Method and device for estimating health degree of battery internal resistance of vehicle, controller and vehicle

Abstract

The invention provides a vehicle battery internal resistance health degree estimation method and device, a controller and a vehicle. The method comprises the following steps: acquiring a plurality of impedance spectrums and a plurality of pulse test data of a battery of a vehicle; acquiring a first internal resistance of the battery at a preset reference temperature and a reference SOC according to the plurality of impedance spectrums; acquiring a second internal resistance of the battery under the reference temperature and the reference SOC according to the multiple pulse test data; and calculating the internal resistance health degree of the battery according to the first internal resistance and the second internal resistance. The method is used for achieving the effects of accurately estimating the internal resistance of the battery, improving the health degree estimation accuracy of the internal resistance of the battery and further optimizing the multi-aspect performance of the vehicle.

Description

Technical Field

[0001] This application relates to the field of batteries and the field of battery internal resistance estimation, and in particular to a method, apparatus, controller and vehicle for estimating the battery internal resistance health of a vehicle. Background Technology

[0002] With the widespread application of lithium-ion batteries in electric vehicles, accurate online estimation of battery internal resistance has become crucial for ensuring vehicle power performance, charging efficiency, and range. The Battery Management System (BMS) needs to dynamically monitor changes in battery internal resistance, assess battery health based on these changes, and then optimize battery energy management strategies based on the assessment results. This extends battery life and improves overall vehicle operating efficiency.

[0003] Currently, the commonly used method for estimating battery internal resistance is to obtain cell aging data through accelerated aging experiments in a laboratory environment during the battery R&D phase, in order to establish an offline data table. After the vehicle is put into use, the internal resistance value of the current battery pack is estimated by looking up the table based on the collected battery operating condition information.

[0004] However, different ambient temperatures, vehicle mileage, and driving habits affect the aging process of the battery pack. Therefore, the cell aging data tested during the R&D phase cannot accurately reflect the aging state of each battery in actual use. In view of this, how to accurately estimate the changes in the internal resistance of a vehicle's battery during use, and thus accurately estimate the battery's internal resistance health, is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, controller, and vehicle for estimating battery internal resistance health, in order to accurately estimate battery internal resistance, improve the accuracy of battery internal resistance health estimation, and thereby optimize various aspects of vehicle performance such as power performance, charging efficiency, and range.

[0006] In a first aspect, embodiments of this application provide a method for estimating the battery internal resistance health of a vehicle, comprising:

[0007] Multiple impedance spectra and multiple pulse test data of the vehicle's battery are acquired; the multiple impedance spectra are obtained by performing multiple EIS tests on the battery under different test conditions and in a static state; the multiple pulse test data are obtained by performing multiple discharge pulse tests on the battery under different test conditions and in a static state; wherein, the test conditions include battery temperature and SOC.

[0008] Based on the multiple impedance spectra, the first internal resistance of the battery at a preset reference temperature and reference SOC is obtained;

[0009] Based on the multiple pulse test data, the second internal resistance of the battery at the reference temperature and the reference SOC is obtained;

[0010] The internal resistance health of the battery is calculated based on the first internal resistance and the second internal resistance.

[0011] In one possible implementation, calculating the internal resistance health of the battery based on the first internal resistance and the second internal resistance includes:

[0012] Based on the first internal resistance and the initial first internal resistance, the first internal resistance health of the battery is obtained, wherein the initial first internal resistance is obtained based on the first internal resistance of the battery at the reference temperature and the reference SOC obtained in the initial stage after the vehicle is put into use.

[0013] The second internal resistance health of the battery is obtained based on the second internal resistance and the initial second internal resistance. The initial second internal resistance is obtained based on the second internal resistance of the battery at the reference temperature and the reference SOC obtained in the initial stage after the vehicle is put into use.

[0014] The first internal resistance health value and the second internal resistance health value are fused together to obtain the internal resistance health value of the battery.

[0015] In one possible implementation, obtaining the first internal resistance health of the battery based on the first internal resistance and the initial first internal resistance includes:

[0016] Based on the first internal resistance and the average first internal resistance of the battery obtained from the previous test, the average first internal resistance obtained in this test is obtained. The average first internal resistance obtained in each test is obtained based on the first internal resistance obtained in this test, the average first internal resistance obtained from the previous test, and the number of health tests.

[0017] The first internal resistance average value is compared with the initial first internal resistance to obtain the first internal resistance health of the battery;

[0018] Accordingly, obtaining the second internal resistance health of the battery based on the second internal resistance and the initial second internal resistance includes:

[0019] Based on the second internal resistance and the average second internal resistance of the battery obtained from the previous test, a new average second internal resistance is obtained. The average second internal resistance obtained in each test is based on the second internal resistance obtained in this test, the average second internal resistance obtained in the previous test, and the number of health tests.

[0020] The second internal resistance health of the battery is obtained by comparing the new average second internal resistance with the initial second internal resistance.

[0021] In one possible implementation, the first internal resistance health value and the second internal resistance health value are fused to obtain the internal resistance health value of the battery, including:

[0022] The battery's internal resistance health is obtained by fusing the first internal resistance health score and the second internal resistance health score using a Kalman filter.

[0023] In one possible implementation, acquiring multiple impedance spectra of the battery includes:

[0024] Under each test condition, based on multiple pre-selected excitation conditions, the controller sends each excitation condition to the DC-DC controller to cause the DC-DC controller to apply AC excitation to the battery. The energy required for the DC-DC controller to apply AC excitation is alternately provided by the battery and a 12V power supply. Each excitation condition includes an excitation frequency and the duration of the excitation frequency.

[0025] During the process of applying AC excitation to the battery, the voltage timing data and current timing data of the battery are collected;

[0026] The voltage timing data and the current timing data are frequency domain converted to obtain the impedance spectrum corresponding to the test conditions.

[0027] In one possible implementation, obtaining the first internal resistance of the battery at a preset reference temperature and reference SOC based on the plurality of impedance spectra includes:

[0028] For each test condition, the first internal resistance of the battery under the test condition is obtained based on the impedance spectrum corresponding to the test condition.

[0029] For each SOC in the test conditions, the first internal resistance obtained at different battery temperatures is normalized to obtain the first internal resistance of the battery at the SOC and reference temperature.

[0030] Based on the battery's first internal resistance at different SOC and reference temperatures, the SOC is normalized to obtain the battery's first internal resistance at the reference SOC and reference temperature.

[0031] In one possible implementation, each pulse test data includes current changes and voltage changes;

[0032] The step of obtaining the second internal resistance of the battery at the reference temperature and the reference SOC based on the multiple pulse test data includes:

[0033] For each test condition, the second internal resistance of the battery under the test condition is obtained based on the current and voltage changes corresponding to the test condition.

[0034] For each SOC in the test conditions, the second internal resistance obtained at different battery temperatures is normalized to obtain the second internal resistance of the battery at the SOC and reference temperature.

[0035] Based on the second internal resistance of the battery at different SOC and reference temperatures, the SOC is normalized to obtain the second internal resistance of the battery at the reference SOC and reference temperature.

[0036] In one possible implementation, the method further includes:

[0037] Calculate the ratio of the difference between the first internal resistance obtained in this test and the average first internal resistance obtained in the previous test to the first number of health tests.

[0038] The sum of the first ratio and the average first internal resistance obtained from the previous test is taken as the average first internal resistance obtained in this test.

[0039] In one possible implementation, the method further includes:

[0040] Calculate the difference between the second internal resistance obtained in this test and the average second internal resistance obtained in the previous test, and the second ratio of the number of health tests.

