A method and device for controlling electric vehicle batteries

Abstract

This invention discloses a battery control method and apparatus for electric vehicles. The control method includes: acquiring a timestamp-synchronized BMS data source and an MCU data source; the BMS data source includes at least cell voltage, temperature, and a first estimated state of charge (SOC); the MCU data source includes at least bus voltage, motor current, a second estimated SOC, and a user current request; acquiring a first weighting factor and a second weighting factor based on weight parameters; the first weighting factor being the weight ratio of the BMS data source in the current scenario; the second weighting factor being the weight ratio of the MCU data source in the current scenario; correcting the first and second weighting factors according to an SOC calibration scenario; and obtaining the final SOC value based on the corrected first and second weighting factors. The technical solution provided by this invention reduces the SOC display error of electric vehicles.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method and apparatus for controlling electric vehicle batteries. Background Technology

[0002] Currently, electric two-wheelers generally use lithium iron phosphate (LiFePO4) batteries as their power source. Their battery management system (BMS) and vehicle controller (MCU) perform the following functions respectively: the BMS is used to monitor the voltage of individual cells, battery temperature, estimate the state of charge (SOC) of the battery, and charge and discharge current; the MCU is used to control the motor output power and manage undervoltage protection points.

[0003] However, due to the conflict between the BMS estimation SOC algorithm and the MCU display logic, the SOC display error is relatively large. Summary of the Invention

[0004] This invention provides a method and apparatus for controlling electric vehicle batteries to reduce the SOC display error of electric vehicles.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling an electric vehicle battery, comprising:

[0006] Obtain timestamp-synchronized BMS data source and MCU data source; the BMS data source includes at least individual unit voltage, temperature and first SOC estimate; the MCU data source includes at least bus voltage, motor current, second SOC estimate and user current request;

[0007] The first weighting factor and the second weighting factor are obtained based on the weighting parameters; the first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least the temperature influence coefficient, SOC deviation, and individual cell voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate.

[0008] The first weighting factor and the second weighting factor are corrected according to the SOC calibration scenario;

[0009] The final SOC value is obtained based on the corrected first and second weighting factors.

[0010] Secondly, embodiments of the present invention also provide an electric vehicle battery control device, which can execute the electric vehicle battery control method provided in any embodiment of the present invention, including: BMS and MCU;

[0011] The MCU is electrically connected to the BMS and is used to obtain timestamp-synchronized BMS data sources and MCU data sources; the BMS data source includes at least individual unit voltage, temperature, and a first SOC estimate; the MCU data source includes at least bus voltage, motor current, a second SOC estimate, and user current requests;

[0012] The MCU is also used to obtain a first weighting factor and a second weighting factor according to the weighting parameters; the first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least a temperature influence coefficient, SOC deviation, and individual cell voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate.

[0013] The controller is also used to correct the first weighting factor and the second weighting factor according to the SOC calibration scenario; and to obtain the final SOC value according to the corrected first weighting factor and second weighting factor.

[0014] In this invention, the State of Charge (SOC) with errors is corrected using both BMS (Battery Management System) and MCU (Microcontroller Unit) data sources. Specifically, timestamped BMS and MCU data sources are acquired; and a first weighting factor and a second weighting factor are obtained based on weighting parameters. The first weighting factor represents the weight ratio of the BMS data source in the current scenario, and the second weighting factor represents the weight ratio of the MCU data source in the current scenario. The weighting parameters include at least data such as temperature influence coefficient, SOC deviation, and single-cell voltage dispersion obtained from the BMS and MCU data sources. Then, the first and second weighting factors are corrected using an SOC calibration scenario, and the final SOC value is obtained according to the corrected first and second weighting factors. This embodiment combines BMS and MCU data sources, and determines the weights of the first SOC estimate obtained from the BMS data source and the second SOC estimate obtained from the MCU data source based on the current scenario. This allows for the acquisition of the final SOC value under dynamic changes, avoiding large errors in the final SOC due to noisy data or extreme conditions, improving the reliability of the final SOC value, and enhancing the accuracy and safety of battery control. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an electric vehicle battery control method provided in an embodiment of the present invention.

[0016] Figure 2 A schematic diagram of a power supply system provided in an embodiment of the present invention;

[0017] Figure 3This is a flowchart illustrating another electric vehicle battery control method provided in an embodiment of the present invention.

