CN105738817A - Battery charge state estimation method based on AEKF and estimation system - Google Patents

Battery charge state estimation method based on AEKF and estimation system Download PDF

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CN105738817A
CN105738817A CN 201610064341 CN201610064341A CN105738817A CN 105738817 A CN105738817 A CN 105738817A CN 201610064341 CN201610064341 CN 201610064341 CN 201610064341 A CN201610064341 A CN 201610064341A CN 105738817 A CN105738817 A CN 105738817A
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time
noise
charge
battery
state
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CN 201610064341
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孔满
关海盈
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深圳市沃特玛电池有限公司
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3627Testing, i.e. making a one-time determination of some variables, e.g. testing ampere-hour charge capacity
    • G01R31/3634Testing, i.e. making a one-time determination of some variables, e.g. testing ampere-hour charge capacity for determining the ampere-hour charge capacity or state-of-charge (SoC)
    • G01R31/3637Testing, i.e. making a one-time determination of some variables, e.g. testing ampere-hour charge capacity for determining the ampere-hour charge capacity or state-of-charge (SoC) based on voltage measurements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3644Various constructional arrangements
    • G01R31/3648Various constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • HELECTRICITY
    • H03BASIC ELECTRONIC CIRCUITRY
    • H03HIMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00Networks using digital techniques
    • H03H17/02Frequency selective networks
    • H03H17/0202Two or more dimensional filters; Filters for complex signals
    • HELECTRICITY
    • H03BASIC ELECTRONIC CIRCUITRY
    • H03HIMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00Networks using digital techniques
    • H03H17/02Frequency selective networks
    • H03H17/0202Two or more dimensional filters; Filters for complex signals
    • H03H2017/0205Kalman filters

Abstract

The invention relates to the battery electrical testing technology, especially relates to a battery charge state estimation method based on AEKF and an estimation system. Battery SOC can be estimated by adopting the self-adaption expansion Kalman filtering algorithm, and the parameter self-adaption adjusting way of the Kalman filtering algorithm can be changed by additionally providing the weighting coefficient based on the forgetting factor, and then the influence of the parameter initial value setting on the whole algorithm is small, and the phenomena of the inaccurate battery SOC initial value calculated by adopting the original ampere-hour integral method and the accumulated error can be overcome, and in addition, the battery SOC can be estimated accurately and reliable. The battery charge state estimation method and the estimation system are advantageous in that the convergence performance is good, the convergence speed is fast, the algorithm transplantability is good, and the use stable and reliable; the estimation method and the estimation system can be used for the electric vehicle battery management field, and can be used for the SOC estimation of the electric vehicle storage battery, and therefore the endurance mileage of the electric vehicle can be calculated accurately, the control of the driver over the vehicle can be facilitated; the estimation method and the estimation system are more suitable for the electric vehicle environment having the strong current fluctuation.

Description

一种基于AEKF的电池荷电状态估计方法及估计系统 AEKF based battery state of charge estimation system estimation method, and

技术领域 FIELD

[0001 ]本发明涉及电池电气测试技术,尤其涉及一种基于AEKF的电池荷电状态估计方法及估计系统。 [0001] The present invention relates to an electrical battery testing technology, particularly to a method of estimating the state of charge of the battery and the estimation system based AEKF.

背景技术 Background technique

[0002] 目前,电池的剩余电量(StateofCharge,S0C)估计方法主要分为两大类:直接法与间接法。 [0002] Currently, the remaining power of the battery (StateofCharge, S0C) estimation methods are mainly divided into two categories: direct method and indirect method. 直接法是指通过实验设备直接测量电池的剩余电量;间接法主要通过电池内部的物化特性,在估计过程中需要高精度的设备,因此在实际中很难实现。 Direct method refers to a measure of remaining battery power directly through the experimental apparatus; indirect method by physicochemical properties mainly inside the battery, in the estimation process requires high-precision equipment, it is difficult to achieve in practice. 安时积分法、开路电压法、内阻法等属于间接法,安时积分法在计算过程中会产生累积误差,导致计算得到的S0C随充放电时间的增加误差增大,同时安时积分法计算S0C初始值的准确性很难确定;开路电压法需要长时间的静置使电池内部电压稳定,在监测汽车运行过程中的电池剩余电量时难以实现;内阻法存在着估算内阻的困难,在硬件上也难以实现。 An integration time, open circuit voltage, resistance, etc. to process the indirect method, when security integration calculation process will produce cumulative errors resulting in increased errors calculated S0C with charge and discharge time is increased, while when the integration Ann calculated S0C accuracy is difficult to determine an initial value; OCV method takes a long time to stabilize the internal voltage of the battery was allowed to stand, it is difficult to achieve when the remaining battery during the monitoring operation of the vehicle; there are difficulties in estimating the internal resistance resistance method , it is difficult to implement in hardware. 此外,还可通过人工神经网络算法、卡尔曼滤波算法等间接法估算电池S0C,但神经网络算法和卡尔曼滤波算法由于其系统设置困难,且在电池管理系统中应用成本高,不具备优势。 In addition, the battery can also be estimated by the indirect method S0C artificial neural network algorithm, Kalman filter algorithm and the like, but the neural network algorithm and the Kalman filter algorithm is difficult because the system configuration, and a battery management system in a high cost application, do not have the advantage.

