Battery charging control method for electric vehicle, computer device and storage medium

Abstract

The application discloses a kind of electric vehicle battery power supply control method, computer device and storage medium, the application can be in the self-learning state of battery is the case where self-learning state has been, according to reliable battery state of charge data, i.e. first state of charge value, whether threshold condition for triggering power supply is satisfied is judged, in the case where the self-learning state of battery is not self-learning state, do not select unreliable state of charge data, but select according to the state parameter of battery, whether threshold condition for triggering power supply is satisfied is judged;It is favorable to reduce the electric energy loss in the process of power supply, maintain the service life of battery and power battery, or with the state parameter of battery to assist in judging, so that the power supply process of battery can be triggered, so that battery can normally power the functional components of electric vehicle, guarantee the normal use of electric vehicle.The present application is widely used in the field of automobile technology.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a battery charging control method, computer device, and storage medium for electric vehicles. Background Technology

[0002] Electric vehicles typically have two sets of batteries: a power battery and a storage battery. The power battery generally has a higher output voltage and larger capacity, used for driving the vehicle and powering high-voltage electrical components (such as air conditioning and steering). The storage battery generally has a lower output voltage and smaller capacity, similar to the battery in a pure gasoline vehicle, used to power systems such as lighting, instruments, windows, entertainment, and electronic controls. When a vehicle is parked for an extended period, the batteries can become depleted due to dark current and self-discharge. The storage battery, with its lower rated voltage, smaller capacity, and the fact that it powers essential vehicle components, is more significantly affected by this depletion. Therefore, electric vehicles typically recharge the storage battery using the energy stored in the power battery; this process is called battery charging.

[0003] Since the process of replenishing the battery requires the power battery to discharge, and the electrical energy is converted by the replenishment components and then charged into the battery, there is energy loss and consumption of the power battery and the battery charge-discharge cycle in this process. Therefore, it is necessary to choose an appropriate time to replenish the battery in order to reduce energy loss and ensure the service life of the power battery and the battery. Summary of the Invention

[0004] In view of the technical problems in current electric vehicle technology, such as the need to perform battery charging at the appropriate time, the purpose of this invention is to provide a battery charging control method, computer device and storage medium for electric vehicles.

[0005] On one hand, embodiments of the present invention include a battery charging control method for an electric vehicle, the battery charging control method for an electric vehicle comprising the following steps:

[0006] Detect the self-learning status of the battery;

[0007] When the self-learning state is the self-learning state, the battery is subjected to state of charge detection to obtain a first state of charge value, and a threshold condition judgment is made based on the first state of charge value.

[0008] When the self-learning state is a non-self-learning state, the battery is detected to obtain state parameters, and a threshold condition is determined based on the state parameters; the type of the state parameters is different from the state of charge.

[0009] When the threshold condition is met, the storage battery is recharged via the power battery.

[0010] Furthermore, the detection of the battery's self-learning state includes:

[0011] When a trigger condition is detected, the charging record of the battery is checked;

[0012] When a fully charged record is detected, the self-learning state is determined to be the self-learned state.

[0013] If no full charge record is detected, the self-learning state is determined to be the non-self-learning state.

[0014] Furthermore, the triggering conditions include:

[0015] The electric vehicle is in a dormant state, ACC state, or IG ON state and the charging components are not working.

[0016] Furthermore, when the self-learning state is a non-self-learning state, the step of replenishing the battery through the power battery includes:

[0017] The storage battery is controlled to provide reverse charging to the power battery until the storage battery is completely discharged;

[0018] The power battery is controlled to replenish the storage battery until the storage battery is fully charged;

[0019] Set the self-learning state to the self-learned state.

[0020] Furthermore, when the self-learning state is a non-self-learning state, the battery charging control method for the electric vehicle further includes the following steps:

[0021] Within a certain time period, the power battery is controlled to replenish the storage battery, the state of charge of the storage battery is detected to obtain a first state of charge change value, and the state of charge of the power battery is detected to obtain a second state of charge change value.

[0022] The first state of charge value is corrected based on the first state of charge change value and the second state of charge change value;

[0023] Set the self-learning state to the self-learned state.

[0024] Further, the step of correcting the first state of charge value based on the first state of charge change value and the second state of charge change value includes:

[0025] The correction amount is determined based on the first state of charge change value and the second state of charge change value;

[0026] The first state of charge value is corrected using the correction amount.

