CN113733984A - Auxiliary equalizing charge device based on SOC estimation - Google Patents
Auxiliary equalizing charge device based on SOC estimation Download PDFInfo
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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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
- B60L58/13—Maintaining the SoC within a determined range
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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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
- B60L58/22—Balancing the charge of battery modules
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
- H02J7/0014—Circuits for equalisation of charge between batteries
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/02—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from ac mains by converters
- H02J7/04—Regulation of charging current or voltage
- H02J7/06—Regulation of charging current or voltage using discharge tubes or semiconductor devices
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M3/00—Conversion of dc power input into dc power output
- H02M3/22—Conversion of dc power input into dc power output with intermediate conversion into ac
- H02M3/24—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters
- H02M3/28—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac
- H02M3/325—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal
- H02M3/335—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only
- H02M3/33507—Conversion of dc power input into dc power output with intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate ac using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of the output voltage or current, e.g. flyback converters
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/20—Charging or discharging characterised by the power electronics converter
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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
- Y02T10/70—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
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/92—Energy efficient charging or discharging systems for batteries, ultracapacitors, supercapacitors or double-layer capacitors specially adapted for vehicles
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Abstract
The invention discloses an auxiliary equalizing charge device based on SOC estimation, which comprises a rectification module, a driving module, an output module, an acquisition module and a machine learning module, wherein the rectification module is used for rectifying the SOC estimation; the driving module transforms input current, and the acquisition module acquires and feeds back voltage and current of the output module; and the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the MCU to generate and output a corresponding waveform. The driving module controls the switching tube in the driving module to be switched on or switched off based on the received waveform signal, and finally the purpose of stabilizing the output current is achieved.
Description
Technical Field
The invention relates to the technical field of equalizing charge of power battery packs, in particular to an auxiliary equalizing charge device based on SOC estimation.
Background
Lithium ion batteries have the advantages of high specific energy and the like, and have been widely applied to industries such as electric vehicles, mobile equipment and the like. Due to the voltage and capacity limitations of single lithium batteries, in practical use, a plurality of single lithium batteries are generally connected in series to form a battery pack. However, the performance of each unit cell is different from one unit cell to another, and therefore, the performance of the unit cell affects the characteristics of the battery pack. The reduction in the capacity and life of the individual unit cells leads to a drastic reduction in the service life and the service capacity of the battery pack. In order to reduce the imbalance among the single batteries, the battery pack needs to be charged in a balanced manner in the charging process. Therefore, an effective equalizing charge circuit is needed to realize equalizing charge of the battery pack.
Disclosure of Invention
The invention aims to provide an auxiliary equalizing charge device based on SOC estimation, which improves the equalizing charge efficiency of a battery pack. The apparatus realizes adaptive auxiliary equalizing charge based on feedback control of soc (state of charge).
In order to achieve the purpose, the invention provides the following technical scheme:
an auxiliary equalizing charge device based on SOC estimation comprises: the device comprises a rectification module, a driving module, an output module, an acquisition module and a machine learning module;
the rectification module is electrically connected with the driving module and is used for rectifying the input commercial power and then transmitting the rectified commercial power to the driving module;
the driving module is electrically connected with the output module, converts the current and transmits the converted current to the output module; the output module rectifies the voltage and the current and then serves as the final output of the device;
the input end of the acquisition module is electrically connected with the output module and used for acquiring and feeding back the voltage and the current of the output module;
the output end of the acquisition module is electrically connected with the machine learning module, and the acquired data is converted and then transmitted to the machine learning module;
