A battery wireless sensor based on Internet of Things technology

Abstract

The present invention discloses a battery wireless sensor based on Internet of Things technology, which belongs to the field of battery detection technology, including MCU 、 EEPROM , communication module, temperature sensor group, power module, backup battery pack, data acquisition unit and detection circuit control unit, solve the technical problems of improving internal resistance detection accuracy, improving life prediction accuracy, and having edge-end online detection capabilities and isolated detection capabilities. The present invention integrates multiple voltage, current, and temperature acquisition channels, carefully monitors the status of each single cell, provides automatically triggered online detection and manually triggered isolated detection, covering more working conditions, MCU The internal resistance sequence and internal resistance change rate are calculated locally to achieve edge prediction. Abnormal batteries are detected based on internal resistance mutations, and automatic tagging and uploading are achieved to trigger isolation detection.

Description

Technical Field

[0001] The invention belongs to the technical field of battery detection and relates to a battery wireless sensor based on Internet of Things technology. Background Art

[0002] With the development of the Internet of Things and intelligent maintenance technologies, battery monitoring systems are increasingly being used in scenarios such as communication base stations, data centers, and rail transit, which require extremely high reliability of the power supply system. The main battery monitoring methods currently used in the market include:

[0003] Offline detection method: manually use a battery tester to periodically detect the battery's internal resistance, capacity and other parameters.

[0004] Online monitoring system: uses fixed sensors and circuits to collect voltage and current, which are then uploaded to the server for analysis by a centralized data collector.

[0005] The shortcomings of the existing technology are:

[0006] Insufficient data granularity: Most common systems can only collect total voltage and current, making it difficult to obtain the detailed status of each battery cell (such as the internal resistance change trend).

[0007] Single detection method: Most traditional systems only provide voltage-based health status assessments and cannot reflect changes in internal resistance that actually affect battery life.

[0008] Large detection disturbance: Online detection often cannot isolate the impact of the load, resulting in large internal resistance detection errors and insufficient accuracy. Summary of the Invention

[0009] The purpose of the present invention is to provide a battery wireless sensor based on Internet of Things technology, which solves the technical problems of improving the internal resistance detection accuracy and the life prediction accuracy, and at the same time has the edge online detection capability and isolated detection capability.

[0010] To achieve the above object, the present invention adopts the following technical solutions:

[0011] A battery wireless sensor based on Internet of Things technology, comprising MCU 、 EEPROM , communication module, temperature sensor group, power module, backup battery pack, data acquisition unit and detection circuit control unit, EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit are all connected with MCU connect;

[0012] The data acquisition unit and the detection circuit control unit are both connected to the battery pack under test; MCU Wireless communication with the host computer through the communication module;

[0013] The backup battery pack is connected to the power module, and the power module is connected to the battery pack under test. MCU 、 EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply;

[0014] The detection circuit control unit includes a first SPDT Module, second SPDT Module, current collection resistor RS And the gear resistor group; the gear resistor group includes resistors R 1. Resistors R 2. Resistance R 3. Resistors R 4 and resistors R 5;

[0015] First SPDT Module D 1 end, D 2 ends and D All three terminals pass through current collection resistors RS Connect the positive pole of the battery pack to be tested, and the negative pole of the battery pack to be tested to the ground wire; SPDT Module S 1 A Output VOUT power supply, S 2 A Output VOUT 1. Power supply, S 3 B Output VTE Power supply; first SPDT Module control terminal connection MCU ;

[0016] second SPDT Module D 1 end, D 2 ends, D 3-terminal and D All 4 terminals are connected VTE Power supply; Second SPDT Module S 1 B end, S 2 B end, S 3 B Duanhe S 4 B Resistors R 1. Resistors R 2. Resistance R 3 and resistors R 4. Connect the resistor R Pin 1 of 5, resistor R 5's 2nd pin is connected to the ground wire; the secondSPDT Module control terminal connection MCU ;

[0017] The data acquisition unit includes a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module. The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively acquire the voltage of the four single cells in the battery pack under test. The first acquisition module also acquires the current acquisition resistance RS Differential signal at both ends;

[0018] MCU It is used to calculate the internal resistance of each single cell in the tested battery pack and estimate the life and abnormal status of each single cell.

[0019] Preferably, the MCU The model is STM 32 F 103 C 8 T 6; EEPROM The model is AT twenty four C 32; the model of the communication module is CC 3100; the temperature sensor group includes multiple digital temperature sensors, the model of the digital temperature sensor is 18 B 20;

[0020] The first SPDT module and the second SPDT The module models are ADG 884; the models of the first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module are all INA 226.

