Battery bms state control method, device, equipment and storage medium

Abstract

This invention belongs to the field of battery control technology and discloses a battery management system (BMS) state control method, device, equipment, and storage medium. The method includes: when the battery BMS enters independent operation mode, controlling the battery BMS to first enter a discharge state; determining the actual discharge state based on the first current; when the discharge state indicates discharge has stopped, entering a battery BMS waiting state, and then switching to a charging state to determine charging demand. Through this scheme, when the discharge state is detected as having stopped, the system enters a battery BMS waiting state, and then enters a charging state to determine the charging current. This mechanism helps maintain system stability and safety, while avoiding over-discharge at inappropriate times, thus improving battery safety. Simultaneously, when the battery BMS operates independently without a host computer, it can autonomously determine the charging and discharging mode and switch modes autonomously, improving the convenience of battery swapping and independent operation.

Description

Technical Field

[0001] This invention relates to the field of battery control technology, and in particular to a battery BMS state control method, apparatus, device, and storage medium. Background Technology

[0002] With the emergence of battery swapping technology for new energy vehicles, its convenience has quickly attracted the attention of manufacturers. Battery swapping is an alternative to charging electric vehicles, providing power by replacing the entire battery pack instead of waiting for it to charge. This technology aims to solve problems such as long charging times and insufficient charging infrastructure for electric vehicles, thereby improving the ease of use of electric vehicles.

[0003] Currently, the battery's BMS cannot operate autonomously during battery swapping. This is because the state transitions within the BMS rely on commands from a host computer. After the BMS is powered on and woken up, if it does not receive a VCM signal, it reports a fault and cannot operate. If it receives a simulated VCM control signal from an external host computer, the BMS completes the corresponding state transition according to the command. Therefore, the battery cannot operate independently. However, in many battery swapping scenarios, such as in vehicles that can perform battery swapping, the battery needs to operate independently. In these cases, an external host computer is required to simulate commands to assist the BMS in control, thus greatly reducing the ease of use.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a battery BMS state control method, apparatus, device, and storage medium, aiming to solve the technical problem of low convenience when batteries operate independently in the prior art.

[0006] To achieve the above objectives, the present invention provides a battery BMS state control method, the method comprising the following steps:

[0007] When the battery BMS enters independent operation mode, it controls the battery to enter the discharge state;

[0008] Obtain the first current and determine the discharge state based on the first current;

[0009] When the discharge state is stopped, the battery enters the BMS standby state.

[0010] Optionally, after entering the battery BMS waiting state when the discharge state is discharge stopped, the method further includes:

[0011] Respond to the charging request and obtain the current battery parameters;

[0012] Determine the charging request current based on the current battery parameters;

[0013] The charging request current is sent to the charging device so that the charging device inputs energy according to the charging request current;

[0014] Obtain the second current and determine the charging state based on the second current;

[0015] When the discharge state is when charging stops, the battery enters the BMS waiting state.

[0016] Optionally, determining the discharge state based on the first current current includes:

[0017] Obtain the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor;

[0018] Determine the current state based on the first current current;

[0019] When the current state is in a convergent state and the first current is within the target current range, the discharge state is determined to be discharge stopped.

[0020] Optionally, determining the current state based on the first current current includes:

[0021] The first current is sampled to obtain a sampled current set;

[0022] The rate of change of the continuous current sampling period is determined based on the aforementioned set of sampled currents;

[0023] The current state is determined based on the rate of change of the current during the continuous sampling period.

[0024] Optionally, determining the current state based on the first current current includes:

[0025] The first current is sampled to obtain a sampled current set;

[0026] The current prediction set is obtained by substituting the sampled current set into a preset extended Kalman filter model.

[0027] Calculate the rate of change of the current continuous sampling period based on the set of predicted current values;

[0028] The current state is determined based on the rate of change of the current during the continuous sampling period.

[0029] Optionally, before obtaining the current prediction value set by substituting the sampled current set into a preset extended Kalman filter model, the method further includes:

[0030] Obtain the battery charge / discharge model and battery cell parameters, and determine the first battery model coefficient matrix and the second battery model coefficient matrix based on the battery charge / discharge model and battery cell parameters;

[0031] A preliminary state vector model is determined based on the coefficient matrix of the first battery model and the coefficient matrix of the second battery model.

