A battery state estimation method, device, medium and battery management system

Abstract

This invention belongs to the field of intelligent vehicle technology, and particularly relates to a battery state estimation method, device, medium, and battery management system. Based on the circuit characteristics of a staged constant current charging process, it detects the voltage and current sequences of the battery pack before and after current fluctuations in the charging circuit. Combined with optimization processes such as temperature compensation, it provides a quantitative estimation process for battery health state (SOH) and state of charge (SOC) based on the monotonicity of characteristic impedance. Furthermore, it considers the impact of SOC fluctuations on SOH estimation and improves the estimation process by limiting the range of SOC values. In addition, it further improves the accuracy of the estimation by optimizing the statistical values ​​of characteristic impedance. The related products and methods can be used in applications such as battery management systems, and can improve the overall operating quality, safety, and lifespan of the battery pack by enhancing the estimation levels of parameters such as SOH and SOC.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicle technology, and particularly relates to a battery state estimation method, device, medium and battery management system. Background Technology

[0002] Battery packs and their accessories are widely used to provide energy to intelligent vehicle systems. The state estimation of indicators such as their State of Charge (SOC) and State of Health (SOH) is crucial for the realization of system safety, control accuracy, robustness, and user experience.

[0003] The main methods for estimating the state of charge (SOH) include capacity aging and internal resistance aging, which respectively detect or estimate the decrease in capacity or the increase in internal resistance during the aging process of the battery pack. Both the decrease in capacity and the increase in internal resistance can be estimated based on the cycle life and calendar life.

[0004] Given that the battery pack undergoes two operating states during its life cycle—charge-discharge cycle and rest—the relationship between its capacity decrease or internal resistance increase and cumulative ampere-hours (Ah) can be obtained by testing the battery pack's cycling process at different temperatures; where different temperatures can be converted using a proportional coefficient.

[0005] Among these tests, the relationship between the capacity decrease and internal resistance increase of the battery pack after resting at different temperatures / SOCs and calendar time can be tested; the capacity decrease or internal resistance increase caused by cycling, plus the capacity decrease and internal resistance increase caused by calendar time, can also be detected.

[0006] The relevant technologies mainly rely on battery aging data obtained from offline measurements in the estimation of the state of health (SOH) of the battery pack, and then estimate the current state through a lookup table process. However, there are often differences between offline testing and actual operating conditions, and the evaluation bias is widespread and will become more and more serious over time. In addition, the bias in SOH estimation will also affect the accuracy of SOC calculation, which will have a chain reaction and affect the performance of the whole vehicle. Summary of the Invention

[0007] This invention discloses a battery state estimation method, including a first signal detection step, a second feature extraction step, and a third parameter estimation step. The first signal detection step acquires the voltage sequence, current sequence, and / or temperature sequence information of the constant current charging sequence of the battery pack. Then, it selects the m-th charging stage and the (m+1)-th charging stage following it as the current sequence group. Next, it acquires the highest voltage Umax before the end of the m-th charging stage, the corresponding charging current, the resting time Tt, and the lowest voltage Umin of the (m+1)-th charging stage in the current sequence group. Then, the second feature extraction step calculates the characteristic impedance Rt = (Umax - Umin) / (It) at the end of the (m+1)-th charging stage, where It is the unsteady current within the defined range of the current sequence. Finally, the third parameter estimation step compares the characteristic impedance Rt with a preset reference value Rs and / or the initial value R1 of the characteristic impedance Rt during the first charging of the battery pack to determine the estimated battery health state SOHp = Rs / Rt or SOHp = R1 / Rt.

[0008] Furthermore, in order to reduce the impedance difference caused by the different states of charge (SOC) of the battery pack, when the battery pack is charged using the standard DC charging process, the size or range of its SOC interval is limited, so that: SOCdown≤SOC≤SOCup.

[0009] If the first signal detection step is performed under different temperature elements of the temperature sequence T(x), compensation is performed according to the conversion coefficient of the corresponding physical quantity at different temperatures.