[0027] Further, the step of controlling the power battery to replenish the storage battery within a certain time period, performing state-of-charge detection on the storage battery to obtain a first state-of-charge change value, and performing state-of-charge detection on the power battery to obtain a second state-of-charge change value includes:

[0028] Within the stated time period, several time segments are defined;

[0029] For any given time segment, within that time segment, the power battery is controlled to replenish the storage battery, the storage battery is subjected to state of charge detection to obtain the component of the first state of charge change value within that time segment, and the power battery is subjected to state of charge detection to obtain the component of the second state of charge change value within that time segment.

[0030] Further, determining the correction amount based on the first state-of-charge change value and the second state-of-charge change value includes:

[0031] A system of equations is established; the system of equations represents the relationship between the energy flow between the storage battery and the power battery. The system of equations uses the components of the first state of charge change value in each time segment and the components of the second state of charge change value in each time segment as parameters. Each equation in the system of equations contains a corresponding correction term.

[0032] Solve the system of equations to determine the value of each correction term;

[0033] The statistical characteristics of each correction term are obtained to determine the correction amount.

[0034] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute a battery charging control method for an electric vehicle according to the embodiments.

[0035] On the other hand, embodiments of the present invention also include a storage medium storing a processor-executable program, which, when executed by a processor, is used to perform a battery charging control method for an electric vehicle according to the embodiments.

[0036] The beneficial effects of this invention are as follows: The battery charging control method for electric vehicles in the embodiments can, when the battery's self-learning state is in a self-learning state, select reliable battery state-of-charge data (i.e., the first state-of-charge value) to determine whether the threshold condition for triggering charging is met. When the battery's self-learning state is not in a self-learning state, unreliable state-of-charge data is not selected; instead, the battery's state parameters are used to determine whether the threshold condition for triggering charging is met. When reliable battery state-of-charge data is available, appropriate threshold conditions can be set to charge the battery at the appropriate time, which helps reduce energy loss during charging and maintain the lifespan of the battery and power battery. When reliable battery state-of-charge data is unavailable, the battery's state parameters can be used to assist in the judgment, thereby triggering the battery charging process, ensuring that the battery can normally supply power to the functional components of the electric vehicle and guaranteeing the normal operation of the electric vehicle. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of a vehicle system to which the battery charging control method for electric vehicles can be applied in the embodiment.

[0038] Figure 2 This is a schematic diagram of the steps of the battery charging control method for an electric vehicle in the embodiment;

[0039] Figure 3 This is a flowchart illustrating the battery charging control method for an electric vehicle in this embodiment.

[0040] Figure 4 This is a schematic diagram illustrating the state-of-charge detection of the storage battery and the power battery in the embodiment. Detailed Implementation

[0041] In this embodiment, the battery charging control method for electric vehicles can be applied to... Figure 1 The vehicle system shown. (Refer to...) Figure 1The system includes a vehicle control unit (VCU), a power battery, a battery management system (BMS), a DC-DC module, a storage battery, a battery charge sensor (EBS), a body control system (BCM), gateways IGM 1 and IGM 2, an instrument cluster (METER), and a motor controller (DU). The VCU controls all controllable components of the vehicle. The BMS manages the charging and discharging of the power battery and monitors its operating parameters. The BMS can determine the state of charge (SOC) of the power battery by detecting its output voltage and using table lookups, or by detecting the charging and discharging current and performing coulomb counting. The DC-DC module receives the high-voltage DC power from the power battery, performs DC-DC conversion to obtain low-voltage DC power, and inputs it to the storage battery to replenish its charge. The module can internally include relays and other switching elements, which are controlled by the Battery Management System (BMS) to control the DC-DC module to charge or stop charging the battery. The Battery State Sensor (EBS) can determine the battery's state of charge (SOC) by detecting the output voltage and using table lookup, or by detecting the charging and discharging current and performing coulomb counting. The EBS is connected to the Vehicle Control Unit (VCU) via a LIN bus, sending the battery's SOC data to the VCU. The gateway IMG... Gateway IMG 1 is used to connect the vehicle controller (VCU) and the instrument cluster (METER). The VCU can send the detected battery state of charge and other information to the METER through gateway IMG 1, and the METER will display the battery state of charge and other information. Gateway IMG 2 is used to connect the VCU and the battery management system (BMS). The VCU can send control commands to the BMS through gateway IMG 2. The BMS can control the power battery to discharge to the motor controller (DU). The motor controller (DU) can use the electrical energy output from the power battery to drive the motor, or perform active discharge (e.g., converting the electrical energy output from the power battery into heat energy through a resistor) to consume the electrical energy output from the power battery.