[0021] Preferably, the power module includes a lithium battery management chip and a 3.3 V The power supply of the voltage regulator and lithium battery management chip is VOUT 1 power supply, 3.3 V The voltage regulator and backup battery pack are connected to the lithium battery management chip, 3.3 V The voltage regulator is MCU 、 EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply;

[0022] The model of lithium battery management chip is BQ 24075, 3.3 V The model of the voltage regulator is TPS 62842.

[0023] Preferably, the positive electrode of the battery pack under test is connected to the 2nd pin of the current collection resistor RS. RS Pin 1 provides the voltage output terminal of the battery pack BAT - OUT ;

[0024] Voltage output terminal BAT - OUT Respectively with the first SPDT Module D 1 end, D 2 ends and D After the 3 ends are connected, the first SPDT The three switches in the module control and output VOUT power supply, VOUT 1 Power supply and VTE power supply, VOUT The power supply is used to provide battery power to external devices. VOUT 1 Power supply is used to provide power to the power module. VTE The power supply is used as the test power supply for internal resistance detection and is input to the second SPDT Module D 1 end, D 2 ends, D 3-terminal and D 4 terminals, through the second SPDT The 4 switches in the module control VTE Power supply access circuit.

[0025] Preferably, the first acquisition module VBUS Connect the terminal to the first battery cell of the battery pack under test. BAT 1's positive electrode, used to detect single cells BAT The voltage on the positive electrode of 1;

[0026] The differential input terminal of the first acquisition module, i.e. IN +End and IN -ends are connected to the current collection resistor RS The two ends of the current collection resistor are used to detect RS The differential signal at both ends of the battery pack is used to detect the current signal in the total loop of the battery pack under test;

[0027] The second acquisition module VBUS Connect the terminal to the second battery cell of the battery pack under test BAT 2's positive pole, used to detect single cells BAT The voltage on the positive electrode of 2;

[0028] The third acquisition module VBUS Connect the terminal to the third battery cell of the battery pack under test. BAT The positive pole of 3 is used to detect single cellsBAT The voltage on the positive electrode of 3;

[0029] The fourth acquisition module VBUS Connect the terminal to the fourth battery cell of the battery pack under test. BAT The positive pole of 4 is used to detect single cells BAT The voltage on the positive terminal of 4;

[0030] The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively use different I 2 C Bus and MCU communication.

[0031] Preferably, the MCU It is used to complete the overall logic control and data processing of the battery wireless sensor, including:

[0032] During the power-on initialization phase, complete the initialization and read EEPROM The configuration parameters and life prediction model saved in the system are used to start the temperature sensor group and data acquisition unit, and control the first SPDT and second SPDT The initial conduction state of the module;

[0033] exist MCU There are three working states:

[0034] State 1 is the normal working state: Under normal working state, MCU Control first SPDT The module maintains the switch on state for supplying power to external devices and the power module;

[0035] The data acquisition unit collects the voltage values ​​of the four cells in the battery pack under test, and collects the current and resistance values. RS The voltage difference between the two ends is used to obtain the loop current;

[0036] Determine whether to enter online internal resistance detection based on current fluctuations. After triggering online internal resistance detection, calculate the internal resistance of each single battery and estimate the life of each battery;

[0037] MCU Periodically record voltage, current, and internal resistance data sequences and upload them to the host computer;

[0038] At the same time, according to EEPROM The battery life prediction model saved in the system is used to estimate the life and to judge the abnormality of the single battery status;

[0039] State 2 Conventional manual detection state: When receiving the instruction from the host computer to enter the manual detection state and no abnormality is found in the battery pack under test during the online detection process, MCUEnter the normal manual detection state. At this time, MCU Control first SPDT Module disconnect VOUT Power and VOUT 1. Turn on the power switch of the power supply. VTE The return path of the power supply and controls the second SPDT The module performs fixed-gear conduction control according to the detection mode it is in, and detects the voltage drop after forming a stable load to calculate the internal resistance; MCU Upload the test data and calculation results to the host computer;

[0040] State 3 Abnormal manual detection state: When receiving the instruction from the host computer to enter the manual detection state and the battery pack under test is found to be abnormal during the online detection process, MCU Enter the abnormal manual detection state. At this time, MCU Control first SPDT Module disconnect VOUT Power and VOUT 1. Turn on the power switch of the power supply. VTE The return path of the power supply and controls the second SPDT The module performs multi-level conduction control according to the detection mode it is in. After forming a stable load, it detects the voltage drop to calculate the internal resistance. ​ Upload the test data and calculation results to the host computer;

[0041] ​ A closed-loop calibration mechanism based on manual detection data is constructed. Through error evaluation calculation and using a time-decay weighting mechanism, a revised battery life prediction model is generated, and battery life prediction is performed based on the revised battery life prediction model.

