限(式(12))概念,使得估计结果更加直观; And both the variance S more comprehensive evaluation of the results of estimation system, and introduce an estimation error limit (of formula (12)) concept, so that more intuitive estimation result;

[0122] [0122]

Figure CN106291381BD001020

为状态观测器H~FX中矩阵: A state observer in the matrix H ~ FX:

Figure CN106291381BD001021

:初始值; : Initial value;

[0123] [0123]

Figure CN106291381BD001022

:为设计者基于系统噪声Wk所设计的对称正定阵,如假如我们提前知道了Wk的第二个元素很大时,那么_丨应该大于 : Designers based on symmetric positive definite matrix Wk designed system noise, as if we knew in advance when the second element Wk is large, then _ Shu should be greater than

Figure CN106291381BD001023

中其它元素; Other elements;

Figure CN106291381BD001024

[0124] [0124]

Figure CN106291381BD001025

„为状态观测器H~FX中矩阵 "Is a state observer in the matrix H ~ FX

Figure CN106291381BD001026

初始值; Initial value;

[0125] [0125]

Figure CN106291381BD001027

为设计者基于测量噪声Vk所设计的对称正定阵,如假如我们提前知道了Vk的第 Designer based on symmetric positive definite matrix Vk designed measurement noise, as if we knew in advance of the Vk

Figure CN106291381BD001028

三个元素非常大时,那么、)应该大于和中其它元素; When the three elements is very large, then,) should be greater than and the other elements;

[0126] Sx为参数观测器H~FX中设计者基于状态X中各分量的关心程度而设定的对称正定阵,如当我们对状态向量Xk的第三个元素非常感兴趣时,那么可以设计Sx (3,3)使得其远远大于Sx中其它元素; [0126] Sx for the parameter observation H ~ FX designer based on a degree of interest in the state of each component in the X set of symmetric positive definite matrix, as when we are very interested in the third element of the state vector Xk, then you can be design Sx (3,3) such that it is much larger than the other elements Sx;

Figure CN106291381BD001029

[0127] ,为参数观测器HeFx中设计者基于系统估计误差所设计的对称正定阵,如当我 [0127], based on a symmetric positive definite matrix system estimates error for the design parameters of the observer HeFx the designer, such as when I

Figure CN106291381BD001030

们对参数向量初值XO的第三个元素一无所知时,那么可以设计 They parameter vector of the initial value XO third element ignorant, you can design

Figure CN106291381BD001031

使得其远远大于中其它元素; Such that it is much larger than other elements;

[0128] λχ为状态观测器H~FX选定的性能边界,选定的性能边界值越大说明算法鲁棒性越强,即能更好地适应外界的干扰(如噪声等),且当性能边界设置为〇(最小值)时,算法退化为卡尔曼滤波算法,但大的性能边界值往往依赖于矩阵 [0128] λχ state observer H ~ FX selected performance boundaries, the performance of the selected limit value the greater the robustness of the algorithm, that is better adapted to the external disturbance (e.g., noise, etc.), and when when the performance of the boundary set square (minimum value), the algorithm is the Kalman filter algorithm degraded, but the performance is often dependent on the boundary value matrix

Figure CN106291381BD001032

与的充分合理设计,因而此时 And fully rational design, which at this time

Figure CN106291381BD001033

算法的调试难度较大。 Debugging difficult algorithm is large.

[0129] 当采样时间1^£{1,2,...,^-}时,基于电流、电压等信息的不断输入,计算: [0129] When the sampling time 1 ^ £ {1,2, ..., ^ -}, the input information based on the continuous current and voltage is calculated:

[0130] 步骤①:基于微观时间尺度的状态观测器E„FX的时间更新(先验估计)- [0130] Step ①: Observer E "FX update time (a priori estimation) based on the state of microscopic time scale -

Figure CN106291381BD00111

[0131] 利用k-1采样点的电流值Uk-i = k, ki、系统状态估计值的修正值,以及最近一次采样点1的系统参数估计值的修正值来进行系统状态预估。 [0131] k-1 using the current value of the sampling point Uk-i = k, ki, the correction value of the system state estimates, and the most recent sample value of the estimated value of the correction system parameter points to a primary system state estimate.

Figure CN106291381BD00112

[0132] 同时,利用k-Ι采样点的系统状态设计矩阵预估 [0132] Meanwhile, the use of the sampling point k-Ι design matrix system state estimate

Figure CN106291381BD00113

完成系统状态设计矩阵的时间更新 Complete the system design matrix of time to update status

Figure CN106291381BD00114

,所述系统状态设计矩阵更新 , The system updates the state of the design matrix

Figure CN106291381BD00115

在步骤②中被用来更新状态增益矩阵 In step ② it is used to update the state gain matrix

Figure CN106291381BD00116

,如式(20)所示。 , Formula (20) shown in FIG.

[0133] 系统状态预估: [0133] System Status estimate:

Figure CN106291381BD00117

(16) (16)

[0134] 系统状态设计矩阵预估: [0134] System state design matrix estimate:

Figure CN106291381BD00118

(17) (17)

[0135] 系统状态误差协方差预估:...........—. (18) [0135] The system state error covariance estimate: ...........- (18).

