CN113655282A - Insulation resistance value detection method during connection of power battery of electric automobile - Google Patents
Insulation resistance value detection method during connection of power battery of electric automobile Download PDFInfo
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Abstract
The invention discloses an insulation resistance value detection method during connection of an electric vehicle power battery, which can accurately and quickly calculate the insulation resistance values of a positive electrode and a negative electrode and an equivalent Y capacitance value. Under the condition that electric automobile power battery connects, the influence of system Y electric capacity to sampling voltage has been considered, model building is carried out to sampling voltage, the model parameter is discerned through becoming forgetting factor recurrence least square algorithm, insulating resistance and equivalent Y capacitance value are calculated according to the parameter of discerning, the influence of equivalent Y electric capacity to insulating detection among the electric automobile has been eliminated, the noise has been reduced to the interference of sampling voltage signal, insulating detection result is more accurate reliable, detection algorithm response speed is fast, the ageing is stronger, avoid calculating the wasting of resources simultaneously, reduce the error, the practicality is stronger.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to an insulation resistance value detection method based on a variable forgetting factor recursive least square algorithm when power batteries of electric automobiles are connected.
Background
At present, electric automobiles become more and more popular as automobile pollutant emissions become increasingly severe. The lithium ion battery has higher energy density and power density, and the high voltage of the lithium ion battery can obviously improve the energy utilization rate. Therefore, most electric vehicles use a high-voltage lithium ion battery as a power battery. Because the high-voltage lithium ion battery belongs to a high-voltage direct-current system, the high-voltage insulation performance of the power battery has important significance for safe driving of the electric automobile. The running conditions of the electric automobile are complex, and the electric automobile often faces complex environments such as high temperature, high humidity, high salt fog, vibration collision and the like. These factors all lead to the insulating properties between the positive and negative electrode buses of the power battery and the electric chassis of the vehicle to be reduced, so that the potential of the electric chassis of the vehicle is increased or reduced, the normal work of low-voltage components and high-voltage components is influenced, and the personal safety of drivers and passengers is endangered. Therefore, the method is of great importance for detecting the insulation resistance of the power battery of the electric automobile, is the core content of the electric safety technology of the electric automobile, and has important significance for the safety of the whole automobile and drivers and passengers.
Unlike traditional fuel vehicles, electric vehicles are composed of many high-voltage components, and in order to isolate electromagnetic interference between a high-voltage bus and a high-voltage device, a Y capacitor is generally added at a power inlet of the high-voltage device. For an electric automobile, on one hand, Y capacitors attached to a plurality of high-voltage components on the automobile objectively cause that the Y capacitors are necessarily arranged between two poles of a high-voltage bus and an automobile chassis; on the other hand, an equivalent Y capacitance is also formed between the high-voltage bus cable penetrating the vehicle body and the chassis. The system Y capacitor connected between the high-voltage bus and the electric chassis can change the impedance characteristic between the high-voltage bus and the electric chassis, and the insulation performance between the high-voltage bus and the ground is influenced.
The conventional insulation resistance detection method can be divided into a passive type and an active type according to whether an external power supply exists, and the common methods mainly comprise a bridge method and a signal injection method. The bridge method belongs to passive type, and its basic principle is that a current-limiting resistor is connected between the positive and negative electrode buses of power battery and electric chassis of vehicle, the size of the current-limiting resistor is changed by electronic switch, the voltage division on the tested resistor under the condition of connecting different resistors is measured, and finally the insulation resistance is calculated by solving equation. The bridge method has simple circuit and can directly calculate the insulation resistance, but the method has more defects. For example, switching noise is introduced into a high-voltage bus by an electronic switch added in a circuit, and for example, because a Y capacitor exists between two poles of a power battery and an electric chassis, a bridge method needs to increase a measurement period to improve the detection precision of an insulation resistance, so that the response speed of a measurement system is reduced.
