CN1601295A - Estimation for accumulator loading state of electric vehicle and carrying out method thereof - Google Patents

Estimation for accumulator loading state of electric vehicle and carrying out method thereof Download PDF

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CN1601295A
CN1601295A CN 200410009704 CN200410009704A CN1601295A CN 1601295 A CN1601295 A CN 1601295A CN 200410009704 CN200410009704 CN 200410009704 CN 200410009704 A CN200410009704 A CN 200410009704A CN 1601295 A CN1601295 A CN 1601295A
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battery
state
equation
soc
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王军平
林成涛
陈全世
韩晓东
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清华大学
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Abstract

The invention relates to an estimation and implement method of state of charge (SOC) of battery for electric car, belonging to the field of electric car intelligent niformation processing technology. Said method utilizes the state space equation of battery which is formed from battery charge state equation based on ampere-hour metering method and measurement equation of battery load votlage, and uses the improved extended Kalman filtering equation to calculate and obtain the stale of charge of battery. Said invented advantage lies in that it has stronger adaptability, can eliminate initial error of SOC, and can raise the convergence of error or reduce speed, at the same time it can modify the change of SOC resulted frmo self-discharge of battery. Said invention is applicable to SOC estimation of cell monomer, module and battery.

Description

电动车用蓄电池荷电状态的估计及实现方法 Estimates and implementation of electric vehicle battery state of charge

技术领域 FIELD

本发明涉及电动车用蓄电池荷电状态(SOC)的估计及实现方法,属于电动汽车智能信息处理技术领域。 The present invention relates to an electric vehicle battery implemented method of estimating and the state of charge (SOC), belonging to the technical field of intelligent information processing electric car.

背景技术 Background technique

随着人类环保意识的日渐强烈,汽车领域中越来越多的人将目光投到了零排放的电动车辆上。 With the growing environmental awareness of strong human, the automotive sector more and more people will be eyeing the zero-emission electric vehicles. 电池作为电动汽车的主要或辅助动力源,是电动汽车的关键组成部分。 Battery electric vehicles as the main or auxiliary power source, is a key component of electric vehicles. 如何合理使用电池,充分利用电池中的能量,延长电池的使用寿命是电动汽车及混合动力汽车进一步发展中所必须解决的问题。 How rational use batteries, make full use of energy in the battery, extending battery life is to further the development of electric cars and hybrid vehicles that must be addressed. SOC估计对于电动汽车的作用就像油量计对于内燃机汽车的作用。 SOC estimate the role of electric vehicles like the fuel gauge effect on the internal combustion engine vehicles. 与内燃机汽车油箱不同的是,电池功率提供能力随着电池SOC的变小发生衰减;电池放电倍率越大,放出电量越少;在判断电池是否充满和放空时,没有像油量计那样清晰准确的判据;电池的容量随着温度、循环工作次数发生变化;过充和过放会极大损害电池性能,所以电池SOC估计远比油量计复杂。 The internal combustion engine with different fuel tanks, the ability to provide battery power becomes smaller as the battery SOC is attenuated; the larger the discharge rate of the battery, the less power emit; when determining whether the battery is full and emptying, the gauge is not as accurate as clear criteria; battery capacity changes with temperature, work cycles; overcharge and over discharge can greatly damage the battery performance, the battery SOC estimation complicated than the gauge. 从充分发挥电池能力和提高安全性两个角度来讲,SOC的准确估计是实现电池高效管理的关键因素,同时SOC的准确估计也是进行能量管理策略研究的基础,是电动汽车的关键技术之一,因此提高SOC的估计精度并提高算法的实用性具有很高的理论价值及实际意义。 From the full battery capacity and enhance the two security perspective, an accurate SOC estimation is the key factor in achieving efficient battery management, as well as an accurate estimate of the SOC is basic research energy management strategy, is one of the key technologies of electric vehicles and therefore it has a high theoretical value and practical significance to improve the estimation accuracy of the SOC and improve the practicality of the algorithm.