发明内容 SUMMARY

[0003] 本发明所要解决的技术问题是,针对不同方法的优劣及适用性,提供一种基于AEKF的电池荷电状态估计方法及估计系统,以提高估计的电池S0C的准确性。 [0003] The present invention solves the technical problem, and suitability for the advantages and disadvantages of different methods, a method based AEKF battery state of charge estimation method and the estimation system, to improve the accuracy of the estimated battery S0C. 本发明是这样实现的: The present invention is achieved:

[0004] -种基于AEKF的电池荷电状态估计方法,包括如下步骤: [0004] - Species battery state of charge estimation based AEKF method, comprising the steps of:

[0005] 步骤1:初始化to时刻的XQ、PQ、Q〇、R〇,然后进入步骤2;其中XQ为电池荷电状态初始值,Po为误差协方差初始值,Qo为过程噪声初始值,Ro为观测噪声初始值; [0005] Step 1: XQ initialized to time, the PQ, Q〇, R〇, then proceeds to step 2; wherein XQ is the initial battery state of charge value, Po is the error covariance of the initial value, Qo of the process noise for the initial value, Ro is the initial value of measurement noise;

[0006] 步骤2:预估k时刻的电池荷电状态4及k时刻的状态先验估计误差协方差巧「,然后进入步骤3;其中: [0006] Step 2: Estimate the state of charge of the battery 4 and the state at time k at time k priori estimate error covariance clever ", and then proceeds to step 3; wherein:

[0007]4 ,其中,A为一个采样间隔内的传递矩阵,为k-1时刻的电池荷电状态的后验估计值,B为输入矩阵,ui^Sk-l时刻系统的输入量,noisei^Sk-1时刻加入的白噪声; [0007] 4, wherein, A is an a posteriori estimate of the battery state of charge k-1 time point B of the input matrix, input ui ^ Sk-l timing system, by Noisei sample transfer matrix within the interval, ^ Sk-1 is added to white noise in time;

[0008]尽=,其中,Pk-i为k_l时刻的状态估计后验误差协方差,AT为传递矩阵A的转置矩阵,Qk-i为k-1时刻的过程噪声; [0008] do =, where, Pk-i is the time estimated state k_l posteriori error covariance, the AT is the transposed transfer matrix A, Qk-i noise during the time k-1;

[0009]步骤3:更新k时刻的实际电压信号与模型电压信号之间的差值改和卡尔曼滤波增益Hk,然后进入步骤4;其中: [0009] Step 3: update time k between the actual voltage difference between the voltage signal and the model signal and the modified Kalman filter gain Hk, then proceeds to step 4; wherein:

[0010] ,其中,yk为k时刻采集到的电池的实际电压信号,/($,&)为让时刻的电池模型的模型电压信号,g为k时刻的电池荷电状态先验估计值; [0010] wherein, yk at time k is the actual voltage signal acquired battery, / ($, &) of the battery model so that the model voltage timing signal, g is the battery state of charge at time k priori estimate value;

[0011] 私=(<^TCT +尽),其中,Hk为k时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; [0011] Private = (<^ TCT + do), wherein, for the Kalman filter gain matrix Hk at time k, Rk is the measurement noise at time k, C is an output matrix, CT is the transpose of the output matrix C;

[0012] 步骤4:更新基于遗忘因子的加权系数,然后进入步骤5;dk-Fa-bm-Pr1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,dk-iSk-l时刻的基于遗忘因子的加权系数值; [0012] Step 4: Update on forgetting weighting coefficient factor, and then proceeds to step 5; dk-Fa-bm-Pr1, wherein, b is the forgetting factor, d is based forgetting factor weighting coefficients, dk-iSk-l timing based on the weighting coefficient values ​​of the forgetting factor;

[0013] 步骤5:更新过程噪声Qk和观测噪声Rk,然后进入步骤6: [0013] Step 5: Update process noise and observation noise Qk Rk, and then proceeds to step 6:

[0014] Qk = {\~d,,)〇,,+<lG(//i^e[//;' + />-ARA^G1 ; [0014] Qk = {\ ~ d ,,) square ,, + <lG (// i ^ e [//; '+ /> - ARA ^ G1;

[0015] 馬=(l-;其中: [0015] Ma = (l-; wherein:

[0016] G为白噪声,Qi^Sk-l时刻的过程噪声,Ri^Sk-l时刻的观测噪声,g为k时刻的实际电压信号与模型电压信号之间的差值ek的转置,/^为k时刻的卡尔曼滤波增益矩阵Hk的转置; [0016] G is a white noise, the noise measurement noise process Qi ^ Sk-l time, Ri ^ Sk-l time, g is the transpose of the difference between the actual ek voltage signal voltage signal model at time k, / ^ is the Kalman filter gain matrix Hk at time k transpose;

[0017] 步骤6:更新k时刻的电池荷电状态的后验估计值K和k时刻的电池荷电状态后验估计误差协方差I,然后进入步骤7; +//&,/^ ,1为单位矩阵; [0017] Step 6: K a posteriori estimation value at time k and the state of charge of the battery at time k update the state of charge of the battery estimated posteriori error covariance after I, and then proceeds to step 7; + // &, / ^, 1 is the identity matrix;

[0018] 步骤7:k值增加1,并返回步骤1。 [0018] Step 7: k is incremented by one, and returns to step 1.

[0019] 进一步地,所述b的值为0.95。 [0019] Further, the value of b is 0.95.

