A state of energy (SOE) estimation method based on an equivalent model of a battery

Abstract

This invention relates to the field of battery management system technology for new energy vehicles, specifically disclosing a method for estimating the state of energy (SOE) based on a battery equivalent model. The battery equivalent model is established by estimating the battery's state using an equivalent circuit model. Static theoretical remaining energy estimation involves the battery management system collecting the battery's open-circuit voltage information, correcting the OCV-SOE relationship based on the battery's resting time and its state before resting, and then calculating the corresponding battery SOE value based on the current OCV value. Future remaining discharge energy is predicted based on the current battery state estimation and future state prediction. After the BMS is woken up, it enters a working state and calculates the remaining energy of the battery at the current moment. This invention effectively improves the accuracy of remaining energy estimation, thereby further accurately estimating the driving range and reducing driving problems caused by estimation errors. This is of great significance for promoting the development and popularization of electric vehicles.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology for new energy vehicles, and specifically to a method for estimating the state of energy (SOE) based on a battery equivalent model. Background Technology

[0002] After a new energy vehicle is powered on by the BMS, the theoretical remaining energy of the battery is calculated based on its charging or discharging state and the battery's EMF-SOE characteristics. Combined with the internal resistance value estimated by the SOH (State of Harmonic Hierarchy), the battery energy loss is calculated. Based on this energy loss, the actual remaining energy of the battery is estimated. Finally, the driving range of the electric vehicle is estimated based on this estimated actual remaining energy value.

[0003] The existing technology for estimating the remaining energy of a battery has the following problems:

[0004] (1) The existing scheme does not analyze the impact of ambient temperature, self-discharge rate and cycle life characteristics on battery charge / discharge performance, which affects the estimation accuracy;

[0005] (2) Existing solutions can only estimate the DC internal resistance of the battery, but cannot estimate the energy loss caused by the polarization internal resistance of the battery; battery voltage, SOC and other battery parameters can only characterize the current state, and lack the characteristic characterization of the future state of the battery; and do not take into account the impact of future operating conditions on the prediction of remaining energy.

[0006] Therefore, there is an urgent need to design a state of energy (SOE) estimation method based on a battery equivalent model, considering the influence of various factors on battery characteristics, and to estimate the remaining energy of the battery from an engineering application perspective, so as to effectively solve the problems of accuracy and reliability of remaining energy estimation. Summary of the Invention

[0007] To address the problems existing in the prior art, the purpose of this invention is to provide a method for estimating the state of energy (SOE) based on a battery equivalent model.

[0008] The technical solution adopted by this invention to solve its technical problem is: a method for estimating the state of energy (SOE) based on a battery equivalent model, comprising the following steps:

[0009] S1. Battery equivalent model establishment: The battery state is estimated by using an equivalent circuit model. The equivalent circuit model needs to identify the model parameters for different temperatures and different SOC ranges. Using the commonly used OCV measurement method, the battery is charged and discharged to different SOC stage points, and then left to stand for a long time until the open circuit voltage stabilizes before measuring the OCV.

[0010] S2. Static theoretical remaining energy estimation: The battery management system collects the open circuit voltage information of the battery, corrects the relationship between OCV-SOE based on the battery resting time and the state before resting, and then calculates the corresponding battery SOE value based on the current OCV value.

[0011] S3. Predict future remaining discharge energy: Based on the current state estimation of the battery and the prediction of the future state, including the current state estimation of the battery, the future operating condition estimation of the vehicle, the prediction of the future SOC sequence, the prediction of future parameter changes, the prediction of the future voltage curve, and the calculation of the discharge cutoff point;

[0012] After S4 and BMS are woken up, they enter the working state. They estimate the initial SOE of the battery based on the battery resting time, and calculate the current internal resistance of the battery based on the obtained battery cycle number and battery temperature. Based on the predicted battery charge / discharge cutoff point SOC range, the predicted terminal voltage range, and the predicted current sequence, they simultaneously calculate the internal resistance energy consumption. Based on the calculated static theoretical remaining energy and battery internal resistance energy loss, after self-discharge rate correction, they calculate the remaining energy of the battery at the current moment.