Figure CN106291381BD00119

Figure CN106291381BD001110

.[为tk时刻状态先验估计值, . [Priori estimate for the state value at tk,

Figure CN106291381BD001111

、,即采用tk时刻之前(不包括k)的测量值来预估Xk; Before using ,, i.e. at tk (excluding k) to estimate the measurement values ​​Xk is;

[0136] [0136]

Figure CN106291381BD001112

丨表示模型的状态函数; Shu represents the state function model;

[0137] [0137]

Figure CN106291381BD001113

Atk-I时刻状态后验估计值, After time posteriori state estimate Atk-I,

Figure CN106291381BD001114

·,即采用tk-i时刻与tk-i时刻以前的测量值来估计xk-i,这里基于上一次循环(S卩时刻)的输出直接得到; *, I.e., tk-i using the measurement time and the previous time tk-i estimates xk-i, obtained directly on the cycle (S Jie timing) here based on the output;

[0138] [0138]

Figure CN106291381BD001115

,为tixL时刻参数后验估计值, After inspection parameters tixL time estimate

Figure CN106291381BD001116

,即采用tixL时刻与tixL时刻以前的测量值来估计θΐ,这里基于先前循环的输出直接得到; , I.e. using the time and the tixL tixL before time measurements to estimate θΐ, directly previous cycle output based here;

[0139] Uk为tk时刻系统的输入信息,其为已知量; [0139] Uk as the input information at tk system, which is a known quantity;

[0140] [0140]

Figure CN106291381BD001117

为tk时刻状态设计矩阵β的先验估计结果,与%相对应; A priori estimation result at tk state β of the design matrix, the corresponding%;

[0141] [0141]

Figure CN106291381BD001118

为以 It is to

Figure CN106291381BD001119

+为初值并通过式(17)、(22)不断递推得到的ft»无穷滤波状态设计矩阵; + Is the initial value by the formula (17), (22) continuously recursive obtained. Ft >> endless filter state design matrix;

Figure CN106291381BD001120

时刻的状态设计矩阵d State timing design matrix d

Figure CN106291381BD001121

1:的后验估计结果,与 1: a posteriori estimation results, and

Figure CN106291381BD001122

t相对应,这里基于上一次循环(即tk-l·时刻)的输出直接得到; t corresponds directly previous cycle (i.e., time tk-l ·) output based here;

[0142] [0142]

Figure CN106291381BD001123

^为设计者基于系统噪声Wk所设计的对称正定阵在时刻的值; ^ Designers time value based on a symmetric positive definite matrix Wk designed system noise;

[0143] [0143]

Figure CN106291381BD001124

为tk时刻状态估计误差协方差矩阵骂的先验估计结果,与 State estimation result priori estimate error covariance matrix for the call at tk, and

Figure CN106291381BD001125

..相对应; ..Corresponding;

[0144] [0144]

Figure CN106291381BD001126

为时刻的状态估计误差协方差矩阵Σ。 For the time state estimation error covariance matrix Σ. 的后验估计结果,与 Posteriori estimation results, and

Figure CN106291381BD001127

,相对应,这里基于上一次循环(即时刻)的输出直接得到。 , Corresponds to, based on where the previous cycle (i.e., timing) output directly.

[0145] 步骤②:基于微观时间尺度的状态观测器H~FX的测量更新(后验估计)_ [0145] Step ②: (a posteriori) based on the measurement update state observer microscopic time scale of _ H ~ FX

Figure CN106291381BD001128

Figure CN106291381BD001129

[0146] 基于步骤①中的获得的系统状态预估」和上个参数估计采样点的系统参数估计值修正值 Parameter estimation system estimates the correction value of the sampling points, "and the parameters [0146] Estimated based on the system state obtained in step ① of

Figure CN106291381BD001130

(即参数后验估计值),利用容量-SOC-OCV三维响应面,获得对应的第一开路电压Uoc O (I.e., the posterior estimate of the parameter), utilize the capacity of the three-dimensional response surface -SOC-OCV, to obtain a corresponding first open-circuit voltage Uoc O

[0147] 然后,基于上述11。 [0147] Then, based on the above 11. . 和当前采样点下的电流Uk=iL,k和电压值yk = UL,k,进行当前状态估计微观采样点k下的状态测量更新,即公式(21)所示系统状态估计值修正。 And a current at the current sampling point Uk = iL, k, and the voltage value of yk = UL, k, the state estimate for the current state in the micro-measurement update of sampling points k, i.e. formula (21) shown in the system state estimation value correction.

[0148] 其中如公式⑹所示,U。 [0148] As shown in the formula wherein ⑹, U. . 与直接决定了 And will determine the

Figure CN106291381BD001131

的大小,利用U。 The size, the use of U. . 完成公式(19)所示系统状态估计新息更新。 Finish the formula (19) new information update system state estimation.