The signal injection method belongs to an active type, and the basic principle of the method is to inject a detection signal into a high-voltage system through a vehicle electric chassis and calculate the insulation resistance value by measuring a voltage signal on a sampling resistor. This method does not introduce switching noise on the high voltage bus, as compared to the bridge method. At present, an injection method based on a low-frequency signal is widely applied to electric automobile insulation detection. However, a Y capacitor exists between two poles of the power battery and the electric chassis, due to the charging and discharging processes of the Y capacitor, the sampling voltage cannot reach a stable value due to an excessively short signal period, the measurement result of the insulation resistance value is small, the false alarm of the system is caused, and the response speed of the measurement system is reduced due to an excessively long signal period. And the Y capacitance value is easily influenced by external factors such as vehicle part replacement, environmental temperature and humidity change and the like, and the measurement precision and the measurement cycle of the insulation resistance value are further influenced. In addition, the working condition of the electric automobile is very complicated, and the signal is easily interfered by noise, so that the measurement accuracy is further reduced.
The existing detection method for the insulation resistance value of the power battery of the electric automobile is low in detection precision, poor in timeliness and difficult to guarantee detection efficiency, and cannot meet actual use requirements.
Therefore, how to provide an efficient and accurate method for detecting the insulation resistance value of the power battery of the electric vehicle is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an insulation resistance detection method during connection of an electric vehicle power battery, which is based on an insulation resistance detection algorithm of a forgetting factor recursion least square, can eliminate the influence of a Y capacitor on a measurement process, quickly and accurately calculate the insulation resistance and the size of an equivalent Y capacitor, and effectively solves the problems of low detection precision, poor timeliness, difficulty in ensuring detection efficiency and the like of the existing insulation resistance detection method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for detecting insulation resistance value when a power battery of an electric automobile is connected comprises the following steps:
s1: constructing an insulation detection circuit model containing a Y capacitor when a power battery is connected, and acquiring a continuous time function of sampling voltage in the insulation detection circuit model;
s2: converting the continuous time function of the sampling voltage into a discrete time function;
s3: linearizing the discrete time function of the sampling voltage by adopting a first-order Taylor series expansion algorithm to obtain a least square regression equation form expression of the sampling voltage;
s4: identifying parameters in a least square regression equation form expression of the sampling voltage by adopting a variable forgetting factor recursive least square algorithm;
s5: carrying out arithmetic mean filtering on the power battery voltage sampled in the parameter identification process to obtain a corresponding power battery voltage value;
s6: and calculating to obtain an insulation resistance value and an equivalent Y capacitance value according to the voltage value of the power battery and the identified parameters.
Further, in S1, the insulation detection circuit model when the power battery with the Y capacitor is connected includes an equivalent circuit of the electric vehicle high-voltage system and an insulation detection circuit;
the equivalent circuit of the high-voltage system of the electric automobile comprises a power battery, a positive insulation resistor, a negative insulation resistor, a positive Y capacitor, a negative Y capacitor and an electric chassis, wherein the positive electrode of the power battery is electrically connected with the positive insulation resistor and the positive Y capacitor respectively, the negative electrode of the power battery is electrically connected with the negative insulation resistor and the negative Y capacitor respectively, and the positive insulation resistor, the negative insulation resistor, the positive Y capacitor and the negative Y capacitor are electrically connected with the electric chassis;
the insulation detection circuit comprises a first current-limiting resistor, a second current-limiting resistor, a sampling resistor and a pulse signal generator, wherein one end of the first current-limiting resistor is electrically connected with the anode of the power battery, one end of the second current-limiting resistor is electrically connected with the cathode of the power battery, the other end of the first current-limiting resistor and the other end of the second current-limiting resistor are electrically connected with the sampling resistor, the pulse signal generator is electrically connected with the electric chassis, and the pulse signal generator is grounded with the sampling resistor.
When the insulation detection circuit model works, the pulse signal generator generates an amplitude value of UsThe square wave signal is injected into an equivalent circuit of an automobile high-voltage system through the electric chassis, flows back to the insulation detection circuit through the anode insulation resistor, the cathode insulation resistor, the anode Y capacitor and the cathode Y capacitor of the electric automobile, and returns to the sampling resistor through the first current-limiting resistor and the second current-limiting resistor on the sampling circuit.