[0010] Furthermore, in order to obtain the minimum voltage, i.e. the lowest voltage Umin, it can be determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2. Of course, other methods can also be used to determine the minimum: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

[0011] Specifically, in the application of this battery state estimation method, the battery pack can be a system that uses lithium iron phosphate battery cells for fabrication and / or assembly; in addition, the method for obtaining the state of charge (SOC) of the battery pack can be the ampere-hour integration method.

[0012] Furthermore, the battery state estimation method may also include a fourth iterative compensation step; the fourth iterative compensation step can obtain at least two characteristic impedances Rt by selecting different current sequence groups at different times, different currents or different voltages, and form a characteristic impedance set. Then, the statistical value of the elements in the characteristic impedance set can be used as the initial value R1 of the characteristic impedance Rt and the estimated value SOHp of the battery health state can be obtained again.

[0013] Specifically, in order to improve the accuracy of SOH estimation, the average value of characteristic impedance Rt can be calculated three times during each charging process. The characteristic impedance set includes the first characteristic impedance R1-1, the second characteristic impedance R1-2, and the third characteristic impedance R1-3 calculated in the same charging process. Then, the arithmetic mean of the characteristic impedance set obtained from the above three calculation processes is used as the initial value R1.

[0014] Accordingly, this invention also discloses a battery state estimation device, including a first signal detection unit, a second feature extraction unit, and a third parameter estimation unit; the first signal detection unit acquires the voltage sequence, current sequence, and / or temperature sequence information of the constant current charging sequence of the battery pack; selects the m-th charging stage and the (m+1)-th charging stage that follows it as the current sequence group; and acquires the highest voltage Umax, the corresponding charging current, the resting time Tt, and the lowest voltage Umin of the current sequence group before the end of the m-th charging stage.

[0015] Furthermore, its second feature extraction unit calculates the characteristic impedance Rt=(Umax-Umin) / (It) at the end of the (m+1)th charging stage, where It is the unsteady current within the range of the current sequence; then, its third parameter estimation unit compares its characteristic impedance Rt with the preset reference value Rs and / or the initial value R1 of the characteristic impedance Rt when the battery pack is first charged, in order to determine the estimated value of the battery health state SOHp=Rs / Rt or SOHp=R1 / Rt.

[0016] Similarly, in order to reduce impedance differences caused by different states of charge (SOC) of the battery pack, when the battery pack is charged using a standard DC charging process, the size or range of its SOC interval is limited, such that: SOCdown≤SOC≤SOCup.

[0017] Furthermore, in order to obtain the minimum voltage, i.e. the lowest voltage Umin, it can be determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2. Of course, other methods can also be used to determine the minimum: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

[0018] Specifically, in this battery state estimation device, the battery pack can be a system that uses lithium iron phosphate battery cells for preparation and / or assembly; in addition, the method for obtaining the state of charge (SOC) of the battery pack can be the ampere-hour integration method.

[0019] Furthermore, the battery state estimation device may also include a fourth iterative compensation unit; the fourth iterative compensation unit can obtain at least two characteristic impedances Rt by selecting different current sequence groups at different times, different currents or different voltages, and form a characteristic impedance set. Then, the statistical value of the elements in the characteristic impedance set can be used as the initial value R1 of the characteristic impedance Rt and the estimated value SOHp of the battery health state can be obtained again.

[0020] Specifically, in order to improve the accuracy of SOH estimation, the average value of characteristic impedance Rt can be calculated three times during each charging process. The characteristic impedance set includes the first characteristic impedance R1-1, the second characteristic impedance R1-2, and the third characteristic impedance R1-3 calculated in the same charging process. Then, the arithmetic mean of the characteristic impedance set obtained from the above three calculation processes is used as the initial value R1.