[0018] Figure 4 This is a flowchart illustrating another electric vehicle battery control method provided in an embodiment of the present invention.

[0019] Figure 5 This is a schematic diagram of the structure of an electric vehicle battery control device provided in an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0021] This invention provides a method for controlling an electric vehicle battery, such as... Figure 1 As shown, Figure 1 This is a flowchart illustrating an electric vehicle battery control method according to an embodiment of the present invention. The electric vehicle battery control method specifically includes the following steps:

[0022] Step S101: Obtain the BMS data source and MCU data source with timestamp synchronization; the BMS data source includes at least the individual unit voltage, temperature and first SOC estimate; the MCU data source includes at least the bus voltage, motor current, second SOC estimate and user current request.

[0023] Electric vehicles generally include a Battery Management System (BMS) and a Microcontroller Unit (MCU). The BMS and MCU each perform corresponding functions. The BMS monitors and manages various parameters and data of the electric vehicle's battery, while the MCU monitors the vehicle's data and controls the electric vehicle's motor. In this embodiment, the data obtained from the BMS is referred to as the BMS data source, and the data obtained from the MCU is referred to as the MCU data source. In this embodiment, the required BMS data source includes at least single-cell voltage, temperature, and a first estimated State of Charge (SOC). In this embodiment, the battery includes multiple single cells. For example, if the electric two-wheeler uses a lithium iron phosphate battery, it can include multiple lithium iron phosphate cells. When the BMS measures the battery voltage, it can measure the voltage of each single cell to obtain the single-cell voltage; the temperature of the battery is monitored by a temperature sensor; and the BMS can estimate the battery's SOC value using an ampere-hour integration and open-circuit voltage correction algorithm. In this embodiment, the SOC value estimated by the BMS is referred to as the first estimated SOC value. Of course, in addition to the data mentioned above, the BMS data source can also contain other data, such as SOH (State of Health) and charging / discharging current, etc., which are not limited in this embodiment. The MCU data source includes at least the bus voltage, motor current, second SOC estimate, and user current request. Figure 2 As shown, Figure 2 This is a schematic diagram of a power supply system provided in an embodiment of the present invention. The BMS12 is located near the battery and is used to monitor various parameters of the battery 11 and send these parameters to the MCU. The battery 11 is electrically connected to the motor 15 via the bus 14. The MCU13 is used to monitor the data of the motor 15 and control the motor 15. For example, the second voltage detection circuit can detect the bus voltage and send it to the MCU. Because the bus has a non-negligible resistance 141, its measured voltage differs from the voltage at the battery terminal. Due to the influence of the bus resistance 141, the bus voltage will be less than the battery voltage. The motor current can be obtained through the motor current detection module. The second voltage detection circuit and the motor current detection module are electrically connected to the MCU, and the MCU obtains the bus voltage and motor current. The MCU can also receive externally input instructions to obtain user current requests. The MCU can also estimate the SOC value based on the parameters it obtains; in this embodiment, this can be referred to as the second SOC estimate. Of course, the MCU data source can also include other parameters such as vehicle speed and undervoltage protection points. This embodiment does not specifically limit the parameters of the MCU data source.

[0024] In the process of controlling the battery of an electric vehicle, it is necessary to ensure the alignment of the BMS data source and the MCU data source. This embodiment adopts a timestamp synchronization mechanism to ensure that the BMS data source and the MCU data source are processed under the same time base.

[0025] Step S102: Obtain the first weighting factor and the second weighting factor according to the weighting parameters; the first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least the temperature influence coefficient, SOC deviation and single-unit voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate.

[0026] The first weighting factor W is a dynamically calculated intermediate variable used to quantify the reliability weight of the BMS data source in the current scenario. Similarly, the second weighting factor W2 is used to quantify the reliability weight of the MCU data source in the current scenario. It should be noted that the sum of the first weighting factor W and the second weighting factor W2 is 1. The weighting parameters, such as temperature, various battery parameters, and battery discharge state, differ in different scenarios. The first weighting factor W is calculated based on these weighting parameters in the current scenario. The second weighting factor W2 is obtained by summing it with the first weighting factor W to 1. In this embodiment, the weighting parameters may include temperature influence coefficient, SOC deviation, and single-cell voltage dispersion, etc. This embodiment monitors multiple weighting parameters (including temperature influence coefficient, SOC deviation, and single-cell voltage dispersion, etc.) in real time under different scenarios and calculates them according to a preset formula, reflecting the system's real-time dependence on the BMS data source. For example, in this embodiment, the lower the temperature, the larger the temperature influence coefficient, increasing the weight of the BMS data source and the larger the first weighting factor; the larger the SOC deviation, the larger the weight of the BMS data source and the larger the first weighting factor; the higher the single-cell voltage dispersion, the larger the weight of the BMS data source and the larger the first weighting factor.