[0042] The battery wireless sensor based on Internet of Things technology described in the present invention solves the technical problems of improving the accuracy of internal resistance detection and life prediction, while also providing edge-end online detection capabilities and isolated detection capabilities. The present invention integrates multiple voltage, current, and temperature acquisition channels to carefully monitor the status of each single battery cell, providing automatically triggered online detection and manually triggered isolated detection, covering more working conditions. ​ The internal resistance sequence and internal resistance change rate are calculated locally to achieve edge prediction. Abnormal batteries are detected based on internal resistance mutations, and automatic tagging and uploading are achieved to trigger isolation detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] ​ It is a schematic block diagram of the battery wireless sensor of the present invention;

[0044] ​ It is a schematic block diagram of the power module of the present invention;

[0045] ​ It is a schematic block diagram of the detection circuit control unit of the present invention;

[0046] ​ It is a schematic block diagram of the data acquisition unit of the present invention;

[0047] ​ is a circuit diagram of a detection circuit control unit of the present invention;

[0048] ​ is a circuit diagram of a data acquisition unit of the present invention;

[0049] ​ It is a flow chart of the present invention. DETAILED DESCRIPTION

[0050] Depend on ​ The battery wireless sensor based on Internet of Things technology shown includes ​ 、 ​ , communication module, temperature sensor group, power module, backup battery pack, data acquisition unit and detection circuit control unit, ​ , communication module, temperature sensor group, data acquisition unit and detection circuit control unit are all connected with ​ connect;

[0051] The power module includes a lithium battery management chip and a 3.3 V The power supply of the voltage regulator and lithium battery management chip is ​ 1 power supply, 3.3 V The voltage regulator and backup battery pack are connected to the lithium battery management chip, 3.3 V The voltage regulator is ​ 、 ​ , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply;

[0052] The model of lithium battery management chip is ​ 24075, 3.3 V The model of the voltage regulator is ​ 62842.

[0053] The data acquisition unit and the detection circuit control unit are both connected to the battery pack under test; ​ Wireless communication with the host computer through the communication module;

[0054] The backup battery pack is connected to the power module, and the power module is connected to the battery pack under test. ​ 、 ​ , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply;

[0055] The detection circuit control unit includes a first​ Module, second ​ Module, current collection resistor ​ And the gear resistor group; the gear resistor group includes resistors R 1. Resistors R 2. Resistance R 3. Resistors R 4 and resistors R 5;

[0056] First ​ Module D 1 end, D 2 ends and D All three terminals pass through current collection resistors ​ Connect the positive pole of the battery pack to be tested, and the negative pole of the battery pack to be tested to the ground wire; ​ Module S 1 A Output ​ power supply, S 2 A Output ​ 1. Power supply, S 3 B Output ​ Power supply; first ​ Module control terminal connection ​ ;

[0057] second ​ Module D 1 end, D 2 ends, D 3-terminal and D All 4 terminals are connected ​ Power supply; Second ​ Module S 1 B end, S 2 B end, S 3 B Duanhe S 4 B Resistors R 1. Resistors R 2. Resistance R 3 and resistors R 4. Connect the resistor R Pin 1 of 5, resistor R 5's 2nd pin is connected to the ground wire; the second ​ Module control terminal connection ​ ;

[0058] The positive electrode of the battery pack under test is connected to the 2nd foot of the current collection resistor RS. ​ Pin 1 provides the voltage output terminal of the battery pack ​ -​ ;

[0059] Voltage output terminal ​ - ​ Respectively with the first ​ Module D 1 end, D 2 ends and D After the 3 ends are connected, the first ​ The three switches in the module control and output ​ power supply, ​ 1 Power supply and ​ power supply, ​ The power supply is used to provide battery power to external devices. ​ 1 Power supply is used to provide power to the power module. ​ The power supply is used as the test power supply for internal resistance detection and is input to the second ​ Module D 1 end, D 2 ends, D 3-terminal and D 4 terminals, through the second ​ The 4 switches in the module control ​ Power supply access circuit.

[0060] In this embodiment, the battery pack under test is composed of 4 single cells connected in series, namely, ​ 1. Single battery ​ 2. Single battery ​ 3. Single battery ​ 4.

[0061] First ​ Module is switch U 1, second ​ Module is switch U 2. Switch U1 of ​ Foot connection ​ power supply, D1 Pin connected to current collection resistor ​ 1 foot, ​ 1-pin connection ​ One ​ port, i.e., switch U 1. Normally closed switch of the first switch D 1- S 1 A Connect in series the positive terminal of the battery pack under test and the power supply ​ between.