[0041] The sum of the second ratio and the average second internal resistance obtained from the previous test is taken as the average second internal resistance obtained in this test.

[0042] In one possible implementation, the method further includes:

[0043] If the battery current is less than a preset current value for a duration greater than a preset time, the vehicle battery is determined to be in a static state.

[0044] Secondly, embodiments of this application provide a device for estimating the battery internal resistance health of a vehicle, comprising:

[0045] The first acquisition module is used to acquire multiple impedance spectra and multiple pulse test data of the vehicle's battery; the multiple impedance spectra are obtained by performing multiple EIS tests on the battery under different test conditions while the battery is in a static state; the multiple pulse test data are obtained by performing multiple discharge pulse tests on the battery under different test conditions while the battery is in a static state; wherein, the test conditions include battery temperature and SOC.

[0046] The second acquisition module is used to acquire the first internal resistance of the battery at a preset reference temperature and reference SOC based on the multiple impedance spectra.

[0047] The third acquisition module is used to acquire the second internal resistance of the battery at the reference temperature and the reference SOC based on the multiple pulse test data.

[0048] The calculation module is used to calculate the internal resistance health of the battery based on the first internal resistance and the second internal resistance.

[0049] In one possible implementation, the computing module includes:

[0050] The first acquisition unit is configured to acquire the first internal resistance health of the battery based on the first internal resistance and the initial first internal resistance, wherein the initial first internal resistance is obtained based on the first internal resistance of the battery at the reference temperature and the reference SOC acquired in the initial stage after the vehicle is put into use.

[0051] The second acquisition unit is used to acquire the second internal resistance health of the battery based on the second internal resistance and the initial second internal resistance, wherein the initial second internal resistance is obtained based on the second internal resistance of the battery at the reference temperature and the reference SOC acquired in the initial stage after the vehicle is put into use.

[0052] The fusion unit is used to fuse the first internal resistance health value and the second internal resistance health value to obtain the internal resistance health value of the battery.

[0053] In one possible implementation, the first acquiring unit has the function of:

[0054] Based on the first internal resistance and the average first internal resistance of the battery obtained from the previous test, the average first internal resistance obtained in this test is obtained. The average first internal resistance obtained in each test is obtained based on the first internal resistance obtained in this test, the average first internal resistance obtained from the previous test, and the number of health tests.

[0055] The first internal resistance average value is compared with the initial first internal resistance to obtain the first internal resistance health of the battery.

[0056] Accordingly, the second acquisition unit is specifically used for:

[0057] Based on the second internal resistance and the average second internal resistance of the battery obtained from the previous test, a new average second internal resistance is obtained. The average second internal resistance obtained in each test is based on the second internal resistance obtained in this test, the average second internal resistance obtained in the previous test, and the number of health tests.

[0058] The second internal resistance health of the battery is obtained by comparing the new average second internal resistance with the initial second internal resistance.

[0059] In one possible implementation, the fusion unit is specifically used for:

[0060] The battery's internal resistance health is obtained by fusing the first internal resistance health score and the second internal resistance health score using a Kalman filter.

[0061] In one possible implementation, the first acquisition module is specifically used for:

[0062] Under each test condition, based on multiple pre-selected excitation conditions, the controller sends each excitation condition to the DC-DC controller to cause the DC-DC controller to apply AC excitation to the battery. The energy required for the DC-DC controller to apply AC excitation is alternately provided by the battery and a 12V power supply. Each excitation condition includes an excitation frequency and the duration of the excitation frequency.

[0063] During the process of applying AC excitation to the battery, the voltage timing data and current timing data of the battery are collected;

[0064] The voltage timing data and the current timing data are frequency domain converted to obtain the impedance spectrum corresponding to the test conditions.

[0065] In one possible implementation, the second acquisition module is specifically used for:

[0066] For each test condition, the first internal resistance of the battery under the test condition is obtained based on the impedance spectrum corresponding to the test condition.

[0067] For each SOC in the test conditions, the first internal resistance obtained at different battery temperatures is normalized to obtain the first internal resistance of the battery at the SOC and reference temperature.

[0068] Based on the battery's first internal resistance at different SOC and reference temperatures, the SOC is normalized to obtain the battery's first internal resistance at the reference SOC and reference temperature.

[0069] In one possible implementation, the third acquisition module is specifically used for:

[0070] For each test condition, the second internal resistance of the battery under the test condition is obtained based on the current and voltage changes corresponding to the test condition.

[0071] For each SOC in the test conditions, the second internal resistance obtained at different battery temperatures is normalized to obtain the second internal resistance of the battery at the SOC and reference temperature.

[0072] Based on the second internal resistance of the battery at different SOC and reference temperatures, the SOC is normalized to obtain the second internal resistance of the battery at the reference SOC and reference temperature.

[0073] In one possible implementation, the device further includes:

[0074] The first processing module is used to calculate the difference between the first internal resistance obtained in this test and the average first internal resistance obtained in the previous test, and the first ratio of the number of health tests.

[0075] The sum of the first ratio and the average first internal resistance obtained from the previous test is taken as the average first internal resistance obtained in this test.

[0076] In one possible implementation, the device further includes:

[0077] The second processing module is used to calculate the difference between the second internal resistance obtained in this test and the average value of the second internal resistance obtained in the previous test, and the second ratio of the number of health tests.

[0078] The sum of the second ratio and the average second internal resistance obtained from the previous test is taken as the average second internal resistance obtained in this test.

[0079] In one possible implementation, the device further includes:

[0080] The determination module is used to determine that the vehicle's battery is in a static state when the duration during which the battery current is less than a preset current value is greater than a preset time.

[0081] Thirdly, embodiments of this application provide a controller, including: a memory and a processor;

[0082] The memory stores computer-executed instructions;

[0083] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0084] Fourthly, embodiments of this application provide a vehicle, including a vehicle body and the controller described in the third aspect;

[0085] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0086] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0087] The method, apparatus, controller, and vehicle for estimating the internal resistance health of a vehicle's battery provided in this application acquire multiple impedance spectra and multiple pulse test data of the vehicle's battery. These multiple impedance spectra and pulse test data are obtained by performing multiple electrochemical impedance spectroscopy (EIS) tests and discharge pulse tests under different battery resting conditions and at different temperatures and states of charge (SOC). Based on the multiple impedance spectra, a first internal resistance at a reference temperature and SOC is obtained; based on the multiple pulse test data, a second internal resistance at the same reference temperature and SOC is obtained. Combining these two methods to calculate the internal resistance health directly tests the battery's internal resistance at the vehicle end, improving the estimation accuracy of the vehicle's battery internal resistance. Furthermore, combining the internal resistance from both test sources improves the reliability and accuracy of the battery internal resistance health estimation, providing a reliable basis for battery lifecycle management and ultimately optimizing various aspects of the vehicle's performance, such as power performance, charging efficiency, and range. Attached Figure Description

[0088] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0089] Figure 1 A flowchart illustrating a method for estimating the internal resistance health of a vehicle battery, provided in Embodiment 1 of this application;

[0090] Figure 2 This is a flowchart illustrating a method for estimating the internal resistance health of a vehicle's battery, as provided in Embodiment 2 of this application.

[0091] Figure 3 The fusion effect diagram based on the Kalman filter provided in the embodiments of this application is shown.

[0092] Figure 4 This is a schematic diagram of a structure for estimating the internal resistance health of a vehicle battery, as provided in Embodiment 4 of this application.

[0093] Figure 5 This is a schematic diagram of a structure for estimating the battery internal resistance health of a vehicle, as provided in Embodiment 5 of this application.

