Method and apparatus for determining battery charge status, battery management system

Abstract

The method for determining a battery state of charge includes the steps of: acquiring battery state data (S11), including current data and voltage data; determining element parameter values ​​in the equivalent circuit model by a least-squares RLS prediction model based on an equivalent circuit model of the battery, error information, battery characteristic data, and state data (S12); and determining an estimated battery state of charge value of the battery according to an observer technique based on the element parameter values ​​in the equivalent circuit model, the state data, and the battery characteristic data (S13). The method for determining a battery state of charge, the device for determining a battery state of charge (800), and the battery management system can solve the problem of inaccurate estimation of the battery state of charge.

Description

Technical Field

[0001] (Cross - reference to related applications) This disclosure is based on Chinese Patent Application No. 202010245899.9 filed on March 31, 2020, and claims the priority thereof. All of its contents are incorporated herein by reference.

[0002] This disclosure relates to the technical field of battery management, and particularly to a method and apparatus for determining the state of charge of a battery, and a battery management system.

Background Art

[0003] As a new - energy vehicle, an electric vehicle has advantages such as reducing oil consumption, having less pollution, and less noise, and is considered an important solution to the energy - crisis problem and the environmental - deterioration problem. As the power source of an electric vehicle, the battery not only helps to improve the equalization control efficiency of the battery system and the energy - management efficiency of the electric vehicle by accurately estimating its state of charge, but also relates to the overall safety of the electric vehicle under dynamic operating conditions.

[0004] In related technologies, when estimating the state of charge of a battery, due to insufficient calibration of sampling elements, deterioration of sampling elements, etc., it is difficult to guarantee the measurement accuracy of battery data. Therefore, errors are likely to occur in the equivalent - circuit model, and if the influence of the error of the state of charge of the battery on the parameter - identification result is not considered, the parameter values of the elements become inconsistent, the estimation accuracy of the state of charge of the battery further decreases, and it does not lead to efficient management and reliable operation of the vehicle.

Summary of the Invention

Problems to be Solved by the Invention

[0005] This disclosure provides a method and apparatus for determining the state of charge of a battery, and a battery management system to solve the problem that the estimation of the state of charge of the battery is not accurate.

Means for Solving the Problems

[0006] To achieve the above object, a method for determining the state of charge of a battery according to a first aspect of the present disclosure includes: obtaining state data of a battery including current data and voltage data; determining an element parameter value in the equivalent circuit model by a recursive least square (RLS) prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and the state data; and determining an estimated value of the state of charge (SOC) of the battery according to an observer technique based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data

[0007] An apparatus for determining the state of charge of a battery according to a second aspect of the present disclosure includes: an acquisition module configured to acquire state data of a battery including current data and voltage data; a first determination module configured to determine an element parameter value in the equivalent circuit model by an RLS prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and the state data; and a second determination module configured to determine an estimated value of the SOC of the battery according to an observer technique based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data

[0008] [[ID=********]] A computer-readable storage medium according to a third aspect of the present disclosure stores a computer program which, when executed by a processor, implements the steps of the method according to any one of the first aspects.

[0009] An electronic device according to a fourth aspect of the present disclosure includes: a memory storing a computer program; The system includes a processor that executes the computer program in the memory to realize the steps of the method according to any one of the first embodiments described above.

[0010] A battery management system according to a fifth aspect of this disclosure includes a device for determining the battery charge state as described in any one of the above paragraphs. [Effects of the Invention]

[0011] According to the above technical means, at least the following beneficial effects can be achieved.

[0012] The element parameter values ​​in the equivalent circuit model are determined using the least squares RLS prediction model. Furthermore, when determining the element parameter values ​​in the equivalent circuit model of the battery, by further considering error information including at least the sampling error factor of voltage data or the sampling error factor of current data, or error information including the sampling error factor of both voltage data and current data, the influence of sampling error can be reduced, the accuracy of the determined element parameter values ​​in the equivalent circuit model can be further improved, and ultimately, the accuracy of the determined equivalent circuit model of the battery can be improved. In addition, the state of charge (SOC) value of the battery is determined according to observer technology, the degree of matching between element parameter values ​​and observers is improved, the accuracy of estimating the battery charge state is further improved, and efficient management and reliable operation of the vehicle are ensured.

[0013] Other features and advantages of this disclosure will be described in detail in the following sections on specific embodiments. [Brief explanation of the drawing]

[0014] The drawings provide further understanding of this disclosure and constitute part of the specification, illustrating this disclosure together with the following specific embodiments, but not limiting it.

[0015] [Figure 1]This is a flowchart of a method for determining the battery charge state according to an exemplary embodiment of the present disclosure. [Figure 2] This is a flowchart of a method for determining another battery charge state according to an exemplary embodiment of the present disclosure. [Figure 3] This is a schematic diagram of the initial equivalent circuit of an L-order battery according to an exemplary embodiment of the present disclosure. [Figure 4] This is a flowchart of a method for determining another battery charge state according to an exemplary embodiment of the present disclosure. [Figure 5] This is a flowchart of a method for determining another battery charge state according to an exemplary embodiment of the present disclosure. [Figure 6] This is a diagram illustrating the effect of a method for determining the battery charge state according to an exemplary embodiment of related technology. [Figure 7] This is an effect diagram of a method for determining the battery charge state according to an exemplary embodiment of the present disclosure. [Figure 8] This is a block diagram of an apparatus for determining an equivalent circuit model according to an exemplary embodiment of the present disclosure. [Figure 9] This is a block diagram of an electronic device used to determine an equivalent circuit model according to an exemplary embodiment of the present disclosure. [Modes for carrying out the invention]

[0016] The specific embodiments of this disclosure will be described in detail below with reference to the drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and do not limit this disclosure.

[0017] Furthermore, terms such as "First," "Second," etc., used in the specification, claims, and drawings of this disclosure are intended to distinguish similar subjects and do not necessarily indicate a specific order or priority.

