Detection method of a battery health detection device

Abstract

A battery health status detection device and its detection method. The battery health status detection device includes a slow charging interface connected to a battery or a battery pack, and also includes a power battery detection microcontroller unit, a power battery detection microprocessor, and a power battery detection potentiostat. The power battery detection microcontroller unit is electrically connected to an I / O expansion interface, a data interaction interface, and the power battery detection microprocessor respectively to transmit data to each other. The power battery detection microprocessor, which is used to control the generation of a sine excitation variable DC output, is electrically connected to the power battery detection potentiostat through a micro amplitude sine DDSRAM and a digital-to-analog converter to transmit data. The power battery detection potentiostat electrically connects a voltage input signal and a micro amplitude alternating current input signal to the power battery detection microprocessor through a first analog-to-digital converter and a second analog-to-digital converter respectively to transmit data. The present invention has the characteristics of fast detection speed and accurate detection.

Description

Technical Field

[0001] The present invention relates to a detection method for a battery health status detection device. Background Art

[0002] When detecting the health status of power batteries of existing new energy vehicles in the market, the following processes are generally experienced: start of detection, disassembly of the battery, grouping of the batteries, cleaning of the batteries, connection to the device, battery detection, issuance of a report, recombination of the batteries, installation of the battery, and end of detection; thus, the testing time of the power battery is generally between 5 hours and 8 hours. Since the manufacturers of power batteries are different, the installation methods of power batteries are various, and the types of power batteries are also diverse, there are problems such as complex processes, long time consumption, and large amounts of manpower and material resources during disassembly. Moreover, reinstalling the power battery into the vehicle after detection is also an extremely time-consuming and laborious task. Since a large number of modules are required for battery detection, the detection device is relatively large and is generally fixed in certain places for use. This results in not only time consumption but also high costs for measuring the battery status once.

[0003] In addition, the currently common DC discharge internal resistance measurement method: According to the physical formula R = U / I, the test device forces a large constant DC current, generally 40A - 80A, through the battery in a short time, generally 2 - 3 seconds, measures the voltage across the battery at this time, and calculates the current internal resistance of the battery according to the formula. This DC discharge internal resistance measurement method has obvious deficiencies: 1) It can only measure large-capacity batteries or storage batteries, and small-capacity batteries cannot withstand a large current of 40A - 80A within 2 - 3 seconds; 2) When a large current passes through the battery, the electrodes inside the battery will undergo polarization, resulting in polarization internal resistance. Therefore, the measurement time must be very short, otherwise the measured internal resistance value will have a large error; 3) Passing a large current through the battery will cause certain damage to the electrodes inside the battery. Therefore, in view of these situations, improvement is needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a detection method for a battery health status detection device with fast detection speed and accurate detection, so as to overcome the deficiencies in the prior art.

[0005] A battery health status detection device designed for this purpose includes a slow charging interface connected to a battery or battery pack, and is characterized by further including a power battery detection microcontroller unit, a power battery detection microprocessor, and a power battery detection potentiostat. The power battery detection microcontroller unit is electrically connected to an I / O expansion interface, a data interaction interface, and the power battery detection microprocessor respectively and transmits data to each other. The power battery detection microprocessor for controlling the generation of a sine excitation variable direct current output is electrically connected to the power battery detection potentiostat through a micro amplitude sine DDSRAM and a digital-to-analog converter and transmits data. The power battery detection potentiostat electrically connects the voltage input signal and the micro amplitude alternating current input signal to the power battery detection microprocessor through a first analog-to-digital converter and a second analog-to-digital converter respectively and transmits data; a detection plug electrically connected to the power battery detection potentiostat is electrically connected to the slow charging interface and transmits data; the power battery detection microprocessor includes a main control chip.

[0006] The battery health status detection device described above is characterized in that the power battery detection microcontroller unit is electrically connected to the power battery detection microprocessor through a bus.