[0042] In this embodiment, refer to Figure 2 The battery charging control method for electric vehicles includes the following steps:

[0043] S1. Detect the battery's self-learning status;

[0044] S2. When the self-learning state is already self-learned, perform state of charge detection on the battery to obtain the first state of charge value, and make a threshold condition judgment based on the first state of charge value.

[0045] S3. When the self-learning state is non-self-learning state, the battery is detected to obtain state parameters, and threshold conditions are judged based on the state parameters; the type of state parameters is different from the state of charge.

[0046] S4. When the threshold condition is met, the battery is recharged through the power battery.

[0047] In this embodiment, the flow of the battery charging control method for electric vehicles is as follows: Figure 3 As shown.

[0048] In this embodiment, steps S1-S4 can be executed by the vehicle controller (VCU).

[0049] In step S1, the vehicle's sleep state can refer to the IG OFF state. In this state, only the underlying hardware such as the clock, emergency lights, and memory power supply receives power, while the vehicle's power system, transmission system, drive system, and accessories are all de-energized. The opposite of the IG OFF state includes the ACC state and the IG ON state. In the ACC state, in addition to the underlying hardware in the IG OFF state receiving power, accessories such as power windows, audio-visual entertainment systems, vehicle air conditioning, and external power interfaces also receive power. In the IG ON state, in addition to the components in the ACC state receiving power, components in the power system, transmission system, and drive system also receive power; that is, all electrical components in the vehicle receive power.

[0050] In this embodiment, the vehicle controller (VCU) can call the sensors installed on the vehicle, and when the sensors detect the triggering condition, steps S1-S4 are triggered.

[0051] In this embodiment, the overall vehicle state of the electric vehicle can be used as the trigger condition. Specifically, the overall vehicle state of the electric vehicle includes sleep state (IG OFF state), ACC state, IG ON state, READY state, CHARGE state (at which time the electric vehicle is connected to the charging pile, and the charging pile charges the power battery), etc.

[0052] Reference Figure 3 When the electric vehicle is in sleep mode or ACC mode, the functional components on the vehicle are powered by the battery, and there is a need to recharge the battery. Therefore, the trigger condition is met, and steps S1-S4 are executed to recharge the battery. (Refer to...) Figure 3When the electric vehicle is in the IG ON state, the charging components (mainly the DC-DC module) are further checked for operation. If the charging components are not working, it means that the power battery is not charging the storage battery, that is, the functional components on the electric vehicle are powered by the storage battery, and there is a need to charge the storage battery. Therefore, it is determined that the triggering condition is met, and steps S1-S4 are triggered to charge the storage battery. If the charging components are working, it means that the functional components on the electric vehicle are powered by the power battery, or the power battery is charging the storage battery, and there is no need to charge the storage battery. Therefore, it is determined that the triggering condition is not met, and steps S1-S4 are not triggered to charge the storage battery.

[0053] Reference Figure 3 When the electric vehicle is in the READY or CHARGE state, the functional components on the electric vehicle are powered by the power battery, and there is no need to recharge the battery. Therefore, it is determined that the triggering condition is not met, and the execution steps S1-S4 to recharge the battery are not triggered.

[0054] If the triggering conditions are detected, the vehicle controller VCC begins to execute steps S1-S4.

[0055] In step S1, the self-learning status of the battery detected by the vehicle controller VCC indicates whether the battery has undergone a self-learning process. If the battery has undergone a self-learning process, the actual amount of electricity stored in the battery at a certain point in time can be obtained. Based on the amount of electricity stored at this point in time, the charging and discharging amounts of the battery are recorded, making the detected state of charge of the battery accurate and reliable. Conversely, if the battery has not undergone a self-learning process, since there is no data such as the actual amount of electricity stored in the battery at a certain point in time, only the charging and discharging amounts of the battery can be recorded. Because there is only definite incremental data and no basic stored data, the detected state of charge of the battery is unreliable.