[0021] Furthermore, embodiments of the present invention also disclose a computer storage medium and a battery management system; the former includes a storage medium body for storing a computer program; when the computer program is executed by a microprocessor, it can implement the battery state estimation method as described above; the latter includes the battery state estimation device as described above and / or any of the computer storage media.

[0022] In summary, this invention, based on the circuit characteristics of a staged constant current charging process, detects the voltage and current sequences of the battery pack before and after current fluctuations in the charging circuit. Combined with optimization processes such as temperature compensation, it provides a quantitative estimation process for battery health (SOH) and state of charge (SOC) based on the monotonicity of characteristic impedance. Furthermore, it considers the impact of SOC fluctuations on SOH estimation and improves the estimation process by limiting the SOC value range. In addition, the accuracy of the estimation is further improved by optimizing the characteristic impedance statistics. The related products and methods can be used in applications such as battery management systems, and can improve the overall operating quality, safety, and lifespan of the battery pack by enhancing the estimation levels of parameters such as SOH and SOC.

[0023] It should be noted that the terms "first," "second," and similar terms used in this article are merely for describing the constituent elements of the technical solution and do not constitute a limitation on the technical solution, nor should they be interpreted as an indication or implication of the importance of the corresponding elements; elements with terms such as "first," "second," or similar terms indicate that at least one of the elements is included in the corresponding technical solution. Attached Figure Description

[0024] To more clearly illustrate the technical solution of the present invention and facilitate a further understanding of its technical effects, features, and objectives, the present invention will be described in detail below with reference to the accompanying drawings. The drawings constitute an essential part of the specification and are used together with Embodiment 1 of the present invention to illustrate the technical solution of the present invention, but do not constitute a limitation on the present invention.

[0025] The same reference numerals in the attached diagrams represent the same parts, specifically:

[0026] Figure 1 This is a schematic diagram of the charging process according to an embodiment of the present invention.

[0027] Figure 2 The curve showing the relationship between the state of charge (SOC) (%) and voltage (V) during the charging process in an embodiment of the present invention is shown.

[0028] Figure 3 Attached to this document Figure 2 A magnified view of a portion of the image.

[0029] Figure 4 This is a schematic diagram of the process of an embodiment of the method of the present invention.

[0030] Figure 5 This is a schematic diagram of the structural composition of an embodiment of the device of the present invention.

[0031] Figure 6 This is a schematic diagram of the layout structure of an embodiment of the product of the present invention. Figure 1 .

[0032] Figure 7 This is a schematic diagram of the layout structure of an embodiment of the product of the present invention. Figure 2 .

[0033] Figure 8 This is a schematic diagram of the layout structure of an embodiment of the product of the present invention. Figure 3 .

[0034] Figure 9 This is a schematic diagram illustrating the method for obtaining the voltage minimum value Umin.

[0035] in:

[0036] 001 - The first constant current charging stage of this embodiment of the invention;

[0037] 002 - The second constant current charging stage of this embodiment of the invention;

[0038] 003 - The third constant current charging stage of this embodiment of the invention;

[0039] 010 - The tenth constant current charging stage of this embodiment of the invention;

[0040] 011 - Eleventh constant current charging stage of this embodiment of the invention;

[0041] 012 - The twelfth constant current charging stage of this embodiment of the invention;

[0042] 099 - Voltage minimum value;

[0043] 100 - First signal detection step;

[0044] 101 - Minimum voltage Umin;

[0045] 110 - Temperature parameter T(i) of this invention embodiment;

[0046] 111 - Current sequence group;

[0047] 120-Constant Current Charging Sequence;

[0048] 130 - Charging current sequence I(j), j=1, 2, 3, ..., m, where m is the number of constant current charging stages and m is a positive integer;

[0049] 140 - The highest voltage sequence Umax(j), j=1, 2, 3, ..., m, where m is the number of constant current charging stages and m is a positive integer;

[0050] 150 - Resting time sequence D(j), j=1, 2, 3, ..., m-1, m is the number of constant current charging stages, m is a positive integer, D(m-1)=0 or not included;