[0032] Obtain the technical parameters of the current sensor, and determine the first covariance matrix of the system process noise and the second covariance matrix of the measurement noise based on the current sensor technical parameters;

[0033] The Kalman gain is determined based on the first covariance matrix and the second covariance matrix;

[0034] Based on the preliminary state vector model and the Kalman gain, a preset extended Kalman filter model is determined.

[0035] Optionally, before controlling the battery to enter a discharge state when the battery BMS enters independent operation mode, the following steps are included:

[0036] Respond to power-on wake-up command and detect control signals from the host computer;

[0037] If no control signal from the host computer is detected within a preset time, the system will enter independent operation mode.

[0038] Furthermore, to achieve the above objectives, the present invention also proposes a battery BMS state control device, the battery BMS state control device comprising:

[0039] The processing module is used to control the battery to enter the discharge state when the battery BMS enters the independent operation mode;

[0040] The processing module is further configured to acquire a first current and determine a discharge state based on the first current;

[0041] The switching module is used to enter the battery BMS waiting state when the discharge state is discharge stopped.

[0042] Furthermore, to achieve the above objectives, the present invention also proposes a battery BMS state control device, which includes: a memory, a processor, and a battery BMS state control program stored in the memory and executable on the processor. The battery BMS state control program is configured to implement the steps of the battery BMS state control method described above.

[0043] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a battery BMS state control program, wherein when the battery BMS state control program is executed by a processor, it implements the steps of the battery BMS state control method described above.

[0044] This invention controls the battery to enter a discharge state when the battery management system (BMS) enters independent operation mode; acquires a first current and determines the current discharge state based on the first current; when the discharge state indicates that discharge has stopped, it enters a battery BMS waiting state; and in the charging state, it determines the charging demand and charging current. Through this scheme, when the discharge state is detected as having stopped, the system enters a battery BMS waiting state. This mechanism helps maintain system stability and safety, while avoiding over-discharge at inappropriate times. When charging is needed, it outputs a charging request current according to the battery state, improving battery safety. Furthermore, when the battery BMS operates independently without a host computer, it can autonomously determine the charging and discharging mode and switch modes accordingly, improving the convenience of battery swapping and independent operation. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of the battery BMS state control device in the hardware operating environment involved in the embodiments of the present invention;

[0046] Figure 2 This is a flowchart illustrating the first embodiment of the battery BMS state control method of the present invention;

[0047] Figure 3 This is a flowchart illustrating the second embodiment of the battery BMS state control method of the present invention;

[0048] Figure 4 This is a structural block diagram of the first embodiment of the battery BMS state control device of the present invention.

[0049] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0051] Reference Figure 1 , Figure 1 This is a schematic diagram of the battery BMS state control device structure in the hardware operating environment involved in the embodiments of the present invention.

[0052] like Figure 1As shown, the battery management system (BMS) state control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0053] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the battery BMS state control device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0054] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a battery management system (BMS) status control program.

[0055] exist Figure 1 In the battery BMS status control device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the battery BMS status control device of the present invention can be set in the battery BMS status control device, and the battery BMS status control device calls the battery BMS status control program stored in the memory 1005 through the processor 1001 and executes the battery BMS status control method provided in the embodiment of the present invention.

[0056] This invention provides a battery BMS state control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a battery BMS state control method according to the present invention.

[0057] In this embodiment, the battery BMS state control method includes the following steps:

[0058] Step S10: When the battery BMS enters the independent operation mode, control the battery to enter the discharge state.

[0059] It should be noted that the execution subject in this embodiment is a smart terminal. The smart terminal can be a battery BMS or other devices with the same or similar functions as a battery BMS. This embodiment does not limit this, but only uses a battery BMS as an example for illustration.

[0060] It should be noted that this embodiment applies to the independent operation of the battery management system (BMS), that is, during the communication process when the battery is disconnected from the host computer. For example, the most common scenarios are battery swapping or battery removal for testing. The battery management system (BMS) is an intelligent system used to monitor, control, and protect the battery. Its main purpose is to ensure the safety, stability, and lifespan of the battery.