[0013] Preferably, the relevant mathematical relationship of the battery state in the battery equivalent model in step S1 is as follows:

[0014]

[0015] In the formula, OCV represents the open-circuit voltage, R0 represents the battery internal resistance, and R p C represents resistance. p I represents capacitance, and I represents current. In the battery equivalent model, OCV, R0, and R... p C p These four parameters are used for identification: battery internal resistance R0 and polarization parameter R. p C p The identification adopts the HPPC test condition design test scheme.

[0016] Preferably, the battery open-circuit voltage during charging in the battery equivalent model is expressed as:

[0017]

[0018] Ohmic internal resistance R Ω The calculation can be expressed as:

[0019]

[0020] The rebound voltage versus time curve after the battery has been left to stand is calculated using a function: After fitting, parameters b1, τ1, b2, and τ2 are obtained, and then the parameters are calculated:

[0021]

[0022] Preferably, the battery SOE value in step S2 is calculated by looking up the OCV-SOE curve based on the curves after the intermittent charging and discharging of the power battery.

[0023] Preferably, the battery current state estimation in step S3, i.e. the state estimation of the starting point of the remaining energy prediction, is achieved by collecting the current and terminal voltage of the current battery through the BMS to make an accurate online estimation of the current SOC state of the battery.

[0024] Preferably, the future operating conditions of the vehicle in step S3 are represented by future power, and then simultaneously calculated from the instantaneous power w of the vehicle. I The average power of the car during this trip (w) C Average power per unit travel (W) H The future power is predicted by weighting three aspects. After obtaining the future power, it is converted into future current by the model and used as the input of future operating conditions in the prediction process.

[0025] Future power is calculated based on vehicle history and real-time information, representing the instantaneous power of the vehicle. I The average power consumption of the car during this trip was [w]. C Total travel of the car H The weighting process is performed, using the vehicle's current mileage (Lc) and the remaining battery energy calculated subsequently as parameters, combined with various power values ​​(P). I P C P H Calculations are performed to obtain the processed power, which is then used as the car's future power to calculate the remaining battery energy.

[0026]

[0027] Based on the equivalent circuit model, the formula for calculating the future current is:

[0028]

[0029] At the current time t, with respect to a future time t pre Conducting future SOC pre The prediction is based on the ampere-hour integral method, which uses the integration of future current with time to predict future state of charge (SOC). pre Changes:

[0030]

[0031] Predicting and calibrating the future discharge cutoff point requires input of future current and temperature. By conducting discharge experiments on batteries of different capacities under different operating conditions, the discharge cutoff law of individual cells can be obtained, and the discharge cutoff range can be determined.

[0032] Preferably, in step S3, the predicted future parameter changes are such that the battery parameters at a future point in time depend only on the current battery temperature and the future battery SOC state, and the previously predicted SOC(t) is used to calculate the future battery parameters. pre The battery's future temperature t is obtained by interpolating the data with the current battery temperature T and the parameter identification data. pre The basic parameters R0(t) pre ), R p (t pre ), C p (t pre ) and τ.

[0033] Preferably, the predicted future voltage curve in step S3 uses a first-order equivalent circuit model as the voltage prediction model. The formula for calculating the future terminal voltage is:

[0034] U t (t pre ) = OCV(SOC pre )-I pre (t pre )·R0(t pre )-U(t pre );

[0035] U p (t pre ) = I pre (t pre )·R p (t pre )·(1-e -t / τ ).

[0036] Preferably, the discharge cutoff point in step S3 is calculated based on future current and temperature as parameters, and the SOC cutoff point is calibrated experimentally.

[0037] The present invention has the following beneficial effects:

[0038] The energy state (SOE) estimation method based on the battery equivalent model designed in this invention effectively improves the estimation accuracy of remaining energy, thereby further accurately estimating the driving range and reducing driving problems caused by estimation errors. This is of great significance for promoting the development and popularization of electric vehicles. Attached Figure Description

[0039] Figure 1 It is a voltage curve of the battery's remaining discharge energy.