[0027] Step S103: Correct the first weight factor and the second weight factor according to the SOC calibration scenario.

[0028] After obtaining the first weight factor W and the second weight factor W2 based on the weight parameters of the current scenario, the first weight factor W and the second weight factor W2 can be corrected or forcibly intervened according to the SOC calibration scenario. Because different scenarios have different effects on SOC display error—for example, low-temperature environments increase SOC display error—this embodiment needs to correct the first weight factor W and the second weight factor W2 according to the SOC calibration scenario to further reduce SOC error. For example, based on the temperature range, battery discharge state range, and SOC deviation range of the current scenario, the SOC calibration scenario is determined, and the first weight factor W and the second weight factor W2 are corrected or intervened according to the weight rules of that SOC calibration scenario to obtain more accurate first weight factor W and second weight factor W2, thereby improving the accuracy of the final SOC value.

[0029] Step S104: Obtain the final SOC value based on the corrected first weighting factor and second weighting factor.

[0030] In this embodiment, the final SOC value SOC_Z = W × SOC_BMS + W2 × SOC_MCU. SOC_BMS is the first estimated SOC value; SOC_MCU is the second estimated SOC value. After the first weighting factor W and the second weighting factor W2 are corrected, the accuracy of the final SOC value is effectively improved. For example, at a temperature of -10℃, the SOC display error is reduced from 20% to less than 5%. This is because at low temperatures, an error in the SOC display can easily lead to misjudgment of the battery state, causing the battery output current to exceed its output capacity, resulting in undervoltage or damage to the battery and reducing battery life. This embodiment obtains data updated every second based on sensor data, adapts to dynamically changing environments, quantifies the impact of different factors on the weights, avoids one-sided decisions based on a single parameter, and covers dynamic calculation results in key scenarios (such as low temperature and high load) to intervene in the weights, ensuring system safety and effectively preventing the algorithm from making erroneous decisions due to noisy data or extreme conditions.

[0031] In this embodiment of the invention, the State of Charge (SOC) with errors is corrected using a BMS data source and an MCU data source. Specifically, a timestamp-synchronized BMS data source and an MCU data source are acquired; and a first weighting factor and a second weighting factor are obtained based on weighting parameters. The first weighting factor represents the weight ratio of the BMS data source in the current scenario, and the second weighting factor represents the weight ratio of the MCU data source in the current scenario. The weighting parameters include at least data such as the temperature influence coefficient, SOC deviation, and single-cell voltage dispersion obtained from the BMS and MCU data sources. Then, the first and second weighting factors are corrected using an SOC calibration scenario, and the final SOC value is obtained according to the corrected first and second weighting factors. This embodiment combines the BMS and MCU data sources, and determines the weights of the first SOC estimate obtained from the BMS data source and the second SOC estimate obtained from the MCU data source based on the current scenario, obtaining the final SOC value under dynamic changes. This avoids large errors in the final SOC due to noisy data or extreme conditions, improves the reliability of the final SOC value, and enhances the accuracy and safety of battery control.

[0032] The above is the core idea of ​​this invention. The technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] Optionally, in terms of hardware structure, the individual cell voltage can be acquired by an ADC chip that is electrically connected to each individual cell voltage; a first voltage detection circuit is set in parallel with the battery; the first voltage detection circuit is used to measure the total battery voltage and transmit it to the MCU; a second voltage detection circuit is set at the motor end; the second voltage detection circuit is used to measure the bus voltage; the first voltage detection circuit and the second voltage detection circuit are used for mutual verification.