[0007] A detection method for a battery health status detection device is characterized by including the following steps:

[0008] Step 1, after electrically connecting the detection plug of the battery health status detection device to the slow charging interface, start the detection; enter Step 2;

[0009] Step 2, manually select the model accuracy through the control panel of the battery health status detection device; enter Step 3; among them, the value range of the model accuracy is 85% - 99.5%;

[0010] Step 3, the main control chip of the battery health status detection device controls the generation of a 0.1μA sine excitation variable direct current and transmits it to the slow charging interface through the detection plug; enter Step 4;

[0011] Step 4, the main control chip of the battery health status detection device detects whether there is a change in the battery test data transmitted back from the battery or battery pack twice before and after. When there is a change, enter Step 5. When there is no change, enter Step 10; the time interval between the two detections before and after is 0.1 - 1 second;

[0012] Step 5, the main control chip receives the changed battery test data and enters Step 6;

[0013] Step 6, the main control chip inputs the received changed battery test data into the force-electrochemical coupling model database for model matching until a suitable force-electrochemical coupling model is obtained, and enter Step 7;

[0014] Step 7: The main control chip calculates whether it is qualified through the closed-loop verification algorithm according to the obtained force-electrochemical coupling model. When it is yes, go to Step 8; when it is no, go to Step 11;

[0015] Step 8: The main control chip generates a power battery report and enters Step 9;

[0016] Step 9: Turn off the detection and end;

[0017] Step 10: The main control chip changes the magnitude of the current of the sine excitation converted to direct current within the range of 0.1 μA to 10 μA and enters Step 3;

[0018] Step 11: The main control chip re-selects another force-electrochemical coupling model in the force-electrochemical coupling model database and enters Step 7.

[0019] After the present invention adopts the above technical solution, the integrated circuit technology is fully utilized to integrate the detection module so as to reduce the volume of the product, making it convenient to carry. And during detection, only by electrically connecting the detection plug of the product to the slow charging interface connected to the battery or battery pack, the health status of the battery or battery pack can be detected. The entire detection process is controlled by the main control chip serving as a microprocessor, and the product can be directly connected to the slow charging port to automatically complete the battery detection, avoiding the difficulties of the traditional detection method that requires disassembling and reinstalling the battery, thereby improving the detection efficiency and saving a large amount of time, manpower and material resources.

[0020] During the detection of the present invention, a quasi-direct current reverse injection technology with a micro-amplitude alternating current input, a fast calculation method based on the force-electrochemical M2EC model, an integrated circuit technology based on multi-source data fusion and high-throughput data screening, and an electrochemical model matching technology based on closed-loop verification calculation are adopted. Among them, the quasi-direct current reverse injection technology with a micro-amplitude alternating current input can directly connect the detection circuit to the battery or battery pack, simplifying the detection process and avoiding the disadvantages such as high detection technology threshold and long learning time of the detection process; the fast calculation method based on the M2EC model improves the matching speed of the electrochemical model of the battery, greatly reducing the test time and avoiding problems such as long detection time; the integrated circuit technology based on multi-source data fusion and high-throughput data screening can integrate the four parts of battery detection, making the product more miniaturized and convenient to move, while reducing the equipment cost and avoiding the disadvantages of the traditional detection equipment such as large volume, high detection cost and unique detection location; the electrochemical model matching technology based on closed-loop verification calculation makes the detection report data more accurate, avoiding the drawbacks of the traditional detection method that only relies on empirical formulas or big data machine learning to match and obtain the detection results; therefore, the present invention has the characteristics of fast detection speed and accurate detection results, which helps to reduce the occurrence of safety accidents of the battery or battery pack.

[0021] The present invention is applicable not only to the batteries or battery packs of new energy vehicles, but also to other transportation means, communication tools or household electrical appliances that use batteries or battery packs as the energy power output. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a front view structural schematic diagram of an embodiment of the present invention.

[0023] Figure 2 It is a front view enlarged structural schematic diagram of the present invention after removing the detection plug and the outer cover.