[0018] Before introducing the methods, apparatus, storage media, and electronic devices for determining the battery charge state according to this disclosure, we will first describe the application scenarios of each embodiment of this disclosure. Each embodiment of this disclosure can be used to determine the charge state of a battery, and the battery may be, for example, a ternary lithium battery or a lithium iron phosphate battery.

[0019] Taking electric vehicles as an example, batteries, as the power source of electric vehicles, not only help improve the equalization control efficiency of the battery system and the energy management efficiency of electric vehicles by accurately estimating their charge state, but also affect the overall safety of the electric vehicle under dynamic operating conditions. In related technologies, the state of the battery can be further analyzed by determining the corresponding equivalent circuit model, and these states may be, for example, the State of Charge (SOC), the State of Energy (SOE), the State of Power (SOP), and the State of Health (SOH).

[0020] The applicant discovered that failure to calibrate the sampling element, and subsequent degradation of the sampling element, can lead to additional errors in the measurement results of battery data. This makes the identified equivalent circuit model in related technologies prone to errors, further contributing to errors in estimating the battery state and detrimental to the safe operation and efficient management of vehicles. For example, with the use of electric vehicles, the sampling device of the BMS (Battery Management System) constantly degrades, causing the measurement offset to recur. As a result, the BMS measurement noise changes from white noise to colored noise, ultimately leading to errors in the identified equivalent circuit model and a decrease in the accuracy of battery state estimation. Furthermore, because the impact of battery charge state errors on the parameter identification results is not considered, the element parameter values ​​become inconsistent with the battery SOC, further reducing the accuracy of battery charge state estimation.

[0021] Therefore, this disclosure provides a method for determining the battery charge state, and with reference to the flowchart of the method for determining the battery charge state shown in Figure 1, this method includes the following S11 to S13.

[0022] In S11, battery status data, including current data and voltage data, is acquired.

[0023] The state data may further include battery temperature data, capacity data, open-circuit voltage-charge state curves, etc.

[0024] Taking electric vehicles as an example, when implementing this specifically, the BMS may acquire battery status data directly, indirectly, or both directly and indirectly. For example, the BMS may directly acquire battery current data using a current sensor. Alternatively, the BMS may acquire battery temperature data using a temperature sensor. In some embodiments, the BMS may indirectly acquire status data via a corresponding data interface, for example, by acquiring open-circuit voltage-charge state curve information of the battery stored in memory via the data interface.

[0025] In S12, the element parameter values ​​in the equivalent circuit model are determined using a least squares RLS prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and state data.

[0026] The equivalent circuit model is obtained based on offline tests performed on the battery, and the order of the equivalent circuit model may also be obtained based on offline tests performed on the battery, for example, a first-order equivalent circuit model, a second-order equivalent circuit model, etc., and the element parameter values ​​can characterize the values ​​of each element in the equivalent circuit model.

[0027] Error information may include a sampling error factor for voltage data that explains the difference between the battery voltage data collected by the sampling element and the actual voltage data of the battery. For example, battery voltage data collected by the sampling element

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[0028] Thus, the above technical means, by further considering the sampling error factor of voltage data when determining the element parameter values ​​in the equivalent circuit model of the battery, can reduce the influence of voltage sampling error, further improve the accuracy of the element parameter values ​​in the determined equivalent circuit model, and ultimately achieve the effect of improving the accuracy of the determined equivalent circuit model.

[0029] Furthermore, in some embodiments, the error information may include a sampling error factor for current data that explains the difference between the battery current data collected by the sampling element and the actual current data of the battery. For example, the battery current data collected by the sampling element

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[0030] Thus, the above technical means, by further considering the sampling error factor of current data when determining the element parameter values ​​in the equivalent circuit model of the battery, can reduce the influence of current sampling error, further improve the accuracy of the element parameter values ​​in the determined equivalent circuit model, and ultimately achieve the effect of improving the accuracy of the determined equivalent circuit model.

[0031] It should be noted that in some embodiments, the error information may include both the sampling error factor for voltage data and the sampling error factor for current data. In such cases, step S12 is performed. The process includes the step of determining the element parameter values ​​in the equivalent circuit model using a least squares RLS prediction model, based on the equivalent circuit model of the battery, the sampling error factor of the voltage data, the sampling error factor of the current data, and the state data.

[0032] By using the above technical means to determine the element parameter values ​​in the equivalent circuit model of the battery, and further considering the sampling error factors of voltage data and current data, the influence of sampling errors can be further reduced, the accuracy of the element parameter values ​​in the determined equivalent circuit model can be further improved, and ultimately, the effect of improving the accuracy of the determined equivalent circuit model can be achieved.

[0033] It should be noted that in some embodiments, the error information may further include the sampling time difference between voltage data and current data and the error in the battery open-circuit voltage. In other words, in specific implementations, the sampling error may include, but is not limited to, one or more of the sampling error factors for voltage data, sampling error factors for current data, the sampling time difference between voltage data and current data, and the error in the battery open-circuit voltage.

[0034] In one possible embodiment, Figure 2 shows a flowchart of a method for determining the equivalent circuit model, as shown in the figure, In step S21, initial attribute information of the battery, including the open-circuit voltage-charge state curve and the hysteresis voltage-charge state curve, is obtained based on the battery's offline test.

[0035] Initial attribute information may further include battery capacity information, initial covariance values ​​of battery model parameters, etc., and offline testing may include capacity testing, pulse testing, and typical operating condition testing.

[0036] In some embodiments, the capacity test is performed, and in one embodiment, the capacity test is performed. (1) Adjust the temperature to 25°C, discharge the battery to the lower voltage limit using the capacity test current value (e.g., 1C) suggested by the battery manufacturer, and leave it standing for 30 minutes (step (1)), (2) After charging the battery to the upper voltage limit (e.g., 4.25V) with the capacity test current value (e.g., 1C) suggested by the battery manufacturer, switch to constant voltage charging (the constant voltage value may be the value suggested by the battery manufacturer, e.g., 4.25V), and leave it undisturbed for 30 minutes. (3) Statistically analyze the capacity values ​​accumulated in steps (1) and (2), and repeat steps (1) and (2) until the difference in capacity values ​​between adjacent cycles is less than 0.1 Ah. The capacity value at this time is the battery capacity Q. m This includes step (3) which states the following.