Due to the existence of the Y capacitor of the system, the sampling voltage U on the sampling resistor is causedfNo longer a square wave signal, therefore, in S1, the continuous-time function of the sampled voltage is:
Uf(t)＝a1+a2 exp(-t/a3)
wherein, a1In response to the steady-state component, the sampling voltage value is not only a steady value of the sampling voltage, but also a sampling voltage value without a Y capacitor; a is2In response to the gain, i.e. the difference between the value of the sampled voltage at the step of the square-wave signal and the value of the sampled voltage at the plateau, due to a2The insulation resistance value and the Y capacitance value are not calculated, so that the insulation resistance value and the Y capacitance value are not considered; a is3Is the response time constant, i.e., the time constant of the equivalent Y capacitance.
Further, in S2, the discrete-time function of the sampling voltage is:
Uf(k)＝a1(k)+a2(k)exp(-kΔt/a3(k))
where Δ t is the sampling time interval.
Due to the sampling voltage UfThe discrete time function model under the time t is a nonlinear model, and the invention adopts a first order in consideration of the calculation complexity and the estimation precisionThe taylor series expansion linearizes it. Finally, in S3, the least squares regression equation form expression of the sampled voltage is:
Uf(k)＝H(k)A(k)+Y(k)+V(k)
where h (k) is an observation matrix at time k, a (k) is a parameter value at time k, and a (k) ═ a1(k),a2(k),a3(k)]Y (k) is a constant error term, and V (k) is a one-dimensional random observation noise and is a zero-mean, independent Gaussian white noise sequence.
Further, the S4 specifically includes:
s401: setting parameter initial value of least square
An initial value P (0) of the error covariance, and a parameter lambda of a forgetting factor0、λ1And beta, and setting convergence parameters w and epsilon;
s402: the calculation formula of the forgetting factor lambda (k) at the moment of calculating the forgetting factor lambda, k is as follows:
λ(k)＝λ1-(λ1-λ0)exp(-βkΔt)
s403: the calculation formula for calculating the observation matrix H (k) at the moment of the observation matrix H, k is as follows:
s404: the calculation formula for calculating the gain matrix K, the gain matrix K (K) at the moment K is:
K(k)＝P(k-1)HT(k)[λ+H(k)P(k-1)H(k)]-1
s405: calculating an error covariance matrix P (k) at time k and an estimated value of a parameter at time k
The calculation formulas are respectively as follows:
P(k)＝[1-K(k)H(k)]P(k-1)/λ
s406: judging whether the algorithm is converged or not, and calculating the parameter estimation value at the time k in S405
Then, calculate a in the sliding window w1And a3Mean value of
Recalculating the standard deviation in the sliding window
The calculation formulas are respectively as follows:
compare separately
If the two are smaller than the convergence precision epsilon at the same time, the algorithm is judged to be converged, otherwise, the error covariance matrix and the parameter estimation value obtained by the calculation of the S405 are used as initial values in the S401, and the S402 is returned for iteration until the algorithm is converged; after the algorithm converges, the mean value in the sliding window w is used
As parameter a1And a3The identification result of (1);
s407: according to the pulse signal generator, the value of U is generated in the positive half periods+At the step voltage of (3), the voltage U is sampledf+As a function of the value of (a) at time t, identifies a of the positive half-cycle1+And a3+And generating a value of U in the negative half period according to the pulse signal generators-At the step voltage of (3), the voltage U is sampledf-As a function of the value of (a) at time t, identifies a of the negative half-cycle1-And a3-。
The invention adopts recursive least square, has the advantage of small memory occupation, and is more suitable for being used in a vehicle-mounted embedded system. Meanwhile, the variable forgetting factor is adopted, and the method has the advantages of high convergence speed and high convergence precision. In S406, by setting a suitable basis for determining convergence, the response speed of the detection algorithm can be increased, thereby avoiding the waste of computing resources and reducing errors.
