Battery health state estimation method and related equipment

Abstract

The invention provides a battery health state estimation method and related equipment, and relates to the technical field of energy storage. The method comprises the following steps: acquiring the historical battery capacity of a to-be-estimated battery and a battery charge state sequence in a preset time period; according to the battery charge state sequence, determining charging and discharging data of the to-be-estimated battery in a target charging and discharging process; calculating the current battery capacity by adopting an exponential smoothing method according to the charging and discharging data and the historical battery capacity; and calculating a first battery health state according to the current battery capacity.

Description

Background Art

[0002] In industrial and commercial energy storage systems, the battery state of charge (SoC) and battery state of health (SoH) are important parameters that characterize the current state of the battery. SoH can reflect the capacity of the battery in its current state, and SoC reflects the available state of the remaining battery power. Accurate estimation of battery SoH can provide basic data for SoC estimation and battery balancing control. Since the battery pack is a highly nonlinear time-varying system and is greatly affected by environmental factors, it is difficult to accurately estimate SoH and SoC.

[0003] In the related art, the SOH estimation method usually requires a large amount of battery data and computing power consumption, which easily leads to the problems of low efficiency and poor accuracy of SoH estimation. Summary of the invention

[0004] The present disclosure provides a battery health state estimation method and related devices, which at least to a certain extent overcome the problems of low efficiency and poor accuracy of battery health state estimation methods in related technologies.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a battery health state estimation method is provided, including: obtaining a historical battery capacity of a battery to be estimated and a battery charge state sequence within a preset time period; determining charge and discharge data of a target charge and discharge process of the battery to be estimated based on the battery charge state sequence; calculating a current battery capacity based on the charge and discharge data and the historical battery capacity using an exponential smoothing method; and calculating a first battery health state based on the current battery capacity.

[0007] In one embodiment of the present disclosure, determining the charge and discharge data of the target charge and discharge process of the battery to be estimated based on the battery state of charge sequence includes: determining the charging process or discharging process with the deepest battery charge and discharge depth in the battery state of charge sequence as the target charge and discharge process; determining the charge and discharge data of the target charge and discharge process, wherein the charge and discharge data includes the starting data collection time of the target charge and discharge process, the historical battery health status corresponding to the starting data collection time, the ending data collection time, and the ending battery state of charge corresponding to the ending data collection time.

[0008] In one embodiment of the present disclosure, the method of using an exponential smoothing method to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity includes: calculating the initial battery capacity according to the charge and discharge data of the target charge and discharge process; smoothing the initial battery capacity and the historical battery capacity using a smoothing exponential method to obtain the current battery capacity; and calculating the first battery health state according to the current battery capacity includes: calculating the ratio of the current battery capacity to the rated capacity of the battery, and determining the ratio as the first battery health state.

[0009] In one embodiment of the present disclosure, the calculating of the first battery health state according to the current battery capacity includes: if the battery state of charge sequence has a full charge to full discharge condition or a full discharge to full charge condition, calculating the second battery health state according to the battery charge and discharge efficiency, the charge and discharge current and the battery rated capacity; calculating the average value of the first battery health state and the second battery health state as the battery health state of the battery; if the battery state of charge sequence does not have a full charge to full discharge condition or a full discharge to full charge condition, using the first battery health state as the battery health state of the battery to be estimated.

[0010] In one embodiment of the present disclosure, the battery state of charge sequence within the preset time period is obtained in the following manner, including: acquiring the historical battery health status and the current value and voltage value of the battery to be estimated at multiple data collection moments within the preset time period; using an extended Kalman filter algorithm to process the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection moment within the preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated, determine the battery state of charge at each data collection moment, and obtain the battery state of charge sequence.

[0011] In one embodiment of the present disclosure, after determining the battery state of charge at each data collection moment, the method further includes: if the current value and voltage value of the battery to be estimated meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition, then using the corresponding correction strategy to correct the battery state of charge at the current data collection moment, to obtain the target battery state of charge at the current data collection moment, and using the target battery state of charge to update the battery state of charge sequence.

[0012] In one embodiment of the present disclosure, the method also includes: determining the continuous standing time of the battery to be estimated at the current data collection moment according to the relationship between the current value of the battery to be estimated at the multiple data collection moments and the preset current threshold; if the continuous standing time of the battery to be estimated at the current data collection moment is greater than or equal to the first preset standing time threshold, and the voltage value of the battery to be estimated at the current data collection moment is less than or equal to the starting voltage value of the battery platform voltage, or the voltage value of the battery to be estimated at the current data collection moment is greater than or equal to the ending voltage value of the battery platform voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition; if the continuous standing time of the battery to be estimated at the current data collection moment is greater than the second preset standing time threshold, and the voltage value of the battery to be estimated at the current data collection moment reaches the maximum working voltage or reaches the minimum working voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the full charge and discharge correction condition.