[0037] According to some embodiments of this disclosure, the battery capacity Q m After obtaining the result, a pulse test may be performed on the battery, and the pulse test may include two parts, charging and discharging, each consisting of 20 pulse combination sequences.

[0038] For example, in the preceding 18 pulse combination sequences in the charging section, each pulse combination sequence is: (1) Adjust the temperature to 25°C and apply a constant current charging pulse (amplitude 1C), and the cumulative pulse ampere-hour change is Q m Step (1) stopping until it reaches 5% or more, (2) Step (2) of letting it stand for 2 hours, (3) Step (3) of applying a constant current charging pulse (with an amplitude of 0.5C) for 10 seconds, (4) Step (4) Allow to stand for 40 seconds, (5) Step (5) of applying a constant current discharge pulse (with an amplitude value of 0.1C) for 10 seconds, (6) After replacing 0.1C with 0.5C, 1C, 2C, 3C, 4C, and 5C respectively, repeat step (3) to (5) (6), (7) Step (7) Allow to stand for 24 hours, (8) Adjust the temperature to 55°C and leave it to stand for 2 hours (8), (9) Replace 55℃ with 40℃, 25℃, 10℃, 0℃, -10℃, -20℃, and -30℃ respectively, and repeat step (8) (9), (10) Step (10) to adjust the temperature to 25℃, (11) The step of letting it stand for 24 hours may also be included.

[0039] In the last two pulse combination sequences in the charging section, each set of pulses Combination Sequence teeth, (1) A constant current-constant voltage pulse is applied, and the cumulative ampere-time change of the pulse is Q m Step (1) stopping until it reaches 5% or more, (2) Step (2) of letting it stand for 2 hours, (3) Step (3) of applying a constant current charging pulse (with an amplitude of 0.5C) for 10 seconds, (4) Step (4) Allow to stand for 40 seconds, (5) Step (5) of applying a constant current discharge pulse (with an amplitude value of 0.5C) for 10 seconds, (6) After replacing 0.5C with 1C, 2C, 3C, 4C, and 5C respectively, repeat step (3) to (5), (7) Step (7) Allow to stand for 24 hours, (8) Adjust the temperature to 55°C and leave it to stand for 2 hours (8), (9) Replace 55℃ with 40℃, 25℃, 10℃, 0℃, -10℃, -20℃, and -30℃ respectively, and repeat step (8) (9), (10) Step (10) to adjust the temperature to 25℃, (11) The step of leaving it undisturbed for 24 hours (11) is included.

[0040] Furthermore, regarding the discharge portion, in the 20 pulse combination sequences within the discharge portion, each set of pulses Combination Sequence teeth, (1) Apply a constant current discharge pulse (with an amplitude of 1C), and the cumulative ampere-time change of the pulse is Q m Step (1) stopping until it reaches 5% or more, (2) Step (2) of letting it stand for 2 hours, (3) Step (3) of applying a constant current discharge pulse (with an amplitude of 0.5C) for 10 seconds, (4) Step (4) Allow to stand for 40 seconds, (5) Step (5) of applying a constant current charging pulse (with an amplitude value of 0.5C) for 10 seconds, (6) After replacing 0.5C with 1C, 2C, 3C, 4C, and 5C respectively, repeat step (3) to (5), (7) Step (7) Allow to stand for 24 hours, (8) Adjust the temperature to 55°C and leave it to stand for 2 hours (8), (9) Replace 55℃ with 40℃, 25℃, 10℃, 0℃, -10℃, -20℃, and -30℃ respectively, and repeat step (8) (9), (10) Step (10) to adjust the temperature to 25℃, (11) The step of letting it stand for 24 hours may also be included.

[0041] Thus, the pulse test described above allows each set of pulses in the charging section to be detected. Combination SequenceBased on the battery voltage after standing for 2 h in step (8), change curves of the charging OCV (Open Circuit Voltage) of the battery with respect to the battery SOC under different battery SOCs and different temperature conditions can be obtained. Similarly, for each set of pulses in the discharging part Combination Sequence Based on the battery voltage after standing for 2 h in step (8), change curves of the discharging OCV of the battery with respect to the battery SOC under different battery SOCs and different temperature conditions can be obtained. Under the condition of the same SOC, the average value of the charging OCV and the discharging OCV of the battery is the battery OCV. Half of the difference between the charging OCV and the discharging OCV is denoted as the hysteresis voltage. The change curve of the battery OCV with respect to the battery SOC and temperature is the battery OCV-SOC curve, and the change curve of the hysteresis voltage with respect to the battery SOC and temperature is the hysteresis voltage-state of charge curve.

[0042] Thus, in step S22, based on the initial attribute information, a plurality of initial equivalent circuit models with different orders can be established respectively.

[0043] For example, the L-th order initial equivalent circuit model established based on the initial attribute information is shown in FIG. 3. Here, U OCV and U hys represent the battery open circuit voltage and the battery hysteresis voltage respectively, I and U represent the battery current (discharge is positive) and the battery voltage respectively, R0 is the ohmic internal resistance in the battery circuit model, and R1 to R L are the polarization resistances corresponding to the RC networks 1 to L, C 1 ~C L are the polarization capacitances corresponding to the RC networks 1 to L,

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[0044] In practical implementation, for each initial equivalent circuit model of a given order, the values ​​of each element parameter in such an initial equivalent circuit model can be determined using multiple multi-objective optimization algorithms. Taking a particle swarm algorithm as an example, the values ​​of each element parameter are initialized randomly, and the root mean square of the voltage prediction residual under typical operating conditions of such an equivalent circuit model is used as the fitted value. By continuously iteratively optimizing, the initial element parameter values ​​for such an equivalent circuit model are selected (the optimization conditions may be that the root mean square of the voltage prediction residual under typical operating conditions of such an equivalent circuit model is smaller than a predetermined threshold, or that the number of iterations reaches a threshold), ultimately obtaining initial equivalent circuit models of various orders. In addition, in some embodiments, the order of the equivalent circuit model may be restricted to reduce the complexity of the model. For example, the order L of the equivalent circuit model may be restricted to L ≤ 3, where L is a positive integer.