[0149] 系统状态估计新息更新: [0149] estimated that the new system status information update:

Figure CN106291381BD00121

(19) (19)

[0150] 系统状态增益矩阵: [0150] The system state gain matrix:

Figure CN106291381BD00122

[0152] 在系统状态预估%的基础上,利用系统状态估计新息更新< 和系统状态增益矩阵 [0152]% of the estimated system state based on the use of new information to update the system state estimation <system state gain matrix and

Figure CN106291381BD00123

对..进行修正,得到系统状态估计值修正 .. corrected to obtain correction system state estimate

Figure CN106291381BD00124

(20) (20)

[0151] [0151]

Figure CN106291381BD00125

[0153] 系统状态估计值修正: [0153] The system state estimate correction:

Figure CN106291381BD00126

(2:1) (2: 1)

[0154] 其中,利用步骤①获得的系统状态设计矩阵预估值 [0154] wherein the step of using a system state ① matrix estimate is obtained of the design

Figure CN106291381BD00127

_完成系统状态设计矩阵的 _ Complete system status of the design matrix

Figure CN106291381BD00128

测量更新 Measurement update

Figure CN106291381BD00129

,所述系统状态设计矩阵更新..被用来预估下一时刻的系统状态设计矩阵 The design matrix system state estimate is used to update the system state .. design matrix next time

Figure CN106291381BD001210

,见式(17)。 See formula (17).

[0155] 系统状态设计矩阵更新: [0155] System design matrix status update:

Figure CN106291381BD001211

(2:2:) (2: 2 :)

[0156] 由于最终状态估计结果呈现以 [0156] As the final state estimation results are presented in

Figure CN106291381BD001212

J为期望值, J desired value,

Figure CN106291381BD001213

+为方差的近似正态分布,因此利用状态估计误差协方差评价状态估计值的精确度与稳定性。 + Variance is approximately normal distribution, thus using the state estimation accuracy and stability state evaluation variance estimate error covariance.

[0157] 系统状态估计误差协方差更新 [0157] The system state estimate error covariance update

Figure CN106291381BD001214

(23) (twenty three)

[0158] 系统状态估计误差限(95%置信区间): [0158] The system state estimation error limit (95% confidence interval):

Figure CN106291381BD001215

(24) (twenty four)

Figure CN106291381BD001216

为状态估计新息,即测量值的预估误差; To estimate a new state information, i.e. the estimated error value of the measure;

[0Ί59] yk为tk时刻系统的测量值; [0Ί59] yk is the system measurement at tk;

[0160] [0160]

Figure CN106291381BD001217

丨表示模型的观测函数; Shu represents the observation function model;

[0161] [0161]

Figure CN106291381BD001218

为状态增益矩阵; State gain matrix;

[0162] [0162]

Figure CN106291381BD001219

为设计者基于测量噪声vk所设计的对称正定阵在tk时刻的值; Designer based on symmetric positive definite matrix measurement noise vk designed value time tk;

[0163] I为单位矩阵; [0163] I is the identity matrix;

[0164] α为常向量,用于提取矩阵巧'+(此矩阵对角线上元素远大于其所在行其它元素)对角线上的元素,且a=[l I 1]τ。 [0164] α is a constant vector for extracting matrix clever '+ (diagonal elements of this matrix is ​​much larger than the other elements in its row) on the diagonal elements, and a = [l I 1] τ.

[0165] 步骤③:判断k-1是否能被L整除,如果能,则令I = 1+1,并继续下一步;否则,则返回准备下一采样时刻的计算。 [0165] Step ③: k-1 is determined whether L can be divisible, if so, to make I = 1 + 1, and continue to the next step; otherwise, it returns ready to calculate the next sampling instant.

[0166] 步骤④:基于宏观时间尺度的状态观测器E„Fe的时间更新(先验估计)- [0166] Step ④: macroscopic time scale based on a state observer E "Fe update time (a priori estimate) -

Figure CN106291381BD001220

[0167] 系统参数预估: [0167] system parameter estimates:

Figure CN106291381BD001221

(2.5) (2.5)

[0168] 系统参数设计矩阵预估: [0168] system parameter design matrix estimate:

Figure CN106291381BD001222

(26) (26)

Figure CN106291381BD001223

~为tlxL时刻参数先验估计值, ~ TlxL time parameter is a priori estimate,

Figure CN106291381BD001224

·,即采用tlxL时刻之前(不包括tlXL)的测量值来预估θΐ; *, I.e. before use measurements tlxL time (not including tlXL) to estimate θΐ;

[0169] [0169]

Figure CN106291381BD001225

_ StiXL时刻状态设计矩阵Pif^先验估计结果,与相对应; _ StiXL time state of the design matrix Pif ^ priori estimation result corresponding to;

Figure CN106291381BD001226

[0170] P10为以 [0170] P10 as to

Figure CN106291381BD001227

为初值并通过式(26)、(30)不断递推得到的He»无穷滤波参数设计矩阵;1为ta-D XL时刻的状态设计矩阵 Is the initial value by the formula (26), (30) of He continued recursive obtained »endless filter parameters design matrix; a matrix for the state design time ta-D XL

Figure CN106291381BD00131

的后验估计结果,与 Posteriori estimation results, and

Figure CN106291381BD00132

相对应,这里通过上一次循 Correspondingly, where a cycle through the upper

Figure CN106291381BD00133

环的计算结果直接得到; The results obtained directly ring;

[0171] [0171]

Figure CN106291381BD00134

为设计者基于系统噪声Ph所设计的对称正定阵在时刻ta-Di的值。 Designer-Di ta value at a time based on a symmetric positive definite matrix Ph designed system noise.

