CN109655751A - A kind of method and system using Gaussian process regression estimates battery charging state - Google Patents

A kind of method and system using Gaussian process regression estimates battery charging state Download PDF

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CN109655751A
CN109655751A CN201910148355.8A CN201910148355A CN109655751A CN 109655751 A CN109655751 A CN 109655751A CN 201910148355 A CN201910148355 A CN 201910148355A CN 109655751 A CN109655751 A CN 109655751A
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姚之琳
王海英
王远远
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Harbin University of Science and Technology
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Abstract

本发明公开了一种使用高斯过程回归估计电池充电状态的方法和系统，包括以下步骤：A、传感器组采集电池充电过程中的参数，包括充电温度，充电电压和充电电流；B、传感信号采集单元采集传感器组采集的参数后进行优化处理，并通过AD转换模块将模拟信号转换为数字信号；C、控制器接收到数字信号后根据温度值、电压值、电流值构建训练样本集；D、基于训练样本集学习出高斯过程回归模型；E、最后输出估算值，本发明采用的估算方法能够实现对电池充电状态的充电参数进行高精度估算，误差小，能够有效的提高后续数据处理效率。

The invention discloses a method and a system for estimating the charging state of a battery by using Gaussian process regression, comprising the following steps: A. A sensor group collects parameters in the charging process of the battery, including charging temperature, charging voltage and charging current; B. Sensing signal The acquisition unit collects the parameters collected by the sensor group and performs optimization processing, and converts the analog signal into a digital signal through the AD conversion module; C. After the controller receives the digital signal, a training sample set is constructed according to the temperature value, voltage value, and current value; D . Learning a Gaussian process regression model based on the training sample set; E. The estimated value is finally output. The estimation method adopted in the present invention can realize high-precision estimation of the charging parameters of the battery state of charge, with small errors, and can effectively improve the efficiency of subsequent data processing. .

