CN112114184A - PWM signal optimization method, equipment, medium and device in charging process of charging pile - Google Patents
PWM signal optimization method, equipment, medium and device in charging process of charging pile Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/04—Measuring peak values or amplitude or envelope of ac or of pulses
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/10—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles characterised by the energy transfer between the charging station and the vehicle
- B60L53/14—Conductive energy transfer
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/02—Measuring characteristics of individual pulses, e.g. deviation from pulse flatness, rise time or duration
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/14—Plug-in electric vehicles
Abstract
The invention provides a PWM signal optimization method in a charging process of a charging pile, which comprises the following steps: initializing a prediction equation, performing prediction processing and updating a state. The invention relates to an electronic device and a storage medium, and particularly relates to a PWM signal optimization method for executing a charging process of a charging pile. The invention also relates to a PWM signal optimization device in the charging process of the charging pile. The Kalman filtering algorithm used by the invention can process the sampled data in real time, occupies small memory, does not need to keep other historical data except the previous state quantity, has high calculation speed, and can carry out new value estimation of the current state according to the state equation of the system and the calculated estimation value and the measured value of the previous state on the basis of the state equation after the new data is measured.
Description
Technical Field
The invention relates to the technical field of alternating current charging of electric automobiles, in particular to a PWM signal optimization method, equipment, a medium and a device in a charging process of a charging pile.
Background
In the electric automobile alternating-current charging in-process, it is connected with the vehicle socket through connected mode C to fill electric pile, change through the CP of judging the connector and carry out the vehicle and shake hands and confirm between the PE, fill electric pile's controller and send the PWM signal of 1KHZ frequency in the charging process, on-vehicle machine of charging and fill electric pile controller and judge maximum output current and connection status through the amplitude and the duty cycle of judging the PWM signal, because charging cable length difference and distribution system ground connection quality are difficult to control among the in-service use environment, so lead to PWM signal to be mingled with interference such as noise and burr.
The filtering method of most alternating current charging piles and vehicle-mounted charger products in the current market comprises two types of software and hardware: the hardware is RC filtering current, and the software is an averaging method; in the two methods, the amplitude of the PWM signal is inaccurate due to the hardware filtering scheme, and the rising edge and the falling time of the signal cannot meet the requirement of the rising and falling time of the signal in GB/T18487.1 table A.5; the software mean value filtering method needs a longer time window to calculate the sampling value, and if the sampling time window is too long, the requirement of controlling the guide time sequence in the GB/T18487.1 specification cannot be met, namely the charging is stopped within 100ms in a fault state; if the length of the sampling time window is shortened, the calculation accuracy of the duty ratio and the amplitude of the PWM signal is reduced; therefore, based on the defects of the existing design, the Kalman filter is introduced to carry out algorithm optimization processing on the PWM signals in the charging process, and the amplitude and the duty ratio of the PWM signals in the charging process are quickly and accurately calculated.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the PWM signal optimization method for the charging process of the charging pile, and the amplitude and the duty ratio of the PWM signal in the charging process can be quickly and accurately calculated.
The invention provides a PWM signal optimization method in a charging process of a charging pile, which comprises the following steps:
initializing a prediction equation, and calculating a state description equation and a measurement output equation of the acquired PWM signal data system;
predicting, namely predicting the next state according to the system state description equation, and updating a system prediction result and the covariance of the system prediction result;
and updating the state, namely calculating the optimized estimation result and Kalman gain of the current state according to the updated system prediction result and the obtained measured value of the state, and updating the covariance of the optimized estimation result.
Further, in the initialized prediction equation, the system state description equation specifically includes:
xt＝Axt-1+But+wt
the measurement output equation is specifically as follows:
zt＝Hxt+vt
wherein x istIs the system state at time t, utFor systematic control vectors, wtFor predicted noise, ztFor measuring data at time t, vtFor the noise in measurement, A is a prediction matrix, a conversion coefficient for converting a t-1 state into a t state, B is a control matrix, a control vector is converted into a coefficient of a current state, H is a conversion matrix, and a conversion relation between a measured value and a predicted value is obtained.
Further, in the prediction processing step, the specific calculation formula for predicting the next state according to the system state description equation is as follows:
xt|t-1＝A·xt-1|t-1+B·ut
the specific calculation formula for updating the covariance of the system prediction result is as follows:
Pt|t-1＝A·Pt-1|t-1·AT+Q
wherein x ist|t-1For the result of the last state prediction, utIs a control quantity of the current state, xt-1|t-1Optimizing the estimation result for time t-1, Pt|t-1Is xt-1|t-1Q is the system process covariance,
further, in the state updating step, the formula for calculating the optimized estimation result of the current state is as follows:
xt|t＝xt|t-1+Kt(zt-H·xt|t-1)
the formula for calculating the kalman gain is:
Kt＝HT·Pt|t-1/(H·Pt|t-1·HT+R)
the calculation formula of the covariance of the optimized estimation result is as follows:
Pt|t＝(I-KtH)·Pt|t-1
wherein x ist|tFor the optimized estimation result of the current state, KtTo Kalman gain, Pt|tIs xt|tR is the measurement noise covariance,
an electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising a PWM signal optimization method for performing a charging pole charging process.