[0040] Figure 2 This is the circuit diagram of the battery equivalent model.

[0041] Figure 3 This is the flowchart for the OCV identification test.

[0042] Figure 4 This is a graph of experimental data for HPPC parameter identification.

[0043] Figure 5 This is a curve showing the relationship between the OCV and SOE of the power battery.

[0044] Figure 6 This is a flowchart of the calculation process for the residual energy in static theory.

[0045] Figure 7 This is a flowchart for predicting the remaining energy of future batteries. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] The abbreviations of the English terms used in this invention are explained in Table 1 below:

[0048]

[0049]

[0050] A method for estimating the state of energy (SOE) based on a battery equivalent model includes the following specific technical solutions.

[0051] like Figure 1 As shown, the remaining energy of the battery is represented by the area integral on the coordinate system enclosed by the battery terminal voltage and the cumulative discharge capacity, based on the current state estimate and future state prediction.

[0052] The discharge Ah terminal voltage curve is the battery terminal voltage curve under a certain operating condition. The shaded area under this curve represents the battery's remaining energy E at the current moment under this operating condition. RDE This can be expressed as:

[0053]

[0054] Battery remaining energy prediction combines current state estimation and future state prediction. It requires determining the terminal voltage change curve and the start and end points of discharge as the upper and lower limits of integration to perform accurate operating condition prediction and terminal voltage response prediction. This includes theoretical remaining energy estimation and actual remaining energy estimation. The battery remaining energy prediction process of this invention is as follows:

[0055] (1) Static theoretical remaining energy estimation: The battery management system collects the open circuit voltage information of the battery, corrects the relationship between OCV-SOE based on the battery resting time and the state before resting, and then calculates the corresponding battery SOE value based on the current OCV value.

[0056] (2) Vehicle future operating condition estimation: The vehicle's future operating condition is represented by future power, and then simultaneously calculated from the vehicle's instantaneous power w I The average power of the car during this trip (w) C Average power per unit travel (W) H The future power is predicted by weighting three aspects. After obtaining the future power, it is converted into future current through the model and used as the input of future operating conditions in the prediction process.

[0057] (3) Battery current state estimation: that is, the state estimation of the starting point of the remaining energy prediction. The current current and terminal voltage of the battery are collected by the BMS to make an online and accurate estimation of the current SOC state of the battery.

[0058] (4) Predict future SOC sequences.

[0059] (5) Predicting future parameter changes: The battery parameters at a future moment depend only on the current battery temperature and the future battery SOC state. The previously predicted SOC(t) will be used to predict the future battery temperature and future battery SOC state. pre The battery's future temperature t is obtained by interpolating the data with the current battery temperature T and the parameter identification data. pre The basic parameters R0(t) pre ), R p (t pre ), C p (t pre ) and τ.

[0060] (6) Predicting Future Voltage Curves: A first-order equivalent circuit model is selected as the voltage prediction model. The formula for calculating the future terminal voltage is:

[0061] U t (t pre ) = OCV(SOC pre )-I pre (t pre )·R0(t pre )-U(t pre );

[0062] U p (t pre ) = I pre (t pre )·R p (t pre )·(1-e -t / τ ).

[0063] (7) The discharge cutoff point is calculated based on future current and temperature as parameters, and the SOC cutoff point is calibrated through experiments.

[0064] Battery equivalent model establishment: such as Figure 2 As shown, the state of the battery is estimated using an equivalent circuit model.

[0065] The relevant mathematical relationship of battery state in the battery equivalent model is as follows:

[0066]

[0067] In the formula, OCV represents the open-circuit voltage, R0 represents the battery internal resistance, and R p C represents resistance. p I represents capacitance, and I represents current. In the battery equivalent model, OCV, R0, and R... p C p These four parameters are used for identification.

[0068] like Figure 3 As shown, using the common OCV measurement method, the battery is charged and discharged to different SOC stage points, then left to stand for a long time until the open circuit voltage stabilizes, and then the OCV is measured.

[0069] like Figure 4 As shown, the battery internal resistance R0 and polarization parameter R p C p The identification adopts the HPPC test condition design test scheme.