[0013] In one embodiment of the present disclosure, the battery state of charge at the current data collection moment is corrected by adopting a corresponding correction strategy to obtain the target battery state of charge at the current data collection moment, including: when the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition, determining the target battery state of charge at the current data collection moment as a value corresponding to the inverse function of the battery state of charge-open circuit voltage curve and the voltage value of the battery to be estimated.

[0014] In one embodiment of the present disclosure, the battery state of charge at the current data collection moment is corrected by adopting a corresponding correction strategy to obtain the target battery state of charge at the current data collection moment, including: when the current value and voltage value of the battery to be estimated meet the full charge and discharge correction condition, if the voltage value of the battery to be estimated reaches the maximum operating voltage, then the target battery state of charge is determined to be 100%; if the voltage value of the battery to be estimated reaches the minimum operating voltage, then the target battery state of charge is determined to be 0%.

[0015] In one embodiment of the present disclosure, the method further includes: if the current value and voltage value of the battery to be estimated at the current data collection moment do not meet the preset open circuit voltage correction condition or the preset full charge and discharge correction condition, the battery state of charge sequence is not updated.

[0016] In one embodiment of the present disclosure, the extended Kalman filter algorithm is used to process the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection moment within the preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated to determine the battery state of charge at each data collection moment, including: taking the current value, voltage value of the battery to be estimated and the model parameters of the equivalent circuit model as the input of the extended Kalman filter algorithm, and taking the battery state of charge as the output, to construct the state equation and measurement equation of the extended Kalman filter algorithm; initializing the parameters of the extended Kalman filter algorithm, calculating the battery state of charge, polarization voltage and covariance matrix at the current data collection moment; taking the polarization voltage and covariance matrix at the current data collection moment as the initialization parameters of the extended Kalman filter algorithm at the next data collection moment, calculating the battery state of charge polarization voltage and covariance matrix at the next data collection moment, and iterating in sequence until the battery state of charge at the multiple data collection moments is obtained.

[0017] According to another aspect of the present disclosure, a battery health state estimation device is provided, including: a sequence acquisition module, used to obtain the historical battery capacity of a battery to be estimated and a battery charge state sequence within a preset time period; a data determination module, used to determine the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery charge state sequence; a capacity calculation module, used to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity using an exponential smoothing method; and a health state estimation module, used to calculate a first battery health state according to the current battery capacity.

[0018] According to another aspect of the present disclosure, an electronic device is also provided, which includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned battery health status estimation methods by executing the executable instructions.

[0019] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the battery health status estimation method described in any one of the above is implemented.

[0020] According to another aspect of the present disclosure, a computer program product is also provided, including a computer program, wherein when the computer program is executed by a processor, the battery health status estimation method described in any one of the above is implemented.

[0021] In the embodiment of the present disclosure, the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period are obtained, and the charge and discharge data of the target charge and discharge process of the battery to be estimated are determined according to the battery state of charge sequence; the current battery capacity is calculated according to the charge and discharge data and the historical battery capacity using the exponential smoothing method, and the first battery health state is calculated according to the current battery capacity. The method disclosed in the present disclosure for estimating the battery SOH by using the historical battery capacity and the charge state of charge sequence within a preset time period is simple, can reduce the demand for data and computing power consumption, improve the estimation efficiency and estimation accuracy of the battery health state, and has a wider range of applications.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0024] Figure 1 A schematic structural diagram of an energy storage system provided by an embodiment of the present disclosure is shown.

[0025] Figure 2 A flow chart of a battery health status estimation method provided by an embodiment of the present disclosure is shown.

[0026] Figure 3 A flow chart of a battery state of charge estimation method provided by an embodiment of the present disclosure is shown.

[0027] Figure 4 A schematic diagram of an equivalent circuit model of a battery provided by an embodiment of the present disclosure is shown.

[0028] Figure 5 A flow chart of a method for determining a battery state of charge provided by an embodiment of the present disclosure is shown.

[0029] Figure 6 A flow chart of another battery health status estimation method provided by an embodiment of the present disclosure is shown.

[0030] Figure 7 A flow chart of a method for determining whether the current value and voltage value of a battery provided in an embodiment of the present disclosure satisfy a preset open circuit voltage correction condition and / or a preset full charge / discharge correction condition is shown.

[0031] Figure 8A flow chart of a method for determining charge and discharge data of a target charge and discharge process provided in an embodiment of the present disclosure is shown.

[0032] Fig. 9 A flow chart of a method for determining a battery health status provided in an embodiment of the present disclosure is shown.

[0033] Fig.10 A flow chart of another method for determining a battery health status provided in an embodiment of the present disclosure is shown.

[0034] Fig.11 A schematic diagram of the structure of a battery health status estimation device provided by an embodiment of the present disclosure is shown.