[0020] 一种基于AEKF的电池荷电状态估计系统,包括初始化模块、预估模块、电压差值和卡尔曼滤波增益更新模块、基于遗忘因子的加权系数更新模块、过程噪声和观测噪声更新模块、荷电状态后验估计值及其误差协方差更新模块、迭代模块;其中: [0020] A battery state of charge estimation system AEKF includes initialization module, estimates the module, the voltage difference between the gain and the Kalman filter update module based forgetting factor based on the weighting coefficient updating module, the process noise and measurement noise updating module , after the state of charge posteriori error covariance estimate and an update module, the iteration module; wherein:

[0021] 初始化模块用于初始化to时刻的幼、化、(^、办,然后跳转到预估模块;其中幼为电池荷电状态初始值,P〇为误差协方差初始值,Q〇为过程噪声初始值,R〇为观测噪声初始值; [0021] The initialization module for the young, to the time of initialization, (^, run, and then jump to the prediction module; wherein the battery state of charge Immature initial value, the error covariance P〇 initial value of Q〇 The initial process noise value, the initial value measurement noise is R〇;

[0022] 预估模块用于预估k时刻的电池荷电状态4'及k时刻的状态先验估计误差协方差6,然后跳转到电压差值和卡尔曼滤波增益更新模块;其中: [0022] Estimated Estimated battery module status for the state of charge at time k 4 'and time k 6 priori estimate error covariance, and then jump to the voltage difference between the gain and the Kalman filter update module; wherein:

[0023]4 +彻岭m),其中,A为一个采样间隔内的传递矩阵,为k-1时刻的电池荷电状态的后验估计值,B为输入矩阵,uk-i为k-1时刻系统的输入量,noisek-i为k-1时刻加入的白噪声; [0023] 4 + Toru ridge m), where, A is the transfer matrix within one sampling interval for a posteriori estimation of the battery state of charge k-1 time point, B is an input matrix, uk-i for the k-1 timing input system, noisek-i k-1 is added to white noise in time;

[0024] .巧=+0^,其中,Pk-1为k_l时刻的状态估计后验误差协方差,AT为传递矩阵A的转置矩阵,Qk-i为k-1时刻的过程噪声; [0024] Qiao = 0 + ^, where, Pk-1 is a state k_l time posteriori estimation error covariance, the AT is the transposed transfer matrix A, Qk-i noise during the time k-1;

[0025]电压差值和卡尔曼滤波增益更新模块用于更新k时刻的实际电压信号与模型电压信号之间的差值ek和卡尔曼滤波增益Hk,然后跳转到基于遗忘因子的加权系数更新模块;其中: [0025] The voltage difference between the gain and the Kalman filter update module for updating the time k ek difference between the model signal and the actual voltage and signal voltage gain Kalman filter Hk, then jump to update the weighting coefficient based on the forgetting factor module; wherein:

[0026] % =力-乂),其中,yk为k时刻采集到的电池的实际电压信号,/d%)为k 时刻的电池模型的模型电压信号,g为k时刻的电池荷电状态先验估计值; /4=iTC:f/(eirc^ +&),其中,Hdk时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; [0026]% = Force - qe), wherein, yk is the time k acquired actual voltage signal of the battery, / d%) as a model voltage signal of the battery model at time k, g is the battery state of charge at time k to posteriori estimate; / 4 = iTC: f / (eirc ^ + &), wherein the Kalman filter gain matrix Hdk time, Rk is the measurement noise at time k, C is an output matrix, CT is the transpose of the output matrix C ;

[0027]基于遗忘因子的加权系数更新模块用于更新基于遗忘因子的加权系数,然后跳转到过程噪声和观测噪声更新模块;dk-Fa-bKi-bkr1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,ch^Sk-l时刻的基于遗忘因子的加权系数值; [0027] Based on the forgetting factor a weighting coefficient updating means for updating the weighting coefficient based on the forgetting factor, and then jumps to the process noise and measurement noise updating module; dk-Fa-bKi-bkr1, wherein, b is the forgetting factor, d is weighting coefficient value based on a forgetting factor ch ^ Sk-l timing based forgetting factor weighting coefficient;

[0028] 过程噪声和观测噪声更新模块用于更新过程噪声Qk和观测噪声Rk,然后跳转到荷电状态后验估计值及其误差协方差更新模块: _] a =(•-<m, +</, ~^n ^)Gr; After the [0028] process noise and measurement noise updating module for updating the measurement noise and process noise Qk Rk, then jump to the state of charge and the posteriori estimate error covariance update module: _] a = (• - <m, + </, ~ ^ n ^) Gr;

[0030] ^=(1-4-4 +4-C);其中: [0030] ^ = (1-4-4 + 4-C); wherein:

[0031] G为白噪声,Qi^Sk-l时刻的过程噪声,时刻的观测噪声,<Sk时刻的实际电压信号与模型电压信号之间的差值ek的转置,辦'为k时刻的卡尔曼滤波增益矩阵Hk的转置; [0031] G is a white noise, the transpose of the difference between the observed noise ek process Qi ^ Sk-l timing noise, time, <actual voltage signal with a voltage signal Sk timing model, do 'is time k Hk Kalman filter gain matrix transpose;

[0032]荷电状态后验估计值及其误差协方差更新模块用于更新k时刻的电池荷电状态的后验估计值4和k时刻的电池荷电状态后验估计误差协方差C,然后跳转到迭代模块; \ 二4-///A,巧=(/-//,〇/丨,1 为单位矩阵; After the battery state of charge [0032] SOC estimation value and the posteriori error covariance after updating module for updating the time k to the state of charge of the battery 4 and the posteriori estimated value at time k posteriori estimate error covariance C, then Jump to iteration module; \ two 4 - /// A, Qiao = (///, square / Shu, 1 is a unit matrix;

[0033]迭代模块用于将k值增加1,并返回到初始化模块。 [0033] Iterative means for increasing the value of k 1, and returns to the initialization module.