[0062] Similarly, it can be seen from the circuit diagram that the switch U 1's second normally closed switch D 2- S 2A Connect in series the positive terminal of the battery pack under test and the power supply ​ 1 between.

[0063] switch U 1's third normally open switch D 3- S 3 B Then connect the positive electrode of the battery pack under test and the power supply in series. ​ between.

[0064] and ​ The power supply is used for power supply. ​ 1 is to power the battery wireless sensor itself. ​ The power supply is used to isolate and test the battery pack under test.

[0065] The data acquisition unit includes a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module. The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively acquire the voltage of the four single cells in the battery pack under test. The first acquisition module also acquires the current acquisition resistance ​ Differential signal at both ends.

[0066] The first acquisition module ​ Connect the terminal to the first battery cell of the battery pack under test. ​ 1's positive electrode, used to detect single cells ​ The voltage on the positive electrode of 1;

[0067] The differential input terminal of the first acquisition module, i.e. ​ +End and ​ -ends are connected to the current collection resistor ​ The two ends of the current collection resistor are used to detect ​ The differential signal at both ends of the battery pack is used to detect the current signal in the total loop of the battery pack under test;

[0068] The second acquisition module ​ Connect the terminal to the second battery cell of the battery pack under test ​ 2's positive pole, used to detect single cells ​ The voltage on the positive electrode of 2;

[0069] The third acquisition module ​ Connect the terminal to the third battery cell of the battery pack under test. ​ The positive pole of 3 is used to detect single cells ​ The voltage on the positive electrode of 3;

[0070] The fourth acquisition module ​ Connect the terminal to the fourth battery cell of the battery pack under test. ​The positive pole of 4 is used to detect single cells ​ The voltage on the positive terminal of 4;

[0071] The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively use different I 2 C Bus and ​ communication.

[0072] In this embodiment, the first acquisition module is an acquisition chip U 3. The second acquisition module is the acquisition chip U 4. The third acquisition module is the acquisition chip U 5. The fourth acquisition module is the acquisition chip U 6.

[0073] Acquisition chip U 3 for collection ​ 1. The positive voltage and current collection resistor ​ The current signal of the acquisition chip U 3 of A 0 end, A 1 end, ​ end, ​ Duanhe ​ End respectively with ​ Different ​ Port connection.

[0074] Acquisition chip U 3. The signals used to collect include battery ​ 1's positive electrode voltage and the overall loop current.

[0075] The second acquisition module is the acquisition chip U 4. Collection Chip U 3 circuit principle is similar, the acquisition chip U 4. The collection is battery ​ 2 on the voltage.

[0076] The third acquisition module is the acquisition chip U 5. Collection Chip U 3 circuit principle is similar, the acquisition chip U 5. The collection is battery ​ 3 on the voltage.

[0077] The fourth acquisition module is the acquisition chip U 6. Collection Chip U 3 circuit principle is similar, the acquisition chip U 6 The collection is battery ​ 2 on the voltage.

[0078] described ​ The model is​ 32 F 103 C 8 T 6; ​ The model is ​ twenty four C 32; the model of the communication module is ​ 3100; the temperature sensor group includes multiple digital temperature sensors, the model of the digital temperature sensor is 18 B 20;

[0079] The first ​ module and the second ​ The module models are ​ 884; the models of the first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module are all ​ 226.

[0080] ​ It is used to calculate the internal resistance of each single cell in the tested battery pack and estimate the life and abnormal status of each single cell.

[0081] 1. ​ It is used to complete the overall logic control and data processing of the battery wireless sensor, including:

[0082] During the power-on initialization phase, complete the initialization and read ​ The configuration parameters and life prediction model saved in the system are used to start the temperature sensor group and data acquisition unit, and control the first ​ and second ​ The initial conduction state of the module;

[0083] exist ​ There are three working states:

[0084] State 1 is the normal working state: Under normal working state, ​ Control first ​ The module maintains the switch on state for supplying power to external devices and the power module;

[0085] The data acquisition unit collects the voltage values ​​of the four cells in the battery pack under test, and collects the current and resistance values. ​ The voltage difference between the two ends is used to obtain the loop current;

[0086] Determine whether to enter online internal resistance detection based on current fluctuations. After triggering online internal resistance detection, calculate the internal resistance of each single battery and estimate the life of each battery;

[0087] ​ Periodically record voltage, current, and internal resistance data sequences and upload them to the host computer;

[0088] At the same time, according to ​ The battery life prediction model saved in the system is used to estimate the life and to judge the abnormality of the single battery status;