[0035] Fig.12 A structural block diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0037] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0038] Since the energy people need is highly temporal and spatial, in order to rationally use energy and improve energy utilization, it is necessary to use a medium or device to store one form of energy in the same form or convert it into another form of energy, and then release it in a specific form based on future application needs. At present, the main way to generate green electricity is to develop green energy such as photovoltaics and wind power to replace fossil energy.

[0039] At present, the generation of green electricity generally relies on photovoltaics, wind power, and hydropower, etc. However, wind and solar energy generally have problems of strong intermittency and large volatility, which will cause instability in the power grid, insufficient electricity during peak hours, and too much electricity during valley hours. Unstable voltage will also cause damage to electricity. Therefore, insufficient electricity demand or insufficient grid acceptance capacity may cause the problem of "abandoning wind and solar power". To solve these problems, we must rely on energy storage. That is, converting electrical energy into other forms of energy through physical or chemical means and storing it, and converting energy into electrical energy and releasing it when needed. In simple terms, energy storage is like a large "power bank". When photovoltaic and wind energy are sufficient, electrical energy is stored and the stored electricity is released when needed.

[0040] Taking electrochemical energy storage as an example, the present disclosure provides an energy storage device, which has a group of chemical batteries inside. The chemical elements in the batteries are mainly used as energy storage media. The charging and discharging process is accompanied by chemical reactions or changes in the energy storage media. In simple terms, the electricity generated by wind and solar energy is stored in chemical batteries. When the use of external electricity reaches a peak, the stored electricity is released for use, or transferred to places where electricity is scarce for use.

[0041] At present, energy storage (i.e. energy storage) has a wide range of application scenarios, including power generation side energy storage, grid side energy storage and power consumption side energy storage. The corresponding types of energy storage equipment include:

[0042] (1) Large-scale energy storage power stations applied to wind power and photovoltaic power stations can help renewable energy generation meet grid connection requirements and improve the utilization rate of renewable energy. As a high-quality active / reactive regulating power source on the power supply side, energy storage power stations can achieve load matching of electric energy in time and space, enhance the ability to absorb renewable energy, reduce instantaneous power changes, reduce the impact on the power grid, improve the absorption of new energy generation, and are of great significance in power grid system backup, relieving peak load power supply pressure, and peak and frequency regulation.

[0043] (2) Energy storage containers used on the grid side mainly perform peak load regulation, frequency regulation, and grid congestion relief. They can achieve peak load shaving and valley filling of power loads, that is, charging the energy storage battery when the power load is low and releasing the stored electricity during the peak load period, thereby achieving a balance between power production and consumption, such as energy storage power station systems;

[0044] (3) Small energy storage cabinets used on the power consumption side, mainly for self-generation and self-use of electricity, peak-valley price arbitrage, capacity cost management and improving power supply reliability. According to different application scenarios, energy storage on the power consumption side can be divided into industrial and commercial energy storage cabinets, household energy storage equipment, energy storage charging piles, etc., which are generally used in conjunction with distributed photovoltaics. Industrial and commercial users can use energy storage for valley-peak price arbitrage and capacity cost management. In the power market that implements peak-valley electricity prices, by charging the energy storage system at low electricity prices and discharging the energy storage system at high electricity prices, peak-valley electricity price arbitrage is achieved to reduce electricity costs. In addition, industrial enterprises that are subject to two-part electricity prices can use energy storage systems to store energy during low electricity consumption and discharge at peak loads, thereby reducing peak power and the maximum demand reported, and achieving the purpose of reducing capacity electricity charges. Household photovoltaic storage can improve the level of self-generation and self-use of electricity. Due to high electricity prices and poor power supply stability, the demand for household photovoltaic installations has been driven. Considering that photovoltaic power is generated during the day, and users generally have higher loads at night, energy storage can be configured to better utilize photovoltaic power, improve the level of self-generation and self-use, and reduce electricity costs. In addition, communication base stations, data centers and other fields need to configure energy storage for backup power.

[0045] See also Figure 1 , Figure 1 is a schematic diagram of the structure of an energy storage system 100 according to an embodiment of the present disclosure, and Figure 1 The embodiment is described by taking the shared energy storage scenario on the generation / distribution side as an example, but the energy storage system 100 disclosed in the present invention is not limited to the energy storage scenario on the generation / distribution side.

[0046] The present disclosure provides an energy storage system 100, which includes: an energy storage device 110, a high-voltage cable 120, a first electric energy conversion device 130, and a second electric energy conversion device 140. In the case of power generation, the first electric energy conversion device 130 and the second electric energy conversion device 140 are used to convert other forms of energy into electric energy, which is connected to the high-voltage cable 120 and supplied to the power distribution network for use. When the power load is low and the first conversion device 130 and the second electric energy conversion device 140 generate excess power, the excess power is stored in the energy storage device 110, thereby reducing the wind and solar power abandonment rates and improving the problem of new energy power generation and consumption. When the power load is high, the power grid issues an instruction to transmit the power stored in the energy storage device 110 in conjunction with the high-voltage cable 120 in a grid-connected mode to the power consumption side, thereby providing peak load regulation, frequency regulation, standby and other services for the operation of the power grid, giving full play to the peak load regulation of the power grid, promoting peak load reduction and valley filling of the power grid, and alleviating the power supply pressure of the power grid.