[0034] 进一步地,所述b的值为0.95。 [0034] Further, the value of b is 0.95.

[0035] 与现有技术相比,本发明采用自适应扩展卡尔曼滤波算法估计电池S0C,并增加了基于遗忘因子的加权系数改变了卡尔曼滤波算法的参数自适应调整方式,促使整个算法受参数初始值设置的影响很小,克服了原有的安时积分法计算电池S0C初始值不准确及累计误差的现象,可以更准确可靠地估计电池S0C。 [0035] Compared with the prior art, the present invention employs an adaptive extended Kalman filter estimates S0C battery, and the parameter is changed to increase the adaptive Kalman Filter adjustment method based forgetting factor weighting coefficients by the algorithm causes Effect of initial parameters set small, to overcome the initial value of the calculated original cell S0C an integral method is not accurate when the accumulated errors and the phenomenon can be more accurately and reliably estimated battery S0C. 同时,本发明收敛性好,收敛速度快,且算法移植性好,稳定可靠。 Meanwhile, the present invention is good convergence, convergence speed, and the algorithm portability, and reliable. 本发明可应用于电动汽车电池管理领域,将本发明应用于电动汽车蓄电池的S0C估计,可以准确地计算电动汽车的续航里程,便于驾驶者对车辆的掌控,更适用于电流波动剧烈的电动汽车环境。 The present invention can be applied to an electric car battery management areas, the present invention is applied to an electric vehicle battery S0C estimation, the electric vehicle can be calculated accurately mileage, to facilitate the driver's control of the vehicle, and more suitable for an electric vehicle current volatile surroundings.

附图说明 BRIEF DESCRIPTION

[0036] 图1:本发明实施例提供的基于AEKF的电池荷电状态估计方法流程示意图; [0036] Figure 1: a schematic view of embodiment of the present invention is based on the battery state of charge estimation method provided by the embodiment of the flow AEKF;

[0037]图2:本发明实施例提供的基于AEKF的电池荷电状态估计系统结构示意图。 [0037] Figure 2: structural diagram of a system embodiment of the present invention is based on the battery state of charge estimation according AEKF provided.

具体实施方式 detailed description

[0038]为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。 [0038] To make the objectives, technical solutions and advantages of the present invention will become more apparent hereinafter in conjunction with the accompanying drawings and embodiments of the present invention will be further described in detail.

[0039]本发明提供了一种基于AEKF(自适应扩展卡尔曼滤波算法)的电池荷电状态(StateofCharge,S0C)估计方法。 [0039] The present invention provides a method based on AEKF (Adaptive Extended Kalman Filter) of the battery state of charge (StateofCharge, S0C) estimation method. 本方法的总体思想是,首先对各估计参数进行初始化处理,主要包括to时刻的S0C状态、协方差及噪声矩阵(过程噪声、观测噪声)的初始化设置,然后对过程变量进行更新,依据上述卡尔曼滤波算法递推式进行推进,然后对基于遗忘因子的加权系数进行确定,进而确定其遗忘因子,更新算法中的参数,最后得到S0C估计值。 The general idea of ​​this method is that, first of all for each estimation parameter initialization process, including to S0C state time, covariance and noise matrix (process noise, measurement noise) initialization settings, then the process variable is updated, based on the above Carr Man recursive filtering algorithm performed forward, and then the weighted coefficient based on the determined forgetting factor, which in turn determines the forgetting factor, the parameter updating algorithm, to obtain the final estimated value S0C. 通过反复整个过程进行迭代,不断更新得到最优的S0C估计值。 Iterate through the entire process again and again, constantly updated to get the best S0C estimated value.

[0040]在上述总体思想的基础上,本发明提供的基于AEKF的电池荷电状态估计方法包括如下步骤: [0040] Based on the above general concept of the present invention provides a battery state of charge estimation based AEKF method comprising the steps of:

[0041 ]步骤1:初始化to时刻的xo、Po、Qo、Ro,然后进入步骤2;其中xo为电池荷电状态初始值,P〇为误差协方差初始值,Q〇为过程噪声初始值,R〇为观测噪声初始值; [0041] Step 1: Initialization time to xo, Po, Qo, Ro, and then proceeds to step 2; where xo is the initial battery state of charge value, the error covariance P〇 initial value, the initial value of the noise process Q〇, R〇 initial value of measurement noise;

[0042]步骤2:预估k时刻的电池荷电状态4及k时刻的状态先验估计误差协方差iT,然后进入步骤3;其中: [0042] Step 2: Estimate the state of charge of the battery 4 and the state at time k at time k priori estimate error covariance iT, and then proceeds to step 3; wherein:

[0043] 右=夂<_, 其中,A为一个采样间隔内的传递矩阵,^C,为k-1时刻的电池荷电状态的后验估计值,B为输入矩阵,uk-i为k-1时刻系统的输入量,noisek-i为k-1时刻加入的白噪声; [0043] Right = Fan <_, wherein, A is a sampling interval of the transfer matrix, ^ C, the posterior estimate of the battery state of charge k-1 time point, B is an input matrix, uk-i for the k -1 input timing system, noisek-i k-1 is added to white noise in time;

[0044] 乃.=十这-1,其中,Pk-i为k_l时刻的状态估计后验误差协方差,AT为传递矩阵A的转置矩阵,Qk-i为k-1时刻的过程噪声; [0044] This is the ten = -1, where, Pk-i is the time estimated state k_l posteriori error covariance, the AT is the transposed transfer matrix A, Qk-i noise during the time k-1.;

