Rapid capacity estimation method for battery system based on Kalman filtering

Abstract

The invention relates to the technical field of battery capacity estimation, in particular to a battery system rapid capacity estimation method based on Kalman filtering. Comprising the following steps: collecting battery charging and discharging experiment data, obtaining a relation curve and identification parameters of battery residual capacity and open-circuit voltage at different environment temperatures, and establishing an equivalent circuit model of a battery; calculating the residual capacity of the battery at the discharge tail end by adopting a mode of weighting a self-adaptive extended Kalman filtering method of an equivalent circuit model and an ampere-hour integral method; according to the method, the SOC-OCV curve at multiple temperatures and the parameter curve of the battery model are obtained, so that the applicability of the second-order RC Thevenin equivalent circuit model at different temperatures is enhanced; the SOC is calculated by using the self-adaptive extended Kalman filtering method and the ampere-hour integral method for weighting at the discharge tail end of the battery, so that the SOC of the battery is corrected in real time, and the accuracy of predicting the SOC of the battery pack is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery capacity estimation, and in particular to a method for fast capacity estimation of a battery system based on Kalman filtering. Background Art

[0002] In a battery management system, it is crucial to accurately estimate the battery state of charge (SOC). This not only tells the user how much power is left in the battery, but also serves as the basis for battery charge and discharge management and balanced control management. Since SOC is affected by many factors such as temperature, current size and direction, accurate prediction becomes more difficult. Improving the accuracy of SOC estimation can help extend battery life and improve user experience.

[0003] At present, the most commonly used algorithm in battery management systems is the combination of the ampere-hour integration method and the open-circuit voltage method. However, this algorithm has problems such as difficulty in determining the initial value, large cumulative error, and difficulty in dealing with differences in battery packs. Considering the problem of the inability to estimate in real time due to the complexity of the neural network algorithm itself when it is deployed locally, we consider using a model-based algorithm to solve the above problems.

[0004] There are many classic battery models at present. The parameters of the ideal equivalent models in these models rarely take temperature factors into account, which makes it difficult to accurately predict SOC in working conditions and regions with drastic temperature changes. Moreover, under different working conditions, the plateau problem of lithium iron phosphate batteries is prone to long-term stagnation of SOC prediction values ​​when the Kalman filter algorithm is inserted at the end of battery discharge. Summary of the invention

[0005] The purpose of the present invention is to provide a method for rapid capacity estimation of a battery system based on Kalman filtering to solve the above technical problems; the technical problems solved by the present invention can be achieved by the following technical solutions: a method for rapid capacity estimation of a battery system based on Kalman filtering, comprising: step S1, collecting battery charging and discharging experimental data, obtaining the relationship curve and identification parameters between the remaining battery capacity and the open circuit voltage at different ambient temperatures, and establishing an equivalent circuit model of the battery; step S2, collecting the total battery voltage of the battery and calculating the voltage change rate; step S3, judging whether the voltage change rate is greater than or equal to a preset value, if it is greater than or equal to the preset value, calculating the remaining battery capacity by weighting the adaptive extended Kalman filtering method and the ampere-hour integration method through the equivalent circuit model; if it is less than the preset value, calculating the remaining battery capacity by the ampere-hour integration method.

[0006] Preferably, in step S1, the method for collecting battery charge and discharge experimental data includes: step S11, allowing the fully charged single battery to stand at room temperature for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage when the remaining capacity of the battery is 100%; step S12, performing a first constant current pulse discharge on the battery for a first set time, allowing the battery to stand, and again performing a first constant current pulse charge on the battery for the first set time; step S13, performing a second constant current pulse discharge on the battery until the remaining capacity of the battery reaches a preset value, allowing the battery to stand for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage of the current remaining capacity of the battery; step S14, judging whether the battery is fully discharged, if so, executing step S15; if not, returning to execute step S12; step S15, judging whether the experiment of all preset ambient temperatures is completed, if not, changing the ambient temperature, and returning to execute step S11; if completed, obtaining the relationship curve and identification parameters between the remaining capacity of the battery and the open circuit voltage at all ambient temperatures.