对于电动汽车动力电池SOC的估计,目前国内外采用的方法主要有放电试验法、安时(Ah)计量法、开路电压法、负载电压法、电化学阻抗频谱法、内阻法、线性模型法、神经网络法和卡尔曼滤波法等。 For estimation of electric vehicle battery SOC, current methods used mainly domestic and discharge test, ampere hours (Ah) measurement method, open circuit voltage, load voltage, electrochemical impedance spectroscopy method, a resistance, linear model method , neural networks and Kalman filtering method.

Ah计量法是最通用的SOC估计方法。 Ah measurement method is the most common method of SOC estimation. 其原理主要是通过电流积分来累计放电量,从而计算SOC。 The main principle is to be accumulated in the discharge amount by current integration, thereby calculating the SOC. 但应用中存在三个主要问题:(1)电流测量不准确将增大误差,长时积累误差会越来越大;(2)必须考虑电流充放的损失;(3)在高温状态和电流波动很大的情况下误差较大。 However, three major problems in the application: (1) the current measurement inaccuracies will increase the error, the accumulated error will grow long; (2) loss of charge and discharge currents must be considered; (3) high temperature and current error in the case of large fluctuations. 卡尔曼滤波法是将SOC作为电池状态空间模型中的一个状态,然后利用卡尔曼滤波方程进行状态估计。 Kalman filtering method is state of the battery SOC as a state space model, then the equation of the Kalman filter state estimation. 该方法尤其适合于电流变化比较剧烈的混合动力汽车电池SOC的估计,卡尔曼滤波法不仅给出了SOC的估计值,还给出了SOC的估计误差。 This method is particularly suitable for more intense current variation hybrid vehicle battery SOC estimation method not only gives the Kalman filter estimates the SOC, SOC estimation error also gives the. 由于卡尔曼滤波涉及大量的矩阵运算,该方法的缺点在于对电池模型准确性和计算能力要求高,最重要的是对于电池自放电的影响,单纯利用该方法没有有效地解决,而是重新构造自放电模型来估计。 Since the Kalman Filter involves a large number of matrix operations, disadvantage of this process is the high accuracy of the battery model and computational power requirements, the most important is the effect on self-discharge, this method alone does not effectively solve, but reconfigured self-discharge model to estimate.

发明内容 SUMMARY

本发明的目的是提供一种估计误差小,计算简单,可以解决电池自放电影响的蓄电池荷电状态估计方法。 Object of the present invention is to provide an estimation error is small, simple calculation method for estimating a state of charge of the battery self-discharge effects can be solved.

本发明的方法是用基于安时计量法的电池荷电状态方程,以及电池负载电压的量测方程所构成的电池的状态空间方程,再用改进的扩展卡尔曼滤波方程计算来获得电池的荷电状态。 The method of the present invention is based on the state equation composed of the measurement equation of state of charge of the battery ampere measurement method, and the battery voltage of the battery load space equation, then the improved extended Kalman filter equation used to obtain the battery charge power status.

所说的电池的荷电状态方程:令xk=SOCk Said equation of state of charge of the battery: Let xk = SOCk 所说的电池的负载电压量测方程:y(k)=f(Ik,xk)+vk=K0-RIk-K1/xk-K2xk+K3ln(xk)+K4ln(1-xk)+vk式中n为匹克特(Peukert)系数,η为充电效率;Ki为拟合系数;Vk,ik分别为k采样时刻电池的负载电压信号,电流信号;vk为量测噪声,其方差为Q。 Load voltage of said battery measurement equation: y (k) = f (Ik, xk) + vk = K0-RIk-K1 / xk-K2xk + K3ln (xk) + K4ln (1-xk) + vk where n is Pickett (the Peukert) coefficients, the charging efficiency [eta]; of Ki for the fitting coefficients; Vk, ik k sampling instants respectively load the battery voltage signal, a current signal; VK is a measurement noise variance is Q.