[0045]步骤3:更新k时刻的实际电压信号与模型电压信号之间的差值%和卡尔曼滤波增益Hk,然后进入步骤4;其中: [0045] Step 3: the Kalman filter gain and% difference between the update time k Hk actual voltage signal voltage signal and the model, and then proceeds to step 4; wherein:

[0046]& =凡-/^,心),其中,yk为k时刻采集到的电池的实际电压信号,/($,&)为k 时刻的电池模型的模型电压信号,&为k时刻的电池荷电状态先验估计值; [0046] & = Van - / ^, heart), in which the actual voltage signal yk for the k time collected battery / ($, &) model voltage signal of the battery model at time k, & is the time k battery state of charge prior estimate;

[0047] //,, = 6CWqC'; +A),其中,Hk为k时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; [0047] // ,, = 6CWqC '; + A), where, Hk at time k is the Kalman filter gain matrix, Rk is the measurement noise at time k, C is an output matrix, CT is the transpose of the output matrix C;

[0048] 步骤4:更新基于遗忘因子的加权系数,然后进入步骤5;如-1=(1-13)(1-沪广1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,dk-iSk-l时刻的基于遗忘因子的加权系数值; [0048] Step 4: Update the weighting coefficients based on the forgetting factor, and then proceeds to step 5; such as -1 = (1-13) (l-wide Shanghai 1, wherein, b is a forgetting factor, d is a weighting coefficient based on a forgetting factor , dk-iSk-l timing based forgetting factor weighting coefficient values;

[0049] 步骤5:更新过程噪声Qk和观测噪声Rk,然后进入步骤6: [0049] Step 5: Update process noise and observation noise Qk Rk, and then proceeds to step 6:

[0050] Qk = (l-dk_xY2k_y +d^G(HketelHTk +1^ - APk AT)GT ; [0050] Qk = (l-dk_xY2k_y + d ^ G (HketelHTk + 1 ^ - APk AT) GT;

[0051]馬,)& 丨-R/,丨其中: [0051] Ma,) & Shu -R /, Shu wherein:

[0052]G为白噪声,Qk-i为k-1时刻的过程噪声,Rk-i为k-1时刻的观测噪声,< 为k时刻的实际电压信号与模型电压信号之间的差值ek的转置,",「为k时刻的卡尔曼滤波增益矩阵Hk的转置; [0052] G is a white noise, Qk-i for the process noise time k-1, Rk-i of the observation noise time k-1, <ek is the difference between the actual voltage signal with a voltage signal model at time k transpose, "" for the time k transpose the Kalman filter gain matrix Hk;

[0053] 步骤6:更新k时刻的电池荷电状态的后验估计值i〖和k时刻的电池荷电状态后验估计误差协方差巧+,然后进入步骤7; =X,、+ //,<q,斤=(7 - //,C; ,I为单位矩阵; [0053] Step 6: After updating the battery state of charge of the battery state of charge at time k posteriori estimation value at time k and i 〖posteriori estimate error covariance + Qiao, then proceeds to step 7; = X ,, + // , <q, kg = (7 - //, C;, I is the identity matrix;

[0054] 步骤7:k值增加1,并返回步骤1。 [0054] Step 7: k is incremented by one, and returns to step 1.

[0055]图1是本实施例估计方法的简化流程示意图。 [0055] FIG. 1 is a simplified flow estimation method of the present embodiment FIG. 在本实施例步骤4中,b的值设为0.95。 In this embodiment the procedure of Example 4, the value b is set to 0.95. 整个S0C估计过程利用基于遗忘因子的加权系数对卡尔曼滤波算法递推式进行更新, 不断优化更新估计的电池S0C值。 S0C estimation process utilizes the entire update the Kalman filter algorithm based forgetting factor recursive weighting coefficients to continuously optimize the value of the updated estimated battery S0C. 步骤4是本发明区别于现有技术的很重要的一步。 Step 4 is a very important step in the present invention is distinguished from the prior art. 通过增加基于遗忘因子的加权系数,改变了卡尔曼滤波算法的参数自适应调整方式,促使整个算法受参数初始值设置的影响较小,可以更好地优化估计的电池S0C值,相比利用现有的安时积分法估计电池S0C,本发明更可靠。 By increasing the weighting coefficient based on the forgetting factor, changing the parameters of an adaptive manner to adjust the Kalman filter algorithm, the algorithm causes less dependent on the initial parameter set, to better optimize the value of the estimated battery S0C, now compared using when the estimated battery S0C some security integration, the present invention more reliable. 本发明可应用于电动汽车(包括纯电动汽车和混合动力汽车)的电池管理中,便于驾驶者实时准确掌握电动汽车的续航里程。 The present invention can be used in electric vehicles (including electric cars and hybrid vehicles) battery management, real-time accurate information to facilitate the driver of an electric vehicle mileage.

[0056]基于上述电池荷电状态估计方法,本发明还提供了一种基于AEKF的电池荷电状态估计系统。 [0056] The battery state estimation method based on the present invention further provides a AEKF based on the battery state of charge estimation system. 如图2所示,该系统包括: As shown, the system 2 comprises:

[0057]初始化模块1、预估模块2、电压差值和卡尔曼滤波增益更新模块3、基于遗忘因子的加权系数更新模块4、过程噪声和观测噪声更新模块5、荷电状态后验估计值及其误差协方差更新模块6、迭代模块7;其中: [0057] Initialization module 1, module 2 estimates, and Kalman filter gain voltage difference value updating module 3, the forgetting factor based on a weighting coefficient updating module 4, the process noise and measurement noise updating module 5, the state of charge posteriori estimate their error covariance update module 6, 7 iteration module; wherein:

[0058]初始化模块1用于初始化to时刻的xo、Po、Qo、Ro,然后跳转到预估模块2;其中xo为电池荷电状态初始值,Po为误差协方差初始值,Qo为过程噪声初始值,Ro为观测噪声初始值; xo [0058] The initialization module for initializing to 1 time, Po, Qo, Ro, and then jump to the prediction module 2; where xo is the initial battery state of charge value, Po is the error covariance of the initial value, Qo of the process The initial value of the noise, Ro is the initial value of measurement noise;

[0059] 预估模块2用于预估k时刻的电池荷电状态<及k时刻的状态先验估计误差协方差6,然后跳转到电压差值和卡尔曼滤波增益更新模块3;其中: [0059] Prediction module 2 estimates the state of charge of the battery state for <time k and time k priori estimate error covariance 6, and then jump to the voltage difference between the gain and the Kalman filter update module 3; wherein:

[0060] % = 與其中,A为一个米样间隔内的传递矩阵,^为k_l时刻的电池荷电状态的后验估计值,B为输入矩阵,uk-i为k-1时刻系统的输入量,noisek-i为k-1时刻加入的白噪声; [0060]% = and where, A is the transfer matrix in a one meter sample interval, ^ posteriori estimate the battery state of charge k_l time, B is an input matrix, uk-i is input k-1 timing system amount, noisek-i k-1 is added to white noise in time;

[0061 ] 尽'_= +d,其中,Pk-i为k-1时刻的状态估计后验误差协方差,AT为传递矩阵A的转置矩阵,Qk-i为k-1时刻的过程噪声; [0061] Doing '_ = + d, where, Pk-i to state k-1 time estimated posteriori error covariance, AT to pass the transpose matrix A, Qk-i for the process k-1 time noise ;

[0062] 电压差值和卡尔曼滤波增益更新模块3用于更新k时刻的实际电压信号与模型电压信号之间的差值ek和卡尔曼滤波增益Hk,然后跳转到基于遗忘因子的加权系数更新模块4;其中: [0062] The voltage difference between the gain and the Kalman filter update module for updating the 3 k ek time difference between the model signal and the actual voltage and signal voltage gain Kalman filter Hk, then jumps to a weighting coefficient based on the forgetting factor updating module 4; wherein:

[0063] q ,其中,yk为k时刻采集到的电池的实际电压信号,/(^,七)为k 时刻的电池模型的模型电压信号,X/丨为k时刻的电池荷电状态先验估计值; [0063] q, wherein, yk at time k is the actual voltage signal acquired battery, / (^, g) a voltage signal as the model of the battery model at time k, X / Shu battery state of charge at time k priori estimated value;

[0064] 巧=iff/+為),其中,Hdk时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; [0064] Qiao = iff / + to), wherein the Kalman filter gain matrix Hdk time, Rk is the measurement noise at time k, C is an output matrix, CT is the transpose of the output matrix C;

[0065] 基于遗忘因子的加权系数更新模块4用于更新基于遗忘因子的加权系数,然后跳转到过程噪声和观测噪声更新模块sj^ia-bKi-bkr1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,ch^Sk-l时刻的基于遗忘因子的加权系数值; [0065] Based on the forgetting factor weighting coefficient updating module 4 for updating the weighting coefficient based on the forgetting factor, and then jumps to the process noise and measurement noise updating module sj ^ ia-bKi-bkr1, wherein, b is the forgetting factor, d is weighting coefficient value based on a forgetting factor ch ^ Sk-l timing based forgetting factor weighting coefficient;

[0066] 过程噪声和观测噪声更新模块5用于更新过程噪声Qk和观测噪声Rk,然后跳转到荷电状态后验估计值及其误差协方差更新模块6: After the [0066] process noise and measurement noise updating module for updating the process noise Qk 5 and observation noise Rk, then jump to the state of charge and the posteriori estimate error covariance update module 6:

[0067] 这=(卜-1 [0067] It = (-1 Bu

[0068] 愚=(1-4 冰,-W/,,W,: -OK");其中: [0068] Yu = (1-4 ice, -W / ,, W ,: -OK "); wherein:

[0069] G为白噪声,Qk-Ak-1时刻的过程噪声,Rk-Ak-1时刻的观测噪声,ef为k时刻的实际电压信号与模型电压信号之间的差值ek的转置,成为k时刻的卡尔曼滤波增益矩阵Hk的转置; [0069] G is a white noise, the process noise Qk-Ak-1 in time, the measurement noise Rk-Ak-1 time point, EF for the transposition of the difference between the actual ek voltage signal voltage signal model at time k, become time k Hk Kalman filter gain matrix transpose;

[0070] 荷电状态后验估计值及其误差协方差更新模块6用于更新k时刻的电池荷电状态的后验估计值4和k时刻的电池荷电状态后验估计误差协方差巧+,然后跳转到迭代模块7; + 尸/KZ-"A),】,1 为单位矩阵; After the state of charge of the battery after the [0070] state of charge of the battery state of charge posteriori estimation values ​​and error covariance update module for updating the time k 6 posteriori estimation value at time k and 4 posteriori estimate error covariance clever + and then jumps to the iterator module 7; + dead / KZ- "A),], 1 is a unit matrix;

[0071]迭代模块7用于将k值增加1,并返回到初始化模块1。 [0071] Iterative module 7 for increasing the value of K 1, and a return to the initialization module.

[0072] 其中,b的值为0.95。 [0072] wherein, b is 0.95.