[0007] Preferably, in step S2, after the current and voltage of the battery charging and discharging are stabilized, when the battery is in a discharging condition through current identification, the total battery voltage is obtained at a set frequency, and the voltage change rate is calculated by the difference between the total battery voltages obtained twice before and after.

[0008] Preferably, in step S3, the method for calculating the remaining battery capacity in a weighted manner using the adaptive extended Kalman filter method and the ampere-hour integration method is to operate the adaptive extended Kalman filter method and the ampere-hour integration method in parallel, and the calculation result selects the remaining battery capacity of the battery with the lowest single cell voltage as the remaining battery capacity of the entire battery pack.

[0009] Preferably, in step S3, the system noise covariance matrix, the observation noise covariance matrix and the covariance matrix of the system state quantity are set, and the adaptive extended Kalman filtering method includes: step A1, initializing the state quantity at the current moment, the covariance matrix of the system state quantity, the system noise covariance matrix and the covariance matrix of the system state quantity; step A2, predicting the updated state quantity of the next time step by the state space equation to obtain the predicted state quantity; step A3, calculating the Kalman gain; step A4, correcting the predicted state quantity using the Kalman gain and the new information to obtain the corrected state quantity; step A5, judging whether to stop estimating the remaining capacity of the battery, and if so, ending the execution of step S3; otherwise, updating the covariance matrix of the system state quantity and the system noise covariance matrix, and returning to execute step A2.

[0010] Preferably, in step S1, the equivalent circuit model is a second-order RC model, including an ideal voltage source; a first resistor, a first end of the first resistor is connected to the positive electrode of the ideal voltage source; a first RC module, a first end of the first RC module is connected to the second end of the first resistor, the first RC module includes a second resistor and a first capacitor in parallel; a second RC module, a first end of the second RC module is connected to the second end of the first resistor, the first RC module includes a third resistor and a second capacitor in parallel; the second end of the second RC module and the negative electrode of the ideal voltage source form the terminal voltage of the battery.

[0011] Preferably, in step S3, the discrete state space equation of the equivalent circuit model is:

[0012] The observation equation of the equivalent circuit model is:

[0013] in,

[0014]

[0015]

[0016] in, Indicates the rated capacity of the battery, Indicates the current moment, represents the voltage of the first RC module, represents the voltage of the second RC module, Indicates the remaining capacity of the battery, represents the sampling time, represents the current state quantity, represents the state quantity at the next moment, Indicated in The current value at the moment, represents the terminal voltage of the battery at the current moment, represents the open circuit voltage at the current moment, represents the state coefficient matrix at the current moment, represents the control coefficient matrix at the current moment, represents the resistance value of the first resistor, represents the resistance value of the second resistor, represents the resistance value of the third resistor, represents the capacitance of the first capacitor, represents the capacitance of the second capacitor.

[0017] Preferably, step S3 also includes detecting the current of the battery, determining that the battery is in a small current discharge state when the current of the battery is less than a set value, and judging whether the duration of the small current discharge state exceeds the set value; if not, using the battery remaining capacity at the previous moment to calculate the battery remaining capacity at the next moment; if exceeded, using the relationship curve between the battery remaining capacity and the open circuit voltage at the current ambient temperature to estimate the battery remaining capacity.

[0018] Preferably, in step S3, the method for calculating the remaining battery capacity at the next moment using the remaining battery capacity at the previous moment is to obtain the corrected state quantity in step A4, determine whether to switch to the adaptive extended Kalman filter method for calculation, and if not, perform ampere-hour integration on the corrected state quantity, and use the integration result as the initial value of the ampere-hour integration at the next moment; if so, use the predicted state quantity obtained in step A2 as the initial value of the state quantity at the next time step after the adaptive extended Kalman filter method is restarted, and re-execute step A2 to regain the corrected state quantity.