所说的卡尔曼滤波方程为量测阵Ck=∂f(Ik,xk)∂xk=K1/(xk)2-K2+K3/xk-K4/(1-xk)]]>状态预测: Said Kalman filter equations for the measurement matrix Ck = & PartialD; f (Ik, xk) & PartialD; xk = K1 / (xk) 2-K2 + K3 / xk-K4 / (1-xk)]]> state prediction : 计算量测阵:Ck=K1/(xk/k-1)2-K2+K3/xk/k-1-K4/(1-xk/k-1)滤波增益:Kk=Pk/k-1CkT[CkPk/k-1CkT+Q]-1]]>预测均方差: Pk/k-1=Pk-1/k-1估计均方差: Pk/k=(I-KkCk)Pk/k-1状态估计: xk/k=xk/k-1+αKk[yk-f(ik,xk/k-1)]本发明的特点在于:1.电池的荷电状态方程采用分段模型,即充电和放电时采用不同的状态方程以提高初始估计精度;2.在扩展卡尔曼滤波的状态估计方程中,加入滤波放大倍数α对滤波增益进行放大,从而可以提高误差的收敛速度,同时可以修正电池由于自放电所引起的SOC的变化。 Calculated measurement matrix: Ck = K1 / (xk / k-1) 2-K2 + K3 / xk / k-1-K4 / (1-xk / k-1) the filter gain: Kk = Pk / k-1CkT [ CkPk / k-1CkT + Q] -1]]> mean square prediction: Pk / k-1 = Pk-1 / k-1 are the estimated variance: Pk / k = (I-KkCk) Pk / k-1 state estimate : xk / k = xk / k-1 + αKk [yk-f (ik, xk / k-1)] characteristics of the present invention is that: when a state of charge of the battery using the equation model segment, i.e., charging and discharging. different equations of state to improve the accuracy of the initial estimate;. 2 in the extended Kalman filter state estimation equation, was added to the filter magnification α amplifying filter gain, which can improve the convergence speed of the error, can be corrected while the battery self-discharge changes in SOC caused.

本发明的优点在于具有很强的自适应性,即对于SOC的初始误差不敏感,通过改进的扩展卡尔曼滤波的迭代算法可以逐步消除SOC的初始误差,在改进的扩展卡尔曼滤波算法中,滤波放大倍数的引入可以提高误差的收敛或减小速度。 Advantage of the present invention is highly adaptive, i.e., insensitive to the initial error of the SOC, SOC can be gradually eliminate the initial errors by improving iterative algorithm extended Kalman filter, modified in the extended Kalman filter algorithm, as filter magnification can be increased or decrease the speed of convergence error. 同时本发明的该特性可以用于修正电池由于自放电所引起的SOC的变化,即如果由于自放电使得SOC变小,但计算时所使用的SOC初值大于真实的SOC值,通过该方法可以逐步消除二者之间的误差,从而在不改变算法复杂性的同时解决自放电对于SOC估计的误差。 Meanwhile, the feature of the present invention may be used for correcting the change in the SOC of the battery due to self-discharge, i.e., if the self-discharge that SOC becomes small, the initial value of SOC used for calculating the SOC is larger than the true value, which can be the gradual elimination of the error between the two, so that without changing the algorithm complexity for simultaneously solving the self-discharge SOC estimation error. 本发明采用的电池模型为单变量模型,因此卡尔曼滤波所涉及大量的矩阵运算在本算法中为数值运算,计算量不大。 The battery model used in the present invention is a single variable model, the Kalman filter so that a large number of matrix operations in the algorithm for numerical calculation, the calculation amount is not directed. 本发明适用于电池单体、模块和电池组的SOC估计。 The present invention is applicable to the battery cells, SOC estimation module and a battery pack.