the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls an MCU (micro controller unit) to generate and output a corresponding waveform;
the output end of the machine learning module is electrically connected with the driving module and is used for transmitting the output waveform to the driving module and controlling the switching tube in the driving module to be switched on or switched off;
furthermore, a data processing chip is arranged in the machine learning module, and the acquired data is learned and trained by utilizing an artificial neural network;
further, preferably, a cyclic neural network mode is adopted to train data, and the data in a time period is used as input to obtain the output of the current time;
furthermore, a main transformer is arranged in the driving module, and the secondary side of the main transformer is provided with two coil outputs which are respectively defined as a first coil and a second coil; the first coil is used as auxiliary output, the second coil is used as main output, and the acquisition module acquires the output of the second coil as feedback control;
furthermore, the driving module is provided with a switching tube, and the switching-on and switching-off frequency of the switching tube is limited between 50KHz and 110 KHz;
furthermore, the acquisition module further comprises an error proportional amplification circuit for scaling the received current and voltage signals;
further, the driving module is provided with a driving circuit, and the driving circuit comprises a pulse chip MCU1, a capacitor C1, a capacitor C2, a capacitor C3, a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R5, a resistor R6, a resistor R7, a resistor R8, a resistor R9, a resistor R10, a diode D1, a diode D2, a diode D3, a diode D4, a diode D5, a first transistor Q1, a second transistor Q2, a third transistor Q3, a fourth transistor Q4, and a transformer T1; pins 1, 2 and 7 of the pulse chip MCU1 are connected with the machine learning module; pin 5 of the pulse chip MCU1 is connected with the gate of the third transistor Q3; the drain of the third transistor Q3 is connected with the gate of the first transistor Q1 and the gate of the fourth transistor Q4; the emitter of the first transistor Q1 is connected with the emitter of the fourth transistor Q4, the cathode of the diode D4 and the resistor R8; the grid of the second transistor Q2 is connected with the anode of the diode D4 and the resistor R8, the drain of the second transistor Q2 is connected with the anode of the diode D2 and the negative end of the primary side of the transformer T1, and the source of the second transistor Q2 is connected with the resistors R9 and R10; the pulse chip MCU1 generates a corresponding type of waveform by using data sent by the machine learning module, and outputs the waveform from a pin 5 of the pulse chip MCU1, so as to drive the third transistor Q3 to be switched on and off; when the third transistor Q3 is turned on, the first transistor Q1 is turned on, and the fourth transistor Q4 is turned off, so that the second transistor Q2 is turned on; when the third transistor Q3 is turned off, the first transistor Q1 is turned off, and the fourth transistor Q4 is turned on, so that the second transistor Q2 is turned off; the amplification of the driving current is realized through the cooperative work of the first transistor Q1, the third transistor Q3 and the fourth transistor Q4, and finally the driving of the primary side current of the transformer T1 is realized through the second transistor Q2.
The beneficial effects of the invention include but are not limited to:
(1) the invention provides an auxiliary equalizing charge device based on SOC estimation, which comprises: the device comprises a rectification module, a driving module, an output module, an acquisition module and a machine learning module;
the driving module transforms input current, and the acquisition module acquires and feeds back voltage and current of the output module; and the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the MCU to generate and output a corresponding waveform.
The driving module controls the switching tube in the driving module to be switched on or switched off based on the received waveform signal, and finally the purpose of stably outputting the current is achieved.
(2) The invention also comprises a main transformer; the primary side of the main transformer is provided with a coil input; the secondary side has two coil outputs, which are respectively defined as a secondary side first coil and a secondary side second coil. The first secondary winding is used as auxiliary output, and the second secondary winding is used as main output. And the output of the secondary side first coil supplies power to the machine learning module. The output of the secondary second coil supplies power to the acquisition module, and the acquisition module acquires the output of the secondary second coil as feedback control. The acquisition module further comprises an error proportional amplification circuit for scaling the received current and voltage signals.
(3) The auxiliary equalizing charge device based on SOC estimation provided by the invention can estimate the SOC data of the battery according to the collected voltage and current data, and determine the equalizing charge parameter according to the SOC data, thereby improving the efficiency of the power supply and simultaneously improving the equalizing charge efficiency. Meanwhile, in the charging process, the machine learning module continuously collects the charging current, the pulse duty ratio and frequency, and the voltage and temperature change of the battery pack, and an estimation model of the SOC is optimized by adopting unsupervised learning.