[0019] Preferably, the method further includes step S4, wherein after each calculation of the battery remaining capacity is completed, whether the battery is in a fully charged or discharged state is judged by the terminal voltage of the battery; if the battery is in a fully charged state, the battery remaining capacity is set to 100%; if the battery is in an empty state, the battery remaining capacity is set to 0%.

[0020] Beneficial effects of the present invention: Due to the adoption of the above technical scheme, the present invention enhances the applicability of the second-order RC Thevenin equivalent circuit model at different temperatures by acquiring the SOC-OCV curves and parameter curves of the battery model at multiple temperatures; by using the adaptive extended Kalman filter method and the ampere-hour integration method to calculate the SOC in a weighted manner at the end of battery discharge, the real-time correction of the battery SOC is achieved, thereby improving the accuracy of the battery pack SOC prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the steps of a method for rapid capacity estimation of a battery system based on Kalman filtering in an embodiment of the present invention; Figure 2 It is a flow chart of a method for rapid capacity estimation of a battery system based on Kalman filtering in an embodiment of the present invention; Figure 3 A schematic diagram of the steps of obtaining battery data in an embodiment of the present invention; Figure 4 A schematic diagram of the steps of an adaptive extended Kalman filtering method in an embodiment of the present invention; Figure 5 It is a curve comparison diagram in the embodiment of the present invention; Figure 6 Schematic diagram of an equivalent circuit model in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0025] A fast capacity estimation method for battery system based on Kalman filtering, such as Figure 1 As shown, the method includes: step S1, collecting battery charge and discharge experimental data, obtaining the relationship curve and identification parameters of the battery remaining capacity and open circuit voltage at different ambient temperatures, and establishing an equivalent circuit model of the battery; step S2, collecting the total battery voltage of the battery, and calculating the voltage change rate; step S3, judging whether the voltage change rate is greater than or equal to a preset value, if it is greater than or equal to the preset value, using the adaptive extended Kalman filter method of the equivalent circuit model and the ampere-hour integration method to calculate the battery remaining capacity; if it is less than the preset value, using the ampere-hour integration method to calculate the battery remaining capacity.

[0026] Specifically, the present invention proposes a method for fast capacity estimation of a battery system based on Kalman filtering, establishes a second-order RC Thevenin battery simulation model, and in step S1, performs parameter identification on a lithium power battery through a hybrid power pulse capability characteristic (HPPC) test, explores the relationship between the remaining capacity (SOC) of the battery and the open circuit voltage and internal resistance, and on this basis, takes into account the temperature, a factor affecting battery performance; the power battery involved in the present invention is a lithium iron phosphate battery, and since the lithium iron phosphate battery has a plateau period, an edge algorithm is implanted for correction after the SOC reaches the critical value of the plateau period; at the same time, since the system error in the basic extended Kalman filter (EKF) algorithm is a constant, the present invention corrects this and proposes an adaptive extended Kalman filter method (AEKF), which uses the difference (new information) between the observed value and the model predicted value to adaptively adjust the system error, thereby reducing the cumulative error in the calculation of the EKF due to ignoring the high-order terms of the Taylor expansion of the nonlinear function.

[0027] At the same time, it also takes into account that the actual voltage values ​​at the corresponding entry points of different battery charge and discharge cycles are different due to the different internal parameters of the battery RC model actually obtained under different working conditions, and the parameter list obtained by HPPC is fixed - the voltage value obtained by the model under the same working condition is relatively fixed, so the SOC value may stagnate at the entry point for a long time. The present invention combines the ampere-hour integration and the AEKF weighted method, completes the deployment and conducts experiments, and verifies the superiority of the method of the present invention.