[0051] 200 - Second feature extraction step;

[0052] 300 - Third parameter estimation step;

[0053] 400 - Fourth iteration compensation step;

[0054] 600 - Battery State Estimation Device;

[0055] 610 - First signal detection unit;

[0056] 620 - Second Feature Extraction Unit;

[0057] 630 - Third parameter estimation unit;

[0058] 640 - Fourth Iteration Compensation Unit;

[0059] 900 - Vehicles;

[0060] 901 - Controller, operating system, or autopilot;

[0061] 903 - Computer storage media;

[0062] 909 - Battery Management System;

[0063] 999 - Battery or battery pack. Implementation

[0064] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described below are merely illustrative of the technical solutions of the present invention, and not intended to limit the invention. Furthermore, the parts described in the embodiments or drawings are merely illustrative examples of relevant parts of the present invention, and not the entirety of the invention.

[0065] like Figure 1 , Figure 4 and Figures 6 to 8 The battery state estimation method shown includes a first signal detection step 100, a second feature extraction step 200, and a third parameter estimation step 300; wherein, the first signal detection step 100 is used to obtain the detection information of the voltage sequence U(j) 140, the current sequence I(j) 130, and / or the temperature sequence T(x) 110 of the constant current charging sequence 120 of the battery pack 999; j and x are the serial numbers of the sampling points, and their values ​​are all positive integers.

[0066] Furthermore, the m-th charging stage and the (m+1)-th charging stage following it can be selected as the current sequence group 111; and after obtaining the highest voltage Umax before the end of the m-th charging stage, the corresponding charging current, the resting time Tt, and the lowest voltage Umin of the current sequence group 111, i.e., the lowest voltage 101, the characteristic impedance at the end of the (m+1)-th charging stage can be calculated through the second feature extraction step 200, i.e., Rt=(Umax-Umin) / (It).

[0067] Where It is the unsteady current within the range of the current sequence I(j)130.

[0068] Furthermore, in the third parameter estimation step 300, the estimated value of the battery health status is determined by comparing the characteristic impedance Rt with a preset reference value Rs and / or the initial value R1 of the characteristic impedance Rt when the battery pack 999 is first charged.

[0069] That is, SOHp = Rs / Rt or SOHp = R1 / Rt.

[0070] In order to reduce the impedance difference caused by the different states of charge (SOC) of the battery pack, when the battery pack 999 is charged using the standard DC charging process, the execution of the first signal detection step 100 can be limited to the SOC range within a preset range, such that: SOCdown≤SOC≤SOCup.

[0071] Specifically, temperature compensation can also be performed based on the conversion coefficients of corresponding physical quantities at different temperatures; for example, the effect of temperature on static capacity, or the effect of different temperatures on SOC; these effects can all be optimized through the conversion coefficients between different temperatures.

[0072] Furthermore, the minimum voltage can be obtained by finding the minimum value: the minimum voltage Umin can be determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

[0073] Specifically, the battery pack 999 can be fabricated and / or assembled using lithium iron phosphate battery cells; of course, it is equally applicable to other types of battery cells; in addition, the battery pack 999 can obtain the state of charge (SOC) using the ampere-hour integration method; since the accuracy of SOH has been improved, the accuracy of SOC calculation will also be improved accordingly.

[0074] Furthermore, the battery state estimation method may also include a fourth iterative compensation step 400; the fourth iterative compensation step 400 can solve for at least two characteristic impedances Rt and form a characteristic impedance set by selecting different current sequence groups 111 at different times, different currents or different voltages; if the statistical value of the elements in the characteristic impedance set is used as the initial value R1 of the characteristic impedance Rt, the estimated value SOHp of SOH can be obtained again.