[0070] In the battery equivalent model, the open-circuit voltage of the battery during charging is expressed as:

[0071]

[0072] Ohmic internal resistance R Ω The calculation can be expressed as:

[0073]

[0074] The rebound voltage versus time curve after the battery has been left to stand is calculated using a function: After fitting, parameters b1, τ1, b2, and τ2 are obtained, and then the parameters are calculated:

[0075]

[0076] Static theoretical remaining energy estimation: such as Figures 5-6 As shown, the battery management system collects the open-circuit voltage information of the battery, corrects the relationship between OCV and SOE based on the battery's resting time and its state before resting, and then calculates the corresponding battery SOE value based on the current OCV value.

[0077] Predicting future remaining discharge energy: such as Figure 7 As shown, future power is calculated based on vehicle history and real-time information, representing the instantaneous power w of the vehicle. I The average power of the car during this trip (w) C Total power of the car (W) H A weighted calculation is performed, using the vehicle's current mileage (Lc) and the remaining battery energy calculated later as parameters, combined with various power values, to obtain the processed power as the vehicle's future power for calculating the remaining battery energy.

[0078]

[0079] Based on the equivalent circuit model, the formula for calculating the future current is:

[0080]

[0081] At the current time t, with respect to a future time t pre Conducting future SOC pre The prediction is based on the ampere-hour integral method, which uses the integration of future current with time to predict future changes in state of charge (SOC).

[0082]

[0083] Predicting and calibrating the future discharge cutoff point requires input of future current and temperature. By conducting discharge experiments on batteries of different capacities under different operating conditions, the discharge cutoff law of individual cells can be obtained, and the discharge cutoff range can be determined.

[0084] After the BMS is woken up, it enters the working state and estimates the initial SOE of the battery based on the battery resting time. Based on the obtained battery cycle count and battery temperature, it calculates the current internal resistance value of the battery. Based on the predicted battery charge / discharge cutoff point SOC range, the predicted terminal voltage range, and the predicted current sequence, it calculates the internal resistance energy consumption simultaneously. Based on the calculated static theoretical remaining energy and battery internal resistance energy loss, after self-discharge rate correction, it calculates the remaining energy of the battery at the current moment.

[0085] 1. By incorporating factors such as battery aging, temperature, and self-discharge, a more accurate assessment of the battery's state of energy is achieved.

[0086] 2. Battery static state of energy estimation, and logic for future battery state of energy estimation.

[0087] 3. Battery HPPC offline parameter identification and operating condition fitting conditions.

[0088] 4. In addition to estimating the DC internal resistance of the battery, the energy loss caused by the battery polarization internal resistance can also be estimated; the battery voltage, SOC and other battery parameters are used to characterize the future state characteristics of the battery; the impact of future changes in operating conditions on the prediction of remaining energy is also taken into account.

[0089] This invention is not limited to the above-described embodiments. Anyone should know that any structural changes made under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention.

[0090] The technologies, shapes, and structures not described in detail in this invention are all known technologies.