[0089] State 2 Conventional manual detection state: When receiving the instruction from the host computer to enter the manual detection state and no abnormality is found in the battery pack under test during the online detection process, ​ Enter the normal manual detection state. At this time, ​ Control first ​ Module disconnect ​ Power and ​ 1. Turn on the power switch of the power supply. ​ The return path of the power supply and controls the second ​ The module performs fixed-gear conduction control according to the detection mode it is in, and detects the voltage drop after forming a stable load to calculate the internal resistance; ​ Upload the test data and calculation results to the host computer;

[0090] State 3 Abnormal manual detection state: When receiving the instruction from the host computer to enter the manual detection state and the battery pack under test is found to be abnormal during the online detection process, ​ Enter the abnormal manual detection state. At this time, ​ Control first ​ Module disconnect ​ Power and ​ 1. Turn on the power switch of the power supply. ​ The return path of the power supply and controls the second ​ The module performs multi-level conduction control according to the detection mode it is in. After forming a stable load, it detects the voltage drop to calculate the internal resistance. ​ Upload the test data and calculation results to the host computer;

[0091] ​ A closed-loop calibration mechanism based on manual detection data is constructed. Through error evaluation calculation and using a time-decay weighting mechanism, a revised battery life prediction model is generated, and battery life prediction is performed based on the revised battery life prediction model.

[0092] When in use, this embodiment can be based on the battery detection logic process implemented on the solution architecture of the battery wireless sensor + host computer based on the Internet of Things technology. The battery wireless sensor has the following three working states:

[0093] State 1: Normal working state:

[0094] First ​ In the module, D 1- S 1 A 、D 2- S 2 A It is a normally closed switch. When powered on normally, D 1- S 1 A 、 D 2- S 2 A are all turned on, and the voltage of the battery pack under test is loaded to the first ​ Module S 1 A and S 2 A The output is ​ and ​ 1. At this time ​ To power external devices, ​ 1 is to directly supply power to the power module, that is, to ​ Wait for the chip to be powered and charge the backup battery pack;

[0095] At this time D 3- S 3 B It is a normally open switch and is not conducting at this time.

[0096] Under normal working conditions, through the test ​ The current is used to determine whether there is voltage fluctuation in the power supply of the external device, and the fluctuation threshold is used to determine whether the online internal resistance detection logic can be entered.

[0097] If it can, then enter the detection logic of the online internal resistance. If not, then wait.

[0098] The battery wireless sensor locally records the single-cell voltage, total voltage, and total current generated during normal operation of the tested battery pack. It also records the internal resistance of each cell, calculated online, during each fluctuation. It then constructs a sequence of internal resistance changes for each cell. Based on this sequence, it makes an online prediction of each cell's lifespan. This is the first prediction, recorded as Prediction 1. This data is regularly uploaded to the host computer, which performs statistical analysis and optimizes the battery lifespan prediction model. The optimized model is then sent to the battery wireless sensor for application.

[0099] At intervals, a judgment is made based on the internal resistance change sequence: when the internal resistance of a single battery is found to change sharply in a short period of time, the single battery is judged to be abnormal, the single battery is marked, an alarm message is generated, and the alarm message is sent to the host computer.

[0100] In this state, ​ After power-on initialization, it directly enters the normal working state, which can also be called the online detection state.

[0101] ​ The initialization content includes I 2 C 、 ​ 、 ​ 、 ​ , initialize the communication module ( ​ 3100) Connect and load ​ Configuration and model saved in, start 18 B 20 Temperature sensor acquisition thread, start ​ 226 Voltage and current data acquisition module, configuration first and second ​ ( ​ 884) Module default state ( ​ and ​ 1 is on, ​ is disconnected), start the timer, periodically collect data and judge the working status.

[0102] At runtime, ​ Keep D 1- S 1 A 、 D 2- S 2 A Normally closed state is turned on to supply power to the power module and external load, and collect 4 single cell voltages (i.e., collect 4 ​ 226 ​ terminal input voltage), collect the first ​ 226 ​ + and ​ -The differential signal at the end, that is ​ Signal-current I .

[0103] Determine whether to enter internal resistance detection based on current fluctuations:

[0104] If the current changes ​ Exceeding the threshold I thresh , and duration> T thresh , then the online internal resistance detection is triggered.

[0105] When the online internal resistance test is triggered, the collected data includes:

[0106] Voltage before fluctuation of each single cell V before,i ; The voltage of each single cell after fluctuation V after,i ; The current value passing through the single cell at the moment of mutation I ;

[0107] Calculate the internal resistance of each single battery online R online-cell,i :

[0108] R online-cell,i =( V before,i - V after,i ) / I ;

[0109] in i Indicates the battery number, indicating ​ 1. ​ 2. ​ 3. ​ 4.

[0110] The internal resistance of the single cell obtained each time R online-cell,i Perform statistics and obtain a historical sequence for each battery. R online-1,i 、 R online-2,i 、 。。。 、 R online-t,i ], t Indicates a moment in time.