[0056] In this embodiment, the vehicle controller (VCC) can call the DC-DC module to detect the battery's charging record. The battery's charging record indicates that the battery has undergone at least one full charge, thus allowing the system to obtain the actual amount of electricity stored in the battery at a given point in time. Based on this principle, when a full charge record is detected, the battery's self-learning state can be determined to be in a self-learning state; conversely, when no full charge record is detected, the battery's self-learning state can be determined to be in a non-self-learning state.

[0057] Reference Figure 2If, in step S1, the battery's self-learning state is detected as "already learned," then step S2 is executed to perform state of charge (SOC) detection on the battery, obtaining a first SOC value. Then, a threshold condition is determined based on this first SOC value, i.e., the magnitude of the first SOC value is judged. For example, a threshold (e.g., 60%) can be set to determine the relationship between the first SOC value and 60%. If the first SOC value is less than 60%, the threshold condition is considered met; if the first SOC value is greater than or equal to 60%, the threshold condition is considered not met.

[0058] Reference Figure 2 If, in step S1, the battery's self-learning state is detected as non-self-learning, then step S3 is executed to detect the battery and obtain state parameters. The type of these state parameters differs from the state of charge (SOC); that is, the state parameters are data of a type other than SOC. For example, "discharge voltage" or "discharge current" can be selected as the state parameters. Taking "discharge voltage" as an example, the battery's discharge voltage is detected, and a threshold (e.g., 11V) is set. The relationship between the discharge voltage and 11V is determined. If the discharge voltage is less than 11V, the threshold condition is met; if the discharge voltage is greater than or equal to 11V, the threshold condition is not met.

[0059] Reference Figure 3 Whether it is step S2 or step S3 that determines that the threshold condition is met, the vehicle controller (VCU) will execute step S4, which calls the DC-DC module and other power replenishment components to obtain the discharge energy of the power battery, convert it into voltage and current that match the battery, and charge the battery with the energy, thereby replenishing the battery.

[0060] In this embodiment, by executing steps S1-S4, when the battery's self-learning state is in a self-learning state, the system selects reliable battery state-of-charge (SOC) data, i.e., the first SOC value, to determine whether the threshold condition for triggering recharging is met. When the battery's self-learning state is not in a self-learning state, unreliable SOC data is not selected; instead, the system selects battery state parameters (e.g., discharge voltage) to determine whether the threshold condition for triggering recharging is met. When reliable SOC data is available, setting appropriate threshold conditions allows for timely recharging of the battery, which helps reduce energy loss during recharging and maintains the lifespan of the battery and power battery. When reliable SOC data is unavailable, battery state parameters can be used to assist in the determination, thereby triggering the recharging process and ensuring that the battery can supply power to the functional components of the electric vehicle, guaranteeing the normal operation of the electric vehicle.

[0061] In this embodiment, when step S1 is executed and the self-learning state of the battery is detected to be non-self-learning, step S4, which is the step of replenishing the battery with power battery, can specifically perform the following steps:

[0062] S401. Control the storage battery to provide reverse charging to the power battery until the storage battery is completely discharged;

[0063] S402. Control the power battery to replenish the storage battery until the storage battery is fully charged;

[0064] S403. Set the self-learning state to "already self-learning".

[0065] In step S401, the vehicle controller (VCU) can control the direction of power conversion of the DC-DC module, that is, to make the battery discharge to the DC-DC module. The DC-DC module converts the power energy discharged by the battery into voltage and current that match the power battery, thereby charging the power battery and realizing the reverse charging of the power battery, until the battery is discharged, that is, the discharge voltage and discharge current of the battery can no longer sustain the power conversion process of the DC-DC module.

[0066] After completing the reverse charging of the power battery, the vehicle controller (VCU) can control the direction of power conversion of the DC-DC module, so that the power battery charges the storage battery until the storage battery is fully charged.

[0067] During the execution of steps S401-S402, the battery power sensor EBS can record the SOC of the charging and recharging process, so that the battery can undergo at least one full discharge and full charge process, thereby realizing the battery's self-learning.

[0068] After the battery completes its self-learning process, the vehicle control unit (VCU) sets the battery's self-learning status to "self-learned".