[0028] In a preferred embodiment, Figure 3 As shown, in step S1, the method for collecting battery charge and discharge experimental data includes: step S11, keeping a fully charged single battery stationary at room temperature for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage of the battery remaining capacity of 100%; step S12, performing a first constant current pulse discharge on the battery for a first set time, keeping the battery stationary, and again performing a first constant current pulse charging on the battery for the first set time; step S13, performing a second constant current pulse discharge on the battery until the battery remaining capacity reduced by the battery reaches a preset value, keeping the battery stationary for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage of the current battery remaining capacity; step S14, judging whether the battery is fully discharged, if so, executing step S15; if not, returning to executing step S12; step S15, judging whether the experiment of all preset ambient temperatures is completed, if not, changing the ambient temperature, and returning to executing step S11; if completed, obtaining the relationship curve and identification parameters of the battery remaining capacity and open circuit voltage at all ambient temperatures.

[0029] Step S1 of the present invention is used to obtain basic battery data. In order to accurately estimate and correct the SOC, a charge and discharge experiment is performed on the battery to obtain a curve of the relationship between the remaining capacity and the open circuit voltage of the battery at different operating temperatures (SOC-OCV curve) and to identify the second-order RC model parameters offline.

[0030] In a specific embodiment, the discharge device simulates the charging and discharging conditions of the electric ship, and collects the data required for the experiment through the group control module and data acquisition module implanted in each battery pack and the cluster control module in the high-voltage box. In this embodiment, the experiment includes the following steps: Step 1, let the fully charged single battery stand at room temperature of 25°C for 1 hour to reach a balanced state, and record the open circuit voltage OCV when the terminal voltage of the battery is 100% SOC; Step 2, perform 10s of 1 / 2C (C represents the unit current of the battery capacity) constant current pulse discharge on the battery, let the battery stand for 40s, and then perform 10s of 1 / 2C constant current pulse charging on it; Step 3, discharge the battery to 95% SOC with a constant current of 1 / 3C, let the battery stand for 1 hour to reach a balanced state, and record the open circuit voltage OCV when the terminal voltage of the battery is 95% SOC; Step 4, repeat steps 2 and 3 until the battery is fully discharged; Step 5, respectively at 45 , 35 , 15 , 5 , -5 , -15 The above experiment is repeated at the ambient temperature to obtain the SOC-OCV curve and identification parameters under the variable temperature required for the experiment.

[0031] Specifically, the SOC-OCV curve under the variable temperature required by the experiment is obtained through the experimental results of S1 and the identification is as follows: Figure 6 The parameters of the equivalent model shown are used to construct the battery equivalent circuit model.

[0032] More specifically, the temperature data of the minimum voltage single cell is read through the cluster control, and the corresponding SOC-OCV curve and model internal parameters are selected to improve the model accuracy and thus the estimation accuracy.

[0033] In a preferred embodiment, in step S2, after the current and voltage of the battery charge and discharge are stable, when the battery is in a discharge condition through current identification, the total battery voltage is obtained at a set frequency, and the voltage change rate is calculated by the difference between the total battery voltages obtained twice.

[0034] Specifically, Figure 2 As shown in the figure, when the current and voltage data of the battery charge and discharge are stable, when the current size determines that it belongs to the discharge condition, the total voltage value of the cluster is refreshed every minute, and the voltage change rate is obtained by calculating the difference between the current voltage value V_Current and the previous voltage value V_Last. , by changing the voltage rate With the default value For comparison, if the voltage change rate Less than the preset value , the battery is judged to be in the discharge plateau period, and the remaining battery capacity is calculated using the ampere-hour integration method; if the voltage change rate Greater than the preset value , it is judged that the battery is at the end of discharge, and the remaining capacity of the battery is calculated by weighted method using adaptive extended Kalman filter method and ampere-hour integration method.

[0035] The ampere-hour integration method estimates SOC by integrating current. The disadvantage of the ampere-hour integration method is that at the end of discharge, the ampere-hour integration method will produce a large error because the voltage is close to the cut-off value.

[0036] In a preferred embodiment, in step S3, the method for calculating the remaining battery capacity by weighted method of adaptive extended Kalman filter method and ampere-hour integration method is to parallelly operate the adaptive extended Kalman filter method and the ampere-hour integration method, and the calculation result selects the remaining battery capacity of the single battery with the lowest single cell voltage as the remaining battery capacity of the entire battery pack.