[0172] 步骤⑤:基于宏观时间尺度的状态观测器Hc„Fe的测量更新(后验估计) [0172] Step ⑤: macroscopic time scale based on a state observer Hc "measurement update of Fe (posteriori)

Figure CN106291381BD00135

[0173] 基于步骤④中的获得的系统参数预估貪和最近一次步骤②中获得的系统状态估计值修正f,利用所述容量-SOC-OCV三维响应面,再次更新当前系统开路电压U。 [0173] Estimated corrupt the system state and the last step ② estimation value obtained in the correction step ④ system parameter f is obtained based on the capacity -SOC-OCV using the three-dimensional response surface, the system updates the current open circuit voltage again U. . ;

[0174] 基于上述再次更新的当前系统开路电压U。 [0174] Based on the above system again updates the current open circuit voltage U. . 、当前采样点下的电流uk=iL,k和电压值yk=Ul , k,进行当前容量估计宏观采样点下的系统参数估计值的修正。 , The current at current sampling point uk = iL, k, and the voltage value of yk = Ul, k, for the current capacity estimation system estimates the correction parameter in macroscopic samples.

[0175] 其中如公式⑹所示,U。 [0175] As shown in the formula wherein ⑹, U. . 与直接决定了、 And will determine,

Figure CN106291381BD00136

啲大小,利用U。 GOD size, the use of U. . 完成系统状态估计新息更新,见公式(27)。 Complete new system state estimation information updates, see equation (27).

[0176] 系统参数估计新息更新: [0176] The new information system parameter estimation update:

Figure CN106291381BD00137

m m

[0177] 系统参数增益矩阵: [0177] Gain matrix system parameters:

Figure CN106291381BD00138

[0178] (28) [0178] (28)

[0179] 在系统参数预估 [0179] estimate the system parameters

Figure CN106291381BD00139

「的基础上,利用系统参数估计新息更新 "Based on the use of new information to update the system parameter estimation

Figure CN106291381BD001310

和系统参数增益矩阵 And system parameters gain matrix

Figure CN106291381BD001311

1对—进行修正,得到系统状态估计值修正 1 pair - correction, the correction to obtain the system state estimate

Figure CN106291381BD001312

Figure CN106291381BD001313

[0180] 系统参数估计值修正: [0180] System correction parameter estimates:

Figure CN106291381BD001314

(29) (29)

[0181] 参数设计矩阵更新:. [0181] parameters of the design matrix update:

Figure CN106291381BD001315

_(30)_ _ (30) _

[0182] 其中,利用步骤④获得的系统参数设计矩阵预估, [0182] wherein the step ④ obtained using the system parameter estimates matrix design,

Figure CN106291381BD001316

_完成系统参数设计矩阵的测量更新 _ Complete the design matrix system parameter measurement update

Figure CN106291381BD001317

,所述系统参数设计矩阵更新 , Updates the system parameter design matrix

Figure CN106291381BD001318

用来更新下一时刻的参数增益矩阵,见公式(26)与(28)。 Parameters used to update the gain matrix next time, see equation (26) and (28).

Figure CN106291381BD001319

[0183] [0183]

Figure CN106291381BD001320

,为状态估计新息,即测量值的预估误差; , A new estimate for the state information, i.e. the estimated error value of the measure;

[0184] [0184]

Figure CN106291381BD001321

伺样表示模型的观测函数; It represents a sample servo observation function model;

[0185] [0185]

Figure CN106291381BD001322

,为参数增益矩阵; For the parameter gain matrix;

[0186] [0186]

Figure CN106291381BD001323

同样为设计者基于测量噪声Vk所设计的对称正定阵在tk时刻的值; The same measurement noise value based on the designer designed Vk symmetric positive definite matrix at time tk;

[0187] 经过上述五步之后,获得了tk时刻下系统参数估计值修正, [0187] After above five steps, to obtain an estimate of the time correction system parameters tk,

Figure CN106291381BD001324

与系统状态估计值修正笔,然后相应结果将会返回到步骤①中,并将其作为下一时刻tk+1下参数与状态估计的初值。 And system state estimation value correction pen, then the result will be returned to the appropriate step ①, and the parameters tk + 1 and the state of the next time is estimated as the initial value.