A computer-readable storage medium having stored thereon a computer program for executing by a processor a PWM signal optimization method of a charging pole charging process.
The PWM signal optimization device comprises a voltage follower and a filter circuit, wherein a PWM signal output port of a control guide line of the alternating-current charging pile is connected with a first input end of the voltage follower, a second input end and an output end of the voltage follower are connected with the filter circuit, and the filter circuit is connected with an analog-to-digital converter port of the control guide line.
Furthermore, the filter circuit is an RC low-pass filter circuit, the RC filter circuit includes a resistor and a first capacitor, the second input end and the output end of the voltage follower are connected to the resistor, the resistor is connected to one end of the first capacitor and the port of the analog-to-digital converter of the control guide line, and the other end of the first capacitor is grounded.
Further, the voltage follower is an SGM8273 rail-to-rail operational amplifier.
Furthermore, the high-voltage switch also comprises a second capacitor, one end of the second capacitor is connected between the power supply terminal of the SGM8273 rail-to-rail operational amplifier and a power supply, and the other end of the second capacitor is grounded.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a PWM signal optimization method in a charging process of a charging pile, which comprises the following steps: initializing a prediction equation, and calculating a state description equation and a measurement output equation of the acquired PWM signal data system; predicting, namely predicting the next state according to a system state description equation, and updating a system prediction result and the covariance of the system prediction result; and updating the state, namely calculating the optimized estimation result and Kalman gain of the current state according to the updated system prediction result and the obtained measured value of the state, and updating the covariance of the optimized estimation result. The invention relates to an electronic device and a storage medium, and particularly relates to a PWM signal optimization method for executing a charging process of a charging pile. The invention also relates to a PWM signal optimization device in the charging process of the charging pile. The Kalman filtering algorithm used by the invention is applied to PWM signal filtering in the alternating current charging process of the electric automobile, has the characteristics of short calculation period and high precision, and does not need to store all data processed in the past in the use process; the filtering delay time is longer than that of the traditional PWM signal software and hardware, and a method for storing a large amount of data is needed; the sampling time period is short, and secondly, the recursion algorithm is easy to realize by a computer. The Kalman filtering algorithm can process the sampled data in real time, occupies small memory, does not need to keep other historical data except the previous state quantity, has high calculation speed, and carries out new value estimation of the current state according to a specified recursion method only according to the state equation of the system and the combination of the estimated value and the measured value which are calculated in the previous state after the new data is measured.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a main functional architecture diagram of an electric vehicle ac charging apparatus according to the present invention;
FIG. 2 is a Kalman filtering algorithm processing flow diagram of the present invention;
FIG. 3 is a flowchart of a PWM signal optimization method for charging a charging pile according to the present invention;
FIG. 4 is a schematic diagram of Kalman filtered data in accordance with the present invention;
FIG. 5 is a schematic diagram of an error curve according to the present invention;
fig. 6 is a circuit diagram of the PWM signal optimizing apparatus for the charging process of the charging pile according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
In one embodiment, the maximum output power of the alternating current charging pile of the electric automobile is 7KW, the specific specification is a 220V32A connection mode C, the related national standard and the energy standard GB/T18487.1GB/T20234.2NB/T33002 of the charging pile are met, and the main functional architecture of the equipment is shown in FIG. 1. The energy output direction of the whole system is AC- > AC, the whole process is monitored by a control unit, the control unit and a vehicle-mounted charger interact with each other through the change of a CP (control guide line) signal in front, PWM (pulse width modulation) signals between the CP and PE (ground wire) are mainly collected, the charging state is switched according to the change of the amplitude and the duty ratio of the signals, the control unit sends out the PWM signals in the charging process, meanwhile, the PWM signals are sampled through AD, the time window is 10ms, then, the sampling values are subjected to Kalman filtering algorithm processing to obtain the sampling values, and then, the amplitude and the duty ratio are calculated.