[0024] Figure 3 It is a schematic diagram of the detection process of the present invention.

[0025] Figure 4 It is a block diagram of the control principle of the present invention.

[0026] Figure 5 It is a schematic diagram of the measurement process of the alternating current impedance spectrum of the present invention.

[0027] Figure 6 It is a waveform diagram of converting sine excitation to direct current in the present invention.

[0028] In the figure: 1 is the detection plug, 2 is the power switch, 3 is the handle, 4 is the NFC identification area, 5 is the LED display screen, 6 is the signal indicator light, 7 is the data port, 8 is the power protection switch, 9 is the power input, 10 is the data interaction interface, 11 is the signal generator, 12 is the potentiostat, 13 is the main control chip, 13 is the frequency response analyzer, DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The present invention will be further described below in conjunction with the drawings and embodiments.

[0030] The following will be described in detail with new energy vehicles as an example.

[0031] See Figures 1 - 6, this battery health detection device includes a slow charging interface connected to a battery or a battery pack, and also includes a power battery detection microcontroller unit, a power battery detection microprocessor, and a power battery detection potentiostat. The power battery detection microcontroller unit is electrically connected to an I / O expansion interface, a data interaction interface, and the power battery detection microprocessor respectively to transmit data to each other. The power battery detection microprocessor, which is used to control the generation of a sine excitation variable DC output, is electrically connected to the power battery detection potentiostat through a micro amplitude sine DDSRAM and a digital-to-analog converter to transmit data. The power battery detection potentiostat electrically connects a voltage input signal and a micro amplitude alternating current input signal to the power battery detection microprocessor through a first analog-to-digital converter and a second analog-to-digital converter respectively to transmit data; a detection plug electrically connected to the power battery detection potentiostat is electrically connected to the slow charging interface to transmit data; the power battery detection microprocessor includes a main control chip 13.

[0032] In this embodiment, the input is alternating current, and the output through the detection plug is a sine excitation variable DC. The waveform of the sine excitation variable DC is shown in Figure 6 as shown.

[0033] The sine excitation variable DC can be approximately considered as the synthesis of many DC currents. By continuously changing the magnitude of the DC current, it can be approximately considered as a sine excitation variable DC.

[0034] Implementation method: Turn off the large-period GTR with variable square wave output, a high-power transistor with a switching frequency of 1 - 5 KHz, to form a large number of rectangular DC currents with equal amplitude and pulse width equal to the sine wave amplitude in a half-wave period. The power battery detection microprocessor generates a PWM control signal to control the GTR to turn off, forming a quasi-direct current input with a sine excitation variable DC.

[0035] A sine excitation variable DC can be generated by the power battery detection microprocessor and an AD9240 standard high-speed analog-to-digital converter.

[0036] The integrated circuit technology of multi-source data fusion and high-throughput data screening integrates the signal generator, potentiostat, and frequency response analyzer required for the power battery detection part into one through the power battery detection microprocessor. Then, the signal generator generates a micro amplitude sine excitation variable DC signal, presets the frequency through a knob, and displays the frequency through a display screen. The amplitude is amplified by an amplifier. The potentiostat applies the reference signal and the control potential to the comparison amplifier after impedance transformation. After comparison and amplification, a signal proportional to the error is output.

[0037] The frequency response analyzer has all the functions of power calculation, and also has functions such as real-time waveform and harmonic analysis. Integrating the three parts of the circuit can realize functions such as directly outputting a micro amplitude sine excitation variable DC from one circuit and collecting and analyzing the response data generated by the power battery.

[0038] In this embodiment, the power battery detection microcontroller unit is electrically connected to the power battery detection microprocessor through a bus.

[0039] The signal generator integrated with integrated circuit technology generates a micro-amplitude sine excitation variable direct current of 0.1 μA to 10 μA under the control of the main control chip 13. The micro-amplitude sine excitation variable direct current can be directly used as a kind of direct current at the slow charging interface of new energy vehicles to perform reverse injection on the power battery of new energy vehicles, that is, the battery or battery pack. At the same time, the micro-amplitude sine excitation variable direct current can be continuously adjusted according to the different types of power batteries used in new energy vehicles.