Drawings
Fig. 1 is a schematic structural diagram of an auxiliary equalizing charge device based on SOC estimation according to the present invention;
FIG. 2 is a circuit diagram of the output of the first coil of the secondary side of the driving circuit according to the embodiment of the present invention;
FIG. 3 is a circuit diagram of the secondary side second coil output provided by an embodiment of the present invention;
FIG. 4 is a circuit diagram of a sampling feedback circuit provided by an embodiment of the present invention;
fig. 5 is a diagram of SOC estimation data provided by the embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to examples, but the present invention is not limited to these examples.
Fig. 1 is a schematic flow chart of an auxiliary equalizing charge device based on SOC estimation according to the present application, as shown in fig. 1, including: the device comprises a rectification module, a driving module, an output module, an acquisition module and a machine learning module;
the rectification module is electrically connected with the driving module and is used for rectifying the input commercial power and then transmitting the rectified commercial power to the driving module;
the driving module is electrically connected with the output module, converts the current and transmits the converted current to the output module. The output module rectifies the voltage and the current and then serves as the final output of the device;
the input end of the acquisition module is electrically connected with the output module and used for acquiring and feeding back the voltage and the current of the output module;
the output end of the acquisition module is electrically connected with the machine learning module, and the acquired data is transmitted to the machine learning module after being converted;
and the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the MCU to generate and output a corresponding waveform.
The output end of the machine learning module is electrically connected with the driving module and is used for transmitting the output waveform to the driving module and controlling the switching tube in the driving module to be switched on or switched off;
furthermore, a data processing chip is arranged in the machine learning module, and the acquired data are learned and trained by utilizing an artificial neural network.
Further, preferably, the data is trained in a cyclic neural network manner, and the data in a time period is used as input to obtain the output of the current time.
Furthermore, a main transformer is arranged in the driving module; the primary side of the main transformer is provided with a coil input; the secondary side of the main transformer has two coil outputs which are respectively defined as a primary coil and a secondary coil. The secondary side first coil is used for auxiliary output, the secondary side second coil is used for main output, and the acquisition module acquires the output of the secondary side second coil as feedback control.
Further, the driving module is provided with a switch tube, and the conduction and cut-off frequency of the switch tube is limited between 50KHz and 110 KHz.
Further, the acquisition module further comprises an error proportional amplifying circuit for scaling the received current and voltage signals.
In the invention, a driving module transforms input current, and an acquisition module acquires and feeds back voltage and current of an output module; and the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the MCU to generate and output a corresponding waveform. The driving module controls the switching tube in the driving module to be switched on or switched off based on the received waveform signal, and finally the purpose of stabilizing the output current is achieved.
The invention also comprises a main transformer; the primary side of the main transformer is provided with a coil input; the secondary side has two coil outputs, which are respectively defined as a secondary side first coil and a secondary side second coil. The first secondary winding is used as auxiliary output, and the second secondary winding is used as main output. And the output of the secondary side first coil supplies power to the machine learning module. The output of the secondary second coil supplies power to the acquisition module, and the acquisition module acquires the output of the secondary second coil as feedback control. The acquisition module further comprises an error proportional amplification circuit for scaling the received current and voltage signals.
The auxiliary equalizing charge device based on SOC estimation provided by the invention can estimate the SOC data of the battery according to the collected voltage and current data, and determine the equalizing charge parameter according to the SOC data, thereby improving the efficiency of the power supply and simultaneously improving the equalizing charge efficiency. Meanwhile, in the charging process, the machine learning module continuously collects the charging current, the pulse duty ratio and frequency, and the voltage and temperature change of the battery pack, adopts unsupervised learning, optimizes the estimation model of the SOC, automatically adjusts the pulse duty ratio and frequency, and outputs proper current.
Fig. 2 is a circuit diagram of the driving circuit and the first coil output provided in this embodiment, and as shown in fig. 2, the driving circuit includes a pulse chip MCU1, a capacitor C1, a capacitor C2, a capacitor C3, a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R5, a resistor R6, a resistor R7, a resistor R8, a resistor R9, a resistor R10, a diode D1, a diode D2, a diode D3, a diode D4, a diode D5, a first transistor Q1, a second transistor Q2, a third transistor Q3, a fourth transistor Q4, and a transformer T1.