[0073] 该估计系统中的各模块与上述估计方法中的各步骤一一对应,可参考上述估计方法中的各步骤,在此对各模块的功能和工作原理不再一一赘述。 [0073] The estimation system in each module and the estimation method in each step one correspondence, the steps may refer to the above-described estimation method, this is not detailed in the function and operating principle of each module.

[0074] 以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。 [0074] The foregoing is only preferred embodiments of the present invention but are not intended to limit the present invention, any modifications within the spirit and principle of the present invention, equivalent substitutions and improvements should be included in the present within the scope of the invention.

Claims (4)

  1. 1. 一种基于AEKF的电池荷电状态估计方法,其特征在于,包括如下步骤: 步骤1:初始化to时刻的xo、Po、Qo、Ro,然后进入步骤2;其中xo为电池荷电状态初始值,Po为误差协方差初始值,Q〇为过程噪声初始值,R〇为观测噪声初始值; 步骤2:预估k时刻的电池荷电状态4及k时刻的状态先验估计误差协方差6,然后进入步骤3;其中: 4 + ,其中,A为一个采样间隔内的传递矩阵,<,为k-1时刻的电池荷电状态的后验估计值,B为输入矩阵,uk-i为k-1时刻系统的输入量,noisek-i为k-1时刻加入的白噪声; 斤其中,Pk-1为k-1时刻的状态估计后验误差协方差,AT为传递矩阵A的转置矩阵,Qk-iSk-l时刻的过程噪声; 步骤3:更新k时刻的实际电压信号与模型电压信号之间的差值ek和卡尔曼滤波增益Hk, 然后进入步骤4;其中: m-/d七),其中,yk为k时刻采集到的电池的实际电压信号,/dwAJ为让 A method of estimating the state of charge of a battery based AEKF, characterized by comprising the following steps: Step 1: Initialization time to xo, Po, Qo, Ro, and then proceeds to step 2; where xo is the initial state of charge of the battery value, Po is the error covariance of the initial value, the initial value of the process noise is Q〇, R〇 initial value for the measurement noise; step 2: 4 and estimates the state of charge of the battery state at the time k at time k priori estimate error covariance 6, and then proceeds to step 3; wherein: 4 +, wherein, a is a sample transfer matrix within the interval <posteriori estimate the battery state of charge k-1 time point, B is an input matrix, uk-i is input k-1 time system, noisek-i is added to k-1 time white noise; kg wherein, Pk-1 is a state k-1 time estimated posteriori error covariance, AT is the transfer matrix a turn process matrix, Qk-iSk-l timing of noise; step 3: the difference between the update time k ek actual voltage signal with a voltage signal and a model of the Kalman filter gain Hk, then proceeds to step 4; wherein: m- / d VII), wherein, yk at time k is acquired the actual battery voltage signal, / dwAJ to make 刻的电池模型的模型电压信号,冗为k时刻的电池荷电状态先验估计值; 私=6C"/(qC+為),其中,HAk时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; 步骤4:更新基于遗忘因子的加权系数,然后进入步骤sd^ia-bKi-bkr1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,dk-iSk-l时刻的基于遗忘因子的加权系数值; 步骤5:更新过程噪声Qk和观测噪声Rk,然后进入步骤6: 炙=〇- + 4-;其中: G为白噪声,Qk-1为k-1时刻的过程噪声,Rk-dk-l时刻的观测噪声,e【为k时刻的实际电压信号与模型电压信号之间的差值ek的转置,祀为k时刻的卡尔曼滤波增益矩阵Hk的转置; 步骤6:更新k时刻的电池荷电状态的后验估计值g和k时刻的电池荷电状态后验估计误差协方差珍,然后进入步骤7; \ = \ + //&,# =(ZW,I为单位矩阵; 步骤7:k值增加1,并返回步 Voltage signal of the battery model carved model, redundant battery state of charge at time k priori estimate value; private = 6C "/ (qC + is), wherein the Kalman filter gain matrix HAk time, Rk is the observation at time k noise, C is an output matrix, CT output matrix C transpose; step 4: update the weighting coefficients based on the forgetting factor, and then proceeds to step sd ^ ia-bKi-bkr1, wherein, b is the forgetting factor, d is based on the forgetting factor weighting factor, the weighting coefficient value based on forgetting factor dk-iSk-l timing; step 5: update process noise Qk and Rk observation noise, and then proceeds to step 6: 4- Sunburn + = 〇-; provided wherein: G is a white noise , Qk-1 k-1 is the noise process time, measurement noise Rk-dk-l time, E [ek transpose of the difference between the actual voltage signal with a voltage signal model at time k, k is the time worship Hk Kalman filter gain matrix transpose; step 6: update the state of charge of the battery posteriori estimation value g k and the moment the battery state of charge at time k posteriori estimate error covariance Jane, and then proceeds to step 7; \ = \ + // &, # = (ZW, it is a unit matrix; step 7: k is incremented by one, and returns to step 1。 1.
  2. 2. 如权利要求1所述的基于AEKF的电池荷电状态估计方法,其特征在于,所述b的值为0.95〇 2. The value of claims 0.95〇 battery state of charge estimation based AEKF, wherein said 1, the b