[0188] 3、SOC 与SOH 提取 [0188] 3, SOC and SOH extraction

[0189] 基于上述多时间尺度Η~滤波算法,得到实时的电池参数 [0189] Based on the multi-time scale Η ~ filtering algorithm, battery parameters in real time

Figure CN106291381BD001325

与状态$,通过式(31) 提取出状态量Zk,参数量Ca,i、Ro,i与Rp,i。 $ State, the state quantity extracted Zk, parameter amount of Ca, i, Ro by the formula (31), i and Rp, i.

[0190] [0190]

Figure CN106291381BD00141

(31) (31)

[0191] Ca, i表示tlxL时刻下更新的电池容量值; [0191] Ca, i represents a battery capacity value is updated at the time tlxL;

[0192] Rtu与Rp,^别表示tlxL时刻下更新的电池欧姆内阻与极化内组值。 [0192] Rtu and Rp, ^ denote the battery ohmic resistance polarization within the set of values ​​of the updated time tlxL.

[0193] [0193]

Figure CN106291381BD00142

(32J 式中,状态量Zk即为系统状态估计值修正茗,是实时的荷电状态(SOC);参数量Ca, 和RP,1与系统参数估计值修正相关,则能直接实时地反映电池的健康状态(SOH)。 (Wherein 32J, Zk is the state quantity correction system state estimate Ming, a real state of charge (the SOC); the amount of parameters Ca, and RP, 1 estimates the correction parameter associated with the system, the battery can be directly reflected in real time state of health (SOH).

Figure CN106291381BD00143

[0194] 在某一固定使用情况(如温度恒定等)下,电池容量越小,反应电池老化越严重,同时意味着电池健康状态(SOH)越差,在此算法过程中,电池容量精度较高,可以以此作为主要的SOH衡量参数;同时,对于一定的使用条件(如温度恒定、SOC固定)而言,电池内阻越大, 电池老化越严重,同样意味着电池SOH越差,考虑到电池内阻估计精度未得到充分检验,因而仅将其作为SOH的辅助衡量参数。 [0194] In the case of using a fixed (e.g., constant temperature, etc.), the smaller the battery capacity, the more severe aging reaction of the battery, the battery state of health while means (the SOH) worse, this algorithmic process, the precision is better than the battery capacity high, as may the main parameters measured SOH; Meanwhile, for certain conditions (such as constant temperature, the SOC is fixed), the larger the internal resistance of the battery, the more serious the battery aging, a battery SOH also means worse, consider the estimation accuracy of the battery internal resistance has not been fully tested, and therefore only the SOH as an auxiliary measure parameters.

[0195] 下面通过选用某一型号镍钴锰三元电池为例进行试验,进而比对本发明的基于多时间尺度的估计值相对于基于单时间尺度的估计值。 [0195] The following test model by selecting a nickel cobalt manganese batteries, for example three yuan, and further than the estimated value based on a multi-time scale of the present invention with respect to the estimated value based on a single time scale.

[0196] 选用镍钴锰三元电池为研究对象,其额定容量为2Ah,充放电截止电压分别为4.1¥、3.0¥。 [0196] Selection three yuan nickel cobalt manganese battery as the research object, which is a rated capacity of 2Ah, the charge and discharge cut-off voltage of 4.1 ¥, 3.0 ¥ respectively. 准备试验包括三个固定温度点(10°(:、25°(:、45°(:)下的基础容量、开路电压、05丁(Dynamic Stress Test,动态应力测试工况)循环工况三项试验,以及室温条件下的基础容量与DST循环工况试验。其中,三个固定温度点下的试验主要用于容量、SOC与OCV三者函数关系的获取、SOC与容量联合估计算法程序的调试;室温条件下的试验则用来验证算法的精度与稳定性。 Test preparation comprising a three-point fixed temperature (10 ° (:, 25 ° (:, 45 ° base capacity (:) in the open-circuit voltage, 05 D (Dynamic Stress Test, dynamic stress testing conditions) drive cycle three DST basic commissioning tests and cycle capacity condition under test, and ambient conditions that a test temperature is fixed at three points used to acquire the capacity, the SOC and OCV as a function of the three, the SOC estimation algorithm to the combined capacity of the program ; test at room temperature is used to verify the accuracy and stability of the algorithm.

[0197] 基于基础容量试验,得到不同温度下的最大可用容量,如表1所示。 [0197] Based on the basis of capacity test, to give the maximum available capacity at different temperatures, as shown in Table 1.

[0198] 表1不同温度下,该电池单体最大可用容量 [0198] at different temperatures in Table 1, the maximum available capacity of the cell

[0199] [0199]

Figure CN106291381BD00144

[0200] 基于不同容量下(这里以温度为例)的开路电压与SOC关系曲线,建立电池容量-SOC-OCV三维响应面,如图3所示。 [0200] Based on the open-circuit voltage of different capacities (here, at a temperature for example) and the SOC curve, establishing a three-dimensional battery capacity -SOC-OCV response surface, as shown in FIG. 通过对容量、SOC和OCV三者关系,采用式(1)、(2)进行拟合,从而获得常数矩阵Λ。 Using the formula (1), (2) by fitting of capacity, SOC, and the relations between OCV, so as to obtain a constant matrix Λ.