本发明的具体方法可总结为如下的估计过程:1)初始化:给定电池SOC的初值SOC0(即下述的x0);给定预测误差协方差阵初值P0;给定量测噪声方差Q;给定滤波放大倍数α;对于k=1,2,…,等各个采样时刻,分别进行如下计算:2)获取k采样时刻电池的负载电压信号Vk,电流信号Ik;3)电池模型由如下的荷电状态方程和负载电压量测方程组成:电池的荷电状态方程:令xk=SOCk Specific methods of the invention can be summarized as follows for the estimation process: 1) Initialization: Given the initial value of the battery SOC SOC0 (i.e., x0 below); given the initial value of the prediction error covariance matrix P0; to quantitative measurement noise variance Q ; magnification given filter [alpha]; for k = 1,2, ..., etc. each sampling time, were calculated as follows: 2) obtaining a load voltage sampling timing signal Vk k battery current signals Ik; 3) by the following cell model the equation of state of charge and load voltage measurement equation: the equation of state of charge of the battery: Let xk = SOCk 电池的负载电压量测方程:y(k)=f(Ik,xk)+vk=k0-RIk-K1/xk-K2xk+K3ln(xk)+K4ln(1-xk)+vk式中,vk为量测噪声,其方差为Q。 Load voltage measurement equation battery: y (k) = f (Ik, xk) + vk = k0-RIk-K1 / xk-K2xk + K3ln (xk) + K4ln (1-xk) + vk where, vk is measurement noise variance is Q.

令量测阵Ck=∂f(Ik,xk)∂xk=K1/(xk)2-K2+K3/xk-K4/(1-xk)]]>则卡尔曼滤波方程为状态预测: So that the measurement matrix Ck = & PartialD; f (Ik, xk) & PartialD; xk = K1 / (xk) 2-K2 + K3 / xk-K4 / (1-xk)]]> the Kalman filter equations for the state prediction: 计算量测阵:Ck=K1/(xk/k-1)2-K2+K3/xk/k-1-K4/(1-xk/k-1)滤波增益:Kk=Pk/k-1CkT[CkPk/k-1CkT+Q]-1]]>预测均方差: Pk/k-1=Pk-1/k-1估计均方差: Pk/k=(I-KkCk)Pk/k-1状态估计: xk/k=xk/k-1+αKk[yk-f(Ik,xk/k-1)]对于k=1,2,…,等各个采样时刻,循环2~3的计算过程即可获得每个时刻电池的SOC。 Calculated measurement matrix: Ck = K1 / (xk / k-1) 2-K2 + K3 / xk / k-1-K4 / (1-xk / k-1) the filter gain: Kk = Pk / k-1CkT [ CkPk / k-1CkT + Q] -1]]> mean square prediction: Pk / k-1 = Pk-1 / k-1 are the estimated variance: Pk / k = (I-KkCk) Pk / k-1 state estimate : xk / k = xk / k-1 + αKk [yk-f (Ik, xk / k-1)] for k = 1,2, ..., etc. each sampling time, the calculation process cycle of 2 to 3 can be obtained SOC each time the battery. 通过实验证明,本发明所提出的方法具有很强的自适应性,对于不同使用环境及不同种类的电池该方法可以比较准确地估算出电池的SOC,重要的是对于初值误差不敏感,可以部分地纠正自放电带来的估计误差,达到了预期的目的。 The experimental results show that the proposed method of the present invention has strong adaptability, can more accurately estimate the SOC of the battery for different environments and different types of batteries of the method, it is important not sensitive to the initial value error can be partially correct estimation error caused by self-discharge, to achieve the desired purpose.

附图说明 BRIEF DESCRIPTION

图1为基于改进的扩展卡尔曼滤波的电动车用蓄电池荷电状态的估计方法的流程框图。 Figure 1 is a flow diagram of the extended Kalman filter based on improved electric vehicle battery estimation method of the state of charge.

图2为发明实施例中所测得的电流信号I(t)。 FIG 2 is a signal current invention, I (t) measured in the examples.

图3为发明实施例中所测得的电压信号V(t)。 FIG 3 is a disclosure voltage signal V (t) measured in the embodiment.

图4为发明实施例中当SOC存在初始误差时SOC的估计结果。 FIG 4 is a present invention when initial SOC estimation error in the results of Examples SOC embodiment.

图5为发明实施例中假设存在自放电时SOC的估计结果。 Figure 5 embodiment is assumed that there SOC estimation result of self-discharge embodiment of the invention.