[0069] By executing steps S401-S403, the battery can complete its self-learning within the vehicle's range through reverse charging and charging processes even when the battery has not completed self-learning (e.g., a new battery has been replaced, or the battery has been disconnected from the vehicle's circuit and connected to another vehicle for jump-starting). This ensures that the battery's state of charge data is reliable.

[0070] In this embodiment, when step S1 is executed and the battery's self-learning state is detected to be non-self-learning, the battery charging control method for electric vehicles further includes the following steps:

[0071] S5. Within a certain time period, control the power battery to replenish the storage battery, perform state of charge detection on the storage battery to obtain the first state of charge change value, and perform state of charge detection on the power battery to obtain the second state of charge change value.

[0072] S6. Correct the first state of charge value based on the first state of charge change value and the second state of charge change value;

[0073] S7. Set the self-learning state to "already self-learning".

[0074] The time period in step S5 can be the period during which the electric vehicle is parked and not in use. This time period can be precisely set by the user according to their own usage needs, or it can be predicted by the onboard system based on the user's usage habits. It can also be set as a time period with a fixed start and end point, such as 23:00 on a certain day to 6:00 the next day, or three consecutive days, or one consecutive week. This time period is denoted as T.

[0075] During the execution of steps S5-S7, although the battery's self-learning state is not self-learning, the battery's state of charge can still be detected at a certain moment to obtain the battery's state of charge data at that moment, which can also be called the first state of charge value, denoted as SOC1.

[0076] When executing step S5, the vehicle controller (VCU) calls the battery power sensor (EBS) to track the state of charge (SOC) of the battery and obtains the change in SOC of the battery within the time period T, which is recorded as the first SOC change value ΔSOC1; the vehicle controller (VCU) calls the battery management system (BMS) to track the SOC of the power battery and obtains the change in SOC of the power battery within the time period T, which is recorded as the second SOC change value ΔSOC2.

[0077] In step S6, the vehicle controller (VCU) corrects the first state of charge value SOC1 based on the first state of charge change value ΔSOC1 and the second state of charge change value ΔSOC2, and then executes step S7 to set the battery's self-learning state to the self-learning state.

[0078] In this embodiment, the principle of executing steps S5-S7 is as follows: Since the quality, calibration, capacity, stability, and self-discharge performance of the storage battery are generally inferior to those of the power battery, and the storage battery has the characteristics of easy disassembly and replacement, the state of charge data (including the first state of charge value SOC1 detected at a certain moment and the first state of charge change value ΔSOC1 detected over a period of time) obtained by the storage battery without self-learning is unreliable compared with the state of charge data obtained by the power battery. The second state of charge change value ΔSOC2 obtained by the power battery can be regarded as a reliable and accurate value. By comparing the reliable and accurate second state of charge change value ΔSOC2 with the unreliable first state of charge change value ΔSOC1, the magnitude of the error faced in the state of charge detection process of the storage battery can be determined, thereby correcting the first state of charge value SOC1 to reduce the impact of the error and obtain more reliable state of charge data of the storage battery. Since the error faced in the state of charge detection process of the storage battery can be corrected in the above process, the self-learning effect of the storage battery is realized. Therefore, the self-learning state of the storage battery is set to the self-learned state.

[0079] As can be seen from the above principle, by executing steps S5-S7, the self-learning effect of the battery can be achieved without fully charging the battery.

[0080] In this embodiment, when the vehicle control unit (VCU) executes step S5, which involves detecting the state of charge (SOC) of the battery within a certain time period to obtain a first SOC change value and detecting the SOC of the power battery to obtain a second SOC change value, the following steps are specifically performed:

[0081] S501. Within a time period, identify several time segments;

[0082] S502. For any given time segment, within that time segment, control the power battery to replenish the storage battery, perform state of charge detection on the storage battery to obtain the component of the first state of charge change value within that time segment, and perform state of charge detection on the power battery to obtain the component of the second state of charge change value within that time segment.

[0083] In step S501, refer to Figure 4 The time period T can be divided into time segments such as T1, T2, T3, T4, T5, T6, T7, T8, and T9. When dividing the time period T, it can be divided into equal time segments, meaning each time segment has the same length. Alternatively, the time segments within time period T where the battery's state of charge (SOC) needs to be checked can be determined (e.g., [specific time segments]). Figure 4The shaded segments (T1, T3, T5, T7, T8, and T9) represent time segments with increasing durations (i.e., the relative lengths are T1 < T3 < T5 < T7 < T8 < T9) that do not involve battery state-of-charge detection (e.g., T1, T3, T5, T7, T8, and T9). Figure 4 T2, T4, T6, and T8 (which are not marked with shaded areas) can have the same duration.