[0094] Figure 6 A schematic diagram of the controller provided in this application.

[0095] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0096] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0097] To facilitate understanding of the technical content of this application, the background technology is described in detail below:

[0098] With the rapid development of new energy technologies, lithium-ion batteries have become the main energy source for electric vehicles. As a core component of electric vehicles, the rate performance and lifespan of the battery directly determine the vehicle's power, charging time, and service life. Generally speaking, the battery's internal resistance directly affects its charging and discharging power, discharge energy, and lifespan. As the vehicle's service life increases, the battery's internal resistance increases with the aging of the battery pack, resulting in decreased acceleration performance, longer charging time, and shorter driving range.

[0099] Therefore, for a Battery Management System (BMS), accurately estimating the internal resistance change of the battery pack within its design lifecycle online, obtaining an accurate battery health estimate based on the internal resistance change, and then improving the accuracy of the BMS's estimation or calculation of the battery's State of Power (SOP), State of Energy (SOE), State of Time (SOT), State of Certified Energy (SOCE), and driving range based on the accurate health assessment results, can optimize the battery energy management strategy and thereby improve the vehicle's power and the electric vehicle's lifespan.

[0100] Currently, the commonly used method for estimating battery internal resistance is to conduct accelerated life tests on the cells under specific operating conditions during the R&D phase to obtain offline cell aging data, and then construct an offline data table based on the cell aging data. After the vehicle is put into use, the battery's internal resistance is estimated by combining some battery operating information, such as time, cell temperature, and capacity throughput, with the offline table.

[0101] However, considering that the increase in battery internal resistance is related to operating conditions, different ambient temperatures, vehicle mileage, and driving habits will all affect the aging of the battery pack, thereby affecting the trajectory of the increase in battery pack internal resistance and the aging process of the battery. Furthermore, the driving conditions of each vehicle are different, and cell aging data preset under specific operating conditions during the R&D phase cannot truly reflect the internal resistance aging state of each battery pack.

[0102] Therefore, how to estimate the change in internal resistance in real time and accurately, and thus accurately estimate the health of the battery's internal resistance, remains a problem worth solving.

[0103] In light of the above background technology, the inventors discovered during their research that the internal resistance of a vehicle's battery can be tested directly on-vehicle using various methods. The calculated internal resistance directly reflects the true internal resistance state of the vehicle's battery, enabling accurate estimation of the battery's internal resistance. Furthermore, by integrating highly accurate battery internal resistance data from different sources, the battery's internal resistance health can be calculated, achieving a reliable and accurate calculation of the battery's internal resistance health.

[0104] In view of the inventors’ technical conception described above, the present invention provides a method, apparatus, controller and vehicle for estimating the battery internal resistance health of a vehicle, in order to address the technical problems in the background art described above.

[0105] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0106] Figure 1 This is a flowchart illustrating a method for estimating the internal resistance health of a vehicle battery, as provided in Embodiment 1 of this application. Figure 1 As shown, the method provided in this embodiment includes:

[0107] S101. Obtain multiple impedance spectra and multiple pulse test data of the vehicle's battery.

[0108] Among them, multiple impedance spectra were obtained by performing multiple EIS tests on the battery under different test conditions while the battery was in a static state; multiple pulse test data were obtained by performing multiple discharge pulse tests on the battery under different test conditions while the battery was in a static state; the test conditions included battery temperature and SOC.

[0109] It should be noted that this solution is applicable to controllers in vehicles, especially to battery management systems (BMS). When applied to the actual use of vehicles, it enables accurate online estimation of battery internal resistance health.

[0110] In this step, it is necessary to obtain multiple impedance spectra and multiple pulse test data of the battery by performing EIS tests and discharge pulse tests on the battery respectively.

[0111] The battery can be a single cell in a vehicle or a battery pack composed of multiple cells. This application does not impose specific limitations on this.

[0112] It should be understood that a static state refers to a battery in a stable state, such as when the vehicle has been parked for more than an hour. It should also be understood that in order to obtain stable test data and ensure that the test results truly reflect the inherent electrochemical characteristics of the battery, EIS testing and discharge pulse testing must be performed on the battery while it is in a static state.

[0113] In one possible implementation, the determination of whether a vehicle is stationary can be made in the following way:

[0114] The vehicle battery is determined to be in a static state when the battery current is less than a preset current value for a period of time exceeding a preset time. The preset current value and preset time can be determined based on the design requirements of the specific application of this solution, and this solution does not impose specific restrictions on them. For example, the preset current value can be 0.03C, 0.04C, 0.05C, 0.06C, etc.; the preset time can be, for example, 20 minutes, 25 minutes, 30 minutes, 35 minutes, etc.

[0115] Furthermore, it should be understood that testing under a single test condition may lead to biased experimental results. Therefore, this scheme conducts multiple tests at different SOC and battery temperatures to ensure the comprehensiveness of the test results. At the same time, this scheme also comprehensively captures the variation of internal resistance with these two key parameters through multiple tests at different SOC and battery temperatures. This provides data support for normalizing the internal resistance values ​​under different test conditions to the preset reference temperature and reference SOC, ensuring that the final internal resistance is comparable to the initial internal resistance of the battery.

[0116] Here, SOC refers to the percentage of current remaining power relative to its rated capacity.

[0117] It is worth noting that the test conditions selected for multiple EIS tests and multiple discharge pulse tests in this application can be set independently (i.e., the two can be performed at different battery temperatures and SOCs), and the test conditions for the two tests do not need to be strictly consistent.

[0118] EIS testing is a test method that applies a small-amplitude, wide-frequency-range (e.g., 0.1Hz~3kHz) sinusoidal AC excitation signal to a battery and simultaneously measures the battery’s voltage and current responses at different frequencies to obtain EIS test data.

[0119] In practical applications, the real and imaginary parts of the battery impedance at different frequencies can be calculated based on the voltage and current responses, and then the impedance spectrum can be plotted based on the change of battery impedance with the frequency of the excitation signal.

[0120] Among them, the discharge pulse test is a test method that generates a short-duration, constant DC discharge current pulse (such as lasting 1-10 seconds, with a current ratio of 0.5C-2C) that meets the test requirements by observing the discharge behavior of the battery, and records the changes in battery terminal voltage and current during the pulse application process.

[0121] In practical applications, discharge pulses can be applied to the battery by triggering typical pulse discharge conditions such as high voltage on the vehicle, reverse pre-charging, or engine start.

[0122] Optionally, the battery temperature can be detected and obtained using a temperature sensor installed at the battery. Alternatively, a model of the relationship between impedance and temperature can be established in advance using the significant change of battery impedance parameters with temperature. Then, during the actual testing phase, the current battery temperature can be obtained based on the impedance spectrum obtained from the test and the model of the relationship.

[0123] S102. Based on multiple impedance spectra, obtain the first internal resistance of the battery at a preset reference temperature and reference SOC.

[0124] In this step, in order to ensure that the first internal resistance is comparable to the initial internal resistance obtained during the initial stage of vehicle use in the subsequent health calculation process, it is necessary to obtain the first internal resistance of at least one battery at a preset reference temperature and reference SOC based on multiple impedance spectra obtained under different test conditions.

[0125] The reference temperature and reference SOC need to be consistent with the test conditions corresponding to the initial battery obtained by EIS testing, and the specific values ​​are not specifically limited in this application. For example, the reference temperature and reference SOC are 25°C and 50%, respectively.

[0126] S103. Based on multiple pulse test data, obtain the second internal resistance of the battery at the reference temperature and reference SOC.