Further, in S407, the voltage U is sampledf+The function of the value of (d) at time t is:
Uf+(t)＝a1++a2+exp(-t/a3+)
sampling voltage Uf-The function of the value of (d) at time t is:
Uf-(t)＝a1-+a2-exp(-t/a3-)
further, in S6, the insulation resistance value includes an insulation resistance value of the positive electrode of the power battery relative to the electric chassis of the automobile and an insulation resistance value of the negative electrode of the power battery relative to the electric chassis of the automobile;
the calculation formula of the insulation resistance value of the power battery anode relative to the electric chassis of the automobile is as follows:
the calculation formula of the insulation resistance value of the power battery cathode relative to the electric chassis of the automobile is as follows:
wherein R is the resistance of the current limiting resistor, RfFor sampling resistance value, U is power battery voltage value, Us+For step voltage values, U, generated by the pulse signal generator during the positive half-cycles-The step voltage value generated by the pulse signal generator in the negative half period.
Further, in S6, the equivalent Y capacitance value is calculated as:
wherein, Cp//CnIs an equivalent Y capacitance value, RpIs the insulation resistance value R of the anode of the power battery relative to the electric chassis of the automobilenIs the insulation resistance value R of the cathode of the power battery relative to the electric chassis of the automobilefThe sampled resistance value.
According to the technical scheme, compared with the prior art, the invention discloses the insulation resistance value detection method during connection of the power battery of the electric automobile, and the method can accurately and quickly calculate the insulation resistance values of the positive electrode and the negative electrode and the equivalent Y capacitance value. Under the condition that electric automobile power battery connects, the influence of system Y electric capacity to sampling voltage has been considered, model building is carried out to sampling voltage, the model parameter is discerned through becoming forgetting factor recurrence least square algorithm, insulating resistance and equivalent Y capacitance value are calculated according to the parameter of discerning, the influence of equivalent Y electric capacity to insulating detection among the electric automobile has been eliminated, the noise has been reduced to the interference of sampling voltage signal, insulating detection result is more accurate reliable, detection algorithm response speed is fast, the ageing is stronger, avoid calculating the wasting of resources simultaneously, reduce the error, the practicality is stronger.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of an implementation of the method for detecting the insulation resistance value when the power battery of the electric vehicle is connected according to the present invention;
FIG. 2 is a schematic diagram illustrating an implementation principle of an insulation resistance detection method when an electric vehicle power battery is connected;
fig. 3 is a schematic structural diagram of an insulation detection circuit model when a power battery with a Y capacitor is connected.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawings 1 and 2, the embodiment of the invention discloses a method for detecting the insulation resistance value when a power battery of an electric automobile is connected, which comprises the following steps:
s1: and constructing an insulation detection circuit model containing a Y capacitor when the power battery is connected, and acquiring a continuous time function of sampling voltage in the insulation detection circuit model.
Considering the influence of the Y capacitor between the two poles of the power battery and the electric chassis of the vehicle on the measurement of the insulation resistance value, on the basis of the detection principle of the low-frequency injection method, the insulation detection circuit model containing the Y capacitor is constructed in the embodiment when the power battery is connected.
Referring to fig. 3, the insulation detection circuit model when the power battery with the Y capacitor is connected includes an equivalent circuit of the high-voltage system of the electric vehicle and an insulation detection circuit;
the left side of the equivalent circuit schematic diagram is an equivalent circuit schematic diagram of an electric automobile high-voltage system, the equivalent circuit of the electric automobile high-voltage system comprises a power battery, a positive electrode insulation resistor, a negative electrode insulation resistor, a positive electrode Y capacitor, a negative electrode Y capacitor and an electric chassis, the positive electrode of the power battery is electrically connected with the positive electrode insulation resistor and the positive electrode Y capacitor respectively, the negative electrode of the power battery is electrically connected with the negative electrode insulation resistor and the negative electrode Y capacitor respectively, and the positive electrode insulation resistor, the negative electrode insulation resistor, the positive electrode Y capacitor and the negative electrode Y capacitor are electrically connected with the electric chassis.
In FIG. 3, U is the power cell voltage, Rp、RnRespectively a positive insulation resistance value and a negative insulation resistance value, Cp、CnPositive and negative Y capacitance values, respectively.
The right side is an equivalent circuit schematic diagram of the insulation detection circuit, the insulation detection circuit comprises a first current-limiting resistor, a second current-limiting resistor, a sampling resistor and a pulse signal generator, one end of the first current-limiting resistor is electrically connected with the anode of the power battery, one end of the second current-limiting resistor is electrically connected with the cathode of the power battery, the other end of the first current-limiting resistor and the other end of the second current-limiting resistor are electrically connected with the sampling resistor, the pulse signal generator is electrically connected with the electric chassis, and the pulse signal generator and the sampling resistor are both grounded.