Description

A kind of method and system using Gaussian process regression estimates battery charging state
Technical field
The present invention relates to battery charging state estimating system technical fields, specially a kind of to use Gaussian process regression estimates The method and system of battery charging state.
Background technique
Charged state is defined as the percentage of charge available remaining in battery.SoC provides when cope with battery charging Instruction, which can enable battery management system to improve electricity by making battery from overdischarge or overcharge situation The pond service life.Therefore, it accurately measures SoC for suitable battery management and is very important.
Rechargeable battery carrys out storage energy by reversible chemical reaction.Traditionally, rechargeable battery provides lower use Cost, and the result is that supporting towards the green proposal for influencing environment compared with non-rechargeable battery.For example, including consumer In extensive application in electronic product, Roof of the house solar photovoltaic system, electric vehicle, smart electric grid system etc., lithium ion (Li Ion) rechargeable battery has been widely deployed as main energy storage member.Li ion battery is more than to have different chemical reactions Other kinds of battery at least some major advantages be low self-discharge rate, high single battery voltage, high-energy-density, lightweight, Long-life and low-maintenance.
However, Li ion battery and other kinds of battery are chemical energy storage sources, and the chemical energy can not directly make With.The problem leads to the SoC for being difficult to estimate battery.Because battery model can not capture nonlinear kinetics based on physics and Associated parameter uncertainty, so the accurate estimation of SoC is still extremely complex and is difficult to carry out.Though current evaluation method Charged state can so be estimated, but estimation process is considerably complicated, and error is big.
Summary of the invention
The purpose of the present invention is to provide a kind of method and system using Gaussian process regression estimates battery charging state, To solve the problems mentioned in the above background technology.
To achieve the above object, the invention provides the following technical scheme: a kind of filled using Gaussian process regression estimates battery The method of electricity condition, comprising the following steps:
A, the parameter in sensor group acquisition battery charging process, including charging temperature, charging voltage and charging current；
B, optimization processing is carried out after the parameter of transducing signal acquisition unit acquisition sensor group acquisition, and will by AD conversion module Analog signal is converted to digital signal；
C, controller receives and constructs training sample set D, D={ T according to temperature value, voltage value, current value after digital signali, Vi, Ii}n i=1；
D, Gaussian process regression model out is learnt based on training sample set；
E, estimated value is finally exported.
Preferably, optimization method is as follows in the step B:
A, the transducing signal in the battery charging process of acquisition is converted into signal matrix；
B, the Fourier transform of traveling every trade is pressed to signal matrix；
C, based on obtained row Fourier transform results, transducing signal signal filtering factor is iterated to calculate, to obtained row Fourier Leaf transformation result is modified；
D, judge whether current iteration number is equal to the length of transducing signal sequence；If so, to currently available correction result, The transducing signal sequence for having filtered out noise is generated by traveling every trade inverse fourier transform.
Preferably, Gaussian process regression model learning method is as follows in the step D:
A, the input x and x ' of any two sample is defined kernel function k (x, x ') are as follows:
, wherein x T For the transposition of x, δ For Kronecker delta function, i.e.,, wherein σ f, c and σ n are
4 different hyper parameters in kernel function, and σ f is that signal standards is poor, c is yardstick adjustment factor, and σ n is noise criteria Difference；
B, signal standards difference the σ f, yardstick adjustment factor c and noise criteria difference σ in hyper parameter are initialized:
c=0.2*σf；
C, observed value vector y=[y1, y2 ..., yn] is obtained according to training sample set T , and root
Covariance matrix is calculated according to kernel function k (x, x '):
；
D, value vector y and covariance matrix K y defines the log likelihood of training sample set according to the observation:
；
E, according to initialization hyper parameter, the log likelihood logp (y | D) of training sample set is maximized using conjugate gradient method Optimal hyper parameter is obtained, determines Gaussian process regression model with these optimal hyper parameters.
Preferably, a kind of system using Gaussian process regression estimates battery charging state, including sensor group, sensing letter Number acquisition unit, AD conversion unit, controller, evaluation unit, training sample set generation unit, is learned transducing signal optimization unit Gaussian process regression model unit and output unit are practised, the sensor group input terminal connects rechargeable battery, the sensor group Including temperature sensor, voltage sensor and current sensor, the temperature sensor acquires temperature when rechargeable battery work, Voltage when the voltage sensor acquisition rechargeable battery work, the electricity when current sensor acquisition rechargeable battery works Stream, the sensor group output end connect transducing signal by transducing signal acquisition unit and optimize unit, and the transducing signal is excellent To change unit and controller is connected by AD conversion unit, the controller is separately connected evaluation unit, training sample set generation unit, The evaluation unit, training sample set generation unit are all connected with study Gaussian process regression model unit, the study Gauss mistake Journey regression model unit connects output unit.