[0047] Optionally, the first electric energy conversion device 130 and the second electric energy conversion device 140 can convert at least one of solar energy, light energy, wind energy, thermal energy, tidal energy, biomass energy and mechanical energy into electric energy.

[0048] The number of energy storage devices 110 can be multiple, and multiple energy storage devices 110 are connected in series or in parallel. Multiple energy storage devices 110 are supported and electrically connected by isolation plates (not shown). In this embodiment, "multiple" refers to two or more. An energy storage box can also be provided outside the energy storage device 110 to accommodate the energy storage device 110.

[0049] Optionally, the energy storage device 110 may include but is not limited to a single cell, a battery module, a battery pack, a battery system, etc. The actual application form of the energy storage device 110 provided in the embodiment of the present disclosure may be but is not limited to the listed products, and may also be other application forms. The embodiment of the present disclosure does not strictly limit the application form of the energy storage device 110. The embodiment of the present disclosure is only described by taking the energy storage device 110 as a single cell as an example. When the energy storage device 110 is a single cell, the energy storage device 110 may be at least one of a cylindrical battery, a square battery, etc.

[0050] The energy storage system 100 also includes a battery management unit (BMU), an energy management unit (EMU), and a power conversion system (PCS). BMU can also be called a battery nanny or a battery butler. The battery management system BMS is mainly composed of a detection module, a control module, a communication module, etc. Its main function is to monitor and control the state of the battery in real time, including but not limited to monitoring and controlling the battery's voltage, current, temperature, state of charge (SoC), state of health (SOH) and other parameters. At the same time, the battery management system can also perform protection control on the battery, such as overcharging, over-discharging, over-current, etc., to ensure the safety and life of the battery.

[0051] The energy storage converter is mainly composed of inverters, transformers, controllers, etc. It converts the electric energy stored in the battery into AC power to supply the power grid or the user's electrical equipment. The main functions of the energy storage converter include converting DC power into AC power, controlling the input or output of electric energy, and ensuring the safety and stability of the energy storage system. The performance of the energy storage converter directly affects the operating efficiency and service life of the energy storage system.

[0052] The energy management unit is responsible for the control strategy of the energy storage system. The control strategy can affect the attenuation rate and cycle life of the batteries in the system, thereby determining the economic efficiency of the energy storage. In addition, the energy management unit can also monitor faults in the operation of the energy storage system, and play a role in timely protecting equipment and ensuring safety. The energy management unit can generate the control strategy of the energy storage system based on the operating parameters of the battery or the real-time operating parameters of energy storage devices such as the battery management system and energy storage inverter, and then control the operating status of the battery device or other equipment.

[0053] In the related art, the battery health status SoH estimation algorithm of the energy storage system generally uses the ampere-hour integration method combined with the open circuit voltage method, the full charge and discharge method, the Kalman filter algorithm, the curve-based feature recognition method, the data-driven method and other correction methods for estimation. However, the above methods have the following disadvantages:

[0054] 1. For the ampere-hour integration method, affected by data accuracy and time interval, the SoC can be accurately estimated in a short period of time, but as time goes on, the estimation error will gradually accumulate, and a correction algorithm needs to be combined, resulting in reduced SoH estimation efficiency;

[0055] 2. For the full charge and discharge correction method, in order to prevent overcharge and over discharge, the general battery management system has overvoltage and undervoltage limits, making it difficult for the battery to reach a full charge or full discharge state;

[0056] 3. The method of correcting the Kalman filter algorithm generally sets the observation equation and state equation based on the Thevenin equivalent circuit model, and requires the identification of other battery parameters. However, battery parameters are related to parameters such as temperature and aging, and there are great obstacles to engineering applications.

[0057] 4. For the curve-based feature recognition correction method, due to cost issues, in engineering applications, energy storage equipment cannot use high-precision sensors on a large scale to detect battery current, voltage and other indicators at high frequency, making the battery capacity increment curve (IC curve) or voltage differential curve (DV curve) inaccurate and difficult to apply, resulting in low SoH estimation accuracy;

[0058] 5. The data-driven correction method requires a large amount of battery data and computing power consumption, and there is an instability in SoC and SoH estimation, which makes it difficult to apply on a large scale.

[0059] How to develop a method for estimating battery health status with higher accuracy has become a technical problem that needs to be solved urgently.

[0060] In order to solve at least part of the above technical problems, the battery health state estimation method provided by the embodiment of the present disclosure obtains the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period, and determines the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence; uses the exponential smoothing method to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity, and calculates the first battery health state according to the current battery capacity. The method disclosed in the present disclosure for estimating the battery SOH through the historical battery capacity and the charge state of charge sequence within a preset time period is simple, can reduce the demand for data and computing power consumption, can improve the estimation efficiency and estimation accuracy of the battery health state, and has a wider range of applications.