[0040] Power batteries in different health states correspond to different electrochemical models. During the use of power batteries, as the internal resistance of the power battery changes, a force-electrochemical coupling M2EC model is established, and the force-electrochemical coupling model is matched according to the measured data of the power battery strain.

[0041] The signal generator actively inputs a micro-amplitude sine excitation variable direct current with a frequency range of 10 μHz to 1 MHz to the power battery. According to the set sweep frequency range, sweep frequency points, and methods, as well as the superposition of the amplitude of the excitation sine signal and the DC polarization potential, it is applied to the power battery detection potentiostat to control the electrolytic cell. By continuously changing the frequency of the sine excitation variable direct current, the impedance spectrum test data of the measured object is obtained by continuously testing the complex impedance at different frequencies. Then, the test data is continuously input into the force-electrochemical coupling model in the pre-stored database, and the output value of the force-electrochemical coupling model is compared with the actual measurement of the battery until a force-electrochemical coupling model is found that matches the actual measured value of the battery.

[0042] Based on the obtained force-electrochemical coupling model, relying on the parameter change characteristics of the power battery at different frequencies detected, these parameter characteristics are compared and matched with a large amount of data corresponding to the force-electrochemical coupling model in the existing database, and the test data of this time is added to the database to expand the database data. The health state of the battery is evaluated through a closed-loop verification calculation method, and a battery health status evaluation report is generated.

[0043] The entire detection process is controlled by the power battery detection microprocessor. The detection signal module is responsible for generating a kind of direct current-like electricity that changes from a micro-amplitude sine excitation to direct current. The acquisition module collects the electrochemical parameters of the power battery under different frequencies of direct current-like electricity and returns the parameters to the calculation module. The calculation module matches the force-electrochemical coupling model based on the collected data. Through the matching module, the data in the existing database is fitted with the measured data. After fitting, some points in the existing data model are randomly parsed and transmitted to the detection signal module, which generates direct current-like electricity of the corresponding frequency. The acquisition module returns the electrochemical parameters at this specific frequency to the calculation module to verify the determined electrochemical model. Through such a closed-loop verification calculation matching algorithm, the accuracy of the battery health status can be improved.

[0044] A detection method for a battery health status detection device includes the following steps:

[0045] Step 1: After electrically connecting the detection plug 1 of the battery health status detection device to the slow charging interface, start the detection; enter Step 2;

[0046] Step 2: Manually select the model accuracy through the control panel of the battery health status detection device; enter Step 3; wherein, the value range of the model accuracy is 85% - 99.5%;

[0047] Step 3: The main control chip 13 of the battery health status detection device controls the generation of 0.1 μA sine excitation to direct current and transmits it to the slow charging interface through the detection plug 1; enter Step 4;

[0048] Step 4: The main control chip 13 of the battery health status detection device detects whether there is a change in the battery test data transmitted back by the battery or battery pack for the first and second times. When there is a change, enter Step 5; when there is no change, enter Step 10; the time interval between the two detections is 0.1 - 1 second;

[0049] Step 5: The main control chip 13 receives the changed battery test data and enters Step 6;

[0050] Step 6: The main control chip 13 inputs the received changed battery test data into the force-electrochemical coupling model database for model matching until a suitable force-electrochemical coupling model is obtained, and then enters Step 7;

[0051] Step 7: The main control chip 13 calculates whether it is qualified through the closed-loop verification algorithm according to the obtained force-electrochemical coupling model. When it is yes, enter Step 8; when it is no, enter Step 11;

[0052] Step 8: The main control chip 13 generates a power battery report and enters Step 9;

[0053] Step 9: Close the detection and end;

[0054] Step ten, the main control chip 13 changes the magnitude of the current for converting the sinusoidal excitation to direct current within the range of 0.1 μA to 10 μA, and enters step three;

[0055] Step eleven, the main control chip 13 selects another force-electrochemical coupling model from the force-electrochemical coupling model database again, and enters step seven.