图6为实现本发明的电池管理系统框图,其中I为电池(包括单体、模块或电池组),II为数据采集模块,III为中央处理器,IV为存储模块,V为报警模块。 FIG 6 is a block diagram of a battery management system according to the present invention is implemented, where I is the battery (including monomeric, module or battery pack), II data acquisition module, III central processor, IV of the storage module, V is the alarm module.

具体实施方式 Detailed ways

结合附图说明本发明的具体实施方式。 DETAILED DESCRIPTION OF THE DRAWINGS The embodiments of the invention.

在实施SOC估计算法前应该确定电池模型的相关参数,方法可以按照公知的新一代汽车合作伙伴计划试验手册(PNGV)中的脉冲功率试验确定。 In an SOC estimation algorithm should be determined before the parameters of the battery model, the method can determine the pulse power test (PNGV) in accordance with well-known New Generation of Vehicles program manual test partners.

电池的荷电状态方程:令xk=SOCk Equation of state of charge of the battery: Let xk = SOCk 电池的负载电压量测方程:y(k)=f(Ik,xk)+vk=k0-RIk-K1/xk-K2xk+K3ln(xk)+K4ln(1-xk)+vk在本实施例中按照图2的电流对80Ah镍氢电池组进行循环充放电试验。 Load voltage measurement equation battery: y (k) = f (Ik, xk) + vk = k0-RIk-K1 / xk-K2xk + K3ln (xk) + K4ln (1-xk) + vk present embodiment the cycle of charge and discharge test performed 80Ah NiMH battery pack 2 in accordance with the current map. 图3为循环充放电试验时所采集的负载电压V(t)。 FIG 3 is a charge-discharge cycle test when the acquired load voltage V (t). 利用图2和图3的试验数据利用公知的最小二乘法可以确定电池的负载电压量测方程中的未知参数,即k0=534.0017,R-=0.1799,R+=0.3461,K1=2.6273,K2=-131.7037,K3=95.4526,K4=-6.2601,其中R-为放电时对应的内阻,R+为充电时对应的内阻。 Load voltage measurement equation using experimental data using well-known FIGS. 2 and 3 of the least squares method can determine that the battery in the unknown parameters, i.e. k0 = 534.0017, R- = 0.1799, R + = 0.3461, K1 = 2.6273, K2 = - 131.7037, K3 = 95.4526, K4 = -6.2601, wherein R- is the corresponding discharge resistance, R + is the charge corresponding to the internal resistance. 电池的荷电状态方程中n=1.041,η=0.96。 Equation of state of charge of the battery of n = 1.041, η = 0.96.

SOC估计的具体过程为:系统运行时首先进行初始化,初始化时即给定电池SOC的初值SOC0(即下述的x0),SOC0从电池管理系统的存储区获得,即为上次系统停止运行时电池的SOC值,在首次系统运行时该值为用户根据电池的充电状况输入估计值,从后面的实验结果可以看出SOC0的取值误差不会影响后面的估计结果,这也是本发明的一个优势;量测噪声方差Q,一般取Q为电压传感器的量测方差;预测误差协方差阵初值P0,该值可取一个较小的正数,初值的取法不影响计算;滤波放大倍数α,α=5~20。 DETAILED SOC estimation process is as follows: First, initialization system is running, i.e., a given initial value of the battery SOC SOC0 (i.e., x0 below), is obtained from the storage area SOC0 battery management system initialization, the system is stopped is the last when the battery SOC value, the system is running in the first user input value is the estimated value according to the charge condition of the battery, from the experimental results it can be seen behind SOC0 error value does not affect the estimation results back, which is the present invention one advantage; the variance of the measurement noise Q, Q is generally the variance of the measurement voltage sensor; initial value of the prediction error covariance matrix P0, the value is preferably a positive number smaller, emulated not affect the calculation of the initial value; filtering magnification α, α = 5 ~ 20.