Compared with prior art, the beneficial effects of the present invention are:
(1) evaluation method that the present invention uses, which can be realized, carries out high-precision estimation to the charge parameter of battery charging state, accidentally Difference is small, can effectively improve follow-up data treatment effeciency.
(2) signal optimizing method that the present invention uses can effectively, quickly filter out the impulsive noise in transducing signal, into One step improves subsequent estimation accuracy.
(3) the Gaussian process regression model learning method that the present invention uses can further increase estimation precision, reduce and miss Difference.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is transducing signal optimization method flow chart of the present invention；
Fig. 3 is present system functional block diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
Fig. 1-3 is please referred to, the present invention provides a kind of technical solution: a kind of using Gaussian process regression estimates battery charging shape The method of state, comprising the following steps:
A, the parameter in sensor group acquisition battery charging process, including charging temperature, charging voltage and charging current；
B, optimization processing is carried out after the parameter of transducing signal acquisition unit acquisition sensor group acquisition, and will by AD conversion module Analog signal is converted to digital signal；
C, controller receives and constructs training sample set D, D={ T according to temperature value, voltage value, current value after digital signali, Vi, Ii}n i=1；
D, Gaussian process regression model out is learnt based on training sample set；
E, estimated value is finally exported.
In the present invention, optimization method is as follows in step B:
A, the transducing signal in the battery charging process of acquisition is converted into signal matrix；
B, the Fourier transform of traveling every trade is pressed to signal matrix；
C, based on obtained row Fourier transform results, transducing signal signal filtering factor is iterated to calculate, to obtained row Fourier Leaf transformation result is modified；
D, judge whether current iteration number is equal to the length of transducing signal sequence；If so, to currently available correction result, The transducing signal sequence for having filtered out noise is generated by traveling every trade inverse fourier transform.
The signal optimizing method that the present invention uses can effectively, quickly filter out the impulsive noise in transducing signal, into one Step improves subsequent estimation accuracy.
In the present invention, Gaussian process regression model learning method is as follows in step D:
A, the input x and x ' of any two sample is defined kernel function k (x, x ') are as follows:
, wherein x T For the transposition of x, δ For Kronecker delta function, i.e.,, wherein σ f, c and σ n are
4 different hyper parameters in kernel function, and σ f is that signal standards is poor, c is yardstick adjustment factor, and σ n is noise criteria Difference；
B, signal standards difference the σ f, yardstick adjustment factor c and noise criteria difference σ in hyper parameter are initialized:
c=0.2*σf；
C, observed value vector y=[y1, y2 ..., yn] is obtained according to training sample set T , and root
Covariance matrix is calculated according to kernel function k (x, x '):
；
D, value vector y and covariance matrix K y defines the log likelihood of training sample set according to the observation:
；
E, according to initialization hyper parameter, the log likelihood logp (y | D) of training sample set is maximized using conjugate gradient method Optimal hyper parameter is obtained, determines Gaussian process regression model with these optimal hyper parameters.
The Gaussian process regression model learning method that the present invention uses can further increase estimation precision, reduce error.
In the present invention, a kind of system using Gaussian process regression estimates battery charging state, including sensor group, sensing Signal acquisition unit 1, transducing signal optimization unit 2, AD conversion unit 3, controller 4, evaluation unit 5, training sample set generate Unit 6, study Gaussian process regression model unit 7 and output unit 8, the sensor group input terminal connect rechargeable battery 9, institute Stating sensor group includes temperature sensor 10, voltage sensor 11 and current sensor 12, and the acquisition of temperature sensor 10 is filled Temperature when battery works, the voltage sensor 11 acquire voltage when rechargeable battery work, the current sensor 12 Electric current when rechargeable battery work is acquired, the sensor group output end connects transducing signal by transducing signal acquisition unit 1 Optimize unit 2, the transducing signal optimization unit 2 connects controller 4 by AD conversion unit 3, and the controller 4 is separately connected Evaluation unit 5, training sample set generation unit 6, the evaluation unit 5, training sample set generation unit 6 are all connected with study Gauss Process regression model unit 7, the study Gaussian process regression model unit 7 connect output unit 8.Sensor group acquires battery Parameter in charging process, including charging temperature, charging voltage and charging current；Transducing signal acquisition unit acquires sensor group Optimization processing is carried out after the parameter of acquisition, and digital signal is converted analog signals by AD conversion module；Controller receives Training sample set is constructed according to temperature value, voltage value, current value after to digital signal；Learn Gauss mistake out based on training sample set Journey regression model；Finally export estimated value.
In conclusion the charge parameter progress that the evaluation method that the present invention uses can be realized to battery charging state is high-precision Degree estimation, error is small, can effectively improve follow-up data treatment effeciency.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding And modification, the scope of the present invention is defined by the appended.