[0056] During specific use, the charging plug of the new energy vehicle is used as the data access port and is directly connected to the slow charging port of the new energy vehicle. Through the coordination of the microprocessor, a slightly amplitude sinusoidal excitation to direct current input and data acquisition are simultaneously performed on the power battery.

[0057] The following takes the ternary cylindrical battery as the detection object.

[0058] Select the model accuracy α to be 90%, and the model accuracy will affect the calculation and solution time.

[0059] The integrated circuit for multi-source data fusion and high-throughput data screening generates a 0.1 μA sinusoidal excitation to direct current externally: I m is the maximum value of the current, that is, the amplitude, ωt is the angular frequency of the alternating current, is called the phase of I at time t, is the phase of I at time t0, called the initial phase or initial phase angle. i(t) is the sinusoidal excitation expression.

[0060] Since the current is a 0.1 μA sinusoidal excitation to direct current and the current amplitude is very small, it can be regarded as a quasi-direct current with a slightly amplitude alternating current input for reverse injection.

[0061] After the battery receives the 0.1 μA sinusoidal excitation to direct current, strong chemical-mechanical and chemical-electrical couplings will be generated inside the battery, and the internal resistance of the battery will cause obvious axial displacement and electric potential. By continuously changing the current frequency, it will be found that its impedance will have an obvious inflection point at a certain point, which occurs at about 300 seconds. At this inflection point, the internal resistance of the battery approaches 57 mΩ. Taking 70 Hz as an example, the fluctuation range of the battery temperature T is: 24°C - 28.6°C.

[0062] When the main control chip 13 receives the changed battery test data, it is input into the pre-stored force-electrochemical coupling model database for model matching until a suitable force-electrochemical coupling model is obtained, and then the closed-loop verification algorithm is entered for calculation according to the obtained force-electrochemical coupling model.

[0063] When performing calculations, the momentum balance equation of the mechanical field, the Maxwell equation of the magnetic and electric field, the entropy expression of the thermal field, and the mass conservation equation in the chemical field are combined. The following gives the calculation formula based on the mechanical-electrochemical (M2EC) model:

[0064]

[0065] where; Ie is the current density per unit volume; T is the absolute temperature, is the Hamiltonian operator; α is the chemical potential; φ is the electromagnetic potential; Leq and Leα are coupling coefficients, which are set to zero in the simplest case. It can be obtained from Fourier's law of heat transfer and Fick's law of diffusion. Lee is the reciprocal of the standard resistivity.

[0066] The resistance calculation formula is R = τ*(E / Ie), where τ is the resistance correction coefficient, which is selected according to the type of battery and the battery discharge condition during detection, and the value range is 1 to 1.5. Among them, E is the detected voltage.

[0067] From the above formula, the force-electrochemical model formula of the battery at different frequency bands can be measured. By matching this formula with the existing database model, the same curve segment can be quickly matched, and this model can be selected for closed-loop verification.

[0068] From the matched complete model, a sinusoidal alternating current at a random frequency is selected. Here, taking 200Hz as an example, the model shows that the battery impedance changes from 62mΩ to 57mΩ at 200Hz. The control module enables the quasi-DC signal generation module with a small AC input to generate a 200Hz sinusoidal alternating current and inject it into the battery, and collects the change of the battery impedance. Check whether it is consistent with the graph in the model library. When the change error is not greater than 90%, it is considered to meet the requirements and enter the next step.

[0069] That is, ΔR = R d ±(1 - α). Here, taking 200Hz as an example, if the collected resistance change range changes from 62 ± 0.62mΩ to 57 ± 5.7mΩ, it is considered qualified. At this time, the data obtained from the second detection: the internal resistance of 57mΩ at the resistance convergence point is used as the currently detected internal resistance and substituted into the next step to calculate the battery health state of health (SOH).