[0075] In addition, the initial value can be optimized by using the characteristic impedances obtained from three separate calculations in each charging process; specifically, the characteristic impedance set includes the first characteristic impedance R1-1, the second characteristic impedance R1-2 and the third characteristic impedance R1-3 calculated in the same charging process; furthermore, the arithmetic mean of the elements in the characteristic impedance set can be used as the initial value R1.

[0076] Specifically, the method and product of this invention can evaluate the state of health (SOH) of various battery packs, including lithium-ion batteries, online: charging is performed according to the standard DC charging process of the battery pack, and voltage, current and temperature signals are obtained during different charging stages.

[0077] During this process, a certain charging stage is selected and the highest voltage Umax reached before its end is recorded; the current of this charging stage is denoted as I, and then the resting process of the battery pack and the lowest voltage Umin of the next charging stage are adopted; after the data acquisition is complete, the impedance at the end of the next charging stage is calculated as Rt; so that Rt=(Umax-Umin) / I (non-steady state), and is denoted as Rt=R1.

[0078] According to the above method, if the preset conditions are met during each charging process, the battery pack can obtain the corresponding characteristic impedance Rt; where the values ​​of Rt are denoted as R1, R2...Rn.

[0079] This invention uses the change in characteristic impedance Rt to sense the change in the state of health (SOH) of the battery pack. Generally, it is believed that if the characteristic impedance Rt increases, the aging degree of the battery pack is increased, that is, the SOH deteriorates. Of course, it can also be measured by the ratio of the characteristic impedance Rt to the preset parameter Rs or the initial value R1 of the characteristic impedance Rt when the battery pack is first charged at 999°C. That is, the SOH of the battery pack is measured by SOHp=Rs / Rt or SOHp=R1 / Rt.

[0080] In actual testing, such as Figures 1 to 3 As shown, taking a certain type of lithium iron phosphate battery as an example, such as... Figures 6 to 8 The process of DC charging the battery pack 999 of the vehicle 900 shown at 25°C comprises 11 stages, which constitutes the process described above. Figure 1 The constant current charging sequence 120; in which, when there is a current switching at different stages, the polarization of the battery will be different due to the different current magnitudes.

[0081] Specifically, during charging, since terminal voltage = open-circuit voltage + polarization voltage, the polarization voltage decreases when the large current stops or switches to a small current, which in turn causes the terminal voltage to decrease: for example... Figure 1 , Figure 2 As shown, in this stage, the current sequence I(j)130 stops at 1.2C, remains still for 1s, and then switches to 1C; as Figure 2 As shown, its terminal voltage exhibits a significant decrease; at this point, a minimum value Umin appears; during this period, the battery's state of charge (SOC) is limited to between 50% and 60%; the reduced SOC fluctuation range can effectively reduce the disturbance of SOC fluctuations on SOH.

[0082] Therefore, based on the maximum voltage before the voltage drop is Umax and the voltage drop Umax-Umin, and combined with the current I in this stage, the characteristic impedance Rt=(Umax-Umin) / (It) can be calculated; where It is the value of the battery pack current I at the detection time.

[0083] Furthermore, to improve the estimation of the monitored state of equilibrium (SOH), a three-stage iterative compensation process is introduced:

[0084] During this process, the range of SOC variation is also limited to adapt to and simulate the SOC shift when the battery pack is charged to a certain stage or voltage after aging; and to minimize the impedance difference caused by different SOC.

[0085] Specifically, in order to increase the accuracy of SOH estimation, three characteristic impedances are calculated in each charging process, and the average of these three characteristic impedances is used as the final characteristic impedance for this charging; the specific process is as follows.

[0086] First, the charging process enters stage n1, where the charging cutoff voltage is U1. The current system SOC is preset to be within the interval "SOC1≤SOC≤SOC2". The characteristic impedance calculation process is triggered, and the result is recorded as R1-1. The current in stage n1 is I1, and Umax is U1. The acquisition of Umin is achieved by comparing the sampled values ​​at three time points: when switching from charging stage n1 to charging stage n2, the voltage change is collected and recorded, and the voltage magnitude in each sampling period is compared.