Claims (4)

1.一种使用高斯过程回归估计电池充电状态的方法，其特征在于：包括以下步骤：1. a method for estimating battery state of charge using Gaussian process regression is characterized in that: comprise the following steps: A、传感器组采集电池充电过程中的参数，包括充电温度，充电电压和充电电流；A. The sensor group collects parameters during battery charging, including charging temperature, charging voltage and charging current; B、传感信号采集单元采集传感器组采集的参数后进行优化处理，并通过AD转换模块将模拟信号转换为数字信号；B. The sensor signal acquisition unit collects the parameters collected by the sensor group and performs optimization processing, and converts the analog signal into a digital signal through the AD conversion module; C、控制器接收到数字信号后根据温度值、电压值、电流值构建训练样本集D，D={Ti，Vi，Ii}n i=1；C. After the controller receives the digital signal, a training sample set D is constructed according to the temperature value, voltage value and current value, D={T i , V i , I i } n i=1 ; D、基于训练样本集学习出高斯过程回归模型；D. Learning a Gaussian process regression model based on the training sample set; E、最后输出估算值。E. The final output estimated value. 2.根据权利要求1所述的一种使用高斯过程回归估计电池充电状态的方法，其特征在于：所述步骤B中优化方法如下：2. a kind of method using Gaussian process regression estimation battery state of charge according to claim 1, is characterized in that: in described step B, the optimization method is as follows: a、将采集的电池充电过程中的传感信号转换为信号矩阵；a. Convert the collected sensing signals during battery charging into a signal matrix; b、对信号矩阵按行进行行傅立叶变换；b. Perform row Fourier transform on the signal matrix by row; c、基于得到的行傅立叶变换结果，迭代计算传感信号信号滤波因子，对得到的行傅立叶变换结果进行修正；c. Based on the obtained row Fourier transform result, iteratively calculate the filter factor of the sensing signal signal, and modify the obtained row Fourier transform result; d、判断当前迭代次数是否等于传感信号序列的长度；若是，则对当前得到的修正结果，按行进行行傅立叶反变换生成滤除了噪声的传感信号序列。d. Determine whether the current number of iterations is equal to the length of the sensing signal sequence; if so, perform inverse line Fourier transform on the currently obtained correction result row by row to generate a noise filtered sensing signal sequence. 3.根据权利要求1所述的一种使用高斯过程回归估计电池充电状态的方法，其特征在于：所述步骤D中高斯过程回归模型学习方法如下：3. a kind of method using Gaussian process regression estimation battery state of charge according to claim 1, is characterized in that: in described step D, Gaussian process regression model learning method is as follows: a、任意两个样本的输入x和x′，定义核函数k(x,x′)为：a. Input x and x' of any two samples, define the kernel function k(x, x') as: ，其中x T 为x的转置，δ为Kronecker delta函数，即，其中，σf 、c和σn 为 , where x T is the transpose of x and δ is the Kronecker delta function, i.e. , where σf , c and σn are 核函数中4个不同的超参数，且σf 为信号标准差， c为尺度调节系数，σn 为噪声标准差；There are 4 different hyperparameters in the kernel function, and σf is the signal standard deviation, c is the scale adjustment coefficient, and σn is the noise standard deviation; b、初始化超参数中的信号标准差σf ，尺度调节系数c和噪声标准差σ：b. The signal standard deviation σf in the initialization hyperparameters, the scale adjustment coefficient c and the noise standard deviation σ: c=0.2*σf；c=0.2*σf; c、根据训练样本集合得到观察值向量y＝[y1 ,y2 ,...,yn ] T ，并根c. Obtain the observation value vector y=[y1 , y2 ,...,yn ] T according to the training sample set, and root the 据核函数k(x,x′)计算协方差矩阵：Calculate the covariance matrix according to the kernel function k(x,x'): ； ; d、根据观察值向量y和协方差矩阵K y 定义训练样本集合的对数似然度：d. Define the log-likelihood of the training sample set according to the observation vector y and the covariance matrix Ky: ； ; e、根据初始化超参数，利用共轭梯度法最大化训练样本集合的对数似然度logp(y|D)得到最优超参数，用这些最优超参数确定出高斯过程回归模型。e. According to the initialization hyperparameters, use the conjugate gradient method to maximize the log-likelihood logp(y|D) of the training sample set to obtain the optimal hyperparameters, and use these optimal hyperparameters to determine the Gaussian process regression model. 4.一种使用高斯过程回归估计电池充电状态的系统，其特征在于：包括传感器组、传感信号采集单元（1）、传感信号优化单元（2）、AD转换单元（3）、控制器（4）、估算单元（5）、训练样本集生成单元（6）、学习高斯过程回归模型单元（7）和输出单元（8），所述传感器组输入端连接充电电池（9），所述传感器组包括温度传感器（10）、电压传感器（11）和电流传感器（12），所述温度传感器（10）采集充电电池工作时的温度，所述电压传感器（11）采集充电电池工作时的电压，所述电流传感器（12）采集充电电池工作时的电流，所述传感器组输出端通过传感信号采集单元（1）连接传感信号优化单元（2），所述传感信号优化单元（2）通过AD转换单元（3）连接控制器（4），所述控制器（4）分别连接估算单元（5）、训练样本集生成单元（6），所述估算单元（5）、训练样本集生成单元（6）均连接学习高斯过程回归模型单元（7），所述学习高斯过程回归模型单元（7）连接输出单元（8）。4. A system for estimating the state of charge of a battery using Gaussian process regression, characterized in that it comprises a sensor group, a sensor signal acquisition unit (1), a sensor signal optimization unit (2), an AD conversion unit (3), a controller (4), an estimation unit (5), a training sample set generation unit (6), a learning Gaussian process regression model unit (7), and an output unit (8), the input end of the sensor group is connected to a rechargeable battery (9), the The sensor group includes a temperature sensor (10), a voltage sensor (11) and a current sensor (12), the temperature sensor (10) collects the temperature of the rechargeable battery during operation, and the voltage sensor (11) collects the voltage of the rechargeable battery during operation , the current sensor (12) collects the current when the rechargeable battery is working, the output end of the sensor group is connected to the sensing signal optimization unit (2) through the sensing signal acquisition unit (1), and the sensing signal optimization unit (2) ) is connected to the controller (4) through the AD conversion unit (3), the controller (4) is respectively connected to the estimation unit (5), the training sample set generation unit (6), the estimation unit (5), the training sample set The generating units (6) are all connected to the learning Gaussian process regression model unit (7), and the learning Gaussian process regression model unit (7) is connected to the output unit (8).
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Application publication date: 20190419