[0045] In step S23, for each initial equivalent circuit model of the order, the calculation error information and calculation time information for the initial equivalent circuit model under the target operating conditions are tested, respectively.

[0046] Following the previous embodiment, after determining initial equivalent circuit models of various orders, calculation error information and calculation time information under typical operating conditions for each initial equivalent circuit model of each order may be tested.

[0047] Thus, in step S24, the degree of matching for each initial equivalent circuit model is calculated based on the number of parameters, calculation error information, and calculation time information for each initial equivalent circuit model. Then, in step S25, the initial equivalent circuit model with the optimal degree of matching is determined as the equivalent circuit model for the battery.

[0048] By performing an offline test on the battery using the above technical means, initial attribute information of the battery is obtained. According to some embodiments of this disclosure, the accuracy of the equivalent circuit model can be improved by establishing initial equivalent circuit models of different orders based on the initial attribute information and calculating the degree of matching.

[0049] According to some embodiments of this disclosure, the equivalent circuit model is an RC circuit model, and step S11 is A step in which the battery management system (BMS) acquires status data, wherein the status data includes battery temperature data and battery charging Includes step, which further includes state data.

[0050] For example, the BMS may directly acquire battery current data using a current sensor. Alternatively, the BMS may acquire battery temperature data using a temperature sensor. In some embodiments, the BMS may indirectly acquire state data via a corresponding data interface, for example, by acquiring open-circuit voltage-charge state curve information and hysteresis voltage-charge state curve information of the battery stored in memory via the data interface.

[0051] Step S12 is The process includes determining a target open-circuit voltage and a target hysteresis voltage from open-circuit voltage-charge state curves and hysteresis voltage-charge state curves corresponding to battery temperature data, based on charge state data.

[0052] It should be understood that after obtaining open-circuit voltage-charge state curves and hysteresis voltage-charge state curves at different temperatures of the battery through offline testing, the BMS can determine the current target open-circuit voltage and target hysteresis voltage based on the acquired current SOC information, open-circuit voltage-charge state curves, and hysteresis voltage-charge state curves of the battery.

[0053] Thus, in step S12, the RLS prediction model can determine the element parameter values ​​of the battery model based on the equivalent circuit model, error information, current data, voltage data, target open-circuit voltage, and target hysteresis voltage, thereby achieving the effect of identifying the battery model parameter values ​​of the battery online. At the same time, by further considering the sampling error of the BMS sampling device in the identification process, the accuracy of the element parameter values ​​in the determined equivalent circuit model can be further improved, and the accuracy of the equivalent circuit model can be further improved.

[0054] The error information includes at least one of the following: the sampling error factor for voltage data, the sampling error factor for current data, the sampling time difference between voltage data and current data, and the error in the battery open-circuit voltage.

[0055] According to some embodiments of this disclosure, the error information includes sampling error factors for voltage data and sampling error factors for current data, and the identification format of the RLS prediction model is

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[0056] Using Figure 3 as an example, we can derive a general formula for the initial equivalent circuit of an L-order battery in Laplace space based on the L-order initial equivalent circuit model shown in Figure 3.

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[0057]

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[0058] If defined by the following formula,

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[0059] When L=1, the formula is as shown in equation (5) below.

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[0060] When L=2, the formula is as shown in equation (6) below.

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[0061] When L=3, the formula is as shown in equation (7) below.

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[0062] Equation (4) allows us to obtain the discretized representation of the equivalent circuit model, which is shown in equation (8).

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[0063] Furthermore, in some embodiments, since the measurement error of the in-vehicle BMS is colored noise, the following two types of errors, (1) and (2), may be considered. (1) Measurement error of voltage data:

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[0064] According to equations (9) and (10), in an in-vehicle environment,

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[0065] If defined by the following formula,

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[0066] Afterward, by using the method for solving simultaneous equations of multiple variables to solve equations (3), (5) to (7) in reverse, we can obtain the battery element parameter value P. parameter =[R0,R1~R L ,C1~C L ] can be found. For example, by solving equations 12 and 13 simultaneously, θ(k) can be found, and if θ(k) is known, the battery element parameter value P can be found by solving equations 3, 5-7 (determined based on the order of the battery model) in reverse. parameter =[R0,R1~R L ,C1~C L This allows us to obtain [the following], and furthermore, by substituting the battery model parameters, we can determine the equivalent circuit model of the battery.

[0067] The applicant found the following: In several scenarios, the error information includes the sampling error factor for voltage data, the sampling error factor for current data, the sampling time difference between voltage data and current data, and the error in the battery open-circuit voltage, and the identification format of the RLS prediction model is,

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[0068] Following the explanation of equation (10) in the previous embodiment, in some scenarios, the following error (3) may also be considered. (3) The sampling time difference between current data and voltage data, i.e.

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[0069] In this way, we can perform a Taylor expansion on equation (14) to obtain equation (15). I'(k+ε3)=I'(k)+ε3·I'(k)(15)

[0070]

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[0071] Furthermore, since the parameter estimation process for the equivalent circuit model is separated from the battery SOC estimation process, the influence of battery SOC errors may be further considered in the parameter estimation process. That is, if there is an error in the battery SOC, the battery OCV error may be considered in the parameter estimation.