[0127] In this step, based on multiple pulse test data obtained under different test conditions, it is necessary to obtain the second internal resistance of at least one battery at a preset reference temperature and reference SOC, so that the data is comparable to the initial internal resistance of the battery.

[0128] The reference temperature and reference SOC need to be consistent with the test conditions corresponding to the initial battery obtained by the discharge pulse test, and the specific values ​​are not specifically limited in this application. For example, the reference temperature and reference SOC are 25°C and 50%, respectively.

[0129] It should be noted that the reference temperature and reference SOC corresponding to the first internal resistance obtained by EIS testing may be the same as or different from the reference temperature and reference SOC corresponding to the second internal resistance obtained by discharge pulse testing. This application does not restrict their consistency.

[0130] S104. Calculate the battery's internal resistance health based on the first internal resistance and the second internal resistance.

[0131] In this step, to avoid large deviations in internal resistance estimation due to interference from environmental factors caused by a single data source, the first and second internal resistances obtained by different testing methods are combined to jointly determine the battery's internal resistance health, ensuring the reliability and accuracy of the estimation results.

[0132] Among them, the State of Health based on Resistance (SOHR) of a battery refers to an indicator that quantifies the health status of a battery by comparing its current internal resistance with that of the initial stage.

[0133] The battery internal resistance health estimation method provided in this application obtains multiple impedance spectra and multiple pulse test data of the vehicle's battery. These impedance spectra and pulse test data are obtained by performing multiple EIS tests and discharge pulse tests under different temperature and SOC test conditions, respectively, while the battery is in a static state. Based on the multiple impedance spectra, a first internal resistance at a reference temperature and SOC is obtained; based on the multiple pulse test data, a second internal resistance at the same reference temperature and SOC is obtained. Combining these two methods to calculate the internal resistance health directly tests the battery's internal resistance at the vehicle end, improving the estimation accuracy of the battery's internal resistance. Furthermore, combining the internal resistance from two test sources improves the reliability and accuracy of the battery internal resistance health estimation, providing a reliable basis for battery lifecycle management. Ultimately, this achieves the effect of optimizing various aspects of vehicle performance, such as power performance, charging efficiency, and range.

[0134] Furthermore, in one possible implementation, the multiple impedance spectra of the battery can be obtained using the methods described in steps 1.1 to 1.3:

[0135] Step 1.1: Under each test condition, based on multiple pre-selected excitation conditions, the controller sends each excitation condition to the DC-DC (DCDC) controller so that the DCDC controller applies AC excitation to the battery.

[0136] Each excitation condition includes the excitation frequency and the duration of the excitation frequency; the DC-DC controller refers to the control unit in the vehicle used to convert DC input voltage (boost, buck, buck-boost).

[0137] In practical applications, it is necessary to select multiple frequency points in advance within a pre-selected frequency variation range (such as 0.1Hz~3kHz) as excitation frequencies, and determine the duration of each excitation frequency to obtain multiple excitation conditions.

[0138] In this implementation, an EIS test needs to be performed under each test condition to obtain the impedance spectrum corresponding to that test condition. Specifically, when an EIS test is required on the battery, the BMS will sequentially send frequency-changing excitation conditions to the DCDC controller, so that the DCDC controller outputs the corresponding AC excitation signal according to the excitation frequency and duration of the corresponding excitation condition, thereby applying the frequency-changing AC excitation signal to the battery.

[0139] The DC-DC controller can adjust the frequency of its output excitation by adjusting the duty cycle of the digital pulse width modulation (PWM) wave or the duration of the current under different amplitudes, based on the acquired excitation frequency.

[0140] In addition, the energy required for the DC-DC controller to apply AC excitation is alternately provided by a battery and a 12V power supply.

[0141] Specifically, the DC-DC controller operates in two modes when applying AC excitation to the battery, and these two modes alternate to apply AC excitation. In one mode, powered by the high-voltage battery, a sinusoidal current in the discharge direction is pumped, stepped down by the DC-DC controller, and then used to charge the 12V power supply, forming the discharge direction excitation. In this mode, the DC-DC controller outputs the "positive half-axis" of the AC excitation. In the other mode, powered by the 12V power supply, a time-varying discharge current is pumped, stepped up by the DC-DC controller, and then used to charge the battery, forming the charging direction excitation. In this mode, the DC-DC controller outputs the "negative half-axis" of the AC excitation. This allows for the application of AC excitation to the battery at the vehicle end. It should be understood that during the application of AC excitation, this converted energy flows between the 12V low-voltage power supply and the high-voltage battery being tested.

[0142] Step 1.2: During the process of applying AC excitation to the battery, collect the battery's voltage timing data and current timing data.

[0143] In this step, throughout the entire process of applying frequency-varying AC excitation to the battery, it is necessary to acquire the battery voltage timing data collected by the voltage sensor located at the battery and the battery current timing data collected by the current sensor located at the battery through the Analog Front End (AFE).

[0144] Step 1.3: Perform frequency domain conversion on the voltage and current time series data to obtain the impedance spectrum corresponding to the test conditions.

[0145] In this step, for each test condition, the voltage and current timing data corresponding to the test condition need to be frequency domain transformed to obtain the real and imaginary parts of the battery impedance at different frequencies, forming the impedance spectrum corresponding to that test condition.

[0146] The frequency domain transformation process is, for example, a Fourier transform. This application does not impose specific restrictions on the frequency domain transformation method.

[0147] It should be understood that in practical applications, the voltage and current timing data of the battery at all excitation frequencies can be obtained first, and then the timing data can be uniformly converted to the frequency domain. Alternatively, the timing data can be converted to the frequency domain after the voltage and current timing data of the battery at a single excitation frequency has been obtained. This application does not impose any restrictions on this.

[0148] As a specific example, when an EIS test is required on a battery, the BMS first sends the excitation frequency corresponding to the first excitation condition to the DC-DC controller, as well as the duration to be sustained at this excitation frequency. After the DC-DC controller responds, the BMS records the current excitation frequency and records the battery's voltage timing data and current timing data within this duration in the Random Access Memory (RAM). It should be noted that the timing data collected here should be the data after considering communication delay synchronization.

[0149] After the duration of the first excitation frequency ends, the collected battery voltage and current timing data are subjected to discrete Fourier transforms to convert them into frequency domain signals. This yields the real and imaginary parts of the battery impedance at that excitation frequency. The BMS mainboard then transmits the excitation frequency and duration corresponding to the next excitation condition to the DC-DC controller. This process is repeated until the real and imaginary parts of the battery impedance at all excitation frequencies for all excitation conditions are obtained. It should be noted that, to improve processing efficiency, the data acquisition and discrete Fourier transform process can also be performed by the BMS slave controller, which then returns the real and imaginary parts of the impedance after the Fourier transform to the BMS mainboard controller.

[0150] The method provided in this implementation utilizes a DC-DC controller to apply multi-frequency AC excitation to the battery under multiple test conditions, thereby enabling EIS testing of the battery at the vehicle end. By combining timing data acquisition and frequency domain conversion, the impedance spectrum of the battery under different test conditions is obtained, providing a reliable basis for the subsequent calculation of the first internal resistance.

[0151] Furthermore, Figure 2 This is a flowchart illustrating a method for estimating the internal resistance health of a vehicle battery according to Embodiment 2 of this application. Figure 2 As shown, this embodiment provides a detailed description of the specific implementation of step S204 in the above embodiments. The method includes:

[0152] S201. Obtain the first internal resistance health status of the battery based on the first internal resistance and the initial first internal resistance.

[0153] The initial first internal resistance is obtained based on the battery's first internal resistance at a reference temperature and a reference SOC, acquired during the initial phase after the vehicle is put into use.