Claims

1. A method for estimating the state of energy (SOE) based on a battery equivalent model, characterized in that, Includes the following steps: S1. Battery equivalent model establishment: The battery state is estimated by using an equivalent circuit model. The equivalent circuit model needs to identify the model parameters for different temperatures and different SOC ranges. The OCV measurement method is used to charge and discharge the battery to different SOC stage points, and then let it stand for a long time until the open circuit voltage stabilizes, and then the OCV is measured. S2. Static theoretical remaining energy estimation: The battery management system collects the open circuit voltage information of the battery, corrects the relationship between OCV-SOE based on the battery resting time and the state before resting, and then calculates the corresponding battery SOE value based on the current OCV value. S3. Predict future remaining discharge energy: Based on the current state estimation of the battery and the prediction of the future state, including the current state estimation of the battery, the future operating condition estimation of the vehicle, the prediction of the future SOC sequence, the prediction of future parameter changes, the prediction of the future voltage curve, and the calculation of the discharge cutoff point; After S4 and BMS are woken up, they enter the working state. The initial SOE of the battery is estimated based on the battery resting time. Based on the obtained battery cycle number and battery temperature, the current internal resistance value of the battery is equivalently calculated. Based on the predicted battery charge and discharge cutoff point SOC range, the predicted terminal voltage range, and the predicted current sequence, the internal resistance energy consumption is calculated simultaneously. Based on the calculated static theoretical remaining energy and battery internal resistance energy loss, and after correction for self-discharge rate, the remaining energy of the battery at the current moment is calculated. The relevant mathematical relationship of the battery state in the battery equivalent model in step S1 is as follows: ； In the formula, OCV represents the open-circuit voltage, R0 represents the battery internal resistance, and R p C represents resistance. p I represents capacitance, and I represents current. In the battery equivalent model, OCV, R0, and R... p C p These four parameters are used for identification: battery internal resistance R0 and polarization parameter R. p C p The identification process employs an HPPC test condition design test scheme. In the battery equivalent model, the open-circuit voltage of the battery during charging is expressed as: ； Ohmic internal resistance R Ω The calculation can be expressed as: ； The rebound voltage versus time curve after the battery has been left to stand is calculated using a function: After fitting, parameters b1, τ1, b2, and τ2 are obtained, and then the parameters are calculated: ； In step S3, the future operating conditions of the vehicle are represented by future power, and then simultaneously the instantaneous power w of the vehicle is calculated. I The average power of the car during this trip (w) C Average power per unit travel (W) H The future power is predicted by weighting three aspects. After obtaining the future power, it is converted into future current by the model and used as the input of future operating conditions in the prediction process. Future power is calculated based on vehicle history and real-time information, representing the vehicle's instantaneous power (W). I The average power of the car during this trip (w) C Total power of the car (W) H The weighting process is performed, using the vehicle's current mileage (Lc) and the remaining battery energy calculated subsequently as parameters, combined with various power values ​​(P). I P C P H Calculations are performed to obtain the processed power, which is then used as the car's future power to calculate the remaining battery energy. ； Based on the equivalent circuit model, the formula for calculating the future current is: ； At the current time t, with respect to a future time t pre Conducting future SOC pre The prediction is based on the ampere-hour integral method, which uses the integration of future current with time to predict future state of charge (SOC). pre Changes: ； Predicting and calibrating the future discharge cutoff point requires input of future current and temperature. By conducting discharge experiments on batteries of different capacities under different operating conditions, the discharge cutoff law of individual cells can be obtained, and the discharge cutoff range can be determined.

2. The method for estimating the state of energy (SOE) based on a battery equivalent model according to claim 1, characterized in that, The battery SOE value in step S2 is calculated by looking up the OCV-SOE curve after the power battery has finished intermittently charged and after intermittently discharged.

3. The method for estimating the state of energy (SOE) based on a battery equivalent model according to claim 1, characterized in that, The battery current state estimation in step S3, which is the state estimation of the starting point of the remaining energy prediction, is achieved by collecting the current and terminal voltage of the current battery through the BMS to make an accurate online estimation of the current SOC state of the battery.

4. The method for estimating the state of energy (SOE) based on a battery equivalent model according to claim 1, characterized in that, In step S3, the predicted future parameter changes are assumed to mean that the battery parameters at a future point in time depend only on the current battery temperature and the future battery SOC state. The previously predicted SOC (t) is then used to calculate the future battery parameters. pre The battery's future temperature t is obtained by interpolating the data with the current battery temperature T and the parameter identification data. pre The basic parameters R0(t) pre ), R p (t) pre ), C p (t) pre ) and τ.

5. The method for estimating the state of energy (SOE) based on a battery equivalent model according to claim 4, characterized in that, The predicted future voltage curve in step S3 uses a first-order equivalent circuit model as the voltage prediction model, and the formula for calculating the future terminal voltage is: ； 。 6. The method for estimating the state of energy (SOE) based on a battery equivalent model according to claim 1, characterized in that, The discharge cutoff point calculation in step S3 is based on future current and temperature as parameters, and the SOC cutoff point is calibrated through experiments.

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