[0070] If it is not within this range, that is, enter step ten, change the magnitude of the excitation current, that is, re-modify the current frequency to generate a new sinusoidal excitation current, and then compare the detection data on the two closest sides.

[0071] Battery health state of health calculation: R E represents the internal resistance of the battery at the end of battery life, R is the internal resistance of the battery in the current state, R new is the internal resistance of the battery when it leaves the factory. Here R E = 62mΩ, R = 57mΩ, R new = 56mΩ, and the calculated battery health state of health is 83.33%.

[0072] Since direct current is used for detection, a relatively large direct current needs to be input, generally 40A to 80A. However, large current can damage the battery. In contrast, the present invention uses alternating current and a small current, in the μA level, which will not damage the battery.

[0073] Moreover, when detecting by inputting direct current, a very large constant direct current needs to be passed through in advance, which requires the battery to be a large-capacity battery. Small-capacity batteries will be broken down by large current. However, the sine excitation to direct current input in the present invention is a micro current and has no requirement for battery capacity. Therefore, the measurement range is wider and it is also safer and more reliable.

[0074] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation to the present invention. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features.

[0075] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A detection method for a battery health detection device, including a slow charging interface connected to a battery or a battery pack, characterized in that It also includes a power battery detection microcontroller unit, a power battery detection microprocessor, and a power battery detection potentiostat. The power battery detection microcontroller unit is electrically connected to the I / O expansion interface, the data interaction interface, and the power battery detection microprocessor respectively to transmit data to each other. It is used to control the power battery detection microprocessor that generates a sine excitation variable DC output. The power battery detection microprocessor is electrically connected to the power battery detection potentiostat through a micro amplitude sine DDSRAM and a digital-to-analog converter to transmit data. The power battery detection potentiostat electrically connects the voltage input signal and the micro amplitude alternating current input signal to the power battery detection microprocessor through the first analog-to-digital converter and the second analog-to-digital converter respectively to transmit data. The detection plug electrically connected to the power battery detection potentiostat is electrically connected to the slow charging interface to transmit data. The power battery detection microprocessor includes a main control chip (13). The operation includes the following steps: Step 1: After electrically connecting the detection plug (1) of the battery health status detection device to the slow charging interface, start the detection; proceed to Step 2. Step 2: Manually select the model accuracy through the control panel of the battery health status detection device; proceed to Step 3. Among them, the value range of the model accuracy is 85% - 99.5%. Step 3: The main control chip (13) of the battery health status detection device controls the generation of a 0.1 μA sine excitation variable DC and transmits it to the slow charging interface through the detection plug (1); proceed to Step 4. Step 4: The main control chip (13) of the battery health status detection device detects whether there is a change in the battery test data transmitted back by the battery or battery pack for the previous and next times. When there is a change, proceed to Step 5. When there is no change, proceed to Step 10. The time interval between the previous and next detections is 0.1 - 1 second. Step 5: The main control chip (13) receives the changed battery test data and proceeds to Step 6. Step 6: The main control chip (13) inputs the received changed battery test data into the force-electrochemical coupling model database for model matching until a suitable force-electrochemical coupling model is obtained, and proceeds to Step 7. Step 7: The main control chip (13) calculates whether it is qualified through a closed-loop verification algorithm according to the obtained force-electrochemical coupling model. When it is yes, proceed to Step 8. When it is no, proceed to Step 11. Step 8: The main control chip (13) generates a power battery report and proceeds to Step 9. Step 9: Close the detection and end. Step 10: The main control chip (13) changes the current magnitude of the sine excitation variable DC within the range of 0.1 μA - 10 μA and proceeds to Step 3. Step 11: The main control chip (13) re-selects another force-electrochemical coupling model in the force-electrochemical coupling model database and proceeds to Step 7.

2. The detection method of the battery health detection device according to claim 1, characterized in that The power battery detection microcontroller unit is electrically connected to the power battery detection microprocessor through a bus.

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