[0034] In this embodiment, a high-precision ADC chip can be used to independently sample each individual battery cell. For example, the ADC chip can be a BQ76952 with an error maintained at ±1mV and a sampling frequency of 10Hz. Optionally, an RC filter circuit is set for each ADC chip. This RC filter circuit has a cutoff frequency of 100Hz, which can effectively eliminate high-frequency interference and improve the detection accuracy of the individual cell voltage. This embodiment eliminates the parameter deviation between systems through hardware structure, solving the conversion error between the individual cell voltage reported by the BMS and the bus voltage received by the MCU. For example, this embodiment can reduce the voltage conversion error from the original ±1.5V to ±0.2V. Furthermore, a first voltage detection circuit (voltage division ratio can be 1:20) is connected in parallel between the total positive (P+) and total negative (P-) voltages of the battery. An isolation operational amplifier (e.g., ADI ADuM3190) is used to eliminate common-mode interference, measure the total battery voltage, and upload it to the MCU. A second voltage detection circuit (voltage division ratio 1:15) is set at the motor bus terminal for cross-verification with the total voltage reported by the BMS. For example, the bus voltage should ideally be slightly lower than the total battery voltage. If the total battery voltage is higher than the total battery voltage, it indicates that the detection result is incorrect. The first voltage detection circuit and the second voltage detection circuit are then used to verify each other and improve the accuracy of the detection result.

[0035] Based on the above embodiments, to further improve the voltage acquisition accuracy, the electric vehicle battery control method may further include: acquiring the total battery voltage V after impedance compensation. 实际 =V 测量 +I 放电 ×R 线阻 Among them, R 线阻 The line resistance of the bus between the battery and the motor; I 放电 V is the battery's output current. 测量 The bus voltage measured by the second voltage detection circuit. 放电 This can be provided by a current sensor in the BMS, R 线阻 The system is then calibrated through factory testing. In this embodiment, the acquisition of the total battery voltage improves system compatibility, supports automatic compensation for cables of different wire diameters (2.5-6mm²), adapts to various vehicle configurations, and optimizes voltage sampling accuracy.

[0036] Based on the above embodiments, the process of obtaining and correcting the first weighting factor and the second weighting factor is described in detail, such as... Figure 3 As shown, Figure 3 This is a flowchart illustrating another electric vehicle battery control method provided in an embodiment of the present invention. The electric vehicle battery control method specifically includes the following steps:

[0037] Step S201: Obtain the BMS data source and MCU data source with timestamp synchronization; the BMS data source includes at least the individual unit voltage, temperature and first SOC estimate; the MCU data source includes at least the bus voltage, motor current, second SOC estimate and user current request.

[0038] Step S202: Obtain the dynamic reliability factor.

[0039] Step S203: Obtain the first weight factor based on the weight parameters and dynamic weight factor; and obtain the second weight factor based on the first weight factor.

[0040] Optionally, "obtaining the first weight factor and the second weight factor based on the weight parameters" may include the contents of steps S202 and S203.

[0041] In a specific example, obtaining the first weighting factor and the second weighting factor based on the weighting parameters may include: obtaining the dynamic reliability factor. Where λ1 is the first weighting coefficient; λ2 is the second weighting coefficient; λ1 + λ2 = 1; R BMS Reliability parameters for BMS data sources; R MCU For the reliability parameter of the MCU data source; 0≤R BMS ≤1; 0≤R MCU ≤1; Obtain the first weight factor Among them, T coef V is the temperature influence coefficient; ΔSOC is the SOC deviation; dew α is the individual voltage dispersion; DRF is the dynamic reliability factor; α is the first influencing parameter; β is the second influencing parameter; γ is the third influencing parameter; δ is the fourth influencing parameter; α+β+γ+δ=1; the second weighting factor is obtained based on the first weighting factor; the sum of the first weighting factor and the second weighting factor is 1.

[0042] The dynamic reliability factor can adjust the weights of the BMS data source and also adjust the magnitude of the first weight factor W. Dynamic Reliability Factor Among them, R BMS and R MCU The values ​​of λ1 and λ2 are all in the range of 0 to 1; the sum of λ1 and λ2 is 1.

[0043] First weighting factor The first weighting factor W is jointly determined by the temperature influence coefficient, SOC deviation, individual unit voltage dispersion, and dynamic reliability factor. α is the coefficient of the temperature influence coefficient, β is the coefficient of the SOC deviation, γ is the coefficient of the individual unit voltage dispersion, and δ is the coefficient of the dynamic reliability factor. In this embodiment, the first weighting factor W can be obtained using the above formula, and the second weighting factor W2 can be obtained based on the first weighting factor W + W2 = 1.

[0044] Step S204: Obtain vehicle status parameters in the current scenario; vehicle status parameters include at least temperature, motor current, and SOC deviation.