[0037] Specifically, at the end of discharge, the present invention uses the battery equivalent circuit model to perform AEKF SOC estimation, and runs the SOC calculation of ampere-hour integration in parallel, and performs weighted operations through a specific rule. The SOC value of the battery with the lowest single cell voltage is taken as the SOC value of the entire battery pack. The ampere-hour integration method estimates the SOC by integrating the current. The disadvantage of the ampere-hour integration method is that at the end of discharge, the ampere-hour integration method may produce a large error due to the voltage approaching the cutoff value. Figure 5 As shown in the figure, after the battery SOC is calibrated to be 20% higher, and the AEKF method is inserted at SOC=40%, the ampere-hour integration, AEKF, and the improved method of the present invention are compared. It can be seen that under this working condition, the ampere-hour integration calculation result has a large error until the battery voltage reaches the lower cut-off voltage; while the AEKF method can still correct this error, and its calculated value is close to 0 when the charging and discharging equipment is powered off, but there is a phenomenon of stagnation near the access point; while the ampere-hour integration and AEKF weighted method proposed in the present invention can not only correct the error of the false high calibration, but also solve the SOC stagnation phenomenon near the access point.

[0038] Specifically, the present invention only implants the AEKF method at the end of discharge to avoid the error caused by the lithium iron phosphate plateau period. Its weighting factor makes the AEKF method have a greater weight when the SOC gradually decreases to avoid the situation where the SOC value is still high when the relay is cut off.

[0039] In a preferred embodiment, in step S1, Figure 6As shown, the equivalent circuit model is a second-order RC model, including an ideal voltage source E; a first resistor R1, a first end of the first resistor R1 is connected to the positive electrode of the ideal voltage source E; a first RC module RC1, a first end of the first RC module RC1 is connected to the second end of the first resistor R1, and the first RC module RC1 includes a second resistor R2 and a first capacitor C1 in parallel; a second RC module RC2, a first end of the second RC module RC2 is connected to the second end of the first resistor R1, and the first RC module RC1 includes a third resistor R3 and a second capacitor C2 in parallel; the second end of the second RC module RC2 and the negative electrode of the ideal voltage source E form the terminal voltage of the battery.

[0040] In a preferred embodiment, in step S3, the discrete state space equation of the equivalent circuit model is:

[0041] The observation equation of the equivalent circuit model is,

[0042] in,

[0043]

[0044]

[0045] in, Indicates the rated capacity of the battery. Indicates the current moment, represents the voltage of the first RC module RC1, represents the voltage of the second RC module RC2, Indicates the remaining battery capacity. represents the sampling time, Represents the current state quantity, represents the state quantity at the next moment, Indicated in The current value at the moment, Indicates the terminal voltage of the battery at the current moment, represents the open circuit voltage at the current moment, represents the state coefficient matrix at the current moment, represents the control coefficient matrix at the current moment, represents the resistance value of the first resistor R1, represents the resistance value of the second resistor R2, represents the resistance value of the third resistor R3, represents the capacitance of the first capacitor C1, Represents the capacitance of the second capacitor C2.

[0046] In a preferred embodiment, in step S3, the system noise covariance matrix, the observation noise covariance matrix and the covariance matrix of the system state quantity are set, such as Figure 4 As shown, the adaptive extended Kalman filtering method includes step A1, initializing the current state quantity, the covariance matrix of the system state quantity, the system noise covariance matrix and the covariance matrix of the system state quantity; step A2, predicting the updated state quantity of the next time step through the state space equation to obtain the predicted state quantity; step A3, calculating the Kalman gain; step A4, correcting the predicted state quantity using the Kalman gain and the new information to obtain the corrected state quantity; step A5, judging whether to stop estimating the remaining battery capacity, if so, ending the execution of step S3; otherwise, updating the covariance matrix of the system state quantity and the system noise covariance matrix, and returning to execute step A2.