In this embodiment, the turn-on/off frequency of the first transistor Q1, the second transistor Q2, the third transistor Q3, and the fourth transistor Q4 is limited to 50KHz to 110 KHz.
Fig. 3 is a circuit diagram of the second coil output provided by the present embodiment, and as shown in fig. 3, the second coil output includes a rectifier diode D6, a rectifier diode D7, a capacitor C4, a capacitor C5, a capacitor C6, a capacitor C7, a capacitor C8, a capacitor C9, a capacitor C10, a resistor R11, a resistor R12, a resistor R13, and an output terminal OUT 1.
The negative electrode of the rectifier diode D6 is connected with the capacitor C6, the capacitor C4 and the resistor R11; and then output from the other end of the resistor R11 as a power supply of the machine learning module. The negative electrode of the rectifier diode D7 is connected with the capacitor C9, the capacitor C8 and the capacitor C7 and serves as the main output of the device;
the resistor R12 is a current sampling resistor connected in series in the output loop for collecting the output current.
Fig. 4 is a circuit diagram of the sampling feedback circuit provided in this embodiment, and as shown in fig. 4, the sampling feedback circuit includes a diode D8, a diode D9, a light emitting diode GREEN1, a light emitting diode RED1, a capacitor C11, a capacitor C12, a capacitor C13, a resistor R14, a resistor R15, a resistor R16, a resistor R17, a resistor R18, a resistor R19, a resistor R20, a resistor R21, a resistor R22, a resistor R23, a resistor R24, a resistor R25, a resistor R26, and a proportional amplifier chip LM 1.
The circuit collects the output charging current and charging voltage, and transmits data to the machine learning module from a pin PH1, a pin PH2, a pin PH3 and a pin PH4 after the charging current and the charging voltage pass through the proportional amplification chip.
The light emitting diodes GREEN1 and RED1 are used to indicate the current charging mode.
Fig. 5 is a diagram of estimated SOC data according to an embodiment of the present invention, as shown in fig. 5, a dotted line is a true value of SOC, and a solid line is an estimated SOC value calculated by a machine learning module of the present apparatus.
The driving circuit in the auxiliary equalizing charge device based on SOC estimation provided by the invention transforms the primary current of a main transformer T1; the acquisition module acquires and feeds back the voltage and the current of the output module; the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the pulse chip MCU1 to generate and output a corresponding waveform. The waveform output by the pulse chip MCU1 is used for controlling the on and off of the first transistor Q1, the second transistor Q2, the third transistor Q3 and the fourth transistor Q4, so that the conversion of the primary side current of the main transformer T1 is realized.
The invention also comprises a main transformer; the primary side of the main transformer is provided with a coil input; the secondary side has two coil outputs, which are respectively defined as a secondary side first coil and a secondary side second coil. The first secondary winding is used as auxiliary output, and the second secondary winding is used as main output. And the output of the secondary side first coil supplies power to the machine learning module. The output of the secondary second coil supplies power to the acquisition module, and the acquisition module acquires the output of the secondary second coil as feedback control. The acquisition module further comprises an error proportional amplification circuit for scaling the received current and voltage signals. The output of the secondary side second coil passes through a rectifier diode D7, a capacitor C9, a capacitor C8 and a capacitor C7 and then is used as the main output of the device.
The auxiliary equalizing charge device based on SOC estimation provided by the invention can estimate the SOC data of the battery according to the collected voltage and current data, and determine the equalizing charge parameter according to the SOC data, thereby improving the efficiency of the power supply and simultaneously improving the equalizing charge efficiency. Meanwhile, in the charging process, the machine learning module continuously collects the charging current, the pulse duty ratio and frequency, and the voltage and temperature change of the battery pack, adopts unsupervised learning, optimizes the estimation model of the SOC, automatically adjusts the pulse duty ratio and frequency, and outputs proper current.
The above description is only an example of the present invention, and not intended to limit the present invention in any way, and although the present invention has been disclosed in the preferred embodiments, the present invention is not limited thereto, and those skilled in the art can make various changes and modifications within the technical scope of the present invention without departing from the technical scope of the present invention.