[0087] like Figure 9 As shown, if the voltage from tn-1 to tn is Un, the voltage from tn to tn+1 is Un+1, and the voltage from tn+1 to tn+2 is Un+2, determine the relationship between Un, Un+1, and Un+2. If Un+1-Un<0 and Un+2-Un+1>0, then Umin is the voltage Un+1 at time n+1. At this time, the impedance from charging stage n1 to stage n2 can be calculated as R1-1=(Umax-Umin) / I1=(U1-Un+1) / I1.

[0088] Similarly, when charging enters stage n3, and the charging cutoff voltage of stage n3 is U2, if the SOC calculated by the current system is in a certain range SOC3≤SOC≤SOC4, the characteristic impedance Rt calculation process is triggered again, and the result is recorded as R1-2; where the current in charging stage n3 is I2, and Umax is U2; similarly, the acquisition of Umin: considering the switch from charging stage n3 to charging stage n4: first, start recording voltage changes and compare the magnitude of the voltage in each sampling period. The voltage from tk-1 to tk is Uk, the voltage from tk to tk+1 is Uk+1, and the voltage from tk+1 to tk+2 is Uk+2. Determine the relationship between the magnitudes of Uk, Uk+1, and Uk+2. If Uk+1-Uk<0 and Uk+2-Uk+1>0, then Umin is the voltage Uk+1 at time n+1. During this period, the impedance from charging stage n3 to stage n4 is R1-2=(Umax-Umin) / I1=(U1-Un+1) / I1.

[0089] Similarly, R1-3 can be calculated and the average values ​​of R1-1, R1-2 and R1-3 can be obtained; then, the value of R1 can be updated to the above average value; where R1 is the impedance value calculated during the first charging process of the battery pack.

[0090] For similar battery packs, the characteristic impedance calculation process can be triggered during each charging process. Since the characteristic impedance will increase as the battery ages, using the characteristic impedance Rt as a reference value for assessing the battery's state of health (SOH), i.e., SOHp=Rs / Rt or SOHp=R1 / Rt, is valuable. The method for obtaining the initial value of the characteristic impedance is not unique; it can be the "three-stage" method mentioned above, or a preset reference value Rs can be used instead.

[0091] In addition, considering the impact of factors such as fluctuations in sampled values ​​on intermediate data, a certain stage of filtering or outlier filtering steps can be added during the calculation, iteration, and / or compensation process to prevent unnecessary deviations in SOH estimation.

[0092] Accordingly, such as Figure 1 , Figure 5 and Figures 6 to 8 The battery state estimation device 600 shown includes a first signal detection unit 610, a second feature extraction unit 620, and a third parameter estimation unit 630; wherein, the first signal detection unit 610 acquires information of the voltage sequence U(j) 140, the current sequence I(j) 130, and / or the temperature sequence T(x) 110 of the constant current charging sequence 120 of the battery pack 999, where j and x are positive integers.

[0093] Specifically, the m-th charging stage and the (m+1)-th charging stage following it can be selected as the current sequence group 111; the highest voltage Umax before the end of the m-th charging stage, the corresponding charging current, the resting time Tt, and the lowest voltage Umin of the current sequence group 111 can be obtained; its second feature extraction unit 620 calculates the characteristic impedance Rt=(Umax-Umin) / (It) at the end of the (m+1)-th charging stage.

[0094] Where It is the unsteady current within the range of the current sequence I(j)130.

[0095] Next, its third parameter estimation unit 630 can compare the characteristic impedance Rt with the preset reference value Rs and / or the initial value R1 of the characteristic impedance Rt when the battery pack 999 is first charged, and determine the estimated value of the battery health status.

[0096] That is, SOHp = Rs / Rt or SOHp = R1 / Rt.