[0201] [0201]

Figure CN106291381BD00145

(33) (33)

[0202] 基于上述试验数据与式(32),通过上述多时间尺度滤波算法来实现SOC与容量的联合估计。 [0202] Experimental data based on the formula (32), to achieve joint estimation SOC and capacity by the multi-time scale filtering algorithm. 具体过程为: Specific process is:

[0203] 首先,完成联合估计算法程序的调试。 [0203] First, the completion of joint estimation algorithm program debugging. 即在三个固定温度点(10°C、25°C、45°C)下, 基于相应的DST试验数据,共同完成上述基于双ft»滤波的SOC与容量联合估计算法程序的调试。 I.e., at three points at a fixed temperature (10 ° C, 25 ° C, 45 ° C), based on the respective DST test data, based on the above-bis together. Ft >> filtered SOC and capacity for joint estimation algorithm program debugging.

[0204] 然后将室温下DST试验数据直接调入上述调试好的联合估计算法程序中,并将算法中SOC初值设置为80 % (准确初值为100%)、容量初值设置为I .5Ah (准确初值为2.096Ah),得到室温下单时间尺度下SOC与容量估计结果。 [0204] DST is then transferred directly to the test data at the above temperature for joint estimation debugged program, the algorithm and the initial value set at 80% SOC (initial value as 100% accuracy), set the initial value capacity I. 5AH (accurate initial value 2.096Ah), with the capacity to obtain SOC estimation result at a single time scale at room temperature.

[0205] 为了体现多时间尺度的优势,这里取尺度转换限值L=Is (单尺度)和L = 60s (多尺度)两种情况完成动力电池SOC与容量估计,估计结果分别如图4、图5所示。 [0205] In order to realize the advantages of multi-time scale, the scaling value adopted here L = Is (single dimension) and L = 60s (multiscale) completed both cases and battery capacity SOC estimation, the estimation results are shown in Figure 4, Figure 5. 图5中单尺度表示单时间尺度双Η~滤波算法相应估计结果,多尺度以及未具体指明的曲线均表示多时间尺度双Η~滤波算法相应估计结果。 5 represents a single time scale monoscale Η ~ double filtering algorithm corresponding to the estimation result, multi-scale and unspecified multiple time scales represents the curves Η ~ double filtering algorithm corresponding estimation result.

[0206] 同时,表2给出了直观的数字对比结果。 [0206] Meanwhile, Table 2 shows the results of a visual comparison of figures. 需要说明的是,由于上述所有试验(包括不同温度下的基础容量试验、开路电压试验以及DST循环工况试验)数据采样时间均为ls,当L =I s时,多时间尺度算法退化为单时间尺度算法。 Incidentally, since all the above tests (including basic capacity test at different temperatures, and the open-circuit voltage of the test cycle condition test DST) data sampling times are LS, when L = I s, multi-time scale algorithm degenerates to single time scaling algorithm.

[0207] 表2单时间尺度与多时间尺度双Ε»滤波估计结果对比 [0207] TABLE 2 Single and multi-time scale time scale bis Epsilon »filter estimation result of comparison

[0208] [0208]

Figure CN106291381BD00151

[0209] 注:端电压误差为电池模型端电压预测值与测量值之差。 [0209] Note: the terminal voltage of the battery model error is the difference between the predicted value and the measured voltage value. 测量值即通过试验直接测量得到,在采集精度高的试验情况下,即可认为其近似等于端电压准确值。 I.e., the measured values ​​obtained by experiment directly measured at high precision acquisition test situation, it can be considered approximately equal to the exact value of the terminal voltage.

[0210] SOC误差为上述滤波算法SOC估计值与SOC试验参考值之差。 [0210] SOC difference between the reference error value and the SOC test was estimated SOC value above filtering algorithm. 所述SOC试验参考值基于以下原理得到:在试验条件下,设备传感器精度很高,从而保证了电流电压采集精度很高。 The test reference SOC value obtained based on the principle: Under the experimental conditions, equipment is very high sensor accuracy, thereby ensuring a high current and voltage acquisition accuracy. 在一定试验条件下,按照标准的电流(一般为0.3C)充电使得电池容量充满,此时电池SOC等于100%,倘若需要从90%SOC开始试验,则可以按照标准电流放掉10%SOC的电流,按照此方法可以得到准确的初始SOC值。 Under certain experimental conditions, according to standard current (typically 0.3C) such that the battery capacity fully charged, when the battery SOC is equal to 100%, if the need to start the test from 90% SOC, it is possible to let go of 10% SOC in accordance with the current standard current, according to this method can obtain an accurate initial SOC value. 之后进行相关试验,直至试验完成时,按照标准的电流放电至0%S0C,可以得到准确的试验完成时的SOC值。 After the relevant test until the test is completed, according to the current standard of discharge 0% S0C, the SOC value can be obtained accurately when the test is completed. 在已知初始与终止SOC的条件下,鉴于电流采集精度高、电流充放电效率已知,从而采用安时积分法能得到高精度的SOC参考曲线。 Under known conditions with the initial SOC is terminated, in view of the high current detection accuracy, current charge and discharge efficiency is known, so that when using the safety integration SOC can be obtained with high accuracy reference curve.