[0061] In practical implementation, the state transition of the BMS in the vehicle requires control commands from the upper-level controller. However, in vehicles capable of battery swapping, many battery swapping scenarios require the battery to operate independently. Typically, when the entire battery pack operates independently, the BMS is always in a discharge mode for charging and discharging. This is because the BMS only allows positive and negative current in its running mode. Without external device intervention, the swapped battery may only have a single function, such as charging, leading to numerous inconveniences. The actual BMS charging mode must automatically determine the allowable current for charging the battery pack based on the battery state; otherwise, there are risks such as overcurrent and impact on battery health. External devices may not have this capability, severely limiting the battery's application scenarios. Furthermore, without entering the corresponding mode, current control is impossible. Currently, many swapped battery packs require an external computer to manually operate a simulated VCM to control the BMS and achieve state transitions. For charging and discharging scenarios requiring cyclic control, this is difficult to implement. Therefore, this embodiment proposes an active state transition method for independent BMS operation.

[0062] In some embodiments, in response to a power-on wake-up command, the host computer control signal is detected; if no host computer control signal is detected within a preset time, the system enters an independent operation mode.

[0063] Among them, controlling the battery to discharge means that the battery is turned on as a battery swapping mode by default. The advantage of doing this is that the battery in this state can be used immediately. Generally, when entering the independent operation mode, the battery is woken up from the standby mode. When it is confirmed that no host computer control signal is detected, it enters the independent operation mode. Since it is woken up, there will be a demand, so it can be discharged directly. When it is determined that there is no demand for discharge, it will directly enter the battery BMS waiting state to switch modes.

[0064] Step S20: Obtain the first current and determine the discharge state based on the first current.

[0065] It is understandable that the first current is the discharge current in the discharge state. The discharge state indicates which part of the discharge time period the current time node is in, such as the discharge stage, the discharge cutoff stage, etc. The characteristics of the first current are also different in different discharge states, which can be determined based on the current current magnitude, fluctuation, and convergence / dispersion trend.

[0066] Step S30: When the discharge state is discharge stopped, enter the battery BMS waiting state.

[0067] It should be noted that when the discharge state is "discharge stopped," it indicates that the battery has completed its discharge task, and therefore, the battery BMS enters the battery BMS standby state. After entering independent operation, it first enters DRIVE mode (discharge state) to begin discharging; DRIVE mode analyzes the current using an algorithm to determine whether a mode switch is needed, and if a switch is needed, it enters Stanby (standby state).

[0068] In some embodiments, in response to a charging request, the system obtains current battery parameters; determines a charging request current based on the current battery parameters; sends the charging request current to a charging device so that the charging device inputs energy based on the charging request current; obtains a second current and determines a charging state based on the second current; and enters a battery BMS waiting state when the discharge state is charging stopped.

[0069] Among them, the second current is the charging current in the charging state.

[0070] In the specific implementation, the discharge state is determined by the current, and the charging state is determined in the same way. Based on the state switching, it is also necessary to control the current request value to ensure that the appropriate amount of energy is charged. Specifically, for example: in QC mode (charging state), the allowable charging request current is checked based on the current battery temperature and SOC map; in QC mode, the current is analyzed by algorithm to determine whether a mode switch is needed; Stanby will detect the wake-up signal, and if the wake-up signal is maintained, it will determine whether to re-enter DRIVE mode; if the BMS wake-up level signal is cleared to zero, it will enter sleep mode and wait for the next wake-up.

[0071] This embodiment proposes the following preferred scheme for state switching. For example, after a vehicle battery swap, the BMS can independently control the operating state of the battery pack, which is also beneficial to ensuring the overall vehicle performance after the battery is swapped back into the vehicle. After the BMS is woken up, it first enters the discharge (DRIVE) state. Then the discharge current changes according to the external power demand. Finally, the key is how to identify the stop of discharge and realize active state transition, jumping to the waiting (stanby) state, and then entering the next charging state (QC). The BMS algorithm adds a current sensor zero drift detection range. When the discharge stops, the current sensor will have a positive zero drift range [I1, I2]. The BMS periodically captures the current in real time through extended Kalman filtering. When the current received by the BMS enters this range, it adds continuous calculation of the current change rate k value for judgment. If the k value maintains a unidirectional change and approaches convergence and stability, after n1 sampling periods of time t1, the BMS actively jumps out of the discharge mode, enters the stanby state, and then enters the charging (QC) state. After entering the charging state, the BMS will follow the conventional logic based on the battery temperature T and the current. The system checks the SOC (State of Charge) to output the charging request current Ir. External charging stations and other devices will charge the battery according to the request. The key is how to identify: ① the battery itself has charged to the required level and then actively stops charging; ② the external charging stops first and then the system actively stops charging. When the battery itself has charged to the required level, the BMS identifies that the charging request current Ir = Irn (the current in the last charging window before the end of charging) & the battery voltage V = Vmax (the cutoff voltage at the end of charging). After a certain period of time, the charging request current Ir is changed to 0. Similarly, after the charging current stops, the current sensor will have a reverse zero drift range [I1 = 3, I4]. The BMS makes the same judgment on the current range and k value for time t2. If the external charging stops, the BMS will directly enter the same judgment after the charging current stops, and change the charging request current Ir to 0 before exiting the state.