[0154] In this step, the initial first internal resistance obtained based on the EIS test is the battery's optimal state benchmark measured under a unified reference temperature and reference SOC when the vehicle is first put into use. The first internal resistance obtained based on the EIS test can truly reflect the current electrochemical state of the battery. Therefore, by using the first internal resistance and the first initial internal resistance, the degree of battery performance degradation can be directly quantified, and thus the health of the first internal resistance can be obtained.

[0155] In one specific implementation, the first initial internal resistance can be obtained in the following way:

[0156] In the initial stage after the vehicle is put into use, the same method as in steps S101 and S102 is used to obtain the first internal resistance. Multiple EIS tests are performed on the battery until a preset number of batteries have their first internal resistances at a reference temperature and reference SOC. Then, the preset number of first internal resistances are averaged to obtain the first initial internal resistance. The preset number can be determined according to the actual application of this solution; for example, it can be 1, 5, 10, 15, etc., and this application does not impose specific limitations on it.

[0157] In one possible implementation, step S201 can be implemented using steps 2.1 to 2.2 as follows:

[0158] Step 2.1: Obtain a new average first internal resistance value based on the first internal resistance and the average first internal resistance value of the battery obtained from the previous test.

[0159] The average first internal resistance obtained in each test is calculated based on the first internal resistance obtained in this test, the average first internal resistance obtained in the previous test, and the number of health tests.

[0160] It should be understood that in this scheme, the new first average internal resistance used for internal resistance health estimation is obtained by iteratively updating the first average internal resistance obtained from the previous internal resistance health test, using the first internal resistance obtained at the reference temperature and reference SOC, and the total number of health tests.

[0161] Among them, the number of health tests refers to the total number of health tests conducted since the vehicle was put into use (including this test); the new first average internal resistance can be used to characterize the current battery internal resistance based on the EIS test.

[0162] As a specific example, the average first internal resistance value for each health test can be obtained in the following ways:

[0163] Calculate the difference between the first internal resistance obtained in this test and the average first internal resistance obtained in the previous test, and the first ratio of the number of health tests; take the sum of the first ratio and the average first internal resistance obtained in the previous test as the average first internal resistance obtained in this test.

[0164] Specifically, it can be expressed using the following formula:

[0165]

[0166] in, Indicates reference temperature; Indicates the reference SOC; k indicates the number of health tests; This represents the first internal resistance of the battery at the reference temperature and reference SOC, obtained during the k-th health test. This represents the mean value of the first internal resistance obtained during the (k-1)th health test. This represents the mean value of the first internal resistance obtained during the k-th health test.

[0167] It should be noted that when conducting the first health test after the vehicle is put into use, the "average value of the first internal resistance obtained from the previous test" is used as the initial first internal resistance.

[0168] Step 2.2: Compare the average value of the first internal resistance with the initial first internal resistance to obtain the battery's first internal resistance health status.

[0169] Specifically, it can be expressed using the following formula:

[0170]

[0171] in, This represents the first internal resistance health score obtained from the k-th health test. This represents the initial internal resistance of the battery.

[0172] The method provided in this implementation calculates the average internal resistance of the current test by combining the first internal resistance of the current test, the average first internal resistance of the previous test, and the number of health tests, and then compares it with the initial first internal resistance. This smooths out the random errors of a single test and stabilizes the trend of internal resistance changes, thereby improving the accuracy of the assessment of the health of the battery's first internal resistance.

[0173] S202. Obtain the second internal resistance health of the battery based on the second internal resistance and the initial second internal resistance.

[0174] The initial second internal resistance is obtained based on the second internal resistance of the battery at a reference temperature and a reference SOC, acquired during the initial stage after the vehicle is put into use.

[0175] Similar to the calculation principle of the first internal resistance health, in this step, the initial second internal resistance obtained based on the pulse test is the battery's optimal state benchmark measured under a unified reference temperature and reference SOC when the vehicle is first put into use. The current second internal resistance obtained based on the pulse test can truly reflect the current electrochemical state of the battery. Therefore, by using the second internal resistance and the second initial internal resistance, the degree of battery performance degradation can be directly quantified, and thus the second internal resistance health can be obtained.

[0176] In one specific implementation, the second initial internal resistance can be obtained in the following way:

[0177] In the initial stage after the vehicle is put into use, the same method as in steps S101 and S103 for obtaining the first internal resistance is adopted. Multiple discharge pulse tests are performed on the battery until a preset number of batteries have their second internal resistances at a reference temperature and reference SOC are obtained. Then, the preset number of second internal resistances are averaged to obtain the second initial internal resistance. The preset number can be determined according to the actual application of this solution; for example, it can be 1, 5, 10, 15, etc., and this application does not impose specific limitations on it.

[0178] In one possible implementation, step S202 can be implemented using steps 3.1 to 3.2 as follows:

[0179] Step 3.1: Based on the second internal resistance and the average second internal resistance of the battery obtained in the previous test, obtain the average first internal resistance obtained in this test.

[0180] The average value of the second internal resistance obtained in each test is calculated based on the average value of the second internal resistance obtained in this test, the average value of the second internal resistance obtained in the previous test, and the number of health tests.

[0181] It should be understood that in this scheme, the new average second internal resistance used for internal resistance health estimation is obtained by iteratively updating the average second internal resistance obtained from the previous internal resistance health test, using the second internal resistance obtained at the reference temperature and reference SOC, as well as the total number of health tests.

[0182] The new average second internal resistance can be used to characterize the current internal resistance of the battery obtained from the discharge pulse test.

[0183] As a specific example, the average value of the second internal resistance for each health test can be obtained in the following ways:

[0184] Calculate the difference between the second internal resistance obtained in this test and the average second internal resistance obtained in the previous test, and the second ratio of the number of health tests; take the sum of the second ratio and the average second internal resistance obtained in the previous test as the average second internal resistance obtained in this test.

[0185] Specifically, it can be expressed using the following formula:

[0186]

[0187] in, This represents the second internal resistance of the battery at the reference temperature and reference SOC, obtained during the k-th health test. This represents the average value of the second internal resistance obtained during the (k-1)th health test. This represents the average value of the second internal resistance obtained during the k-th health test.

[0188] Step 3.2: Compare the new average second internal resistance with the initial second internal resistance to obtain the battery's second internal resistance health.

[0189] Specifically, it can be expressed using the following formula:

[0190]

[0191] in, This represents the second internal resistance health score obtained from the k-th health test. This represents the initial second internal resistance of the battery.

[0192] The method provided in this implementation calculates the average internal resistance of the current test by combining the second internal resistance of the current test, the average value of the second internal resistance of the previous test, and the number of health tests, and then compares it with the initial second internal resistance. This smooths out the random errors of a single test and stabilizes the trend of internal resistance changes, thereby improving the accuracy of the assessment of the health of the battery's second internal resistance.

[0193] S203. The first internal resistance health value and the second internal resistance health value are fused to obtain the internal resistance health value of the battery.

[0194] In this step, to avoid the limitations of a single internal resistance health assessment method and to achieve a more comprehensive and accurate quantification of the battery's internal resistance health status, it is necessary to integrate information from both health indicators and eliminate their respective biases through a fusion algorithm.

[0195] Optionally, the fusion method can be weighted summation, Bayesian estimation, Kalman filter and its extended forms, etc., and this application does not impose specific restrictions on it.

[0196] In one possible implementation, the first internal resistance health score and the second internal resistance health score can be fused based on a Kalman filter to obtain the battery's internal resistance health score.