[0061] Under the above system architecture, a method for estimating the state of health of a battery is provided in an embodiment of the present disclosure, and the method can be executed by any electronic device with computing and processing capabilities, such as the above-mentioned battery management unit, which can be a programmable logic controller (PLC) or a single-chip microcomputer, as long as it can realize the corresponding functions.

[0062] Figure 2 A flow chart of a method for estimating a battery health state in an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the battery health status estimation method provided in the embodiment of the present disclosure includes the following steps:

[0063] S202, obtaining the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period.

[0064] In one embodiment, the historical battery capacity is the battery capacity calculated last time. If the historical battery capacity does not exist, the battery rated capacity is determined as the historical battery capacity.

[0065] The preset period may be the time period between two adjacent battery health status estimation operations, or the time period from the time the battery to be estimated is used from the time it leaves the factory until the time when the battery profile status estimation operation is performed for the first time. For example, the duration corresponding to the preset period may be 1 month, 3 months, 6 months, etc.

[0066] The battery state of charge sequence is a sequence composed of battery states of charge corresponding to multiple data collection moments within a preset time period.

[0067] The battery state of charge sequence may be pre-stored in a database, such as a cloud database.

[0068] S204: Determine charging and discharging data of a target charging and discharging process of the battery to be estimated according to the battery state of charge sequence.

[0069] The target charge and discharge process can be a charge process or a discharge process with the deepest charge and discharge depth in the battery state of charge sequence. The charge and discharge depth includes the discharge depth and the charge depth. The discharge depth refers to the ratio between the power consumed by the battery during use and the total power of the battery. The charge depth refers to the ratio between the power received by the battery from the external circuit during the charging process and the power when the battery reaches a fully charged state.

[0070] In one embodiment, the battery state of charge sequence may include at least one charging process and may also include at least one discharging process. The charge and discharge depths corresponding to each charging process and discharging process are calculated respectively, and the charging process or discharging process with the deepest charge and discharge depth is used as the target charging and discharging process.

[0071] The charge and discharge data of the target charge and discharge process may include a start data collection time and an end data collection time of the target charge and discharge process, and a battery state of charge corresponding to the start data collection time and a battery state of charge corresponding to the end data collection time.

[0072] S206, using exponential smoothing method to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity.

[0073] In one embodiment, the initial battery capacity can be calculated based on the charge and discharge data of the target charge and discharge process, the initial battery capacity and the historical battery capacity can be combined using an exponential smoothing method to calculate the current battery capacity, and the battery health status can be calculated based on the current battery capacity.

[0074] S208: Calculate a first battery health state according to the current battery capacity.

[0075] In the embodiment of the present disclosure, the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period are obtained, and the charge and discharge data of the target charge and discharge process of the battery to be estimated are determined according to the battery state of charge sequence; the current battery capacity is calculated according to the charge and discharge data and the historical battery capacity using the exponential smoothing method, and the first battery health state is calculated according to the current battery capacity. The method disclosed in the present disclosure for estimating the battery SOH by using the historical battery capacity and the charge state of charge sequence within a preset time period is simple, can reduce the demand for data and computing power consumption, improve the estimation efficiency and estimation accuracy of the battery health state, and has a wider range of applications.

[0076] In one embodiment, the state of charge sequence of the battery to be estimated is obtained by:

[0077] S302, obtaining the historical battery health status and the current value and voltage value of the battery to be estimated at multiple data collection moments within a preset time period.

[0078] In one embodiment, when the battery to be estimated is used for the first time and participates in the calculation, the historical battery health state is recorded as SoH=1. When the battery is not used for the first time, the SoH obtained by the last estimation can be used as the historical battery health state.

[0079] When the battery to be estimated is in use, the battery management system can set the sampling frequency, control the sensor to collect the battery's current value, voltage value, etc. at the set sampling frequency, and record the data collection time corresponding to the collected current value and voltage value. For example, the sampling frequency is set to collect the battery's current value and voltage value once every 10 seconds, and record the corresponding data collection time.

[0080] Due to the delay and hardware device limitations, the sampling frequency may not be guaranteed, and the final stored battery current is I k , voltage is U t,k , time T k , collection number k = 1, 2, 3,….

[0081] S304, using an extended Kalman filter algorithm, processes the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection moment within a preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated, determines the battery charge state at each data collection moment, and obtains a battery charge state sequence.

[0082] An equivalent circuit model of the battery is established, and model parameters of the equivalent circuit model are determined.

[0083] In one embodiment, the equivalent circuit model of the battery can adopt the Thevenin equivalent circuit, such as Figure 4 As shown, the open circuit voltage U of the Thevenin equivalent circuit is OC , ohmic internal resistance R0, polarization capacitance C0, polarization internal resistance R p , polarization capacitance C0 and polarization internal resistance R p After being connected in parallel, it is connected in series with the ohmic internal resistance R0, and the polarized internal resistance R p The voltage across the two ends is U p The Thevenin equivalent circuit includes the polarization reaction of the battery and can simulate the internal reaction of the battery well. There is only a first-order RC loop in the circuit structure, and the RC order is good, which can reduce the complexity of the calculation.