[0084] In step S502, the state of charge (SOC) of the storage battery and the power battery is detected at time segments T1, T3, T5, T7, and T9, respectively. For example, at time segment T1, the SOC of the storage battery is detected to obtain the change in SOC at the end of time segment T1 relative to the SOC at the beginning of time segment T1, denoted as . The state of charge (SOC) of the power battery is measured to obtain the change in SOC at the end of time segment T1 relative to the SOC at the beginning of time segment T1, denoted as [value to be inserted here]. At time segment T3, the state of charge (SOC) of the battery is measured to obtain the change in SOC at the end of time segment T3 relative to the beginning of time segment T3, denoted as . The state of charge (SOC) of the power battery is measured to obtain the change in SOC at the end of time segment T3 relative to the beginning of time segment T3, denoted as [value to be inserted here].

[0085] The obtained by executing step S502 The components that constitute the first state of charge change value ΔSOC1 are collectively referred to as the first state of charge change value ΔSOC1; the obtained The components that constitute the second state of charge change value ΔSOC2 are collectively referred to as the second state of charge change value ΔSOC2.

[0086] In this embodiment, when executing steps S501-S502, the vehicle controller (VCU) performs the following steps when executing step S6, which is the step of correcting the first state of charge value based on the first state of charge change value and the second state of charge change value:

[0087] S601. Determine the correction amount based on the first state of charge change value and the second state of charge change value;

[0088] S602. Correct the first state of charge value using the correction amount.

[0089] During step S601, a set of equations can be established based on the electrical flow relationship between the storage battery and the power battery. For example, the components of the first state-of-charge change value ΔSOC1 detected within time segment T1. And the second state of charge change value ΔSOC2 For example, based on the electrical flow relationship between the storage battery and the power battery, an equation can be established.

[0090]

[0091] Wherein, ΔSOC loss This refers to the energy loss that occurs during the recharging process within time segment T1, as electrical energy is released from the power battery and converted by the recharging components before being recharged into the storage battery. This refers to the self-discharge loss of the power battery within time segment T1. This refers to the self-discharge loss of the battery within time segment T1. The error in the state-of-charge (SOC) measurement within time segment T1 was due to the quality of the battery, its lack of calibration, or its failure to self-learn. Since time segment T itself is a relatively short period, such as one night or several days, and time segment T1 is even shorter, the self-discharge loss... and All can be considered as 0, therefore equation (1) can be simplified to:

[0092]

[0093] In equation (2), ΔSOC loss It can be considered as a quantity proportional to unit time. When time segments T1, T3, T5, T7, T8, and T9 have equal durations, ΔSOC loss It can be considered a constant, and ΔSOC can be... loss and Combining them into one item, we get:

[0094]

[0095] Based on the above principle, within time segment T3, there is also an equation:

[0096]

[0097] There are also corresponding equations in other time segments, thus allowing us to establish a system of equations:

[0098]

[0099] In the system of equations (5), each equation contains a corresponding correction term. because and The values ​​are the known data detected by the battery power sensor EBS and the battery management system BMS, respectively. Therefore, by solving the system of equations (5), the correction term can be determined. The value of .

[0100] Due to the correction item These are the sum of the energy loss within the corresponding time segment and the error in detecting the state of charge of the battery. Energy loss can be expressed as a multiple of the loss per unit time (a constant), and therefore, statistical characteristics such as the average of all correction terms can be used as the correction amount. For example, the correction amount ΔSOC can be obtained by calculating the arithmetic mean of all correction terms. correction ,Right now

[0101]

[0102] The correction amount ΔSOC is obtained in step S601. correction Then, in step S602, the correction amount ΔSOC is used. correction The first state of charge value SOC1 is corrected, i.e., SOC1 + ΔSOC is calculated. correction This serves as the first state of charge value after correction.