[0072] In this embodiment, when the battery management system (BMS) enters independent operation mode, it controls the battery to enter a discharge state; it acquires a first current and determines the actual discharge state based on the first current; when the discharge state is determined to be a stopped discharge state, it enters a battery BMS waiting state. Through this scheme, when the discharge state is detected to be a stopped discharge state, the system enters a battery BMS waiting state. This mechanism helps maintain system stability and safety, while avoiding over-discharge at inappropriate times, thus improving battery safety. Simultaneously, when the battery BMS operates independently without the host computer, it can autonomously determine the charging and discharging mode and switch modes accordingly, improving the convenience of battery swapping and independent operation.

[0073] refer to Figure 3 , Figure 3 This is a flowchart illustrating a second embodiment of a battery BMS state control method according to the present invention.

[0074] Based on the first embodiment described above, the battery BMS state control method of this embodiment further includes, in step S20:

[0075] Step S21: Obtain the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor.

[0076] It should be noted that in practical applications, the current sensor in the BMS does not quickly reach zero at the end of charging or discharging. Even if it does reach zero, due to zero drift, the displayed result will not be zero but will fluctuate within a small range, gradually approaching the zero drift value. Current sensor zero drift refers to the current value output by the sensor not being zero when no current is flowing. This is caused by some imperfections in the sensor itself. Therefore, based on the current sensor zero drift, a small range can be determined as a standard for judging the battery status.

[0077] Furthermore, zero drift combined with prolonged fluctuations can lead to a problem: the battery's state switching becomes very sluggish or even inaccurate. This can result in over-discharge, over-charge, or even depletion of the battery, and the state switching process becomes very insensitive, affecting the user experience. Therefore, based on the above problems, this embodiment proposes to obtain the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor as a judgment criterion.

[0078] Step S22: Determine the current state based on the first current current.

[0079] It should be noted that current state refers to the fluctuation of current, such as whether the current state is fluctuating or converging. In the convergent state, the current tends to a stable value. When the current experiences discrete fluctuations, it may indicate that the system is experiencing instantaneous load changes, sudden current changes, or other temporary variations.

[0080] In some embodiments, the first current is sampled to obtain a sampled current set; the rate of change of the continuous sampling period of the current is determined based on the sampled current set; and the current state is determined based on the rate of change of the continuous sampling period of the current.

[0081] It should be noted that by evaluating the rate of change of the current over a continuous sampling period, the system can determine the state of the current. For example, a small rate of change may indicate that the current is stabilizing and in a convergent state; while a large rate of change may indicate that the current has experienced fluctuations and is in an unstable state. The specific calculation process can be as follows: Where k is the rate of change of the current during the continuous sampling period, Δi-1 is the change in current during the previous sampling period, Δt-1 is the change in time during the previous sampling period, and the difference in the rate of change of the current during the continuous sampling period is the sum of the rates of change. The current at each moment is filtered and estimated using the extended Kalman filter algorithm.

[0082] In some embodiments, the first current is sampled to obtain a sampled current set; a preset extended Kalman filter model is input into the sampled current set to obtain a current prediction value set; the rate of change of the current continuous sampling period is calculated based on the current prediction value set; and the current state is determined based on the rate of change of the current continuous sampling period.

[0083] It should be noted that the preset extended Kalman filter model is a prediction model set based on the principles of the extended Kalman filter model and the current battery and device parameters. The reason for choosing the extended Kalman filter model is that it is an extension of the Kalman filter, used to handle nonlinear systems, and has good adaptability to signals with high noise. Precisely because of these characteristics, the charging and discharging current has high noise and is a nonlinear system with large signal fluctuations and potential discontinuities. Therefore, it is very suitable for the current signal of battery charging and discharging, and its application in the current scenario can greatly improve the system's response speed and accuracy.