[0047] Since the observation equation of the battery model is nonlinear for the state quantity, the observation equation of the equivalent circuit model is linearized by the first-order Taylor expansion to obtain:

[0048] in:

[0049]

[0050]

[0051] The present invention provides is the system noise covariance matrix, is the observation noise covariance matrix, is the covariance matrix of the system state quantity, and the calculation process of AEKF is as follows: Step a, initialization and , =0; Step b, ; Step c, ; Step d, ; Step e, ; Step f, ; Step g, ; Among them, step a is to initialize the adaptive extended Kalman filter method, step b and step c predict the updated state quantity of the next time step through the state space equation, step d calculates the Kalman gain, step e uses the Kalman gain and the new information to correct the predicted state quantity, and step f updates the covariance matrix of the system state quantity , step g updates the system noise covariance matrix .

[0052] in, for The a posteriori estimate of the state quantity at the moment; for A priori estimate of state quantity at each moment; for The a posteriori estimate of the state quantity at the moment; for The covariance matrix of the state quantity estimated a posteriori at the moment; for Prior estimation of state quantity covariance matrix at each moment; for The covariance matrix of the state quantity estimated a posteriori at the moment; for The moment system noise covariance matrix; for The system noise covariance matrix updated at all times; is the observation noise covariance matrix; is the Kalman gain matrix; for The linearized observation matrix of the time system; For the forgetting factor, For new information, that is (current The terminal voltage measurement value at the moment (use as well as The difference between the estimated value obtained in the equivalent circuit model.

[0053] The relationship between all priors and posteriors is illustrated by example: and In terms of the state space equation, According to step b, the formula is converted to , According to step e, the formula is transformed from prior to posterior.

[0054] In a preferred embodiment, step S3 also includes detecting the current of the battery, determining that the battery is in a low-current discharge state when the current of the battery is less than a set value, and judging whether the duration of the low-current discharge state exceeds the set value; if not, using the battery remaining capacity at the previous moment to calculate the battery remaining capacity at the next moment; if exceeded, using the relationship curve between the battery remaining capacity and the open-circuit voltage at the current ambient temperature to estimate the battery remaining capacity.

[0055] Specifically, if the current is less than a certain value, it is determined to be a small current discharge, and the SOC value at the next moment is calculated by a certain rule. The small current discharge state of the present invention refers to the dynamic behavior when the discharge current of the battery is not enough to significantly change the SOC value, such as the natural discharge phenomenon of the battery when it is stationary. In this case, the battery current changes little, which may lead to inaccurate SOC calculation. Therefore, special calculation rules are required to handle the SOC estimation under small current discharge conditions.

[0056] In a preferred embodiment, in step S3, the method for calculating the remaining battery capacity at the next moment using the remaining battery capacity at the previous moment is to obtain the corrected state quantity in step A4, determine whether to switch to the adaptive extended Kalman filter method for calculation, and if not, perform ampere-hour integration on the corrected state quantity, and use the integration result as the initial value of the ampere-hour integration at the next moment; if so, use the predicted state quantity obtained in step A2 as the initial value of the state quantity at the next time step after the adaptive extended Kalman filter method is restarted, and execute step A2 again to regain the corrected state quantity.

[0057] Specifically, in step S3, the cluster control reads the value of the overall current and the opening and closing states of the relays to determine whether the battery is in a low-current charge and discharge state. Since the battery needs to remain stationary for a certain period of time to stabilize in a low-current charge and discharge state, it will take some time before the OCV-SOC curve calibration method at the current temperature can be used to estimate the SOC value.

[0058] The present invention first determines whether the battery is in a low current charge and discharge state for more than a preset time. If not, it means that the OCV-SOC curve cannot be directly applied at this time. Then, the SOC value stored at the previous moment is used to calculate the SOC value at the next moment by ampere-hour integration, that is, the state quantity corrected in step e. , correct the current through certain rules , if after the AEKF method is switched on, the initial value of the state quantity for the next time step after the method is restarted is obtained through step b , and then calculate in step b to get ; If before the AEKF method is used, the ampere-hour integral is obtained , as the initial value of the ampere-hour integral at the next moment.