初始化后实时采集负载电压信号V(t)和电流信号I(t),V(t)和电流信号I(t)是从电池管理系统的数据采集模块获得。 Real-time acquisition load voltage signal V (t) and current signals I (t) after the initialization, V (t) and current signals I (t) is obtained from the battery management system of data acquisition module. 对应于k时刻的电压Vk和电流Ik,利用卡尔曼滤波方程进行SOC估计。 Corresponding to the voltage Vk at time k and current Ik, the SOC estimation performed using the Kalman filter equations. 卡尔曼滤波估计过程按照以下五式进行。 Kalman filter estimation process performed in the following five formulas.

状态预测: State prediction: 计算量测阵:Ck=K1/(xk/k-1)2-K2+K3/xk/k-1-K4/(1-xk/k-1)滤波增益:Kk=Pk/k-1CkT[CkPk/k-1CkT+Q]-1]]>预测均方差: Pk/k-1=Pk-1/k-1估计均方差: Pk/k=(1-KkCk)Pk/k-1状态估计: xk/k=xk/k-1+αKk[yk-f(Ik,xk/k-1)]式中yk对应为负载电压信号Vk,xk/k即为k时刻SOC的估计值。 Calculated measurement matrix: Ck = K1 / (xk / k-1) 2-K2 + K3 / xk / k-1-K4 / (1-xk / k-1) the filter gain: Kk = Pk / k-1CkT [ CkPk / k-1CkT + Q] -1]]> mean square prediction: Pk / k-1 = Pk-1 / k-1 are the estimated variance: Pk / k = (1-KkCk) Pk / k-1 state estimate : xk / k = xk / k-1 + αKk [yk-f (Ik, xk / k-1)] where yk signals corresponding to the load voltage Vk, xk / k at time k is the estimated value of the SOC. 当xk/k超出充放电极限状态时时电池管理系统发出报警信息。 When xk / k exceeds the discharge limit alarm status information from time to time the battery management system.

图4为发明实施例中当SOC存在初始误差时SOC的估计结果,SOC的真实值1.0,估算中令SOC0=0.70。 FIG 4 is a embodiment of the invention, when there is an initial SOC estimation result of the SOC error, the true value of the SOC of 1.0, in order to estimate SOC0 = 0.70. 图5为发明实施例中假设存在自放电时SOC的估计结果,即在估算中令SOC0=0.95,大于电池SOC的实际状态SOC=0.90,即模拟存在自放电时,检验算法的有效性。 Figure 5 embodiment is assumed that there is a self-discharge embodiment of the invention the estimation results of SOC, i.e., in order to estimate the SOC0 = 0.95, the actual state of the battery SOC is greater than the SOC = 0.90, i.e. simulate the presence of self-discharge, validation algorithm. 在图4~5的实施例中,Q=0.03,Po=0.05,α=10。 In the embodiment of FIGS. 4 to 5, Q = 0.03, Po = 0.05, α = 10. 从图4~5的结果可以看出本方法对于上述两种情况的优势。 This method can be seen that the advantages of the above two cases for the result from FIG. 4 to 5. 本发明的仿真实验以Matlab软件来实施计算,实际应用时可用C语言编程实现。 Simulation results of the invention in the Matlab software implemented calculation, an actual C language programming application. 图6为实现本发明的电池管理系统框图。 FIG 6 is a block diagram of a battery management system implemented according to the present invention. 对于本发明,电池管理系统中的数据采集模块实施负载电压信号V(t),电流信号I(t)的采集,中央处理器实施基于卡尔曼滤波的SOC估计算法,计算和判断所需的参数存储在存储模块中,当达到充放电终止状态时电池管理系统通过报警模块发出报警信息。 For the parameters of the present invention, a battery management system embodiment of the data acquisition module load voltage signal V (t), the current signal acquisition I (t), the central processor to implement the Kalman filter based SOC estimation algorithm to calculate the required judgment and stored in the storage module, when reaching the discharge termination state of the battery management system generates alarm through the alarm module.