[0084] In some embodiments, a battery charge / discharge model and battery cell parameters are acquired, and a first battery model coefficient matrix and a second battery model coefficient matrix are determined based on the battery charge / discharge model and battery cell parameters; a preliminary state vector model is determined based on the first battery model coefficient matrix and the second battery model coefficient matrix; current sensor technical parameters are acquired, and a first covariance matrix of system process noise and a second covariance matrix of measurement noise are determined based on the current sensor technical parameters; Kalman gain is determined based on the first covariance matrix and the second covariance matrix; and a preset extended Kalman filter model is determined based on the preliminary state vector model and the Kalman gain.

[0085] Specifically, the process of determining the Kalman gain based on the first covariance matrix and the second covariance matrix can be as follows: discretize the state vector to obtain the state prediction equation; then determine the first covariance matrix of the system process noise and the second covariance matrix of the measurement noise based on the current sensor technical parameters; determine the covariance prediction equation based on the first covariance matrix and the Jacobian matrix of the state vector prediction equation; and determine the Kalman gain based on the covariance prediction equation and the second covariance matrix.

[0086] In practical implementation, obtaining the battery charge / discharge model and battery cell parameters involves acquiring a mathematical model of the battery's charging and discharging behavior, as well as key internal parameters such as internal resistance and capacity. Determining the first and second battery model coefficient matrices is crucial for using the obtained battery charge / discharge model and cell parameters to determine the model coefficient matrices describing the battery's behavior. These coefficient matrices may relate to characteristics such as the battery's dynamic response and internal resistance. Next, based on the first and second battery model coefficient matrices, a preliminary state vector model is established to describe the battery's state changes during charging and discharging. Current sensor technical parameters include acquiring the technical parameters of the sensor used to measure current, such as sensitivity and accuracy. Determining the first covariance matrix of the system process noise and the second covariance matrix of the measurement noise is achieved using the current sensor technical parameters, thus defining the covariance matrices describing the system process noise and measurement noise. These matrices describe the uncertainties in the system model. Using these covariance matrices, the Kalman gain is calculated; this is a key parameter used to adjust the system state estimation during the filtering process. Based on the preliminary state vector model and the calculated Kalman gain, the final extended Kalman filter model is determined to estimate the current and perform real-time control of the system.

[0087] It should be noted that accurately predicting and estimating the current intensity and its changing trend is crucial for achieving correct state transitions. This embodiment proposes the following preferred implementation scheme, for example: using an extended Kalman filter to estimate the dynamic system composed of the circuit and current velocity states, which is divided into two stages: prediction and update. In the prediction step, the current is predicted using state-space equations; in the update step, the predicted value is corrected using observation equations to obtain the final estimated value. The entire process evaluates and adjusts the results of the extended Kalman filter model based on filtering effect, convergence speed, and stability. ① The basic model for the prediction process is as follows: Using Ohm's law and Kirchhoff's laws, a dynamic equation for the rate of change of current in the charging and discharging circuit, matching the properties of the battery pack itself, is established: dI / dt=A(-R / L)*I+B(1 / L)*V, where R is the internal resistance of the battery pack; battery manufacturers provide internal resistance model (DCR) coefficient maps for different charging and discharging states of the battery, and L is the inductance; A and B are the calibration coefficient matrices of the battery model, where A is the coefficient matrix of the first battery model and B is the coefficient matrix of the second battery model; the battery current h(I)=I collected by the current sensor is used to discretize the current dynamic equation to obtain the current state prediction equation: I_{k|k-1}=I_{k-1}+A dt*(-R / L)*I_{k-1}+B dt*(1 / L)*V, where dt is the time interval, specifically the duration of a single sampling period. The sampling period is determined by the BMS hardware and software. The covariance prediction equation is: P_{k|k-1}=F_{k-1}*P_{k-1|k-1}*F_{k-1}^T+Q, where F_{k-1} is the Jacobian matrix of the state transition equation, F_{k-1}=1-A dt*(R / L), Q (first covariance matrix) is the covariance matrix of the system process noise, which can be obtained through the technical specifications of the current sensor or set through offline testing. ② The basic model of the update process is as follows: The current value is updated using the actual measured current I_m and the predicted current I_{k|k-1}, and the covariance matrix is ​​updated at the same time: State prediction equation update: I_{k|k}=I_{k|k-1}+K_{k}*(I_m-I_{k|k-1}), covariance prediction equation update: P_{k|k}=(1-K_{k})*P_{k|k-1}, where K_{k} is the Kalman gain, K_{k}=P_{k|k-1} / (P_{k|k-1}+R), and R (second covariance matrix) is the covariance matrix of the measurement noise, which can be obtained through the technical specifications of the current sensor.