[0111] according to ​ The prediction model in the paper predicts the life of a single battery:

[0112] Calculating the rate of change ​ i ( t ):

[0113] ​ i ( t )= R online-t,i − R online-（t−1）,i ;

[0114] Single battery life estimation function ​ online-t，i as follows:

[0115] ​ online-t，i =1-[( R online-t,i - R init,i ) / ( R fail - R init,i )];

[0116] in,R init,i is the initial internal resistance of the battery; R fail is a defined failure threshold (e.g. 150% of initial internal resistance).

[0117] To further improve the model prediction accuracy, this embodiment also introduces a closed-loop calibration mechanism based on manual detection data:

[0118] The host computer receives the ​ After obtaining the manual test data (including online prediction results and manual test internal resistance sequence), perform the following steps:

[0119] Error assessment:

[0120] Compare the most recent manual detection of internal resistance sequence [ R manual-1,i , R manual-2,i ,… R manual-t,i ], estimated single battery life from manual testing ​ manua-t,i ; ​ manual-t,i The calculation principle and ​ online-t，i The same.

[0121] Will ​ manual-t,i and ​ Lifespan estimates for online predictions ​ online-t，i Compare and find the error ​ ​ i ( t manual ):

[0122] ​ ( t manual )= ​ manual-t,i ( t manual )- ​ online-t，i ( t manual );

[0123] in, t manual Indicates the time of the most recent manual detection.

[0124] According to the above error ​ i ( t manual), using a time-decay weighting mechanism to generate a revised estimate ​ correct-t,i :

[0125] ;

[0126] ​ is the time decay factor, which suppresses the influence of outdated data; w is the weight coefficient, which determines the error correction amplitude; t is the current time.

[0127] ​ Applying Estimates ​ correct-t,i Make subsequent predictions.

[0128] Manual detection of internal resistance working state: This working state includes two modes:

[0129] Conventional detection mode:

[0130] The conditions for entering this mode are: only when there is no abnormality in the single battery and the host computer sends a notification instruction for manual detection of internal resistance, will it enter the normal detection mode.

[0131] In this mode, ​ First pass the first ​ The module cuts off the battery pack from the power supply ​ and power supply ​ 1, that is, cut off the power supply D 1- S 1 A 、 D 2- S 2 A ;Will D 3- S 3 B Connect, the positive electrode of the battery will pass D 3Connect to S 3 B On, thus the power ​ The positive pole of the battery pack under test is powered, and the battery pack under test also cuts off all external power supplies; at this time ​ The chips are powered by a backup battery pack.

[0132] This allows for isolated measurements of the battery pack under test.

[0133] At this time the power ​ Connect to the second ​ Module D 1 to D 4 ends; MCU Control Second SPDT Module only connected D1- S 1 B , that is, power supply VTE Through the resistor R 1 connected to the resistor R 5, and through the resistor R 5 is grounded to form a test loop.

[0134] and MCU For the second SPDT The module adopts pulse detection method, that is, MCU pass IO Output PWM Pulse square wave to control the second SPDT Module D 1- S 1 B The battery is turned on and off periodically to detect the voltage of each battery in the battery pack being tested.

[0135] In normal detection mode, MCU The principle of detecting the internal resistance of a single battery is: PWM Control the conduction rhythm, form a periodic small current, detect the instantaneous voltage drop to calculate the internal resistance, calculate and upload the test data. The calculation formula used and the online calculation of the internal resistance of each single battery R manual-cell,i The formula principle is the same.

[0136] Anomaly detection mode:

[0137] The condition for entering this mode is: only when an abnormality occurs in a single battery and the host computer receives an abnormality alarm and sends a notification instruction for manual detection of internal resistance, will it enter the abnormality detection mode.

[0138] MCU The internal resistance of the single cell obtained each time R online-cell,i After statistics, retrieve the historical sequence of each battery [ R 1,i 、 R 2,i 、 。。。 、 R t,i ], calculate the rate of change ΔR i ( t ), according to the preset time t , judge the change trend of the internal resistance of each single battery. If the internal resistance change of a single battery exceeds the preset change threshold, that is, the change is too fast, it is judged that the single battery is abnormal. MCU An abnormal alarm will be generated and sent to the host computer to remind the host computer to perform abnormal detection. After the host computer receives the abnormal alarm, it will generate an alarm prompt to remind the user to manually operate to enter the abnormal detection. After the user manually operates, the host computer generates an abnormal detection notification and sends it to MCU , MCU After receiving anomaly detection notification, enter anomaly detection mode.