[0045] Step S205: Based on the vehicle state parameters in the current scenario, correct the results of the first weighting factor and the second weighting factor.

[0046] Optionally, “correcting the first weight factor and the second weight factor according to the SOC calibration scenario” may include steps S204 and S205.

[0047] In a specific example, optionally, the vehicle state parameters in the current scenario are obtained, and the results of the first weighting factor and the second weighting factor are corrected based on the vehicle state parameters in the current scenario. This can include: if the temperature in the current scenario is greater than a first temperature threshold and the motor current is less than or equal to a first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a first ratio; if the temperature in the current scenario is greater than the first temperature threshold and the motor current is greater than the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a second ratio; if the temperature in the current scenario is greater than the first temperature threshold and the SOC deviation is greater than a first deviation threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a third ratio; if the temperature in the current scenario is less than or equal to the first temperature threshold and the motor current is less than or equal to the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a fourth ratio; if the temperature in the current scenario is less than or equal to the first temperature threshold and the motor current is greater than the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a fifth ratio; if the temperature in the current scenario is less than or equal to the first temperature threshold and the SOC deviation is greater than the first deviation threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to a sixth ratio.

[0048] In a specific example, the SOC calibration scenario can be configured as shown in Table 1. Table 1 is the dynamic weighting rule table for the SOC calibration scenario in this embodiment. Temperature, discharge loading, and SOC deviation can all affect the weighting settings. For example, in this embodiment, the first temperature threshold can be 5℃, the first current threshold can be 20A, the first deviation threshold can be 10%, the first ratio can be 0.5 / 0.5, the second ratio can be 0.3 / 0.7, the third ratio can be 0.8 / 0.2, the fourth ratio can be 0.7 / 0.3, the fifth ratio can be 0.8 / 0.2, and the sixth ratio can be 0.3 / 0.7. Therefore, if the temperature in the current scenario is greater than 5℃ and it is a low discharge rate discharge (motor current less than or equal to 20A), then both the first and second weighting factors are adjusted to 0.5; if the temperature in the current scenario is greater than 5℃ and it is a high discharge rate discharge (motor current greater than 20A), then the first weighting factor is adjusted to 0.3, and the ratio of the second weighting factor is 0.7; if the temperature in the current scenario is greater than 5℃ and the SOC deviation is greater than 10%, then the first weighting factor is adjusted to 0.8, and the ratio of the second weighting factor is 0.2 .... If the temperature is less than or equal to 5℃ and the discharge rate is low (motor current less than or equal to 20A), the first weighting factor is adjusted to 0.7 and the ratio of the second weighting factor is 0.3. If the temperature is less than or equal to 5℃ and the discharge rate is high (motor current greater than 20A), the first weighting factor is adjusted to 0.8 and the ratio of the second weighting factor is 0.2. If the temperature is less than or equal to 5℃ and the SOC deviation is greater than 10%, the first weighting factor is adjusted to 0.9 and the ratio of the second weighting factor is 0.1.

[0049] Table 1: Dynamic Weighting Rules for SOC Calibration Scenarios

[0050]

[0051] Step S206: Obtain the final SOC value based on the corrected first weighting factor and second weighting factor.

[0052] In a specific example of this embodiment, it is assumed that the vehicle is running at a current of 15A in an environment of -5℃, and the calculated temperature influence coefficient is 0.9 (indicating a significant impact of low temperature), the SOC deviation is 8% (there is a difference between the first and second SOC estimates), the individual cell voltage dispersion is 0.05V (the consistency of individual cell voltages is good), and the dynamic reliability factor DRF = 0.6 × 0.9 + 0.4 × 0.9 = 0.9. α = 0.6; β = 0.1; γ = 0.1; δ = 0.2. α + β + γ + δ = 1; based on the weight parameters and the dynamic weight factor DRF, the first weight factor W = 0.6 × 0.9 + 0.1 × 0.08 + 0.1 × 0.05 + 0.2 × 0.9 = 0.733 is obtained.

[0053] Based on this, according to the vehicle state parameters in the current scenario (such as temperature, discharge current, SOC deviation, etc.), the first weighting factor W is dynamically adjusted according to rules. Since the temperature is ≤5℃ and the discharge current is 15A, although the calculation formula for the first weighting factor W yields W=0.733, according to the SOC calibration scenario, the first weighting factor can be corrected to W=0.7, resulting in a corrected second weighting factor W2=0.3. The corrected first and second weighting factors are close to the calculated values, but it is still necessary to ensure that the low-temperature strategy is prioritized.