In FIG. 3, UsIs a pulse signal generator which is responsible for emitting square wave signals, and the voltage values generated by the pulse signal generator in positive and negative half periods are respectively Us+And Us-，R1、R2Are respectively a first current limiting resistor resistance value and a second current limiting resistor resistance value, and R1＝R2＝R，RfIs the resistance value of the sampling resistor.
The specific detection principle of the circuit model is as follows: the pulse signal generator generates an amplitude value of UsThe square wave signal is injected into an automobile high-voltage system through an electric chassis and passes through an anode insulation resistor R and a cathode insulation resistor R of the electric automobilep、RnAnd positive and negative electrodes Y capacitor Cp、CnFlows back to the detection circuit, and the signal passes through a first current limiting resistor R on the sampling circuit1And a second current limiting resistor R2Back to the sampling resistor Rf。
Due to the presence of system Y capacitanceSampling resistor RfUpper sampled voltage UfNo longer square wave signal, UfThe function of the value of (d) at time t is:
Uf(t)＝a1+a2 exp(-t/a3)
wherein, a1In response to the steady-state component, both the steady value of the sampled voltage and the value of the sampled voltage without the Y capacitor, a2In response to the gain, i.e. the difference between the value of the sampled voltage at the step of the square wave signal and the value of the sampled voltage at the plateau, due to a2It does not participate in the calculation of the insulation resistance value and the Y capacitance value, and therefore it is not considered. a is3Is the response time constant, i.e. the time constant of the equivalent Y capacitance.
S2: a continuous time function of the sampled voltage is converted to a discrete time function.
Will UfConverting the continuous time function model under the time t into a discrete time function model, wherein the expression is as follows:
Uf(k)＝a1(k)+a2(k)exp(-kΔt/a3(k))
where Δ t is the sampling time interval.
S3: and linearizing the discrete time function of the sampled voltage by adopting a first-order Taylor series expansion algorithm to obtain a least square regression equation form expression of the sampled voltage.
Sampling voltage UfThe discrete time function model at time t is a nonlinear model, and in consideration of computational complexity and estimation accuracy, the present embodiment adopts a first-order taylor series expansion to linearize the discrete time function model. The specific method comprises the following steps:
will Uf(k) Estimate of parameters around last time instant
And expanding into Taylor series, and omitting terms of two or more. Final sampling voltage UfThe function model at time t is converted into a regression equation form of least squares, and the expression is as follows:
Uf(k)＝H(k)A(k)+Y(k)+V(k)
where h (k) is an observation matrix at time k, a (k) is a parameter value at time k, and a (k) ═ a1(k),a2(k),a3(k)]Y (k) is a constant error term, and V (k) is a zero-mean, independent white Gaussian noise sequence with one-dimensional random observation noise.
S4: and identifying parameters in a least square regression equation form expression of the sampling voltage by adopting a variable forgetting factor recursive least square algorithm.
Parameter a is subjected to recursive least square algorithm by adopting variable forgetting factor1、a2、a3The identification is performed in seven steps.
S401: setting parameter initial value of least square
An initial value P (0) of the error covariance, and a parameter lambda of a forgetting factor0、λ1Beta, set the parameters w, epsilon of convergence. The initial value of the least square is used for the first iteration of the recursion algorithm, the forgetting factor parameter is used for calculating the forgetting factor of each iteration, the convergence parameter is used for judging whether the algorithm reaches convergence after each iteration is finished, and if the algorithm stops the iteration.