[0059] If the time exceeds a certain time, the OCV-SOC curve at the current temperature is used for calibration and the accumulated time is cleared and re-timed. The above steps of the present invention can reduce the influence of the battery's self-consumption current on the SOC prediction accuracy.

[0060] In a preferred embodiment, step S4 is also included. After each calculation of the battery remaining capacity is completed, it is determined whether the battery is in a fully charged or discharged state according to the terminal voltage of the battery. If the battery is in a fully charged state, the battery remaining capacity is set to 100%; if the battery is in an empty state, the battery remaining capacity is set to 0%.

[0061] Specifically, in step S4, the minimum voltage of the single cell is read through the cluster control, and two types of three-level warnings are set. The first type is a level one warning for low voltage, a level two warning for low voltage, and a level three warning for low voltage. After the voltage reaches the level one and level two warnings for low voltage, the system will display a pop-up window prompt that the battery is about to run out of power; after the voltage reaches the level three warning limit for low voltage, the relay is automatically cut off, and the SOC is forced to be set to 0; the second type is a level one warning for high voltage, a level two warning for high voltage, and a level three warning for high voltage. After the voltage reaches the level one and level two warnings for high voltage, the system will display a pop-up window prompt that the battery is about to be fully charged; after the voltage reaches the level three warning limit for high voltage, the relay is automatically cut off, and the SOC is forced to be set to 100.

[0062] The above description is only a preferred embodiment of the present invention, and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for fast capacity estimation of a battery system based on Kalman filtering, characterized in that: The method comprises: step S1, collecting battery charge and discharge experimental data, obtaining the relationship curve between the battery remaining capacity and the open circuit voltage at different ambient temperatures and identification parameters, and establishing an equivalent circuit model of the battery; step S2, collecting the total battery voltage of the battery and calculating the voltage change rate; step S3, judging whether the voltage change rate is greater than or equal to a preset value, and if it is greater than or equal to the preset value, calculating the battery remaining capacity by weighting the adaptive extended Kalman filter method and the ampere-hour integration method through the equivalent circuit model; If it is less than the preset value, the remaining capacity of the battery is calculated using the ampere-hour integration method.

2. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 1, characterized in that: In step S1, the method for collecting battery charge and discharge experimental data includes: step S11, keeping the fully charged single battery at room temperature for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage of the battery remaining capacity of 100%; step S12, performing a first constant current pulse discharge on the battery for a first set time, keeping the battery still, and again performing a first constant current pulse charge on the battery for the first set time; step S13, performing a second constant current pulse discharge on the battery until the battery remaining capacity reduced by the battery reaches a preset value, keeping the battery still for a certain period of time, and recording the terminal voltage of the battery in this state as the open circuit voltage of the current battery remaining capacity; step S14, judging whether the battery is fully discharged, if so, executing step S15; if not, returning to executing step S12; step S15, judging whether the experiment of all preset ambient temperatures is completed, if not, changing the ambient temperature, and returning to executing step S11; if completed, obtaining the relationship curve and identification parameters of the battery remaining capacity and open circuit voltage at all ambient temperatures.

3. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 1, characterized in that: In step S2, after the current and voltage of the battery charge and discharge are stable, when the battery is in a discharging condition through current identification, the total battery voltage is obtained at a set frequency, and the voltage change rate is calculated by the difference between the total battery voltages obtained twice.

4. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 1, characterized in that: In step S3, the method of calculating the remaining battery capacity in a weighted manner using the adaptive extended Kalman filter method and the ampere-hour integration method is to parallelly operate the adaptive extended Kalman filter method and the ampere-hour integration method, and the calculation result selects the remaining battery capacity of the battery with the lowest single cell voltage as the remaining battery capacity of the entire battery pack.

5. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 1, characterized in that: In step S3, a system noise covariance matrix, an observation noise covariance matrix and a covariance matrix of a system state quantity are set, and the adaptive extended Kalman filtering method comprises: step A1, initializing a current state quantity, a covariance matrix of the system state quantity, a system noise covariance matrix and a covariance matrix of the system state quantity; Step A2, predicting the updated state quantity of the next time step by the state space equation to obtain the predicted state quantity; Step A3, calculating the Kalman gain; Step A4, correcting the predicted state quantity by using the Kalman gain and the new information to obtain the corrected state quantity; Step A5, determining whether to stop estimating the remaining capacity of the battery, and if so, ending the execution of Step S3; Otherwise, update the covariance matrix of the system state quantity and the system noise covariance matrix, and return to execute step A2.

6. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 5, characterized in that: In step S1, the equivalent circuit model is a second-order RC model, including an ideal voltage source (E); a first resistor (R1), wherein a first end of the first resistor (R1) is connected to a positive electrode of the ideal voltage source (E); a first RC module (RC1), wherein a first end of the first RC module (RC1) is connected to a second end of the first resistor (R1), wherein the first RC module (RC1) includes a second resistor (R2) and a first capacitor (C1) connected in parallel; a second RC module (RC2), wherein a first end of the second RC module (RC2) is connected to a second end of the first resistor (R1), wherein the first RC module (RC1) includes a third resistor (R3) and a second capacitor (C2) connected in parallel; and a second end of the second RC module (RC2) and a negative electrode of the ideal voltage source (E) form a terminal voltage of the battery.

7. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 6, characterized in that: In step S3, the discrete state space equation of the equivalent circuit model is: ； The observation equation of the equivalent circuit model is: ； in, ； ； ； in, Indicates the rated capacity of the battery, Indicates the current moment, represents the voltage of the first RC module (RC1), represents the voltage of the second RC module (RC2), Indicates the remaining capacity of the battery, represents the sampling time, represents the current state quantity, represents the state quantity at the next moment, Indicated in The current value at the moment, represents the terminal voltage of the battery at the current moment, represents the open circuit voltage at the current moment, represents the state coefficient matrix at the current moment, represents the control coefficient matrix at the current moment, represents the resistance value of the first resistor (R1), represents the resistance value of the second resistor (R2), represents the resistance value of the third resistor (R3), represents the capacitance of the first capacitor (C1), Indicates the capacitance of the second capacitor (C2).

8. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 5, characterized in that: Step S3 further includes detecting the current of the battery, determining that the battery is in a low-current discharge state when the current of the battery is less than a set value, judging whether the duration of the low-current discharge state exceeds a set value, and if not, using the battery remaining capacity at the previous moment to calculate the battery remaining capacity at the next moment; If it exceeds, the remaining capacity of the battery is estimated using a relationship curve between the remaining capacity of the battery and the open circuit voltage at the current ambient temperature.

9. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 8, characterized in that: In step S3, the method of using the battery remaining capacity at the previous moment to calculate the battery remaining capacity at the next moment is to obtain the corrected state quantity in step A4, determine whether to switch to the adaptive extended Kalman filter method for calculation, and if not, perform ampere-hour integration on the corrected state quantity, and use the integration result as the initial value of the ampere-hour integration at the next moment; if so, use the predicted state quantity obtained in step A2 as the initial value of the state quantity of the next time step after the adaptive extended Kalman filter method is restarted, and re-execute step A2 to regain the corrected state quantity.

10. The method for rapid capacity estimation of a battery system based on Kalman filtering according to claim 1, characterized in that: The method further includes step S4, wherein after each calculation of the remaining battery capacity is completed, whether the battery is in a fully charged or discharged state is determined by the terminal voltage of the battery; if the battery is in a fully charged state, the remaining battery capacity is set to 100%; if the battery is in an empty state, the remaining battery capacity is set to 0%.

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