[0088] Step S23: When the current state is in a convergent state and the first current is within the target current range for a certain period of time, determine the discharge state as discharge stopped.

[0089] Understandably, when the current is in a convergent state, it means the current is approaching a stable value. The system may have adapted to load changes, and the current tends to remain relatively stable. The fact that the first current is within the target current range indicates a tendency for the current to stop discharging. Based on the convergent current state and the condition that the first current is within the target current range, the system makes a decision to stop discharging. This includes pausing the current discharge operation or completely stopping the discharge to ensure the battery operates in a stable and safe state.

[0090] This embodiment obtains the zero drift of the current sensor and determines the target current range based on the zero drift; it determines the current state based on the first current current; when the current state is in a convergent state and the first current current is within the target current range, it determines the discharge state as discharge stopped. Through this scheme, current stability and attenuation are ensured, avoiding misjudgments of discharge stoppage. This approach optimizes system energy consumption, extends battery life, and ensures the stability of the power system.

[0091] Furthermore, this embodiment of the invention also proposes a storage medium storing a battery BMS state control program, which, when executed by a processor, implements the steps of the battery BMS state control method described above.

[0092] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the battery BMS state control device of the present invention.

[0093] like Figure 4 As shown, the battery BMS state control device proposed in this embodiment of the invention includes:

[0094] Processing module 10 is used to control the battery to enter the discharge state when the battery BMS enters the independent operation mode;

[0095] The processing module 10 is further configured to acquire a first current and determine a discharge state based on the first current.

[0096] The switching module 20 is used to enter the battery BMS waiting state when the discharge state is discharge stopped.

[0097] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0098] In this embodiment, when the battery BMS enters independent operation mode, the processing module 10 controls the battery to enter a discharge state. The processing module 10 acquires a first current and determines the discharge state based on the first current. When the discharge state is "discharge stopped," the switching module 20 enters the battery BMS waiting state. Through this scheme, when the discharge state is detected as "discharge stopped," the system enters the battery BMS waiting state. This mechanism helps maintain system stability and safety, while avoiding over-discharge at inappropriate times, thus improving battery safety. Furthermore, when the battery BMS operates independently without the host computer, it can autonomously determine the charging and discharging mode and switch modes accordingly, improving the convenience of battery swapping and independent operation.

[0099] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0100] In addition, for technical details not described in detail in this embodiment, please refer to the battery BMS state control method provided in any embodiment of the present invention, which will not be repeated here.

[0101] In some embodiments, the switching module 20 is further configured to respond to a charging request and obtain current battery parameters;

[0102] Determine the charging request current based on the current battery parameters;

[0103] The charging request current is sent to the charging device so that the charging device inputs energy according to the charging request current;

[0104] Obtain the second current and determine the charging state based on the second current;

[0105] When the discharge state is when charging stops, the battery enters the BMS waiting state.

[0106] In some embodiments, the processing module 10 is further configured to acquire the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor.

[0107] Determine the current state based on the first current current;

[0108] When the current state is in a convergent state and the first current is within the target current range, the discharge state is determined to be discharge stopped.

[0109] In some embodiments, the processing module 10 is further configured to sample the first current current to obtain a sampled current set;

[0110] The rate of change of the continuous current sampling period is determined based on the aforementioned set of sampled currents;

[0111] The current state is determined based on the rate of change of the current during the continuous sampling period.

[0112] In some embodiments, the processing module 10 is further configured to sample the first current current to obtain a sampled current set;

[0113] The current prediction set is obtained by substituting the sampled current set into a preset extended Kalman filter model.

[0114] Calculate the rate of change of the current continuous sampling period based on the set of predicted current values;

[0115] The current state is determined based on the rate of change of the current during the continuous sampling period.