[0097] Similarly, in order to reduce the interference of the state of charge (SOC) on the state of health (SOH) estimation, when the battery pack 999 is charged using the standard DC charging process, the execution process of its first signal detection unit 610 can be limited to the SOC range within a preset range, such that: SOCdown≤SOC≤SOCup.

[0098] The minimum voltage Umin can be determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

[0099] Specifically, the battery pack 999 can be prepared and / or assembled using lithium iron phosphate battery cells; the method for obtaining the state of charge (SOC) of the battery pack 999 can also be the ampere-hour integration method.

[0100] In addition, to improve the accuracy of the estimation, the battery state estimation device 600 may also be provided with a fourth iterative compensation unit 640; the fourth iterative compensation unit 640 will select different current sequence groups 111 at different times, different currents or different voltages to calculate at least two characteristic impedances Rt and form a characteristic impedance set; the statistical value of the elements in the characteristic impedance set can be used as the initial value R1 of the characteristic impedance Rt, and the estimated value SOHp of SOH can be obtained again.

[0101] Specifically, the characteristic impedance set may include the first characteristic impedance R1-1, the second characteristic impedance R1-2, and the third characteristic impedance R1-3 calculated in the same charging process; and then, the arithmetic mean of the characteristic impedance set can be used as the initial value R1 of the characteristic impedance Rt.

[0102] Accordingly, such as Figure 6 , Figure 7 , Figure 8 As shown, the computer storage medium 903 includes a storage medium body for storing a computer program; when the computer program is executed by a microprocessor, it can implement the battery state estimation method as described above; similarly, its battery management system 909 may include the battery state estimation device 600 as described above and / or the computer storage medium 903 as described above.

[0103] It should be noted that the above embodiments are only for more clearly illustrating the technical solution of the present invention. Those skilled in the art will understand that the implementation of the present invention is not limited to the above content. Any obvious changes, substitutions or replacements made based on the above content do not exceed the scope of the technical solution of the present invention. Other implementations will also fall within the scope of the present invention without departing from the concept of the present invention.

Claims

1. A battery state estimation method, characterized in that: The process includes a first signal detection step (100), a second feature extraction step (200), and a third parameter estimation step (300). The first signal detection step (100) acquires the voltage sequence U(j) (140), current sequence I(j) (130), and temperature sequence T(x) (110) of the constant current charging sequence (120) of the battery pack (999), where j and x are positive integers. The m-th charging stage and the (m+1)-th charging stage following it are selected as the current sequence group (111), where the m-th stage corresponds to a high-current stage, and the (m+1)-th stage corresponds to a low-current stage. The highest voltage Umax before the end of the m-th charging stage is acquired. The corresponding charging current, resting time Tt and the minimum voltage Umin (101) of the current sequence group (111); the second feature extraction step (200) calculates the characteristic impedance Rt=(Umax-Umin) / (It) at the end of the (m+1)th charging stage, where It is the unsteady current within the range of the current sequence I(j) (130); the third parameter estimation step (300) compares the characteristic impedance Rt with the preset reference value Rs or the initial value R1 of the characteristic impedance Rt when the battery pack (999) is first charged, and determines the estimated value of the battery health state SOHp=Rs / Rt or SOHp=R1 / Rt.

2. The battery state estimation method as described in claim 1, wherein: The battery pack (999) is charged using a standard DC charging process; the execution of the first signal detection step (100) is limited to the state of charge (SOC) range within a preset range, such that: SOCdown≤SOC≤SOCup; if the first signal detection step (100) is executed under different temperature elements of the temperature sequence T(x), compensation is performed according to the conversion coefficient of the corresponding physical quantity at different temperatures.

3. The battery state estimation method as described in claim 1 or 2; wherein, The minimum voltage Umin (101) is determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

4. The battery state estimation method as described in claim 3; wherein, The battery pack (999) is prepared and / or assembled using lithium iron phosphate battery cells; the method for obtaining the state of charge (SOC) of the battery pack (999) includes the ampere-hour integration method.