[0084] The circuit equation for the Thevenin equivalent circuit is:

[0085]

[0086] U t =U OC -U p -I L R0 (Formula 1).

[0087] In one embodiment, the relevant parameters in the Thevenin equivalent circuit, namely, the ohmic internal resistance R0, the polarization capacitance C0, the polarization internal resistance R p It should be noted that there are many ways to experimentally determine the relevant parameters of the Thevenin equivalent circuit, and the present disclosure does not specifically limit this.

[0088] The Extended Kalman Filter (EKF) algorithm is a filtering algorithm used to process nonlinear systems. It linearizes nonlinear systems through Taylor series expansion and uses the standard Kalman filter framework to estimate the battery state of charge.

[0089] In one embodiment, the steps of the extended Kalman algorithm include: state prediction, calculation of state transfer matrix and observation matrix, calculation of prediction error covariance matrix, updating step and repetition step. Among them, the state prediction can be based on the nonlinear state equation of the battery, Taylor series expansion around the current estimated value, omitting the second order and above terms, to obtain a linearized state prediction equation; the state matrix and observation matrix calculation are based on the nonlinear function f and function h, and the Jacobian matrix is ​​calculated as the state transfer matrix F and the observation matrix H; the prediction error covariance matrix is ​​calculated based on the state prediction and observation prediction, and the prediction error covariance matrix P is calculated; the updating step includes calculating the Kalman gain K based on the observed value and the predicted value, and updating the state estimate and the error covariance matrix; the repetition step is to use the updated state estimate as the new current estimate, and repeat the above steps until the battery state of charge estimation is completed.

[0090] In one embodiment, the nonlinear state equation of the extended Kalman filter algorithm can take the historical battery health status, the current value and voltage value of the battery at each data collection moment, and the model parameters of the equivalent circuit model as input, and the battery charge state as output.

[0091] The battery state of charge sequence includes the data collection time and the corresponding battery state of charge. For example, the battery state of charge sequence is stored as the battery current I k , voltage is U t,k , the battery state of charge is SoC t,k , time T k , k=1,2,3,…。

[0092] In the embodiment of the present disclosure, the battery state of charge is estimated by extending the Kalman filter algorithm, which can take the battery aging factor into consideration, reduce the estimation error of the battery health state caused by aging, improve the estimation accuracy of the battery health state, and have a wider range of applications.

[0093] Figure 5FIG. 1 is a flow chart of a method for determining a battery state of charge provided by an embodiment of the present disclosure. Figure 5 As shown, the extended Kalman filter algorithm is used to process the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection time within a preset period, and the model parameters of the equivalent circuit model of the battery to be estimated to determine the battery charge state at each data collection time, including:

[0094] S502, taking the current value, voltage value and model parameters of the equivalent circuit model of the battery to be estimated as input quantities of the extended Kalman filter algorithm, taking the battery state of charge as output quantity, and constructing the state equation and measurement equation of the extended Kalman filter algorithm;

[0095] S504, initializing the parameters of the extended Kalman filter algorithm, and calculating the battery state of charge, polarization voltage and covariance matrix at the current data collection moment;

[0096] S506, using the polarization voltage and covariance matrix at the current data collection moment as initialization parameters for the extended Kalman filter algorithm at the next data collection moment, calculating the polarization voltage and covariance matrix of the battery state of charge at the next data collection moment, and iterating in sequence until the battery state of charge at multiple data collection moments is obtained.

[0097] In S502, the state equation of the extended Kalman filter algorithm is expressed as:

[0098] x k =Ax k-1 +Bu k-1 +w k-1 (Formula 2)

[0099] The measurement equation is expressed as:

[0100] y k =h(x k )+Du k +v k ≈Cx k +Du k +v k (Formula 3)

[0101] Among them, w k-1 represents the random error of the state equation at the last data collection moment, v k is the random error of the measurement equation at the current data collection moment; A is the transfer matrix; B is the transfer control matrix; C is the measurement matrix; D is the measurement control matrix; h(x k ) is a nonlinear measurement function, Cx k is h(x k ) in x k The first-order term of the Taylor expansion at xk is the true value of the state at the time of data collection k, x k-1 is the true value of the state at the (k-1) data collection time, u k-1 is the control input at the (k-1) data collection time, y k is the true value of observation at the k data collection moment.