[0054] In this embodiment, a first weighting factor W and a second weighting factor W2 are obtained, and the first weighting factor W and the second weighting factor W2 are corrected or forcibly intervened based on the vehicle state parameters in the current scenario. This adapts to the dynamically changing environment, obtains the final SOC value, quantifies the impact of different factors on the weights, avoids one-sided decisions based on a single parameter, and covers dynamic calculation results under critical scenarios (such as low temperature and high load), improving the reliability of the final SOC value and enhancing the accuracy and safety of battery control.

[0055] Optionally, the electric vehicle battery control method may also include steps to improve low-temperature and low-pressure adaptability, specifically, such as... Figure 4 As shown, Figure 4 This is a flowchart illustrating another electric vehicle battery control method provided in an embodiment of the present invention. The electric vehicle battery control method further includes the following steps:

[0056] Step S301: Based on the discharge current-temperature relationship model, obtain the first maximum discharge limit current of the battery at the current temperature.

[0057] In existing technologies, abnormal power outages during low-temperature riding are caused by a mismatch between the BMS current-limiting command and the MCU power requirements. This embodiment further unifies the BMS current-limiting command and the MCU power requirements, improving the BMS discharge adaptability. Specifically, the upper limit of the discharge current can be dynamically adjusted according to different SOC states to improve the battery's low-temperature adaptability. Furthermore, in existing technologies, because the BMS undervoltage point and the MCU software setting are inconsistent, the undervoltage protection threshold deviation can easily reach 1V. This embodiment can obtain a more accurate maximum discharge current for the BMS, avoiding battery undervoltage problems.

[0058] Optionally, the discharge current-temperature relationship model can be... Among them, I nor T is the battery's nominal maximum discharge current. low =0℃; k=0.05 / ℃ (attenuation coefficient). For example, at T=-10℃, if I... nor =30A, I max_T=30×[1-0.05×(0-(-10))]=15A. In this embodiment, based on the discharge current-temperature relationship model, the first maximum discharge limit current I of the battery at the current temperature is obtained. max_T .

[0059] Step S302: Based on the relationship model between the maximum discharge current and the SOC value range, obtain the SOC value corresponding to the final SOC value as the second maximum discharge limit current.

[0060] The relationship between the maximum discharge current and the range of SOC values ​​can be modeled as follows:

[0061] ;

[0062] Among them, I max_soc The second maximum discharge limiting current; I nor denoted as the battery's nominal maximum discharge current; k is the slope of the relationship model, and b is the intercept of the relationship model. Table 2 shows the parameter diagram of the relationship model for the maximum discharge current-SOC value range. Specifically, in the SOC range of 0%~10%, the slope of the relationship model is k1=0.03, and the intercept is b1=0.3; in the SOC range of 10%~30%, the slope of the relationship model is k2=0.02, and the intercept is b2=0.6; in the SOC range ≥30%, the slope of the relationship model is k3=1.0, and the intercept is 0. It should be noted that in this model, SOC only represents the percentage of the current state of charge relative to the total state of charge, and does not refer to its specific value.

[0063] Table 2: Parameter illustration of the relationship model between maximum discharge current and SOC value range

[0064]

[0065] In the example above, if I nor =30A, SOC=20%, then k2=0.02, b2=0.6, I max_soc =30×(0.02×(20-10)+0.6)=24A.

[0066] Step S303: Take the smaller value between the first maximum discharge limit current and the second maximum discharge limit current as the maximum discharge current of the BMS.

[0067] In this embodiment, the maximum discharge current I of the BMS is selected. max The principle is: select the first maximum discharge limiting current I. max_T With the second maximum discharge limit current I max_soc The smaller value is used as the maximum discharge current I of the BMS. max .

[0068] Based on the above embodiments, optionally, if the difference between the maximum discharge current and the user's current request exceeds a first difference threshold, the smaller value will be used as the battery's discharge current. This embodiment also includes a conflict resolution step, that is, when the maximum discharge current I of the BMS... max If the difference between the current request from the MCU and the user current request exceeds a first difference threshold, for example, 15%, the arbitrator can be activated to execute at the minimum available power. For instance, if the maximum discharge current I of the BMS... max =10A, the MCU user current request is 15A, then the battery will ultimately output 10A.