[0197] It should be understood that using a Kalman filter to dynamically fuse two data sources can eliminate noise interference and operating condition dependence of a single data source, improve the robustness of the estimation, and achieve high-precision, real-time estimation of battery internal resistance.

[0198] For example, the following formula can be used for fusion calculation:

[0199]

[0200] in, It refers to the variance of the first internal resistance of the battery obtained based on EIS testing, reflecting the dispersion or reliability of the EIS test results; This refers to the variance of the second internal resistance of the battery obtained based on the discharge pulse test, reflecting the dispersion or reliability of the discharge pulse test results.

[0201] As a specific example, the variance of the first internal resistance during each health test can be obtained in the following ways:

[0202] Based on the first internal resistance obtained in this test, the mean of the first internal resistance obtained in the previous test, the mean of the first internal resistance obtained in this test, the variance of the first internal resistance obtained in the previous test, and the number of health tests, the variance of the first internal resistance in this test is obtained.

[0203] Specifically, it can be expressed using the following formula:

[0204] ,

[0205] in, This represents the variance of the first internal resistance during the k-th health test; This represents the variance of the first internal resistance obtained during the (k-1)th health test.

[0206] Accordingly, the variance of the second internal resistance during each health test can be obtained in the following ways:

[0207] Based on the second internal resistance obtained in this test, the mean of the second internal resistance obtained in the previous test, the mean of the second internal resistance obtained in this test, the variance of the second internal resistance obtained in the previous test, and the number of health tests, the variance of the second internal resistance in this test is obtained.

[0208] Specifically, it can be expressed using the following formula:

[0209]

[0210] in, This represents the variance of the first internal resistance during the k-th health test; This represents the variance of the first internal resistance obtained during the (k-1)th health test. For example, Figure 3 The fusion effect diagram based on the Kalman filter provided in the embodiments of this application is as follows: Figure 3 As shown, the internal resistance health value obtained based on Kalman filter fusion has the characteristics of smoothness and high stability, which can provide a reliable basis for accurately estimating the battery internal resistance health value.

[0211] This implementation uses a Kalman filter to dynamically fuse the first and second internal resistance health values. By leveraging the Kalman filter's noise suppression capability for multi-source data, it adaptively assigns confidence weights to the two types of internal resistance health values. This effectively eliminates measurement bias, random noise, and outlier interference from a single data source, and the health values ​​from the two sources are complementarily enhanced, thereby improving the stability and accuracy of battery internal resistance health assessment.

[0212] The battery internal resistance health estimation method provided in this application obtains a first internal resistance health value and a second internal resistance health value by using a first internal resistance and a second internal resistance respectively, and then compares them with an initial stage benchmark value to obtain two types of internal resistance health values ​​before fusing them. This effectively reduces the random error of a single test and can improve the stability, reliability and accuracy of battery internal resistance health assessment.

[0213] This third embodiment provides a method for estimating the battery internal resistance health of a vehicle. Based on the above embodiments, this embodiment provides a specific implementation method for steps S102 and S103, as detailed below:

[0214] Step S102 can be implemented using steps 4.1 to 4.3 as follows:

[0215] Step 4.1: For each test condition, obtain the first internal resistance of the battery under the test condition based on the impedance spectrum corresponding to that test condition.

[0216] In this step, for each impedance spectrum, it is necessary to extract parameters related to the internal resistance based on the characteristics of each frequency band in the impedance spectrum, and directly obtain the internal resistance of the battery based on the extracted parameters.

[0217] It should be noted that this solution can directly use conventional calculation methods to extract the first internal resistance of the battery from the impedance spectrum.

[0218] Optionally, to improve the calculation efficiency of the first internal resistance, in the initial stage after the vehicle is put into use, the first internal resistance under the corresponding test conditions can be calculated based on the impedance spectrum of the battery under different test conditions obtained from multiple EIS tests. Then, using the multiple first internal resistances obtained under different test conditions, internal resistance models corresponding to different temperature ranges and SOC ranges can be established. In the subsequent first internal resistance test process, the real and imaginary parts of the impedance corresponding to a specific frequency point can be directly substituted into the model to obtain the first internal resistance of the battery.

[0219] For example, temperature and SOH are divided into several intervals (e.g., temperature is divided into [-30℃, -10℃], [-10℃, 25℃], [25℃, 50℃], and SOC is divided into [80%, 100%], [60%, 80%], [0, 60%]). Each temperature interval and SOC interval are cross-paired to obtain multiple temperature and SOH intervals (e.g., when there are 3 temperature intervals and 3 SOC intervals, there are 9 corresponding temperature and SOH intervals). For each temperature and SOH interval, an internal resistance model is established.

[0220] For example, the expression for the initial internal resistance model is as follows:

[0221]

[0222] in, and For the two specific frequencies selected; i is the polynomial degree, for example, 3; and All are polynomial coefficients of the i-th term; Indicates the battery frequency The impedance (including the real and imaginary parts); Indicates the battery frequency The impedance (including the real and imaginary parts) is given.

[0223] Multiple sets of data were obtained from tests conducted within the corresponding temperature and SOC range. , And the first internal resistance of the battery obtained based on the entire impedance spectrum, in the pre-established initial internal resistance model and Nonlinear fitting is performed to obtain the internal resistance model corresponding to the temperature and SOC range.

[0224] Step 4.2: For each SOC in the test conditions, use the first internal resistance obtained at different battery temperatures, normalize the temperature, and obtain the first internal resistance of the battery at that SOC and reference temperature.

[0225] Step 4.3: Based on the battery's first internal resistance at different SOC and reference temperatures, normalize the SOC to obtain the battery's first internal resistance at the reference SOC and reference temperature.

[0226] In the two steps above, in order to make the calculated first internal resistance comparable, it is necessary to normalize the first internal resistance obtained under different test conditions for temperature and SOC to obtain the first internal resistance at reference temperature and reference SOC.

[0227] In this implementation, firstly, multiple combinations of conditions with the same SOC but different battery temperatures are identified from the test conditions. For each combination of conditions (i.e., for each SOC in the test conditions), the first internal resistance obtained at different battery temperatures in the combination of conditions is normalized to obtain the first internal resistance corresponding to that combination of conditions (i.e., the battery at that SOC and reference temperature). Then, based on the first internal resistance corresponding to different combinations of conditions (i.e., different SOCs and reference temperatures), SOC is normalized to obtain the first internal resistance of the battery at the reference SOC and reference temperature.

[0228] As a specific example, the test conditions include nine types, as follows:

[0229] [SOC1, T1], [SOC1, T2], [SOC1, T3], [SOC2, T1], [SOC2, T2], [SOC2, T3], [SOC3, T1], [SOC3, T2], [SOC3, T3].

[0230] For a fixed SOC (such as SOC1, SOC2, or SOC3), the temperature is normalized based on its three first internal resistances at different temperatures (T1, T2, and T3) to obtain the temperature-internal resistance relationship corresponding to that SOC. Then, a reference temperature (such as 25°C) is substituted into this temperature-internal resistance relationship to obtain the first internal resistance at that SOC and reference temperature. Based on the first internal resistances of SOC1, SOC2, and SOC3 at the reference temperature, the SOC is normalized to obtain the SOC-internal resistance relationship. Then, a reference SOC (such as 50%) is substituted into this SOC-internal resistance relationship to obtain the first internal resistance of the battery at the reference temperature and reference SOC.

[0231] Optionally, the relationship between temperature and SOC can be obtained through curve fitting. Specifically, using the data set corresponding to the SOC (or temperature) that needs to be normalized, the parameters in a pre-selected appropriate mathematical model (such as a polynomial model, exponential model, etc.) are fitted to obtain the corresponding SOC-internal resistance relationship (temperature-internal resistance relationship).