[0139] In this mode, MCU First pass the first SPDT The module cuts off the battery pack from the power supply VOUT and power supply VOUT 1, that is, cut off the power supply D 1- S 1 A 、 D 2- S 2 A ;Will D 3- S 3 B Connect, the positive electrode of the battery will pass D 3Connect to S 3 B On, thus the power VTE The positive pole of the battery pack under test is powered, and the battery pack under test also cuts off all external power supplies; at this time MCU The chips are powered by a backup battery pack.

[0140] This allows for isolated measurements of the battery pack under test.

[0141] At this time the power VTE Connect to the second SPDT Module D 1 to D 4 ends; MCU Control Second SPDT The modules should be connected one by one at this time D 1- S 1 B 、 D 2- S 2 B 、 D 3- S 3 B and D 4- S 4 B ,make VTE Through resistors R 1. Resistors R 2. Resistance R 3 and resistors R 4 These four gear resistors and resistors R 5 into the ground, thus achieving step detection.

[0142] In anomaly detection mode, MCU Each file will be recorded ( R 1- R 4 gears), then calculate the internal resistance and upload it to the host computer:

[0143] R manual-cell,i =( V 0- V i ) / I i ;

[0144] I i = VTE / ( R i + R 5);

[0145] in, R manual-cell,i For the i Manually detect the internal resistance of each single battery; R i is the connected step resistor; R 5 is the fixed resistance in the total test circuit; V 0 is the battery voltage when the circuit is open; V i To access the i The voltage measured when a resistor I i To pass the i The current of the test resistor; VTE Input voltage to the test circuit, VTE power supply.

[0146] The battery wireless sensor based on Internet of Things technology described in the present invention solves the technical problems of improving the accuracy of internal resistance detection and life prediction, while also providing edge-end online detection capabilities and isolated detection capabilities. The present invention integrates multiple voltage, current, and temperature acquisition channels to carefully monitor the status of each single battery cell, providing automatically triggered online detection and manually triggered isolated detection, covering more working conditions. MCU The internal resistance sequence and internal resistance change rate are calculated locally to achieve edge prediction. Abnormal batteries are detected based on internal resistance mutations, and automatic tagging and uploading are achieved to trigger isolation detection.

Claims

1. A battery wireless sensor based on Internet of Things technology, characterized by: include MCU 、 EEPROM , communication module, temperature sensor group, power module, backup battery pack, data acquisition unit and detection circuit control unit, EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit are all connected with MCU connect; The data acquisition unit and the detection circuit control unit are both connected to the battery pack under test; MCU Wireless communication with the host computer through the communication module; The backup battery pack is connected to the power module, and the power module is connected to the battery pack under test. MCU 、 EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply; The detection circuit control unit includes a first SPDT Module, second SPDT Module, current collection resistor RS And the gear resistor group; the gear resistor group includes resistors R 1. Resistors R 2. Resistance R 3. Resistors R 4 and resistors R 5; First SPDT Module D 1 end, D 2 ends and D All three terminals pass through current collection resistors RS Connect the positive pole of the battery pack to be tested, and the negative pole of the battery pack to be tested to the ground wire; SPDT Module S 1 A Output VOUT power supply, S 2 A Output VOUT 1. Power supply, S 3 B Output VTE Power supply; first SPDT Module control terminal connection MCU ; second SPDT Module D 1 end, D 2 ends, D 3-terminal and D All 4 terminals are connected VTE Power supply; Second SPDT Module S 1 B end, S 2 B end, S 3 B Duanhe S 4 B Resistors R 1. Resistors R 2. Resistance R 3 and resistors R 4. Connect the resistor R Pin 1 of 5, resistor R Pin 2 of 5 is connected to the ground wire; second SPDT Module control terminal connection MCU ; The data acquisition unit includes a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module. The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively acquire the voltage of the four single cells in the battery pack under test. The first acquisition module also acquires the current acquisition resistance RS Differential signal at both ends; MCU It is used to calculate the internal resistance of each single cell in the tested battery pack and estimate the life and abnormal status of each single cell.

2. The battery wireless sensor based on Internet of Things technology according to claim 1, characterized in that: described MCU The model is STM 32 F 103 C 8 T 6; EEPROM The model is AT twenty four C 32; the model of the communication module is CC 3100; the temperature sensor group includes multiple digital temperature sensors, the model of the digital temperature sensor is DS 18 B 20; The first SPDT module and the second SPDT The module models are ADG 884; the models of the first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module are all INA 226.

3. The battery wireless sensor based on Internet of Things technology according to claim 1, characterized in that: The power module includes a lithium battery management chip and a 3.3 V The power supply of the voltage regulator and lithium battery management chip is VOUT 1 power supply, 3.3 V The voltage regulator and backup battery pack are connected to the lithium battery management chip, 3.3 V The voltage regulator is MCU 、 EEPROM , communication module, temperature sensor group, data acquisition unit and detection circuit control unit power supply; The model of lithium battery management chip is BQ 24075, 3.3 V The model of the voltage regulator is TPS 62842.