本发明的优点在于具有很强的自适应性,即对于SOC的初始误差不敏感,通过改进的扩展卡尔曼滤波的迭代算法可以逐步消除SOC的初始误差,在改进的扩展卡尔曼滤波算法中,滤波放大倍数的引入可以提高误差的收敛或减小速度。 Advantage of the present invention is highly adaptive, i.e., insensitive to the initial error of the SOC, SOC can be gradually eliminate the initial errors by improving iterative algorithm extended Kalman filter, modified in the extended Kalman filter algorithm, as filter magnification can be increased or decrease the speed of convergence error. 同时本发明的该特性可以用于修正电池由于自放电所引起的SOC的变化,从而在不改变算法复杂性的同时解决自放电对于SOC估计的误差。 Meanwhile, the feature of the present invention may be used for correcting the change in the SOC of the battery due to self-discharge, without altering the complexity of the algorithms for the self-discharge while addressing the SOC estimation error. 本发明采用的电池模型为单变量模型,因此卡尔曼滤波所涉及大量的矩阵运算在本算法中为数值运算,计算量不大。 The battery model used in the present invention is a single variable model, the Kalman filter so that a large number of matrix operations in the algorithm for numerical calculation, the calculation amount is not directed. 因此该发明可直接应用于现有的电池管理系统而不需要提高硬件指标,中央处理器一般采用16位单片机即可实现(例如C167CR)。 Thus the invention can be directly applied to a conventional battery management system without the need to increase the hardware indicators, generally the central processor can be realized using the 16-bit microcontroller (e.g. C167CR).

Claims (1)

1.一种电动车用蓄电池荷电状态估计及实现方法,其特征是用基于安时计量法的电池荷电状态方程,以及电池负载电压的量测方程所构成的电池的状态空间方程,再用改进的扩展卡尔曼滤波方程计算来获得电池的荷电状态,所说的电池的荷电状态方程:令xk=SOCk An electric vehicle battery state of charge estimation and implementation, which is based on the state wherein the configuration of the battery charge measurement equation An equation of state when the measurement method, and the battery voltage of the battery load space equation, then the battery state of charge is calculated by obtaining an improved extended Kalman filter equations, equation of state of charge of said battery: Let xk = SOCk 所说的电池的负载电压量测方程:y(k)=f(Ik,xk)+vk=K0-RIk-K1/xk-K2xk+K3ln(xk)+K4ln(1-xk)+vk式中n为匹克特(Peukert)系数,η为充电效率;Ki为拟合系数;vk,ik分别为k采样时刻电池的负载电压信号,电流信号;vk为量测噪声,其方差为Q。 Load voltage of said battery measurement equation: y (k) = f (Ik, xk) + vk = K0-RIk-K1 / xk-K2xk + K3ln (xk) + K4ln (1-xk) + vk where n is Pickett (the Peukert) coefficients, the charging efficiency [eta]; of Ki for the fitting coefficients; vk, ik k sampling instants respectively load the battery voltage signal, a current signal; VK is a measurement noise variance is Q. 所说的卡尔曼滤波方程为量测阵Ck=∂f(Ik,xk)∂xk=K1/(xk)2-K2+K3/xk-K4/(1-xk)]]>状态预测: Said Kalman filter equations for the measurement matrix Ck = & PartialD; f (Ik, xk) & PartialD; xk = K1 / (xk) 2-K2 + K3 / xk-K4 / (1-xk)]]> state prediction : 计算量测阵:Ck=K1/(xk/k-1)2-K2+K3/xk/k-1-K4/(1-xk/k-1)滤波增益:Kk=Pk/k-1CkT[CkPk/k-1CkT+Q]-1]]>预测均方差:Pk/k-1=Pk-1/k-1估计均方差:Pk/k=(I-KkCk)Pk/k-1状态估计:xk/k=xk/k-1+αKk[yk-f(ik,xk/k-1)]。 Calculated measurement matrix: Ck = K1 / (xk / k-1) 2-K2 + K3 / xk / k-1-K4 / (1-xk / k-1) the filter gain: Kk = Pk / k-1CkT [ CkPk / k-1CkT + Q] -1]]> mean square prediction: Pk / k-1 = Pk-1 / k-1 are the estimated variance: Pk / k = (I-KkCk) Pk / k-1 state estimate : xk / k = xk / k-1 + αKk [yk-f (ik, xk / k-1)].
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