[0211] 容量误差为上述滤波算法容量估计值与试验参考值之差。 [0211] error is the difference between the capacity of the capacity estimation value filter algorithm and the reference test value. 容量试验参考值即为在一定条件下,按照标准的电流充电使得电池容量充满后,同样对电池按标准电流放电至0% SOC的整个过程中所放出的总电量。 The reference value is the capacity test under certain conditions, such that the charge current in accordance with the standard capacity after the battery is full, the same standard of the battery discharge current to 0% of the total amount in the whole process of the released SOC.

[0212] 从上述分析得出,本发明所提出的基于数据驱动的车载动力电池系统容量与SOC 联合估计方法与传统方法相比具有以下优势: [0212] derived from the above analysis, the present invention has the advantage that the proposed system car battery capacity SOC joint estimation method based on the conventional method compared to the data driving:

[0213] (1)采用SOC估计的期望值()与误差限(以95%置信区间为例,但不限于95%)更 [0213] (1) The expected value of the estimated SOC () with error limit (95% confidence interval, for example, but not limited to 95%) more

Figure CN106291381BD00152

加全面、准确地评价动力电池的荷电状态可能分布情况; Plus a comprehensive and accurate assessment of the state of charge of the battery may distribution;

[0214] ⑵图4、图5表明在SOC与容量初值均不准确(20%误差)的情况下均能快收敛到真值,即实现了电池最大容量未知时,SOC的准确估计,解决了传统SOC估计算法以最大可用容量已知为前提而无法成功运用到实车上的难题; [0214] ⑵ FIG. 4, FIG. 5 shows the capacity at the initial value not accurate SOC (20% error) of the case can quickly converge to the true value, i.e., to achieve the maximum capacity of the battery is unknown, an accurate estimate of the SOC, to solve traditional SOC estimation algorithm to maximum available capacity is known as the premise can not be successfully applied to real problems the car;

[0215] (3)电池容量与内阻是衡量电池健康状态(SOH)重要指标,因而上述联合估计算法在一定程度上实现了SOC与SOH的联合估计; [0215] (3) the internal resistance of the battery capacity is an important indicator of battery state of health (SOH), whereby said joint estimation algorithm of joint estimation of SOC and SOH to some extent;

[0216] (4)提出多时间尺度的容量与SOC估计算法,相比单时间尺度算法而言,不仅估计精度得到了较大的提高,而且较大地减少了算法计算量与计算时间。 [0216] (4) proposed a multi-time scale capacity SOC estimation algorithm, in terms of time as compared to a single scaling algorithm, estimation accuracy is not greatly improved, but also greatly reduces the amount of computation time algorithm.

Claims (8)