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[0072] It should be noted that there may be differences in the sampling error factors for voltage data, current data, and the magnitude of the sampling time difference between voltage and current data across different BMS systems. If the magnitude of the sampling time difference between voltage and current data is not negligible, Considering equations (9), (10), (14) to (17), in an in-vehicle environment,

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[0073] Furthermore,

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[0074] Using the above technical means, the element parameter values ​​in the equivalent circuit model can be determined by inversely solving the battery model parameters using the least squares RLS prediction model. Furthermore, in the case of the least squares RLS prediction model, by considering the errors in various data acquired by the BMS sampling device, it is possible to reduce the influence of errors in various data acquired by the BMS sampling device (for example, colored measurement noise due to the constant degradation of the BMS sampling device, and estimation errors of the equivalent circuit model parameter values ​​due to asynchronousness between the current and voltage measurement processes in the BMS measurement process) on the estimation of the equivalent circuit model parameter values ​​under complex in-vehicle conditions, thereby further solving the problem of reduced accuracy in subsequent battery state estimation.

[0075] It should be noted that the above method was described using the example that the error information simultaneously includes the sampling error factor of the voltage data, the sampling error factor of the current data, the sampling time difference between the voltage data and the current data, and the error of the battery open-circuit voltage. However, as those skilled in the art should understand, in specific implementation, the error information may include one or more of the above-mentioned sampling error factors of the voltage data, sampling error factors of the current data, the sampling time difference between the voltage data and the current data, and the error of the battery open-circuit voltage. To avoid unnecessary redundancy, this disclosure does not separately describe various possible combinations.

[0076] In S13, the estimated state of charge (SOC) of the battery is determined according to observer technology, based on the element parameter values, state data, and battery characteristic data in the equivalent circuit model.

[0077] In practical implementation, when power is supplied to an electric vehicle, observers can be initialized based on each initial value condition, for example, by initializing the battery state vector.

[0078] In step S13, one feasible approach is that the state data further includes temperature data, and the observer is an adaptive unscented Kalman filter (AUKF) observer, and accordingly, the step of determining the estimated state of charge of the battery according to the observer technique based on the element parameter values ​​and state data in the equivalent circuit model includes the following steps S131 to S135.

[0079] In S131, a set of state vector feature points, a first weight coefficient, and a second weight coefficient are generated based on the battery state vector estimate and state vector covariance from the previous time step.

[0080] The state vector covariance at the previous time point is calculated based on the process noise variance.

[0081] According to some embodiments of this disclosure, an electric vehicle initializes an AUKF observer based on offline battery test results after power is supplied to the vehicle, at which point the battery state vector estimate and state vector covariance are obtained after initialization, while the battery state vector estimate and state vector covariance at other times are calculated based on the battery SOC estimate at the previous time.

[0082] In S132, the first state vector prior value is calculated using the first state equation of the battery state-space equation, based on the element parameter values, the set of state vector feature points, and the current data.

[0083] In S133, the measurement correction matrix is ​​determined based on the first state vector prior value, measurement noise variance, temperature data, current data, first weighting coefficient, second weighting coefficient, and the second output equation of the battery state space equation.

[0084] In S134, the second state vector posterior value is calculated based on the measurement correction matrix, the first state vector prior value, and the voltage data.

[0085] In S135, the estimated State of Computing (SOC) of the battery is determined based on the second state vector posterior value and the battery state-space equation.

[0086] In some embodiments, initializing the observer based on each initial value condition further includes initializing the battery state vector covariance, process noise variance, and measurement noise variance. Then, the array length is set as shown in the following equation.

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[0087] For example, a state vector feature point set is generated using a symmetric sampling method, and the classification form is shown in the following equation.

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[0088] Furthermore, by using the first state equation in the battery state-space equation, time updates are performed on the set of state vector feature points, and as shown in equation (22), the first state vector prior value

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[0089] As another example, the first weighting coefficient

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[0090] Furthermore, the first weighting coefficient

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[0091] The difference between the first state vector prior value and the first expected value is as follows:

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[0092] For example, the measurement correction matrix can be determined using the following identification format.

[0093] First, the set of state vector feature points.

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[0094] Furthermore, the difference between the first output estimate and the second expected value of the calculated j-th feature point.

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[0095] Equations (24) and (25) are the second output equations of the battery state-space equations.

[0096] Furthermore, based on the measurement correction matrix, the first state vector prior value

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[0097] Finally, the estimated battery state of charge (SOC) is calculated based on the posterior values ​​of the state vector and the battery state-space equations.

[0098] According to some embodiments of this disclosure, the state vector covariance at a previous time is calculated based on the process noise variance, and this calculation is performed Based on the first weight coefficient and the first state vector prior value, the first expected value is calculated, Based on a second weighting coefficient, the difference between the first state vector prior value and the first expected value, and the process noise variance, a state vector covariance prior value is obtained. Based on current data, temperature data, measurement noise variance, element parameter values, and a first state vector prior value, a first output estimate is determined. Based on the first weight coefficient and the first output estimate, the second expected value is calculated, This includes calculating the state vector covariance at the previous time point based on a measurement correction matrix, a prior value of the state vector covariance, and the difference between the first output estimate and the second expected value.

[0099] For example, the first expected value is given by the following formula

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[0100] According to some embodiments of this disclosure, based on the measurement correction matrix and the first output estimate calculated in the above example, the state vector covariance posterior value is calculated using the following formula and used to determine the battery SOC estimate for the next time step.

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[0101] According to some embodiments of the present disclosure, the process noise variance and the measurement noise variance are determining an output residual based on a voltage parameter and voltage data in an element parameter value; calculating an output residual matrix based on a first output residual and the output residual in a SOC estimated value of the battery calculated at a previous time; determining a theoretical process noise variance and a theoretical measurement noise variance based on the output residual matrix; determined by determining a process noise variance and a measurement noise variance based on the theoretical process noise variance and the theoretical measurement noise variance according to a noise correction rule.