  3. 3. -种基于AEKF的电池荷电状态估计系统,其特征在于,包括初始化模块、预估模块、 电压差值和卡尔曼滤波增益更新模块、基于遗忘因子的加权系数更新模块、过程噪声和观测噪声更新模块、荷电状态后验估计值及其误差协方差更新模块、迭代模块;其中: 初始化模块用于初始化to时刻的如、队(^、办,然后跳转到预估模块;其中幼为电池荷电状态初始值,P〇为误差协方差初始值,Q〇为过程噪声初始值,R〇为观测噪声初始值; 预估模块用于预估k时刻的电池荷电状态4及k时刻的状态先验估计误差协方差/r,然后跳转到电压差值和卡尔曼滤波增益更新模块;其中: 4 = 其中,A为一个采样间隔内的传递矩阵,<,为kl时刻的电池荷电状态的后验估计值,B为输入矩阵,uk-i为k-1时刻系统的输入量,noisek-i为k-1时刻加入的白噪声; if=. + ,其中,Ph为k-1时刻的状态估 3. - AEKF species based battery state of charge estimation system, characterized in that it comprises an initialization module, estimates the module, the voltage difference between the gain and the Kalman filter update module, the forgetting factor based on a weighting coefficient updating module, the process noise and observation noise updating module, the state of charge posteriori error covariance estimate and an update module, the iteration module; wherein: initializing means for initializing the time to such team (^, run, and then jump to the prediction module; wherein immature the battery state of charge as an initial value, the error covariance P〇 initial value, the initial value of the process noise is Q〇, R〇 initial value for the measurement noise; means for estimated battery state of charge estimated at time k and k 4 state timing priori estimate error covariance / r, and then jump to the voltage difference between the gain and the Kalman filter update module; wherein: where = 4, a is the transfer matrix within a sampling interval <for timing cell kl posteriori estimates the state of charge, B is an input matrix, uk-i input of k-1 time system, noisek-i is added to k-1 time white noise; if = +, where, Ph to be k. state-1 time estimation 后验误差协方差,AT为传递矩阵A 的转置矩阵,Qk-iSk-l时刻的过程噪声; 电压差值和卡尔曼滤波增益更新模块用于更新k时刻的实际电压信号与模型电压信号之间的差值ek和卡尔曼滤波增益Hk,然后跳转到基于遗忘因子的加权系数更新模块;其中: % =艿-/(i,&),其中,yk为k时刻采集到的电池的实际电压信号,/(.匕,《4_)为k时刻的电池模型的模型电压信号,冗为k时刻的电池荷电状态先验估计值; = /K" /(,其中,Hdk时刻的卡尔曼滤波增益矩阵,Rk为k时刻的观测噪声,C为输出矩阵,CT为输出矩阵C的转置; 基于遗忘因子的加权系数更新模块用于更新基于遗忘因子的加权系数,然后跳转到过程噪声和观测噪声更新模块;dk-^a-bKi-bkr1,其中,b为遗忘因子,d为基于遗忘因子的加权系数,ch^Sk-l时刻的基于遗忘因子的加权系数值; 过程噪声和观测噪声更新模块用于更新 Process posteriori error covariance, AT A is the transfer matrix of the transpose matrix, Qk-iSk-l timing of noise; and Kalman filter gain voltage difference value updating module for updating the time k with the actual voltage signal model voltage signals actual / (i, &), wherein, yk at time k for the collected batteries -% = Nai: wherein; ek, and a difference between the Kalman filter gain Hk, then jumps to the forgetting factor based on the weighting coefficient updating module Kalman; "= / K (4_ model voltage signal of the battery model at time k, redundant battery state of charge at time k priori estimate / (wherein, Hdk voltage timing signal, /. dagger,)" filter gain matrix, Rk is the measurement noise at time k, C is an output matrix, CT is the transpose of the output matrix C; forgetting factor based on a weighting coefficient updating means for updating the weighting coefficients based on the forgetting factor, and then jumps to the process noise and measurement noise updating module; dk- ^ a-bKi-bkr1, wherein, b is the forgetting factor, d is a weighting factor based on the forgetting factor, ch ^ Sk-l timing based forgetting factor weighting coefficient values; process noise and observation updating means for updating the noise 程噪声Qk和观测噪声Rk,然后跳转到荷电状态后验估计值及其误差协方差更新模块: Qk=〇-^;)a;+^; ^ 凡=(1 -4-,成―,-k-;其中: G为白噪声,Qk-i为kl时刻的过程噪声,Rk-i为kl时刻的观测噪声,< 为k时刻的实际电压信号与模型电压信号之间的差值ek的转置,忠为k时刻的卡尔曼滤波增益矩阵Hk的转置; 荷电状态后验估计值及其误差协方差更新模块用于更新k时刻的电池荷电状态的后验估计值K和k时刻的电池荷电状态后验估计误差协方差P/,然后跳转到迭代模块; A=X,,W/A(V /;/ =(/ - //,々)/T,I为单位矩阵; 迭代模块用于将k值增加1,并返回到初始化模块。 After the process noise Qk and Rk observation noise, and then jump to the state of charge and the posteriori estimate error covariance update module: Qk = square - ^;) a; + ^; ^ = where (1-4-, into the - , -K-; provided wherein: G is a white noise, Qk-i for the process noise kl time, Rk-i of the observation noise kl time, the actual voltage between the voltage signal and the model signal at time k <ek is the difference transpose, Zhong Kalman filter gain matrix transpose of Hk at time k; the state of charge and the posteriori estimate error covariance after updating module for updating the battery state of charge at time k, and K a posteriori estimation value When the battery state of charge at time k posteriori estimate error covariance P /, then jumps to the iterator module; a = X ,, W / a (V /; / = (/ - //, 々) / T, I is matrix; iteration module is configured to increase the value of 1 k, and returns to the initialization module.
  4. 4.如权利要求3所述的基于AEKF的电池荷电状态估计系统,其特征在于,所述b的值为0.95〇 As claimed in claim 4. A battery state of charge estimation system based AEKF, wherein said 3, the value of b is 0.95〇
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