[0232] Taking the normalization based on the Allen-Nées formula as an example, the method for obtaining the relation is explained:

[0233] The Allen-Neusch formula is:

[0234] Where T is the absolute temperature in K, T = t + 273.15, where t is the battery temperature, and R(T) is the internal resistance of the battery at absolute temperature T. This is a pre-exponential factor, a constant independent of temperature; It is the activation energy; The gas constant is... =3.3141 K / mol;

[0235] Taking the logarithm of the aforementioned Allende formula, we get Thus, a univariate linear relation is obtained;

[0236] For each SOC in the test conditions, the first internal resistance obtained from testing at different battery temperatures is used to adjust the relationship in the formula. Linear fitting was performed to obtain the temperature-internal resistance relationship.

[0237] Alternatively, in one possible implementation, each pulse test data includes both current and voltage changes; correspondingly, step S103 can be implemented using steps 5.1 to 5.3 as follows:

[0238] Step 5.1: For each test condition, obtain the second internal resistance of the battery under that test condition based on the current and voltage changes corresponding to the test condition.

[0239] In this step, for each test condition, the voltage change and current change corresponding to the test condition are compared to obtain the second internal resistance of the battery under that test condition.

[0240] Specifically, it can be expressed using the following calculation formula:

[0241]

[0242] in, This indicates that the battery is at a battery temperature of and SOC The second internal resistance under the test conditions; This indicates that the battery is at a battery temperature of and SOC Battery voltage changes were collected during discharge pulse testing. This indicates that the battery is at a battery temperature of and SOC The battery current changes were collected during a discharge pulse test.

[0243] Step 5.2: For each SOC in the test conditions, use the second internal resistance obtained at different battery temperatures, normalize the temperature, and obtain the second internal resistance of the battery at that SOC and reference temperature.

[0244] Step 5.3: Based on the battery's second internal resistance at different SOC and reference temperatures, normalize the SOC to obtain the battery's second internal resistance at the reference SOC and reference temperature.

[0245] In this implementation, the method of normalizing the temperature and SOC to obtain the second internal resistance of the battery at the reference SOC and reference temperature can be referred to the implementation method of obtaining the first internal resistance at the reference SOC and reference temperature mentioned above, and will not be repeated here.

[0246] The method for estimating the battery internal resistance health of a vehicle provided in this application first extracts the first internal resistance under each test condition from the impedance spectrum, calculates the second internal resistance under each test condition based on the pulse test data, and then normalizes the temperature and SOC in the data in stages to obtain the first and second internal resistances under comparable reference temperatures and reference SOCs, thus providing data support for the subsequent estimation of battery internal resistance health.

[0247] Figure 4 This is a schematic diagram of a structure for estimating the internal resistance health of a vehicle battery, as provided in Embodiment 4 of this application. Figure 4 As shown, the battery internal resistance health estimation device 30 for a vehicle provided in this embodiment includes:

[0248] The first acquisition module 301 is used to acquire multiple impedance spectra and multiple pulse test data of the vehicle's battery. The multiple impedance spectra are obtained by performing multiple EIS tests on the battery under different test conditions while the battery is in a static state. The multiple pulse test data are obtained by performing multiple discharge pulse tests on the battery under different test conditions while the battery is in a static state. The test conditions include battery temperature and SOC.

[0249] The second acquisition module 302 is used to acquire the first internal resistance of the battery at a preset reference temperature and reference SOC based on multiple impedance spectra.

[0250] The third acquisition module 303 is used to acquire the second internal resistance of the battery at a reference temperature and a reference SOC based on multiple pulse test data.

[0251] The calculation module 304 is used to calculate the internal resistance health of the battery based on the first internal resistance and the second internal resistance.

[0252] The battery internal resistance health estimation device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0253] Figure 5 This is a schematic diagram of a structure for estimating the internal resistance health of a vehicle battery, as provided in Embodiment 5 of this application. Figure 5 As shown, based on the above embodiments, the battery internal resistance health estimation device 30 for vehicles provided in this embodiment further includes:

[0254] The first processing module 305 is used to calculate the difference between the first internal resistance obtained in this test and the average value of the first internal resistance obtained in the previous test, and the first ratio of the number of health tests.

[0255] The sum of the first ratio and the average first internal resistance obtained from the previous test is taken as the average first internal resistance obtained in this test.

[0256] The second processing module 306 is used to calculate the difference between the second internal resistance obtained in this test and the average value of the second internal resistance obtained in the previous test, and the second ratio of the number of health tests.

[0257] The sum of the second ratio and the average second internal resistance obtained from the previous test is taken as the average second internal resistance obtained in this test.

[0258] The determination module 307 is used to determine that the vehicle's battery is in a static state when the battery current is less than a preset current value for a duration greater than a preset time.

[0259] In one possible implementation, the computing module 304 includes:

[0260] The first acquisition unit is used to acquire the first internal resistance health of the battery based on the first internal resistance and the initial first internal resistance. The initial first internal resistance is obtained based on the first internal resistance of the battery at a reference temperature and a reference SOC acquired in the initial stage after the vehicle is put into use.

[0261] The second acquisition unit is used to acquire the second internal resistance health of the battery based on the second internal resistance and the initial second internal resistance. The initial second internal resistance is obtained based on the second internal resistance of the battery at a reference temperature and a reference SOC acquired in the initial stage after the vehicle is put into use.

[0262] The fusion unit is used to fuse the first internal resistance health value and the second internal resistance health value to obtain the internal resistance health value of the battery.

[0263] In one possible implementation, the first acquisition unit has the following features:

[0264] Based on the first internal resistance and the average first internal resistance of the battery obtained in the previous test, the average first internal resistance obtained in this test is obtained. The average first internal resistance obtained in each test is based on the first internal resistance obtained in this test, the average first internal resistance obtained in the previous test, and the number of health tests.

[0265] The battery's first internal resistance health is obtained by comparing the average first internal resistance with the initial first internal resistance.

[0266] Correspondingly, the second acquisition unit is specifically used for:

[0267] Based on the second internal resistance and the average second internal resistance of the battery obtained from the previous test, a new average second internal resistance is obtained. The average second internal resistance obtained in each test is based on the second internal resistance obtained in this test, the average second internal resistance obtained in the previous test, and the number of health tests.

[0268] The battery's second internal resistance health is obtained by comparing the new average second internal resistance with the initial second internal resistance.

[0269] In one possible implementation, the fusion unit is specifically used for:

[0270] Based on the Kalman filter, the first internal resistance health value and the second internal resistance health value are fused to obtain the battery's internal resistance health value.

[0271] In one possible implementation, the acquisition module 401 is specifically used for:

[0272] Under each test condition, based on multiple pre-selected excitation conditions, the controller sends each excitation condition to the DCDC controller to cause the DCDC controller to apply AC excitation to the battery. The energy required for the DCDC controller to apply AC excitation is alternately provided by the battery and the 12V power supply. Each excitation condition includes the excitation frequency and the duration of the excitation frequency.

[0273] During the process of applying AC excitation to the battery, the battery's voltage timing data and current timing data are collected;

[0274] Frequency domain transformation is performed on voltage and current time series data to obtain the impedance spectrum corresponding to the test conditions.

[0275] In one possible implementation, the first acquisition module 401 is specifically used for:

[0276] For each test condition, the first internal resistance of the battery under the test condition is obtained based on the impedance spectrum corresponding to the test condition.

[0277] For each SOC in the test conditions, the first internal resistance obtained at different battery temperatures is used, and the temperature is normalized to obtain the first internal resistance of the battery at SOC and reference temperature.