5. The battery state estimation method as described in claim 1, 2 or 4 further includes a fourth iterative compensation step (400); the fourth iterative compensation step (400) selects different current sequence groups (111) at different times, different currents or different voltages to calculate at least two characteristic impedances Rt to form a characteristic impedance set, and uses the statistical value of the elements in the characteristic impedance set as the initial value R1 of the characteristic impedance Rt, and re-obtains the estimated value SOHp.

6. The battery state estimation method as described in claim 5; wherein, The characteristic impedance set includes the first characteristic impedance R1-1, the second characteristic impedance R1-2, and the third characteristic impedance R1-3 calculated in the same charging process; and then the arithmetic mean of the characteristic impedance set is used as the initial value R1.

7. A battery state estimation device (600), characterized in that: The system includes a first signal detection unit (610), a second feature extraction unit (620), and a third parameter estimation unit (630). The first signal detection unit (610) acquires the voltage sequence U(j) (140), current sequence I(j) (130), and temperature sequence T(x) (110) of the constant current charging sequence (120) of the battery pack (999), where j and x are positive integers. It selects the m-th charging stage and the (m+1)-th charging stage that follows it as the current sequence group (111), where the m-th stage corresponds to a high-current stage, and the (m+1)-th stage corresponds to a low-current stage. It also acquires the highest voltage Umax before the end of the m-th charging stage, and the... The corresponding charging current, resting time Tt and the minimum voltage Umin (101) of the current sequence group (111); the second feature extraction unit (620) calculates the characteristic impedance Rt=(Umax-Umin) / (It) at the end of the (m+1)th charging stage, where It is the unsteady current within the range of the current sequence I(j) (130); the third parameter estimation unit (630) compares the characteristic impedance Rt with the preset reference value Rs or the initial value R1 of the characteristic impedance Rt when the battery pack (999) is first charged, and determines the estimated value of the battery health state SOHp=Rs / Rt or SOHp=R1 / Rt.

8. The battery state estimation device (600) as claimed in claim 7, wherein: The battery pack (999) is charged using a standard DC charging process; the execution process of the first signal detection unit (610) is limited to the state of charge (SOC) range within a preset range, such that: SOCdown≤SOC≤SOCup.

9. The battery state estimation device (600) as described in claim 7 or 8; wherein, The minimum voltage Umin (101) is determined by comparing the voltages U(n), U(n+1), and U(n+2) at three sampling times tn, tn+1, and tn+2: if U(n+1) - U(n) < 0 and U(n+2) - U(n+1) > 0, then Umin = U(n+1).

10. The battery state estimation device (600) as described in claim 9; wherein, The battery pack (999) is prepared and / or assembled using lithium iron phosphate battery cells; the method for obtaining the state of charge (SOC) of the battery pack (999) includes the ampere-hour integration method.

11. The battery state estimation device (600) as described in claim 7, 8 or 10 further includes a fourth iterative compensation unit (640); the fourth iterative compensation unit (640) selects different current sequence groups (111) at different times, different currents or different voltages to calculate at least two characteristic impedances Rt to form a characteristic impedance set, and uses the statistical value of the elements in the characteristic impedance set as the initial value R1 of the characteristic impedance Rt and re-obtains the estimated value SOHp.

12. The battery state estimation device (600) as described in claim 11; wherein, The characteristic impedance set includes the first characteristic impedance R1-1, the second characteristic impedance R1-2, and the third characteristic impedance R1-3 calculated in the same charging process; and then the arithmetic mean of the characteristic impedance set is used as the initial value R1 of the characteristic impedance Rt.

13. A computer storage medium (903) comprising a storage medium body for storing a computer program; wherein the computer program, when executed by a microprocessor, implements the battery state estimation method as described in any one of claims 1 to 6.

14. A battery management system (909) comprising a battery state estimation device (600) as described in any one of claims 7 to 12 and / or a computer storage medium (903) as described in claim 13.

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