[0102] In S504, the parameters of the extended Kalman filter algorithm are initialized, including:

[0103]

[0104] y k =U t,k ;

[0105]

[0106] D = -R0;

[0107] u k-1 =I k-1 ;

[0108] u k =I k ;

[0109] Among them, SOC k-1 is the battery charge state at the last data collection moment, U p,k-1 is the polarization voltage at the last data collection moment; if there is no last data collection moment, let SOC k-1 =f(U t,k-1 ) or U p,k-1 =0, f(U t,k-1 ) is the U in the battery state of charge-open circuit voltage curve measured experimentally t,k-1 The corresponding battery state of charge, U t,k-1 is the voltage value at the last data collection moment, U t,k is the voltage value at the current data collection time; Δt k is the current data collection time T k Compared with the last data collection time T k-1 The time interval between k =T k -T k-1 , R p and C p are the polarization internal resistance and polarization capacitance of the equivalent circuit model respectively; η is the battery charge and discharge efficiency, Q c is the battery capacity, which is determined by the previous battery health status SoH and the battery rated capacity Q e OK, Q c =SoH×Q e; R0 is the ohmic internal resistance of the equivalent circuit model; I k-1 is the current value at the last data collection moment; I k is the current value at the current data collection moment.

[0110] In S504, the parameter initialization process of the extended Kalman filter algorithm can be understood as a process of determining the initial state or initial value of each parameter in the state equation and the measurement equation.

[0111] Substituting the above parameters into Formula 2 and Formula 3, the state equation and measurement equation of the extended Kalman filter algorithm can be obtained as follows:

[0112]

[0113] In one embodiment, the setting parameters of the extended Kalman filter algorithm include an estimation error matrix P, an error matrix Q of the state equation, and a measurement equation error t. Among them, the estimation error matrix P is a state covariance matrix, which represents the error of the state estimation. The estimation error matrix P is a multidimensional matrix whose dimension is consistent with the number of states. For example, the number of states in the present disclosure is 2, which are SOC k and U p,k ; The matrix P is a pair matrix. The elements on the diagonal of the symmetric matrix are the variances of each state, and the remaining elements are the covariances of the corresponding states.

[0114] The estimated error matrix P reflects the uncertainty of the battery state of charge estimation. After each round of prediction and update, it is synchronously updated according to the new observation data to gradually approach the true value. Initially, the setting of the estimated error matrix P can be determined according to actual needs and does not need to be too precise. As the number of iterations increases, the estimated error matrix P gradually converges to a more accurate value.

[0115] The error matrix Q of the state equation is also called the process noise covariance matrix, which is used to describe the random disturbances in the equivalent circuit model of the battery. The error matrix Q of the state equation is part of the state equation error and can adjust the accuracy of the battery state of charge prediction. The error matrix Q of the state equation and the estimated error matrix P can calculate the size of the state covariance matrix P and the Kalman filter algorithm gain K. Through the iterative algorithm, the influence of the Q matrix is ​​reflected in the adjustment of the battery state of charge estimation, optimizing the accuracy of the battery state of charge.

[0116] The measurement equation error r represents the covariance matrix of the observation noise, which is used to represent the error size of the observation data. It is a feature related to the instrument and is a numerical value. It is input into the extended Kalman filter as a known condition. By adjusting the value of the measurement equation error r, the weight of the observation data in the battery state of charge estimation can be controlled, thereby affecting the performance of the extended Kalman filter algorithm. It should be noted that the measurement equation error r can be gradually adjusted by gradually adjusting the value of r, observing the performance changes of the extended Kalman filter algorithm, and gradually finding the optimal r value to obtain the optimal battery state of charge estimation result.

[0117] In one embodiment, the uncorrected state parameter calculated at the current data collection time is It is expressed by the following formula:

[0118]

[0119] The uncorrected covariance matrix at the current data collection time is expressed as follows:

[0120]

[0121] Wherein, f(SoC) represents the experimentally measured battery state of charge-open circuit voltage SoC-OCV curve, and K is the Kalman gain matrix;

[0122] The calculated state parameters after correction at the current data collection time It is expressed by the following formula:

[0123]

[0124] The corrected covariance matrix at the current data collection time is expressed by the following formula:

[0125]

[0126] The Kalman filter algorithm gain K is the intermediate result of the filter. The estimated error matrix P, the error matrix Q of the state equation and the error t of the measurement equation are the setting parameters of the extended Kalman filter algorithm. It will be used as the initialization parameter for the next Kalman filter estimation.

[0127] In one embodiment, after initializing the parameters of the extended Kalman filter algorithm, the battery state of charge, polarization voltage and covariance matrix at the current data collection moment can be calculated, and the polarization voltage and covariance matrix are used as initialization parameters for the next data collection moment. The battery state of charge at the next data collection moment, as well as the corresponding polarization voltage and covariance matrix are calculated, and the iteration is performed gradually until the battery state of charge at the last data collection moment is obtained. The battery SoC estimation is completed, and the battery states of charge are sorted in sequence according to the chronological order of the data collection moments to obtain a battery state of charge sequence.

[0128] In the embodiments of the present disclosure, the battery state of charge can be estimated by extending the Kalman filter algorithm, taking into account the polarization reaction inside the battery, thereby greatly improving the accuracy and effectiveness of the battery state of charge estimation.