The processing of the PWM signal samples through the kalman filter algorithm is shown in fig. 2, and is essentially a set of recursive equations with statistical properties. Specifically, as shown in fig. 3, the PWM signal optimization method for the charging process of the charging pile includes the following steps:
initializing a prediction equation, and calculating a state description equation and a measurement output equation of the acquired PWM signal data system. The system state description equation is specifically as follows:
xt＝Axt-1+But+wt
the measurement output equation is specifically:
zt＝Hxt+vt
wherein x istIs the system state at time t, utFor systematic control vectors, wtFor predicted noise, ztFor measuring data at time t, vtFor the noise in measurement, A is a prediction matrix, a conversion coefficient for converting a t-1 state into a t state, B is a control matrix, a control vector is converted into a coefficient of a current state, H is a conversion matrix, and a conversion relation between a measured value and a predicted value is obtained.
And (3) prediction processing, namely predicting the next state according to a system state description equation, wherein the current state is t, and the t state is predicted from the last t-1 state, and the specific calculation formula is as follows:
xt|t-1＝A·xt-1|t-1+B·ut
updating the system prediction result, and updating the covariance of the system prediction result in the next step, wherein the specific calculation formula is as follows:
Pt|t-1＝A·Pt-1|t-1·AT+Q
wherein x ist|t-1For the result of the last state prediction, utIs a control quantity of the current state, xt-1|t-1Optimizing the estimation result for time t-1, Pt|t-1Is xt-1|t-1Q is the system process covariance,
and updating the state, namely calculating the optimized estimation result of the current state t according to the updated system prediction result and the obtained state measurement value, wherein the specific calculation formula is as follows:
xt|t＝xt|t-1+Kt(zt-H·xt|t-1)
calculating Kalman gain, and the formula is as follows:
Kt＝HT·Pt|t-1/(H·Pt|t-1·HT+R)
the optimal estimated value x in the t state is obtainedt|tAt the same time, in order to calculate the iteration, the optimal estimation x needs to be updatedt|tThe calculation formula of the covariance is as follows:
Pt|t＝(I-KtH)·Pt|t-1
wherein x ist|tFor the optimized estimation result of the current state, KtTo Kalman gain, Pt|tIs xt|tR is the measurement noise covariance,
fig. 4 is a schematic diagram of data, real values, and measured values after kalman filtering. Fig. 5 is a schematic diagram of an error curve, which shows the difference between the kalman filtered data and the actual data, and the difference between the measured data and the actual data. In fig. 4, the intermediate waveform is the kalman filtered data and the real data waveform, and the amplitude variation is large and is the measured value waveform, so that the kalman filtering effect on the PMW wave is more similar to the real data than the actually measured data. In fig. 5, the intermediate waveform is the difference between the data after kalman filtering and the real data, the curve with large amplitude variation is the difference between the measured data and the real data, and it can be seen that the error of the kalman filtering is much smaller than the error caused by actual measurement.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising a PWM signal optimization method for performing a charging pole charging process.
A computer-readable storage medium having stored thereon a computer program for execution by a processor of a PWM signal optimization method of a charging pole charging process.
Charging pile charging process's PWM signal optimizing apparatus, as shown in fig. 6, includes voltage follower, filter circuit, and alternating-current charging pile's control guide line's PWM signal output port is connected with voltage follower's first input, and voltage follower's second input, output are connected with filter circuit, and filter circuit is connected with control guide line's analog-to-digital converter port. Preferably, the filter circuit is an RC low-pass filter circuit, the RC filter circuit includes a resistor and a first capacitor, the second input end and the output end of the voltage follower are connected to the resistor, the resistor is connected to one end of the first capacitor and the port of the analog-to-digital converter controlling the guiding circuit, and the other end of the first capacitor is grounded. Preferably, the voltage follower is an SGM8273 rail-to-rail operational amplifier. Preferably, the amplifier further comprises a second capacitor, one end of the second capacitor is connected between the power supply terminal of the SGM8273 rail-to-rail operational amplifier and the power supply, and the other end of the second capacitor is grounded.
The voltage follower is used for impedance matching, and the output impedance is great after the CP voltage passes through the resistance voltage division of 30K and 10K, if the direct connection ADC Port, receives the inside switching channel of ADC and the interference that the ADC electric capacity charges easily. A voltage follower is added, and the voltage follower has the characteristics of high input impedance and low output impedance, so that the output impedance is reduced, and the driving capability is enhanced. The RC low-pass filter circuit is used for removing high-frequency burrs.
The invention optimizes the noise interference influence of the PWM signal in the alternating current charging process of the electric automobile, adopts a Kalman filter to design a filtering scheme, and realizes the rapid and accurate calculation of the amplitude and the duty ratio of the PWM signal so as to meet the control guide time sequence requirements of the specifications of GB/T18487.1, GB/T34657 and the like on the charging process.