[0102] Exemplarily, as shown in Equation (27), when combining battery voltage data based on the battery model voltage output value, the output residual U e (k) of the current model can be calculated. [Number]

[0103] Combining the output residuals at the previous time, using Equation (28) to calculate the output residual matrix H(k), and obtaining the theoretical noise variances Q id (k) and R id (k) based on Equation (29). [Number] [Number]

[0104] As shown in Equations (30) and (31), calculate the process noise variance Q(k + 1) and the measurement noise variance R(k + 1) according to the noise correction rule. [Number]

number

[0105] According to some embodiments of this disclosure, the noise correction rule is: If the theoretical measurement noise variance is smaller than a predetermined threshold, the output residual from the previous time point is used as the process noise variance and the measurement noise variance.

[0106] According to some embodiments of this disclosure, after the output residuals of the previous time are treated as process noise variance and measurement noise variance, the observer is downgraded from an adaptive unscented Kalman filter (AUKF) observer to an unscented Kalman filter (UKF) observer.

[0107] The noise correction rule includes setting the process noise variance to the larger of the initial value of the process noise variance and the trace of the matrix of the theoretical process noise variance, and setting the measurement noise variance to the larger of the initial value of the measurement noise variance and the theoretical measurement noise variance, if the value of the theoretical measurement noise variance is greater than or equal to a predetermined threshold.

[0108] In step S13, another feasible method is that the state data further includes temperature data and the observer is a Luenberger observer, and accordingly, the step of determining the estimated state of charge of the battery according to observer technology based on the element parameter values ​​and state data in the equivalent circuit model includes the following steps S1301 to S1304.

[0109] In S1301, the second state vector prior value is determined based on the estimated battery state vector, element parameter values, current data, and the second state equation of the battery state space equation.

[0110] In S1302, a second output estimated value is determined based on a second state vector pre-value, temperature data, current data, and a second output equation of the battery state space equation.

[0111] In S1303, a second state vector post-value is determined based on the second output estimated value, voltage data, and a predetermined gain of the Luenberger observer.

[0112] In S1304, a SOC estimated value of the battery is determined based on the second state vector post-value and the battery state space equation.

[0113] According to some embodiments of the present disclosure, after the electric vehicle is powered on, the Luenberger observer is initialized based on the offline test result of the battery. At this time, the battery state vector estimated value is obtained after initialization, and the battery state vector estimated values at other times are calculated based on the battery SOC estimated value at the previous time.

[0114] Exemplarily, the expression form of the second state equation of the battery state space equation is as follows.

Equation

Equation

[0115] Exemplarily, the expression form of the second output equation of the battery state space equation is as follows.

Equation

Equation

[0116] For example, the posterior value of the second state vector can be calculated using the following formula.

number

[0117] According to some embodiments of this disclosure, the battery state-space equation is as follows:

number

[0118] For an L-order RC circuit, the following equation applies.

number

[0119] Taking the problem of estimating the battery SOC under dynamic operating conditions of a ternary lithium-ion battery manufactured by a certain manufacturer as an example, Figure 6 is an effect diagram of a method for determining the battery charge state according to an exemplary embodiment of the relevant technology. As can be seen from the figure, the maximum error in the battery charge state is greater than 16%, making it difficult to meet the 5% accuracy requirement specified in the BMS national standard. Figure 7 is an effect diagram of a method for determining the battery charge state according to an exemplary embodiment of the present disclosure. For the same dynamic operating conditions, the present invention improves upon the estimation of RLS and AUKF, and as can be seen from the figure, the error in the battery charge state is less than 5% in both cases, demonstrating that the accuracy of the present invention can meet the SOC accuracy requirement of the BMS national standard.

[0120] In the above technical means, the element parameter values ​​in the equivalent circuit model are determined by a least squares RLS prediction model. Furthermore, when determining the element parameter values ​​in the equivalent circuit model of the battery, by further considering error information including at least the sampling error factor of voltage data or the sampling error factor of current data, or error information including the sampling error factor of both voltage data and current data, the influence of sampling error can be reduced, the accuracy of the element parameter values ​​in the determined equivalent circuit model can be further improved, and ultimately the effect of improving the accuracy of the determined equivalent circuit model of the battery can be achieved. According to some embodiments of this disclosure, the SOC value of the battery is determined according to observer technology, the degree of matching between element parameter values ​​and observers is improved, the accuracy of estimating the battery charge state is further improved, and efficient management and reliable operation of the vehicle are ensured.

[0121] This disclosure further provides an apparatus for determining the battery charge state, and with reference to a block diagram of an apparatus for determining an equivalent circuit model shown in Figure 8, the apparatus 800 is: An acquisition module 810 acquires battery status data including current data and voltage data, A first determination module 820 determines the element parameter values ​​in the equivalent circuit model using a least squares RLS prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and state data. The system includes a second determination module 830 that determines an estimated state of charge (SOC) of the battery according to observer technology, based on element parameter values, state data, and battery characteristic data in an equivalent circuit model.

[0122] According to some embodiments of this disclosure, a third determination module for determining the order of an equivalent circuit model is: An acquisition submodule that acquires initial attribute information of the battery, including the open-circuit voltage-charge state curve and the hysteresis voltage-charge state curve, based on offline testing of the battery. Based on initial attribute information, an establishment submodule establishes multiple initial equivalent circuit models of different orders, A test submodule that tests the calculation error information and calculation time information of the initial equivalent circuit model under the target operating conditions for each order, A calculation submodule that calculates the degree of matching for each initial equivalent circuit model based on the number of parameters, calculation error information, and calculation time information for each initial equivalent circuit model, It includes a decision submodule that determines the initial equivalent circuit model with the optimal degree of matching as the equivalent circuit model of the battery.