1. 一种联合估计动力电池系统荷电状态与健康状态的方法,其特征在于:所述方法包括: 首先,建立容量-SOC-OCV三维响应面,所述OCV是所述系统的开路电压; 其次,在线数据获取,实时采集动力电池系统的电压和电流; 然后,多时间尺度滤波算法,获得所述动力电池系统的当前宏观时间尺度下的系统容量预估修正值与当前微观时间尺度下的系统SOC预估修正值; 所述多时间尺度滤波算法包括: 步骤①:进行当前SOC估计微观采样点k下的系统SOC预估,得到系统SOC预估值; 步骤②:基于步骤①中的获得的系统SOC预估值和上个容量估计宏观采样点的系统容量预估修正值,利用所述容量-SOC-OCV三维响应面,更新当前系统开路电压得到第一开路电压; 然后,基于上述第一开路电压,进行所述系统SOC预估值的修正,得到所述系统SOC预估修正值; 步骤③:k+1作为新的SO CLAIMS 1. A method for joint estimation battery power system state of charge and state of health, which is characterized in that: said method comprises: Firstly, the capacity of the three-dimensional response surface -SOC-OCV, the OCV is the open circuit voltage of the system; Secondly, the online data acquisition, real-time acquisition of voltage and current of the battery power system; then, multi-time scale filtering algorithm, system capacity is estimated correction value obtained at the power of the current macro-cell system and the current time scale at the microscopic time scale the system estimates SOC correction value; filtering algorithm of the multi-time scale comprises the steps of: ①: the current SOC estimation system for micro sampling point K in SOC estimates, to obtain an estimate SOC system; step ②: ① based on obtained in step the system estimates the SOC and the estimation system with a capacity capacity macroscopic sample point correction value estimated by the capacity -SOC-OCV dimensional response surface, the system updates the current open circuit voltage to obtain a first open circuit voltage; then, based on the first an open-circuit voltage, corrected SOC estimates said system, said system to obtain a correction value SOC estimate; step ③: k + 1 as a new SO C估计微观采样点,判断k-1是否能被L整除,如果能,则进行步骤④;否则返回步骤①; 步骤④:进行容量估计宏观采样点1+1下的系统容量预估,得到系统容量预估值; 步骤⑤:基于步骤④中的获得的系统容量预估值和最近一次步骤②中获得的系统SOC 预估修正值,利用所述容量-SOC-OCV三维响应面,再次更新当前系统开路电压得到第二开路电压; 基于所述第二开路电压,进行容量估计宏观采样点1+1下的系统容量预估值的修正,得到系统容量预估修正值;并返回步骤①; 在每个SOC估计微观采样点下更新所述系统SOC预估修正值,每隔L个所述SOC估计微观采样点为容量估计宏观采样点,在该容量估计宏观采样点下更新所述系统容量预估修正值,每次更新后的所述系统容量预估修正值作为当前容量估计宏观采样点后L次的更新所述系统SOC预估修正值所用的参数; 所述 C micro estimated sampling point, can be determined whether L k-1 is divisible, if so, proceeds to step ④; otherwise, returns to step ①; Step ④: estimating the capacity of macro sampling point system capacity estimated at 1 + 1, to give the system capacity estimates; step ⑤: the correction value estimated based on the system estimate the system capacity obtained in step ④ and the last step of the SOC obtained ②, with the capacity of the three-dimensional response surface -SOC-OCV, again updating the current the system open circuit voltage to obtain a second open-circuit voltage; based on the second open-circuit voltage, the capacity estimation correction system capacity estimated value at a sampling point macro + 1, to give estimates of system capacity correction value; and returns to step ①; in each updated estimate SOC microscopic sample point system estimates SOC correction value, the SOC estimating the L every sampling point to estimate the capacity of micro macroscopic sampling point, the sampling point is estimated macro updating the system capacity in the pre-capacity estimate the correction value, the system capacity after each update of the correction value as a current estimated capacity estimating the macro-L times the sampling point updating the system parameters estimated SOC correction value used; the L是两个以上; 最后,在线SOC与SOH提取,利用所述多时间尺度滤波算法获得的当前所述系统SOC预估修正值与系统容量预估修正值,估计动力电池系统荷电状态与健康状态。 L is two or more; Finally, SOC and SOH extraction line, the multi-time scale by using the current filtering algorithm to obtain the correction value prediction system SOC estimate the system capacity correction value, the estimated battery state of charge and health system status.
2. 如权利要求1所述的方法,其特征在于:步骤①中利用SOC估计微观采样点k-Ι下的系统电流值和系统SOC预估修正值,以及最近一次采样点1的系统容量预估修正值来进行系统SOC预估。 2. The method according to claim 1, wherein: ① the step of utilizing the current value and SOC estimation system Micro System sampling point k-Ι SOC estimate the correction value, and the most recent sample system capacity point 1 pre primary estimated correction value for system SOC estimate.
3. 如权利要求1所述的方法,其特征在于:步骤②中进行所述系统SOC预估值的修正,基于步骤①中的获得的系统SOC预估值、所述第一开路电压、SOC估计微观采样点k下的电流值和电压值。 3. The method according to claim 1, wherein: said correcting step ② the system estimates SOC based on obtained in step ① system SOC estimates, the first open-circuit voltage, SOC estimated current value and the voltage value at the micro-sample point k.
4. 如权利要求1所述的方法,其特征在于:步骤⑤中进行所述系统容量预估值的修正, 基于第二开路电压、容量估计宏观采样点1+1或最近一次SOC估计微观采样点下的所述系统的电流值和电压值。 4. The method according to claim 1, wherein: in the step ⑤ corrected estimate the system capacity based on second open circuit voltage, capacity estimating sampling point 1 + 1 macro or micro-sampling last SOC estimation current and voltage values ​​at the system point.
5. 如权利要求1所述的方法,其特征在于:利用系统容量预估修正值评估所述S0H。 5. The method according to claim 1, wherein: the correction value using the estimated system capacity evaluating the S0H.
6. 如权利要求1所述的方法,其特征在于:所述SOC估计微观采样点的采样频率与所述电流和/或电压的采样频率相同。 6. The method according to claim 1, wherein: said estimated SOC same frequency as the sampling frequency microscopic current sampling point and / or voltage.
7. 如权利要求1所述的方法,其特征在于:建立所述容量-SOC-OCV三维响应面的方法为:对不同容量下的SOC与OCV关系进行拟合,得到各所述不同容量下的组合模型系数,采用二次函数对各组合模型系数与容量的关系进行拟合,完成容量-SOC-OCV三维响应面的建立。 The SOC and OCV of the fitting relation in different capacities, to give each of the different sizes: 7. The method according to claim 1, wherein: the capacity of the method for establishing a three-dimensional response surface -SOC-OCV is the combination of model coefficients, quadratic function and capacity relationship model coefficients each combination fitting, dimensional response surface is completed capacity -SOC-OCV.
8. 如权利要求1所述的方法,其特征在于:通过电池管理系统实时采集动力电池系统的电压、电流和/或温度。 8. The method according to claim 1, wherein: the acquisition battery power system voltage, current and / or temperature in real time by a battery management system.
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