The invention provides a PWM signal optimization method in a charging process of a charging pile, which comprises the following steps: initializing a prediction equation, and calculating a state description equation and a measurement output equation of the acquired PWM signal data system; predicting, namely predicting the next state according to a system state description equation, and updating a system prediction result and the covariance of the system prediction result; and updating the state, namely calculating the optimized estimation result and Kalman gain of the current state according to the updated system prediction result and the obtained measured value of the state, and updating the covariance of the optimized estimation result. The invention relates to an electronic device and a storage medium, and particularly relates to a PWM signal optimization method for executing a charging process of a charging pile. The invention also relates to a PWM signal optimization device in the charging process of the charging pile. The Kalman filtering algorithm used by the invention is applied to PWM signal filtering in the alternating current charging process of the electric automobile, has the characteristics of short calculation period and high precision, and does not need to store all data processed in the past in the use process; the filtering delay time is longer than that of the traditional PWM signal software and hardware, and a method for storing a large amount of data is needed; the sampling time period is short, and secondly, the recursion algorithm is easy to realize by a computer. The Kalman filtering algorithm can process the sampled data in real time, occupies small memory, does not need to keep other historical data except the previous state quantity, has high calculation speed, and carries out new value estimation of the current state according to a specified recursion method only according to the state equation of the system and the combination of the estimated value and the measured value which are calculated in the previous state after the new data is measured.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (10)
1. The PWM signal optimization method in the charging process of the charging pile is characterized by comprising the following steps:
initializing a prediction equation, and calculating a state description equation and a measurement output equation of the acquired PWM signal data system;
predicting, namely predicting the next state according to the system state description equation, and updating a system prediction result and the covariance of the system prediction result;
and updating the state, namely calculating the optimized estimation result and Kalman gain of the current state according to the updated system prediction result and the obtained measured value of the state, and updating the covariance of the optimized estimation result.
2. The PWM signal optimization method for charging pile according to claim 1, characterized in that: in the initialized prediction equation, the system state description equation specifically includes:
xt＝Axt-1+But+wt
the measurement output equation is specifically as follows:
zt＝Hxt+vt
wherein x istIs the system state at time t, utFor systematic control vectors, wtFor predicted noise, ztFor measuring data at time t, vtFor the noise in measurement, A is a prediction matrix, a conversion coefficient for converting a t-1 state into a t state, B is a control matrix, a control vector is converted into a coefficient of a current state, H is a conversion matrix, and a conversion relation between a measured value and a predicted value is obtained.
3. The PWM signal optimization method for the charging pile according to claim 2, wherein: in the prediction processing step, the specific calculation formula for predicting the next state according to the system state description equation is as follows:
xt|t-1＝A·xt-1|t-1+B·ut
the specific calculation formula for updating the covariance of the system prediction result is as follows:
Pt|t-1＝A·Pt-1|t-1·AT+Q
wherein x ist|t-1For the result of the last state prediction, utIs a control quantity of the current state, xt-1|t-1Optimizing the estimation result for time t-1, Pt|t-1Is xt-1|t-1Q is the system process covariance,
4. the PWM signal optimization method for the charging process of the charging pile according to claim 3, wherein; in the state updating step, the formula for calculating the optimized estimation result of the current state is as follows:
xt|t＝xt|t-1+Kt(zt-H·xt|t-1)
the formula for calculating the kalman gain is:
Kt＝HT·Pt|t-1/(H·Pt|t-1·HT+R)
the calculation formula of the covariance of the optimized estimation result is as follows:
Pt|t＝(I-KtH)·Pt|t-1
wherein x ist|tFor the optimized estimation result of the current state, KtTo Kalman gain, Pt|tIs xt|tR is the measurement noise covariance,
5. an electronic device, characterized by comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the method of any one of claims 1-4.
6. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor for performing the method according to any of claims 1-4.
7. Fill PWM signal optimization device of electric pile charging process, its characterized in that: the alternating-current charging pile control system comprises a voltage follower and a filter circuit, wherein a PWM signal output port of a control guide line of the alternating-current charging pile is connected with a first input end of the voltage follower, a second input end and an output end of the voltage follower are connected with the filter circuit, and the filter circuit is connected with an analog-to-digital converter port of the control guide line.
8. The PWM signal optimizing apparatus for charging pile according to claim 7, wherein: the filter circuit is an RC low-pass filter circuit, the RC filter circuit comprises a resistor and a first capacitor, a second input end and an output end of the voltage follower are connected with the resistor, the resistor is connected with one end of the first capacitor and an analog-to-digital converter port of the control guide line, and the other end of the first capacitor is grounded.
9. The PWM signal optimizing apparatus for charging pile according to claim 8, wherein: the voltage follower is an SGM8273 rail-to-rail operational amplifier.
10. The PWM signal optimizing apparatus for charging pile according to claim 9, wherein: the high-voltage switch circuit further comprises a second capacitor, one end of the second capacitor is connected between the power supply terminal of the SGM8273 rail-to-rail operational amplifier and a power supply, and the other end of the second capacitor is grounded.
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