[0123] According to some embodiments of this disclosure, the equivalent circuit model is an RC circuit, the acquisition module acquires state data collected by the battery management system (BMS), and the state data includes battery temperature data and battery charging It further includes state data, The first decision module is, A first determination submodule determines the target open-circuit voltage and target hysteresis voltage from the open-circuit voltage-charge state curve and hysteresis voltage-charge state curve corresponding to the battery temperature data, based on the charge state data. It includes a second determination submodule that determines the element parameter values ​​of the battery's equivalent circuit model using an RLS prediction model, based on the equivalent circuit model, error information, current data, voltage data, target open-circuit voltage, and target hysteresis voltage.

[0124] According to some embodiments of this disclosure, the error information includes at least one of the following: a sampling error factor for voltage data, a sampling error factor for current data, a sampling time difference between voltage data and current data, and an error in the battery open-circuit voltage.

[0125] According to some embodiments of this disclosure, the error information includes the sampling error factor for voltage data, the sampling error factor for current data, the sampling time difference between voltage data and current data, and the error in the battery open-circuit voltage, and the identification format of the RLS prediction model is as follows:

number

number

number

number

number

number

number

number

[0126] According to some embodiments of this disclosure, the observer is an adaptive unscented Kalman filter (AUKF) observer, and accordingly, the second decision module is It includes a generator submodule that generates a set of state vector feature points, a first weighting coefficient, and a second weighting coefficient based on the battery state vector estimate and state vector covariance from the previous time step.

[0127] The state vector covariance at the previous time point is calculated based on the process noise variance.

[0128] The second decision module also includes a fourth decision submodule that calculates a first state vector prior value using the first state equation of the battery state-space equation, based on element parameter values, a set of state vector feature points, and current data. A fifth determination submodule determines the measurement correction matrix based on the first state vector prior value, measurement noise variance, temperature data, current data, first weighting coefficient, second weighting coefficient, and the second output equation of the battery state space equation. A sixth decision submodule that calculates a second state vector posterior value based on a measurement correction matrix, a first state vector prior value, and voltage data, It includes a seventh determination submodule that determines the estimated state of charge (SOC) of the battery based on a second state vector posterior value and the battery state-space equation.

[0129] According to some embodiments of this disclosure, the second decision module is: A first calculation submodule that determines the output residual based on the voltage parameter and voltage data in the element parameter values, A second computation submodule calculates the output residual matrix based on the first output residual and output residual in the battery SOC estimate calculated at the previous time step, A third output submodule that determines the theoretical process noise variance and theoretical measurement noise variance based on the output residual matrix, The system further includes a fourth output submodule that determines the process noise variance and the measured noise variance based on the theoretical process noise variance and the theoretical measured noise variance according to noise correction rules.

[0130] According to some embodiments of this disclosure, the second decision module is: If the theoretical measurement noise variance value is smaller than a predetermined threshold, a first decision submodule uses the output residual from the previous time as the process noise variance and measurement noise variance. The system further includes a second decision submodule which, if the numerical value of the theoretical measurement noise variance is greater than or equal to a predetermined threshold, sets the larger of the initial process noise variance value and the trace of the theoretical process noise variance matrix as the process noise variance, and sets the larger of the initial measurement noise variance value and the theoretical measurement noise variance as the measurement noise variance.

[0131] According to some embodiments of this disclosure, the observer is a Luenberger observer, and accordingly, the second decision module is A twelfth determination submodule determines the second state vector prior value based on the estimated battery state vector, element parameter values, current data, and the second state equation of the battery state space equation, A thirteenth decision submodule that determines a second output estimate based on a second state vector prior value, temperature data, current data, and a second output equation of the battery state space equation, A 14th determination submodule that determines a second state vector posterior value based on a second output estimate, voltage data, and a predetermined gain of the Luenberger observer, It includes a 15th determination submodule for determining the State of Charge (SOC) estimate of the battery based on a second state vector posterior value and the battery state-space equation.

[0132] According to some embodiments of this disclosure, the battery state-space equation is as follows:

number

[0133] The specific methods by which each module performs operations in the apparatus described above are described in detail in the embodiments related to the method and will not be described in detail here.

[0134] Furthermore, for the sake of convenience and conciseness, the embodiments described in this specification are all preferred embodiments, and the parts relating to them are not necessarily essential to the present invention. For example, the first decision module and the second decision module may be independent devices or the same device when actually implementing the invention, and this disclosure does not limit this.

[0135] The disclosure further provides a computer-readable storage medium that stores a computer program which, when executed by a processor, implements a step of a method for determining the battery charge state in any one of the above embodiments.

[0136] This disclosure provides an electronic device which includes a memory in which a computer program is stored, The system includes a processor that executes a computer program stored in memory to perform the steps of the method for determining the battery charge state in any one of the embodiments described above.

[0137] Figure 9 is a block diagram of a device 900 for determining an equivalent circuit model according to one exemplary embodiment. Referring to Figure 9, the device 900 may include one or more assemblies from among a processing assembly 902, a memory 904, a power assembly 906, a multimedia assembly 905, an input / output (I / O) interface 912, a sensor assembly 914, and a communication assembly 916.

[0138] The processing assembly 902 generally controls the overall operation of the device 900, such as data acquisition, sensor data processing, and RLS algorithm determination. The processing assembly 902 may include one or more processors 920 to execute instructions and complete all or some of the steps of the method for determining the battery charge state. The processing assembly 902 may also include one or more modules to facilitate interaction between the processing assembly 902 and other assemblies. For example, the processing assembly 902 may include a multimedia module to facilitate interaction between the multimedia assembly 905 and the processing assembly 902.

[0139] Memory 904 is arranged to store various data to support operations on device 900. Examples of this data include instructions for any application program or method executed on device 900, historical current data, historical voltage data, open-circuit voltage-charge state curves of the battery, hysteresis voltage-charge state curves, and so on. Memory 904 can be implemented with any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0140] The power assembly 906 supplies power to various assemblies of the device 900. The power assembly 906 may include a power management system, one or more power supplies, and assemblies related to generating, managing, and allocating power to the device 900.