Claims (6)
1. An auxiliary equalizing charge device based on SOC estimation is characterized by comprising: the device comprises a rectification module, a driving module, an output module, an acquisition module and a machine learning module;
the rectification module is electrically connected with the driving module and is used for rectifying the input commercial power and then transmitting the rectified commercial power to the driving module;
the driving module is electrically connected with the output module, converts the current, transmits the converted current to the output module, and rectifies the voltage and the current by the output module to be used as the final output of the device;
the input end of the acquisition module is electrically connected with the output module and used for acquiring and feeding back the voltage and the current of the output module;
the output end of the acquisition module is electrically connected with the machine learning module, and the acquired data is converted and then transmitted to the machine learning module;
the machine learning module pre-estimates the SOC of the battery according to the obtained data, calculates to obtain optimal output waveform and frequency data according to the pre-estimated SOC, and controls the MCU to generate and output a corresponding waveform;
the output end of the machine learning module is electrically connected with the driving module and used for transmitting the output waveform to the driving module and controlling the switching tube in the driving module to be switched on or switched off.
2. The auxiliary equalizing charge device based on SOC estimation according to claim 1, wherein a data processing chip is disposed in the machine learning module, and the collected data is learned and trained by using an artificial neural network; preferably, a cyclic neural network mode is adopted to train data, and the data in a time period is used as input to obtain the output of the current time.
3. The auxiliary equalizing charge device based on SOC estimation according to claim 1, wherein the driving module further comprises at least four transistors, the transistors implement current transformation to the primary side of the main transformer, and the on and off of the transistors are determined by the machine learning module.
4. The auxiliary equalizing charge device based on SOC estimation according to claim 1, wherein a main transformer is provided in the driving module;
the secondary side of the main transformer is provided with two coil outputs which are respectively defined as a first coil and a second coil; the first coil is auxiliary output, the second coil is main output, and the acquisition module acquires the output of the second coil as feedback control.
5. The auxiliary equalizing charge device based on SOC estimation according to claim 1, wherein the collection module further comprises an error scaling circuit for scaling the received current and voltage signals.
6. The auxiliary equalizing charge device based on SOC estimation according to claim 1, wherein the driving circuit comprises a pulse chip MCU1, a capacitor C1, a capacitor C2, a capacitor C3, a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R5, a resistor R6, a resistor R7, a resistor R8, a resistor R9, a resistor R10, a diode D1, a diode D2, a diode D3, a diode D4, a diode D5, a first transistor Q1, a second transistor Q2, a third transistor Q3, a fourth transistor Q4, a transformer T1;
pins 1, 2 and 7 of the pulse chip MCU1 are connected with the machine learning module; pin 5 of the pulse chip MCU1 is connected with the gate of the third transistor Q3; the drain of the third transistor Q3 is connected with the gate of the first transistor Q1 and the gate of the fourth transistor Q4; the emitter of the first transistor Q1 is connected with the emitter of the fourth transistor Q4, the cathode of the diode D4 and the resistor R8; the grid of the second transistor Q2 is connected with the anode of the diode D4 and the resistor R8, the drain of the second transistor Q2 is connected with the anode of the diode D2 and the negative end of the primary side of the transformer T1, and the source of the second transistor Q2 is connected with the resistors R9 and R10;
the pulse chip MCU1 generates a corresponding type of waveform by using data sent by the machine learning module, and outputs the waveform from a pin 5 of the pulse chip MCU1, so as to drive the third transistor Q3 to be switched on and off; when the third transistor Q3 is turned on, the first transistor Q1 is turned on, and the fourth transistor Q4 is turned off, so that the second transistor Q2 is turned on; when the third transistor Q3 is turned off, the first transistor Q1 is turned off, and the fourth transistor Q4 is turned on, so that the second transistor Q2 is turned off; the amplification of the driving current is realized through the cooperative work of the first transistor Q1, the third transistor Q3 and the fourth transistor Q4, and finally the driving of the primary side current of the transformer T1 is realized through the second transistor Q2.
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