[0069] In this embodiment, by dynamically adjusting the upper limit of the discharge current based on the current temperature and SOC range, the error of the upper limit of the discharge current can be effectively reduced from ±8% to ±3% in a -10℃ environment. This effectively reduces the conflict between BMS current limiting commands and MCU power requirements (reducing system conflict events by 90%), for example, effectively reducing the probability of inconsistency between BMS and MCU commands during hill climbing. Furthermore, through the battery control method of this embodiment, the driving range can be increased by 15% in a -10℃ environment by optimizing current and compensating for SOC. It also reduces the low-temperature abnormal power failure rate from 12% to 1%, further improving the reliability of the power supply system and enhancing the performance and lifespan of the electric vehicle.

[0070] Based on the same concept, embodiments of the present invention also provide an electric vehicle battery control device, which can execute the electric vehicle battery control method provided in any embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of an electric vehicle battery control device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it may include: BMS12 and MCU13;

[0071] MCU13 is electrically connected to BMS12 and is used to obtain the BMS data source and MCU data source for timestamp synchronization; the BMS data source includes at least individual unit voltage, temperature and first SOC estimate; the MCU data source includes at least bus voltage, motor current, second SOC estimate and user current request.

[0072] MCU13 is also used to obtain a first weighting factor and a second weighting factor based on weighting parameters; the first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least the temperature influence coefficient, SOC deviation and single-cell voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate.

[0073] MCU13 is also used to correct the first weighting factor and the second weighting factor according to the SOC calibration scenario; and to obtain the final SOC value based on the corrected first weighting factor and the second weighting factor.

[0074] In this embodiment, the SOC with errors is corrected using both BMS and MCU data sources. Specifically, timestamped BMS and MCU data sources are acquired; and a first weighting factor and a second weighting factor are obtained based on weighting parameters. The first weighting factor represents the weight ratio of the BMS data source in the current scenario, and the second weighting factor represents the weight ratio of the MCU data source in the current scenario. The weighting parameters include at least data such as temperature influence coefficient, SOC deviation, and single-cell voltage dispersion obtained from the BMS and MCU data sources. Then, the first and second weighting factors are corrected using an SOC calibration scenario, and the final SOC value is obtained according to the corrected first and second weighting factors. This embodiment combines BMS and MCU data sources, and determines the weights of the first SOC estimate obtained from the BMS data source and the second SOC estimate obtained from the MCU data source based on the current scenario. This allows for the acquisition of the final SOC value under dynamic changes, avoiding large errors in the final SOC due to noisy data or extreme conditions, improving the reliability of the final SOC value, and enhancing the accuracy and safety of battery control.

[0075] The electric vehicle battery control device provided in the embodiments of the present invention includes the technical features of the electric vehicle battery control method provided in any embodiment of the present invention, and has the effective effects of the corresponding technical features, which will not be repeated here.

[0076] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for controlling an electric vehicle battery, characterized in that, include: Obtain the BMS data source and MCU data source with timestamp synchronization; The BMS data source includes at least the individual cell voltage, temperature, and first SOC estimate; The MCU data source includes at least the bus voltage, motor current, second SOC estimate, and user current request. The first weighting factor and the second weighting factor are obtained based on the weighting parameters; The first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least the temperature influence coefficient, SOC deviation, and individual cell voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate. The first weighting factor and the second weighting factor are corrected according to the SOC calibration scenario; The final SOC value is obtained based on the corrected first and second weighting factors.

2. The electric vehicle battery control method according to claim 1, characterized in that, The first weighting factor and the second weighting factor are obtained based on the weighting parameters, including: Obtain the dynamic reliability factor; Based on the weight parameters and the dynamic reliability factor, a first weight factor is obtained; and based on the first weight factor, a second weight factor is obtained.

3. The electric vehicle battery control method according to claim 2, characterized in that, The first weighting factor and the second weighting factor are obtained based on the weighting parameters, including: Obtaining dynamic reliability factor Where λ1 is the first weighting coefficient; λ2 is the second weighting coefficient; λ1 + λ2 = 1; R BMS R is the reliability parameter of the BMS data source; MCU Here is the reliability parameter for the MCU data source; 0 ≤ R BMS ≤1; 0≤R MCU ≤1; Obtain the first weight factor Among them, T coef V is the temperature influence coefficient; ΔSOC is the SOC deviation; dew α is the individual voltage dispersion; DRF is the dynamic reliability factor; α is the first influencing parameter; β is the second influencing parameter; γ is the third influencing parameter; δ is the fourth influencing parameter; α+β+γ+δ=1; The second weighting factor is obtained based on the first weighting factor; the sum of the first weighting factor and the second weighting factor is 1.