[0141] The multimedia assembly 905 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to detect gestures on the touch panel, such as touches and slides. The touch sensors can not only sense the boundaries of a touch or slide operation, but also further detect the duration and pressure associated with the touch or slide operation.

[0142] The I / O interface 912 provides an interface between the processing assembly 902 and the peripheral interface module, which may be a click wheel, a button, or the like.

[0143] The sensor assembly 914 includes one or more sensors that provide the device 900 with condition evaluations in various aspects. For example, the sensor assembly 914 can detect the temperature, current, etc. of a battery. In some embodiments, the sensor assembly 914 may include, for example, a temperature sensor, a speed sensor, a current sensor, etc.

[0144] The communication assembly 916 is arranged to facilitate wired or wireless communication between the device 900 and other devices. The device 900 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof.

[0145] In exemplary embodiments, the apparatus 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing units (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements to perform the method for determining the battery charge state.

[0146] In exemplary embodiments, a non-temporary computer-readable storage medium containing instructions, such as a memory 904 containing instructions, is further provided, and the instructions can be executed by the processor 920 of the device 900 to complete the method for determining the battery charge state. For example, the non-temporary computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, flexible disk, and optical data storage device.

[0147] In another exemplary embodiment, a computer program product is further provided, which includes a computer program executable by a programmable device, the computer program having a code portion for performing the method for determining the battery charge state when executed by the programmable device.

[0148] This disclosure further provides a battery management system including a device for determining the battery charge state described in any one of the above paragraphs.

[0149] The specific methods by which each device performs operations in the battery management system described above are explained in detail in the embodiments related to the method and will not be explained in detail here.

[0150] While preferred embodiments of the present disclosure have been described in detail above with reference to the drawings, the present disclosure is not limited to the specific details of the above embodiments. Several simple modifications can be made to the technical means of the present disclosure within the scope of the technical concept of the present disclosure, and all such simple modifications fall within the scope of the protection of the present disclosure.

[0151] Furthermore, each specific technical feature described in the above-described embodiment can be combined in any suitable manner, provided they do not conflict. To avoid unnecessary redundancy, this disclosure does not separately describe all possible combinations.

[0152] Furthermore, the various embodiments of this disclosure can be combined in any way and should be considered as part of the content disclosed herein, provided that they do not deviate from the concept of this disclosure. [Explanation of Symbols]

[0153] Battery charge state determination device-800, acquisition module-810, first determination module 820, second determination module 830, equivalent circuit model device-900, processing assembly-902, memory-904, multimedia assembly-905, power assembly-906, input / output (I / O) interface-912, sensor assembly-914, communication assembly-916, processor-920.

Claims

1. A step of acquiring battery status data, including current data and voltage data, collected by a battery management system (BMS), The steps include determining the element parameter values ​​in the equivalent circuit model using a least squares RLS prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and state data, A method for determining the battery charge state, comprising the steps of determining an estimated battery charge state of the battery according to observer technology based on element parameter values ​​in the equivalent circuit model, state data, and battery characteristic data, The equivalent circuit model described above is: The steps include obtaining initial attribute information of the battery, including an open-circuit voltage-charge state curve and a hysteresis voltage-charge state curve, based on an offline test of the battery; Based on the aforementioned initial attribute information, the steps include establishing multiple initial equivalent circuit models of different orders, For each order of the initial equivalent circuit model, the steps include testing the calculation error information and calculation time information under the target operating conditions of the initial equivalent circuit model, respectively. A step of calculating the degree of matching for each initial equivalent circuit model based on the number of parameters of each initial equivalent circuit model, the calculation error information, and the calculation time information, A method for determining the battery charge state, comprising the steps of determining an initial equivalent circuit model with the optimal degree of matching as the equivalent circuit model of the battery, The error information includes the sampling error factor of the voltage data, the sampling error factor of the current data, the sampling time difference between the voltage data and the current data, and the error of the battery open-circuit voltage, and the identification format of the least squares RLS prediction model is, [Math 1] And, Here, [Math 2] This is the measured value of the output signal at time k of the least squares RLS prediction model, U ＯＣＶ (k) and U ｈｙｓ (k) represents the target open-circuit voltage and target hysteresis voltage of the battery at the kth time, respectively. [Math 3] is the battery voltage value collected by the BMS at time k, and φ(k) is the input signal at time k of the least squares RLS prediction model. [Math 4] is the battery current value collected by the BMS at the k-th moment, θ(k) is the parameter matrix of the k-th moment of the least squares RLS prediction model, and a １ ~a Ｌ 、a ０ 、c １ 、d ０ ~d Ｌ 、c ２ are the parameters in the parameter matrix, L is the order of the initial equivalent circuit model, Here, [Math 5] And U(k) is the true voltage of the battery at the kth time, ε １ This is the sampling error factor for voltage data, [Math 6] And I is the battery current sampled synchronously with the voltage, and ε ２ ε is the sampling error factor for current data. ３ This is the sampling time difference between current data and voltage data. [Number 7] And, [Number 8] and ε ４ The following are methods for representing the battery OCV value and the corresponding error in the battery open-circuit voltage when there is an error in the battery charge state of the battery.

2. The equivalent circuit model of the battery is an RC circuit model, and the step of acquiring the state data of the battery is: A step of acquiring the status data collected by the battery management system (BMS), wherein the status data further includes the temperature data of the battery and the charge status data of the battery. The step of determining the element parameter values ​​in the equivalent circuit model using a least squares RLS prediction model based on the equivalent circuit model of the battery, error information, battery characteristic data, and state data is as follows: Based on the aforementioned charge state data, the steps include determining a target open-circuit voltage and a target hysteresis voltage from the open-circuit voltage-charge state curve and the hysteresis voltage-charge state curve corresponding to the temperature data of the battery, The method according to claim 1, comprising the step of determining element parameter values ​​of the equivalent circuit model of the battery using an RLS prediction model based on the equivalent circuit model, the error information, the current data, the voltage data, the target open-circuit voltage, and the target hysteresis voltage.

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