[0103] In this embodiment, the principle of executing steps S601-S602 is as follows: by using the power battery to replenish the storage battery in each time segment, the state of charge data of the power battery can be used as a benchmark to determine the error in the detection process of the storage battery's state of charge data, thereby obtaining various correction items. By calculating the statistical characteristics of all correction items, the random error in the measurement of different time segments is reduced. The obtained correction amount represents the systematic error in the detection process of the storage battery's state of charge data. By using the correction amount to correct the first state of charge value detected by the storage battery power sensor EBS, the corrected first state of charge value can obtain a reliability level close to the state of charge data of the power battery, thereby improving the reliability of the detection of the storage battery's state of charge data when the storage battery does not self-learn.

[0104] A computer program for executing the electric vehicle battery charging control method in this embodiment can be written into a computer device or storage medium. When the computer program is read out and run, the electric vehicle battery charging control method in this embodiment is executed, thereby achieving the same technical effect as the electric vehicle battery charging control method in the embodiment.

[0105] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a," "an," and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing particular embodiments and is not intended to limit the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.

[0106] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of the invention.

[0107] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0108] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or clearly contradicted by the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes multiple instructions executable by one or more processors.

[0109] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques of the invention, the invention also includes the computer itself.

[0110] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on the display.

[0111] The above are merely preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A battery charging control method for an electric vehicle, characterized in that, The battery charging control method for the electric vehicle includes: Detect the self-learning status of the battery; When the self-learning state is the self-learning state, the battery is subjected to state of charge detection to obtain a first state of charge value, and a threshold condition judgment is made based on the first state of charge value. When the self-learning state is a non-self-learning state, the battery is detected to obtain state parameters, and a threshold condition is determined based on the state parameters; the type of the state parameters is different from the state of charge. When the threshold condition is met, the storage battery is recharged via the power battery; When the self-learning state is a non-self-learning state, the battery charging control method for electric vehicles further includes the following steps: Within a certain time period, the power battery is controlled to replenish the storage battery, the state of charge of the storage battery is detected to obtain a first state of charge change value, and the state of charge of the power battery is detected to obtain a second state of charge change value. The correction amount is determined based on the first state of charge change value and the second state of charge change value; The first state of charge value is corrected using the correction amount; Set the self-learning state to the self-learned state.

2. The battery charging control method for electric vehicles according to claim 1, characterized in that, The detection of the battery's self-learning state includes: When a trigger condition is detected, the charging record of the battery is checked; When a fully charged record is detected, the self-learning state is determined to be the self-learned state. If no full charge record is detected, the self-learning state is determined to be the non-self-learning state.

3. The battery charging control method for electric vehicles according to claim 2, characterized in that, The triggering conditions include: The electric vehicle is in a dormant state, ACC state, or IG ON state and the charging components are not working.

4. The battery charging control method for electric vehicles according to claim 1, characterized in that, When the self-learning state is a non-self-learning state, the step of replenishing the battery through the power battery includes: The storage battery is controlled to provide reverse charging to the power battery until the storage battery is completely discharged; The power battery is controlled to replenish the storage battery until the storage battery is fully charged; Set the self-learning state to the self-learned state.

5. The battery charging control method for electric vehicles according to claim 1, characterized in that, Within a certain time period, controlling the power battery to replenish the storage battery, performing state-of-charge (SOC) detection on the storage battery to obtain a first SOC change value, and performing SOC detection on the power battery to obtain a second SOC change value, including: Within the stated time period, several time segments are defined; For any given time segment, within that time segment, the power battery is controlled to replenish the storage battery, the storage battery is subjected to state of charge detection to obtain the component of the first state of charge change value within that time segment, and the power battery is subjected to state of charge detection to obtain the component of the second state of charge change value within that time segment.

6. The battery charging control method for electric vehicles according to claim 5, characterized in that, The step of determining the correction amount based on the first state-of-charge change value and the second state-of-charge change value includes: A system of equations is established; the system of equations represents the relationship between the energy flow between the storage battery and the power battery. The system of equations uses the components of the first state of charge change value in each time segment and the components of the second state of charge change value in each time segment as parameters. Each equation in the system of equations contains a corresponding correction term. Solve the system of equations to determine the value of each correction term; The statistical characteristics of each correction term are obtained to determine the correction amount.

7. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store at least one program, and the processor being used to load at least one program to execute the battery charging control method for an electric vehicle according to any one of claims 1-6.

8. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the battery charging control method for an electric vehicle according to any one of claims 1-6.

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