S402: a forgetting factor lambda is calculated. The forgetting factor λ (k) at time k is calculated as follows:
λ(k)＝λ1-(λ1-λ0)exp(-βkΔt)
s043: an observation matrix H is calculated. The observation matrix h (k) at time k is calculated as follows:
s404: a gain matrix K is calculated. The gain matrix k (k) at time k is calculated as follows:
K(k)＝P(k-1)HT(k)[λ+H(k)P(k-1)H(k)]-1
s405: calculating an error covariance matrix P (k) at time k and an estimated value of a parameter at time k
The formula is as follows:
P(k)＝[1-K(k)H(k)]P(k-1)/λ
the above-mentioned S402 to S405 may be understood as a parameter identification process, and mainly includes calculation of a forgetting factor, calculation of an observation matrix, calculation of a gain matrix, input of a sampling voltage, update of an error covariance matrix and a parameter estimation value. The parameter identification process is an iterative process of a variable forgetting factor recursion least square algorithm.
S406: and judging whether the algorithm converges. The method specifically comprises the following steps: in S405, the estimated value of the parameter at time k is calculated
Then, calculate a in the sliding window (window size w)1And a3Mean value of
Recalculating the standard deviation in the sliding window
The calculation formula is as follows:
then compare separately
And if the convergence precision epsilon and the convergence precision epsilon are smaller than epsilon at the same time, the algorithm is considered to be converged, otherwise, the error covariance matrix and the parameter estimation value obtained by the calculation in the step S405 are used as initial values in the step S401, and the step S402 is returned to carry out iteration until the algorithm is converged. After the algorithm converges, the mean value in the sliding window w is used
As a model parameter a1And a3The result of the identification.
S407: when the pulse signal generator generates a value of U in the positive half periods+At the step voltage of (3), the voltage U is sampledf+The function of the value of (d) at time t is:
Uf+(t)＝a1++a2+exp(-t/a3+)
when the pulse signal generator generates a value of U in the negative half periods-At the step voltage of (3), the voltage U is sampledf-The function of the value of (d) at time t is:
Uf-(t)＝a1-+a2-exp(-t/a3-)
finally, respectively identifying a of the positive half period and the negative half period by applying a variable forgetting factor recursive least square algorithm1+、a3+、a1-And a3-。
In the step, by setting a proper basis for judging convergence, the response speed of the detection algorithm can be improved, the waste of computing resources is avoided, and errors are reduced.
S5: and carrying out arithmetic mean filtering on the power battery voltage sampled in the parameter identification process to obtain a corresponding power battery voltage value.
Due to the acceleration and braking processes of the electric automobile, the voltage of the power battery fluctuates up and down. In the embodiment of the invention, the period of one square wave signal is set to be 3 seconds, and the time is short. In a short time, the voltage fluctuation of the power battery can be regarded as a random process with a constant average value. Therefore, arithmetic mean filtering is carried out on the power battery voltage value sampled in the identification process so as to eliminate the influence caused by voltage fluctuation and obtain the corresponding power battery voltage value U. And the influence of the voltage fluctuation of the power battery on the calculation result is reduced.
S6: and calculating to obtain the insulation resistance value and the equivalent Y capacitance value according to the voltage value of the power battery and the identified parameters.
Therefore, the insulation resistance value of the power battery anode relative to the electric chassis of the automobile is calculated by the formula:
the calculation formula of the insulation resistance value of the cathode of the power battery relative to the electric chassis of the automobile is as follows:
equivalent Y capacitor Cp//CnThe calculation formula of (2) is as follows:
the method is characterized in that the influence of a Y capacitor in an electric automobile high-voltage system on a measurement process is actually considered, a model of sampled voltage is established, the model is identified by a variable forgetting factor recursive least square algorithm, and finally the insulation resistance value and the equivalent Y capacitor are calculated according to an identification result. On the basis of a low-frequency signal injection method, the variable forgetting factor recursive least square algorithm is applied to insulation detection, and the influence of Y capacitance is eliminated.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A method for detecting the insulation resistance value when a power battery of an electric automobile is connected is characterized by comprising the following steps:
s1: constructing an insulation detection circuit model containing a Y capacitor when a power battery is connected, and acquiring a continuous time function of sampling voltage in the insulation detection circuit model;
s2: converting the continuous time function of the sampling voltage into a discrete time function;
s3: linearizing the discrete time function of the sampling voltage by adopting a first-order Taylor series expansion algorithm to obtain a least square regression equation form expression of the sampling voltage;
s4: identifying parameters in a least square regression equation form expression of the sampling voltage by adopting a variable forgetting factor recursive least square algorithm;
s5: carrying out arithmetic mean filtering on the power battery voltage sampled in the parameter identification process to obtain a corresponding power battery voltage value;
s6: and calculating to obtain an insulation resistance value and an equivalent Y capacitance value according to the voltage value of the power battery and the identified parameters.