4. The battery wireless sensor based on Internet of Things technology according to claim 1, characterized in that: The positive electrode of the battery pack under test is connected to the 2nd foot of the current collection resistor RS. RS Pin 1 provides the voltage output terminal of the battery pack BAT - OUT ; Voltage output terminal BAT - OUT Respectively with the first SPDT Module D 1 end, D 2 ends and D After the 3 ends are connected, the first SPDT The three switches in the module control and output VOUT power supply, VOUT 1 Power supply and VTE power supply, VOUT The power supply is used to provide battery power to external devices. VOUT 1 Power supply is used to provide power to the power module. VTE The power supply is used as the test power supply for internal resistance detection and is input to the second SPDT Module D 1 end, D 2 ends, D 3-terminal and D 4 terminals, through the second SPDT The 4 switches in the module control VTE Power supply access circuit.

5. The battery wireless sensor based on Internet of Things technology according to claim 1, characterized in that: The first acquisition module VBUS Connect the terminal to the first battery cell of the battery pack under test. BAT 1's positive electrode, used to detect single cells BAT The voltage on the positive electrode of 1; The differential input terminal of the first acquisition module, i.e. IN +End and IN -ends are connected to the current collection resistor RS The two ends of the current collection resistor are used to detect RS The differential signal at both ends of the battery pack is used to detect the current signal in the total loop of the battery pack under test; The second acquisition module VBUS Connect the terminal to the second battery cell of the battery pack under test BAT 2's positive pole, used to detect single cells BAT The voltage on the positive electrode of 2; The third acquisition module VBUS Connect the terminal to the third battery cell of the battery pack under test. BAT The positive pole of 3 is used to detect single cells BAT The voltage on the positive electrode of 3; The fourth acquisition module VBUS Connect the terminal to the fourth battery cell of the battery pack under test. BAT The positive pole of 4 is used to detect single cells BAT The voltage on the positive terminal of 4; The first acquisition module, the second acquisition module, the third acquisition module and the fourth acquisition module respectively use different I 2 C Bus and MCU communication.

6. The battery wireless sensor based on Internet of Things technology according to claim 1, characterized in that: described MCU It is used to complete the overall logic control and data processing of the battery wireless sensor, including: During the power-on initialization phase, complete the initialization and read EEPROM The configuration parameters and life prediction model saved in the system are used to start the temperature sensor group and data acquisition unit, and control the first SPDT and second SPDT The initial conduction state of the module; exist MCU There are three working states: State 1 is the normal working state: Under normal working state, MCU Control first SPDT The module maintains the switch on state for supplying power to external devices and the power module; The data acquisition unit collects the voltage values ​​of the four cells in the battery pack under test, and collects the current and resistance values. RS The voltage difference between the two ends is used to obtain the loop current; Determine whether to enter online internal resistance detection based on current fluctuations. After triggering online internal resistance detection, calculate the internal resistance of each single battery and estimate the life of each battery; MCU Periodically record voltage, current, and internal resistance data sequences and upload them to the host computer; At the same time, according to EEPROM The battery life prediction model saved in the system is used to estimate the life and to judge the abnormality of the single battery status; State 2 Conventional manual detection state: When receiving the instruction from the host computer to enter the manual detection state and no abnormality is found in the battery pack under test during the online detection process, MCU Enter the normal manual detection state. At this time, MCU Control first SPDT Module disconnect VOUT Power and VOUT 1. Turn on the power switch of the power supply. VTE The return path of the power supply and controls the second SPDT The module performs fixed-gear conduction control according to the detection mode it is in, and detects the voltage drop after forming a stable load to calculate the internal resistance; MCU Upload the test data and calculation results to the host computer; State 3 Abnormal manual detection state: When receiving the instruction from the host computer to enter the manual detection state and the battery pack under test is found to be abnormal during the online detection process, MCU Enter the abnormal manual detection state. At this time, MCU Control first SPDT Module disconnect VOUT Power and VOUT 1. Turn on the power switch of the power supply. VTE The return path of the power supply and controls the second SPDT The module performs multi-level conduction control according to the detection mode it is in. After forming a stable load, it detects the voltage drop to calculate the internal resistance. MCU Upload the test data and calculation results to the host computer; MCU A closed-loop calibration mechanism based on manual detection data is constructed. Through error evaluation calculation and using a time-decay weighting mechanism, a revised battery life prediction model is generated, and battery life prediction is performed based on the revised battery life prediction model.

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