[0278] Based on the battery's first internal resistance at different SOC and reference temperatures, the SOC is normalized to obtain the battery's first internal resistance at the reference SOC and reference temperature.

[0279] In one possible implementation, the second acquisition module is specifically used for:

[0280] For each test condition, the second internal resistance of the battery under the test condition is obtained based on the current and voltage changes corresponding to the test condition.

[0281] For each SOC in the test conditions, the second internal resistance obtained at different battery temperatures is normalized to obtain the second internal resistance of the battery at that SOC and reference temperature.

[0282] Based on the battery's second internal resistance at different SOC and reference temperatures, the SOC is normalized to obtain the battery's second internal resistance at the reference SOC and reference temperature.

[0283] The battery internal resistance health estimation device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0284] Figure 6 This is a schematic diagram of the controller provided in this application. Figure 6 As shown, the controller 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the controller 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0285] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0286] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0287] It should be understood that the aforementioned controller is, for example, a BMS installed in a vehicle.

[0288] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0289] The memory may include read-only memory and random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0290] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0291] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method.

[0292] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0293] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, EEPROM, EPROM, PROM, ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0294] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0295] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0296] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0297] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0298] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0299] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0300] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for estimating the internal resistance health of a vehicle battery, characterized in that, include: Acquire multiple impedance spectra and multiple pulse test data of the vehicle's battery; The multiple impedance spectra were obtained by performing multiple EIS tests on the battery under different test conditions and in a static state. The multiple pulse test data are obtained by performing multiple discharge pulse tests on the battery under different test conditions while the battery is in a static state; wherein the test conditions include battery temperature and SOC. Based on the multiple impedance spectra, the first internal resistance of the battery at a preset reference temperature and reference SOC is obtained; Based on the multiple pulse test data, the second internal resistance of the battery at the reference temperature and the reference SOC is obtained; The internal resistance health of the battery is calculated based on the first internal resistance and the second internal resistance.

2. The method according to claim 1, characterized in that, The step of calculating the internal resistance health of the battery based on the first internal resistance and the second internal resistance includes: Based on the first internal resistance and the initial first internal resistance, the first internal resistance health of the battery is obtained, wherein the initial first internal resistance is obtained based on the first internal resistance of the battery at the reference temperature and the reference SOC obtained in the initial stage after the vehicle is put into use. The second internal resistance health of the battery is obtained based on the second internal resistance and the initial second internal resistance. The initial second internal resistance is obtained based on the second internal resistance of the battery at the reference temperature and the reference SOC obtained in the initial stage after the vehicle is put into use. The first internal resistance health value and the second internal resistance health value are fused together to obtain the internal resistance health value of the battery.

3. The method according to claim 2, characterized in that, The step of obtaining the first internal resistance health status of the battery based on the first internal resistance and the initial first internal resistance includes: Based on the first internal resistance and the average first internal resistance of the battery obtained from the previous test, the average first internal resistance obtained in this test is obtained. The average first internal resistance obtained in each test is obtained based on the first internal resistance obtained in this test, the average first internal resistance obtained from the previous test, and the number of health tests. The first internal resistance average value is compared with the initial first internal resistance to obtain the first internal resistance health of the battery; Accordingly, obtaining the second internal resistance health of the battery based on the second internal resistance and the initial second internal resistance includes: Based on the second internal resistance and the average second internal resistance of the battery obtained from the previous test, a new average second internal resistance is obtained. The average second internal resistance obtained in each test is based on the second internal resistance obtained in this test, the average second internal resistance obtained in the previous test, and the number of health tests. The second internal resistance health of the battery is obtained by comparing the new average second internal resistance with the initial second internal resistance.

4. The method according to claim 2, characterized in that, The step of fusing the first internal resistance health value and the second internal resistance health value to obtain the internal resistance health value of the battery includes: The battery's internal resistance health is obtained by fusing the first internal resistance health score and the second internal resistance health score using a Kalman filter.

5. The method according to any one of claims 1 to 4, characterized in that, Obtain multiple impedance spectra of the vehicle's battery, including: Under each test condition, based on multiple pre-selected excitation conditions, the controller sends each excitation condition to the DC-DC controller to cause the DC-DC controller to apply AC excitation to the battery. The energy required for the DC-DC controller to apply AC excitation is alternately provided by the battery and a 12V power supply. Each excitation condition includes an excitation frequency and the duration of the excitation frequency. During the process of applying AC excitation to the battery, the voltage timing data and current timing data of the battery are collected; The voltage timing data and the current timing data are frequency domain converted to obtain the impedance spectrum corresponding to the test conditions.

6. The method according to any one of claims 1 to 4, characterized in that, The step of obtaining the first internal resistance of the battery at a preset reference temperature and reference SOC based on the multiple impedance spectra includes: For each test condition, the first internal resistance of the battery under the test condition is obtained based on the impedance spectrum corresponding to the test condition. For each SOC in the test conditions, the first internal resistance obtained at different battery temperatures is normalized to obtain the first internal resistance of the battery at the SOC and reference temperature. Based on the battery's first internal resistance at different SOC and reference temperatures, the SOC is normalized to obtain the battery's first internal resistance at the reference SOC and reference temperature.

7. The method according to any one of claims 1 to 4, characterized in that, Each pulse test data includes current changes and voltage changes; The step of obtaining the second internal resistance of the battery at the reference temperature and the reference SOC based on the multiple pulse test data includes: For each test condition, the second internal resistance of the battery under the test condition is obtained based on the current and voltage changes corresponding to the test condition. For each SOC in the test conditions, the second internal resistance obtained at different battery temperatures is normalized to obtain the second internal resistance of the battery at the SOC and reference temperature. Based on the second internal resistance of the battery at different SOC and reference temperatures, the SOC is normalized to obtain the second internal resistance of the battery at the reference SOC and reference temperature.

8. The method according to any one of claims 2 to 4, characterized in that, The method further includes: Calculate the ratio of the difference between the first internal resistance obtained in this test and the average first internal resistance obtained in the previous test to the first number of health tests. The sum of the first ratio and the average first internal resistance obtained from the previous test is taken as the average first internal resistance obtained in this test.

9. The method according to any one of claims 2 to 4, characterized in that, The method further includes: Calculate the difference between the second internal resistance obtained in this test and the average second internal resistance obtained in the previous test, and the second ratio of the number of health tests. The sum of the second ratio and the average second internal resistance obtained from the previous test is taken as the average second internal resistance obtained in this test.

10. The method according to any one of claims 1 to 4, characterized in that, The method further includes: If the battery current is less than a preset current value for a duration greater than a preset time, the vehicle battery is determined to be in a static state.

11. A device for estimating the internal resistance health of a vehicle battery, characterized in that, include: The first acquisition module is used to acquire multiple impedance spectra and multiple pulse test data of the vehicle's battery; The multiple impedance spectra were obtained by performing multiple EIS tests on the battery under different test conditions and in a static state. The multiple pulse test data are obtained by performing multiple discharge pulse tests on the battery under different test conditions while the battery is in a static state; wherein the test conditions include battery temperature and SOC. The second acquisition module is used to acquire the first internal resistance of the battery at a preset reference temperature and reference SOC based on the multiple impedance spectra. The third acquisition module is used to acquire the second internal resistance of the battery at the reference temperature and the reference SOC based on the multiple pulse test data. The calculation module is used to calculate the internal resistance health of the battery based on the first internal resistance and the second internal resistance.

12. A controller, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-10.

13. A vehicle, characterized in that, It includes the vehicle body and the controller as described in claim 12.

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