2. The method for detecting the insulation resistance value of the power battery of the electric automobile during connection according to claim 1, wherein in the step S1, the insulation detection circuit model of the power battery with the Y capacitor during connection comprises an equivalent circuit of a high-voltage system of the electric automobile and an insulation detection circuit;
the equivalent circuit of the high-voltage system of the electric automobile comprises a power battery, a positive insulation resistor, a negative insulation resistor, a positive Y capacitor, a negative Y capacitor and an electric chassis, wherein the positive electrode of the power battery is electrically connected with the positive insulation resistor and the positive Y capacitor respectively, the negative electrode of the power battery is electrically connected with the negative insulation resistor and the negative Y capacitor respectively, and the positive insulation resistor, the negative insulation resistor, the positive Y capacitor and the negative Y capacitor are electrically connected with the electric chassis;
the insulation detection circuit comprises a first current-limiting resistor, a second current-limiting resistor, a sampling resistor and a pulse signal generator, wherein one end of the first current-limiting resistor is electrically connected with the anode of the power battery, one end of the second current-limiting resistor is electrically connected with the cathode of the power battery, the other end of the first current-limiting resistor and the other end of the second current-limiting resistor are electrically connected with the sampling resistor, the pulse signal generator is electrically connected with the electric chassis, and the pulse signal generator is grounded with the sampling resistor.
3. The method for detecting the insulation resistance value of the power battery of the electric vehicle when being connected according to claim 1, wherein in the step S1, the continuous time function of the sampling voltage is as follows:
Uf(t)＝a1+a2exp(-t/a3)
wherein, a1In response to the steady-state component, the sampling voltage value is not only a steady value of the sampling voltage, but also a sampling voltage value without a Y capacitor; a is2Responding to gain, namely the difference value of the sampling voltage value when the square wave signal has step and the sampling voltage value when the square wave signal is stable; a is3Is the response time constant, i.e., the time constant of the equivalent Y capacitance.
4. The method for detecting the insulation resistance value of the power battery of the electric vehicle when being connected according to claim 1, wherein in the step S2, the discrete time function of the sampling voltage is as follows:
Uf(k)＝a1(k)+a2(k)exp(-kΔt/a3(k))
where Δ t is the sampling time interval.
5. The method for detecting the insulation resistance value of the power battery of the electric vehicle when being connected according to claim 1, wherein in the step S3, the expression of the least squares regression equation form of the sampled voltage is as follows:
Uf(k)＝H(k)A(k)+Y(k)+V(k)
where h (k) is an observation matrix at time k, a (k) is a parameter value at time k, and a (k) ═ a1(k),a2(k),a3(k)]Y (k) is a constant error term, and V (k) is a one-dimensional random observation noise and is a zero-mean, independent Gaussian white noise sequence.