[0116] In some embodiments, the processing module 10 is further configured to acquire a battery charge-discharge model and battery cell parameters, and determine a first battery model coefficient matrix and a second battery model coefficient matrix based on the battery charge-discharge model and battery cell parameters.

[0117] A preliminary state vector model is determined based on the coefficient matrix of the first battery model and the coefficient matrix of the second battery model.

[0118] Obtain the technical parameters of the current sensor, and determine the first covariance matrix of the system process noise and the second covariance matrix of the measurement noise based on the current sensor technical parameters;

[0119] The Kalman gain is determined based on the first covariance matrix and the second covariance matrix;

[0120] Based on the preliminary state vector model and the Kalman gain, a preset extended Kalman filter model is determined.

[0121] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0122] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0124] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A battery BMS state control method, characterized in that, The battery BMS state control method includes: When the battery BMS enters independent operation mode, it controls the battery to enter the discharge state; Obtain the first current and determine the discharge state based on the first current; When the discharge state is discharge stopped, the battery BMS wait state is entered. Before controlling the battery to enter the discharge state when the battery BMS enters independent operation mode, the following is included: Respond to power-on wake-up command and detect control signals from the host computer; If no control signal from the host computer is detected within a preset time, the system will enter independent operation mode. The step of determining the discharge state based on the first current current includes: Obtain the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor; Determine the current state based on the first current current; When the current state is in a convergent state and the first current is within the target current range, the discharge state is determined to be discharge stopped.

2. The method as described in claim 1, characterized in that, After entering the battery BMS waiting state when the discharge state is discharge stopped, the process further includes: Respond to the charging request and obtain the current battery parameters; Determine the charging request current based on the current battery parameters; The charging request current is sent to the charging device so that the charging device inputs energy according to the charging request current; Obtain the second current and determine the charging state based on the second current; When the discharge state is when charging stops, the battery enters the BMS waiting state.

3. The method as described in claim 1, characterized in that, The step of determining the current state based on the first current current includes: The first current is sampled to obtain a sampled current set; The rate of change of the continuous current sampling period is determined based on the aforementioned set of sampled currents; The current state is determined based on the rate of change of the current during the continuous sampling period.

4. The method as described in claim 1, characterized in that, The step of determining the current state based on the first current current includes: The first current is sampled to obtain a sampled current set; The current prediction set is obtained by substituting the sampled current set into a preset extended Kalman filter model. Calculate the rate of change of the current continuous sampling period based on the set of predicted current values; The current state is determined based on the rate of change of the current during the continuous sampling period.

5. The method as described in claim 4, characterized in that, Before obtaining the current prediction value set by substituting the sampled current set into a preset extended Kalman filter model, the method further includes: Obtain the battery charge / discharge model and battery cell parameters, and determine the first battery model coefficient matrix and the second battery model coefficient matrix based on the battery charge / discharge model and battery cell parameters; A preliminary state vector model is determined based on the coefficient matrix of the first battery model and the coefficient matrix of the second battery model. Obtain the technical parameters of the current sensor, and determine the first covariance matrix of the system process noise and the second covariance matrix of the measurement noise based on the current sensor technical parameters; The Kalman gain is determined based on the first covariance matrix and the second covariance matrix; Based on the preliminary state vector model and the Kalman gain, a preset extended Kalman filter model is determined.

6. A battery BMS state control device, characterized in that, The battery BMS state control device includes: The processing module is used to control the battery to enter the discharge state when the battery BMS enters the independent operation mode; The processing module is further configured to acquire a first current and determine a discharge state based on the first current. The switching module is used to enter the battery BMS waiting state when the discharge state is the discharge stop state, and switch to the charging state before charging. The processing module is also used to respond to power-on wake-up commands and detect host computer control signals; If no control signal from the host computer is detected within a preset time, the system will enter independent operation mode. The processing module is also used to obtain the zero drift of the current sensor and determine the target current range based on the zero drift of the current sensor. Determine the current state based on the first current current; When the current state is in a convergent state and the first current is within the target current range, the discharge state is determined to be discharge stopped.

7. A battery BMS state control device, characterized in that, The device includes: a memory, a processor, and a battery BMS state control program stored in the memory and executable on the processor, the battery BMS state control program being configured to implement the steps of the battery BMS state control method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a battery BMS state control program, which, when executed by a processor, implements the steps of the battery BMS state control method as described in any one of claims 1 to 5.

Images