4. The electric vehicle battery control method according to claim 1, characterized in that, The first weighting factor and the second weighting factor are corrected according to the SOC calibration scenario, including: Obtain vehicle status parameters in the current scenario; the vehicle status parameters include at least the temperature, the motor current, and the SOC deviation. Based on the vehicle state parameters in the current scenario, the results of the first weighting factor and the second weighting factor are corrected.

5. The electric vehicle battery control method according to claim 4, characterized in that, Obtain vehicle state parameters in the current scenario, and based on the vehicle state parameters in the current scenario, correct the first weighting factor and the second weighting factor, including: If the temperature in the current scenario is greater than the first temperature threshold and the motor current is less than or equal to the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the first ratio value. If the temperature in the current scenario is greater than the first temperature threshold and the motor current is greater than the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the second ratio. If the temperature in the current scenario is greater than the first temperature threshold and the SOC deviation is greater than the first deviation threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the third ratio. If the temperature in the current scenario is less than or equal to the first temperature threshold and the motor current is less than or equal to the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the fourth ratio. If the temperature in the current scenario is less than or equal to the first temperature threshold and the motor current is greater than the first current threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the fifth ratio. If the temperature in the current scenario is less than or equal to the first temperature threshold and the SOC deviation is greater than the first deviation threshold, then the ratio of the first weighting factor and the second weighting factor is adjusted to the sixth ratio.

6. The electric vehicle battery control method according to claim 1, characterized in that, The individual cell voltage is acquired by an ADC chip that is electrically connected to each individual cell voltage in a one-to-one correspondence. A first voltage detection circuit is connected in parallel with the battery; the first voltage detection circuit is used to measure the total battery voltage and transmit it to the MCU; a second voltage detection circuit is provided at the motor end; the second voltage detection circuit is used to measure the bus voltage; the first voltage detection circuit and the second voltage detection circuit are used to verify each other.

7. The electric vehicle battery control method according to claim 6, characterized in that, Also includes: Obtain the total battery voltage V after impedance compensation 实际 =V 测量 +I 放电 ×R 线阻 Among them, R 线阻 The line resistance of the bus between the battery and the motor; I 放电 V is the output current of the battery; 测量 The bus voltage is measured by the second voltage detection circuit.

8. The electric vehicle battery control method according to claim 1, characterized in that, Also includes: Based on the discharge current-temperature relationship model, obtain the first maximum discharge limit current of the battery at the current temperature; Based on the relationship model between the maximum discharge current and the SOC value range, the SOC value corresponding to the final SOC value is obtained as the second maximum discharge limit current. The smaller of the first maximum discharge limit current and the second maximum discharge limit current is taken as the maximum discharge current of the BMS.

9. The electric vehicle battery control method according to claim 8, characterized in that, If the difference between the maximum discharge current and the user's current request exceeds a first difference threshold, the smaller value will be used as the battery's discharge current.

10. A battery control device for an electric vehicle, characterized in that, The electric vehicle battery control method according to any one of claims 1-9 includes: a BMS and an MCU; The MCU is electrically connected to the BMS and is used to obtain timestamp-synchronized BMS data sources and MCU data sources; the BMS data source includes at least individual unit voltage, temperature, and a first SOC estimate; the MCU data source includes at least bus voltage, motor current, a second SOC estimate, and user current requests; The MCU is also used to obtain a first weighting factor and a second weighting factor according to the weighting parameters; the first weighting factor is the weight ratio of the BMS data source in the current scenario; the second weighting factor is the weight ratio of the MCU data source in the current scenario; the weighting parameters include at least a temperature influence coefficient, SOC deviation, and individual cell voltage dispersion; the SOC deviation is the difference between the second SOC estimate and the first SOC estimate. The MCU is also used to correct the first weighting factor and the second weighting factor according to the SOC calibration scenario; and to obtain the final SOC value according to the corrected first weighting factor and second weighting factor.

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