6. The method for detecting the insulation resistance value of the power battery of the electric vehicle when the power battery is connected according to claim 1, wherein the step S4 specifically includes:
s401: setting parameter initial value of least square
An initial value P (0) of the error covariance, and a parameter lambda of a forgetting factor0、λ1And beta, and setting convergence parameters w and epsilon;
s402: the calculation formula of the forgetting factor lambda (k) at the moment of calculating the forgetting factor lambda, k is as follows:
λ(k)＝λ1-(λ1-λ0)exp(-βkΔt)
s403: the calculation formula for calculating the observation matrix H (k) at the moment of the observation matrix H, k is as follows:
s404: the calculation formula for calculating the gain matrix K, the gain matrix K (K) at the moment K is:
K(k)＝P(k-1)HT(k)[λ+H(k)P(k-1)H(k)]-1
s405: calculating an error covariance matrix P (k) at time k and an estimated value of a parameter at time k
The calculation formulas are respectively as follows:
P(k)＝[1-K(k)H(k)]P(k-1)/λ
s406: judging whether the algorithm is converged or not, and calculating the parameter estimation value at the time k in S405
Then, calculate a in the sliding window w1And a3Mean value of
Recalculating the standard deviation in the sliding window
The calculation formulas are respectively as follows:
compare separately
If the two are smaller than the convergence precision epsilon at the same time, the algorithm is judged to be converged, otherwise, the error covariance matrix and the parameter estimation value obtained by the calculation of the S405 are used as initial values in the S401, and the S402 is returned for iteration until the algorithm is converged; after the algorithm converges, the mean value in the sliding window w is used
As parameter a1And a3The identification result of (1);
s407: according to the pulse signal generator, the value of U is generated in the positive half periods+At the step voltage of (3), the voltage U is sampledf+As a function of the value of (a) at time t, identifies a of the positive half-cycle1+And a3+And generating a value of U in the negative half period according to the pulse signal generators-At the step voltage of (3), the voltage U is sampledf-As a function of the value of (a) at time t, identifies a of the negative half-cycle1-And a3-。
7. The method for detecting the insulation resistance value of the power battery of the electric vehicle as claimed in claim 6, wherein in the step S407, the voltage U is sampledf+The function of the value of (d) at time t is:
Uf+(t)＝a1++a2+exp(-t/a3+)
sampling voltage Uf-The function of the value of (d) at time t is:
Uf-(t)＝a1-+a2-exp(-t/a3-)
8. the method for detecting the insulation resistance value of the power battery of the electric vehicle during connection according to claim 1, wherein in the step S6, the insulation resistance value comprises an insulation resistance value of a positive pole of the power battery relative to an electric chassis of the vehicle and an insulation resistance value of a negative pole of the power battery relative to the electric chassis of the vehicle;
the calculation formula of the insulation resistance value of the power battery anode relative to the electric chassis of the automobile is as follows:
the calculation formula of the insulation resistance value of the power battery cathode relative to the electric chassis of the automobile is as follows:
wherein R is the resistance of the current limiting resistor, RfFor sampling resistance value, U is power battery voltage value, Us+For step voltage values, U, generated by the pulse signal generator during the positive half-cycles-The step voltage value generated by the pulse signal generator in the negative half period.
9. The method for detecting the insulation resistance value of the power battery of the electric vehicle when being connected according to claim 8, wherein in the step S6, the calculation formula of the equivalent Y capacitance value is as follows:
wherein, Cp//CnIs an equivalent Y capacitance value, RpIs the insulation resistance value R of the anode of the power battery relative to the electric chassis of the automobilenIs the insulation resistance value R of the cathode of the power battery relative to the electric chassis of the automobilefThe sampled resistance value.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113655277A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN113655281A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN114137309A (en) * | 2021-12-02 | 2022-03-04 | 奇瑞商用车（安徽）有限公司 | Anti-interference insulation detection method for electric automobile |
| CN114355048A (en) * | 2021-12-28 | 2022-04-15 | 海南大学 | Insulation detection method and system for electric automobile |
| CN115856437A (en) * | 2022-12-30 | 2023-03-28 | 深圳优能新能源科技有限公司 | Method for detecting insulation impedance of high-voltage energy storage battery |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113655277A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN113655281A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN113655281B (en) * | 2021-08-13 | 2023-09-26 | 海南师范大学 | Insulation resistance value detection method during disconnection of electric automobile power battery |
| CN113655277B (en) * | 2021-08-13 | 2023-09-26 | 海南师范大学 | Insulation resistance value detection method during disconnection of electric automobile power battery |
| CN114137309A (en) * | 2021-12-02 | 2022-03-04 | 奇瑞商用车（安徽）有限公司 | Anti-interference insulation detection method for electric automobile |
| CN114355048A (en) * | 2021-12-28 | 2022-04-15 | 海南大学 | Insulation detection method and system for electric automobile |
| CN115856437A (en) * | 2022-12-30 | 2023-03-28 | 深圳优能新能源科技有限公司 | Method for detecting insulation impedance of high-voltage energy storage battery |
| CN115856437B (en) * | 2022-12-30 | 2023-09-08 | 深圳优能新能源科技有限公司 | Method for detecting insulation resistance of high-voltage energy storage battery |
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