[0129] Figure 6 Another battery health status estimation method flow chart provided by the embodiment of the present disclosure is shown. Figure 3 Based on the embodiment, S306 is added after S304 to limit the situation where the battery state of charge is corrected by open circuit voltage and / or full charge and discharge. Figure 6 As shown, in one embodiment, the battery health state estimation method disclosed in the present invention includes S302 to S306. After determining the battery state of charge at each data collection moment in S304 and obtaining the battery state of charge sequence, the method further includes:

[0130] S306, if the current value and voltage value of the battery to be estimated meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition, the corresponding correction strategy is used to correct the battery state of charge at the current data collection time, and the target battery state of charge at the current data collection time is obtained, and the target battery state of charge is used to update the battery state of charge sequence.

[0131] In one embodiment, the method further includes: if the current value and voltage value of the battery to be estimated at the current data collection moment do not meet the preset open circuit voltage correction condition or the preset full charge and discharge correction condition, then the battery state of charge sequence is not updated.

[0132] The current value and voltage value of the battery to be estimated satisfy the preset open circuit voltage correction condition, which can also be understood as the current state of the battery to be estimated is in the open circuit voltage state; and satisfy the preset full charge and discharge correction condition, which can be understood as the current state of the battery to be estimated is in the full charge and discharge state.

[0133] It should be noted that after obtaining the battery state of charge at the current data collection moment, it is necessary to determine whether the current value and voltage value of the battery to be estimated meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition. If so, the corresponding correction strategy is used to correct the battery state of charge at the current collection moment, and the corrected battery state of charge is updated in the battery state of charge sequence. If not, there is no need to update the battery state of charge sequence.

[0134] The above-mentioned use of the target battery state of charge to update the battery state of charge sequence refers to replacing the battery state of charge corresponding to the current data collection moment with the target battery state of charge.

[0135] In one embodiment, the above S306 adopts a corresponding correction strategy to correct the battery state of charge at the current data collection time to obtain the target battery state of charge at the current data collection time, including: when the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition, determining the target battery state of charge at the current data collection time as the value corresponding to the inverse function of the battery state of charge-open circuit voltage curve and the voltage value of the battery to be estimated.

[0136] When the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition, the open circuit voltage correction is performed on the battery state of charge at the current data collection time. At this time, SoC = f -1 (U t ), f -1 (·) represents the inverse function of the SoC-OCV curve, namely the OCV-SoC curve.

[0137] In one embodiment, the above S306 adopts a corresponding correction strategy to correct the battery state of charge at the current data collection time to obtain the target battery state of charge at the current data collection time, including: when the current value and voltage value of the battery to be estimated meet the full charge and discharge correction conditions, if the voltage value of the battery to be estimated reaches the maximum operating voltage, then the target battery state of charge is determined to be 100%; if the voltage value of the battery to be estimated reaches the minimum operating voltage, then the target battery state of charge is determined to be 0%.

[0138] It should be noted that the maximum operating voltage and the minimum operating voltage are determined by the battery's calibrated operating voltage range, which are the right endpoint value and the left endpoint value of the operating voltage range respectively. The maximum operating voltage and the minimum operating voltage can be pre-stored in the battery control system.

[0139] In the embodiments of the present disclosure, by integrating multiple SoC correction methods, corresponding correction methods can be adopted under various working conditions. The requirements for data collection are low, and it is a simple matrix operation, which can greatly improve the efficiency of SoC estimation.

[0140] Figure 7 The flowchart of the method for determining whether the current value and voltage value of the battery provided in the embodiment of the present disclosure meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition is shown. Figure 7 As shown, in one embodiment, the method further includes:

[0141] S702, determining the continuous static time length of the battery to be estimated at the current data collection moment according to the relationship between the current values ​​of the battery to be estimated at multiple data collection moments and the preset current threshold;

[0142] S704, if the continuous static time length of the battery to be estimated at the current data collection time is greater than or equal to the first preset static time length threshold, and the voltage value of the battery at the current data collection time is less than or equal to the starting voltage value of the battery platform voltage, or the voltage value of the battery to be estimated at the current data collection time is greater than or equal to the ending voltage value of the battery platform voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition;

[0143] S706. If the continuous static time of the battery to be estimated at the current data collection moment is greater than the second preset static time threshold, and the voltage value of the battery to be estimated at the current data collection moment reaches the maximum operating voltage or the minimum operating voltage, it is determined that the current value and voltage value of the battery to be estimated meet the full charge and discharge correction condition.

[0144] The static state refers to stopping the operation after the battery to be estimated is charged and discharged, and placing the battery to be estimated in a static state. The continuous static state time refers to the total time that the battery to be estimated is continuously static.

[0145] In one embodiment, the continuous rest time of the battery to be estimated can be determined by the relationship between the current value of the battery to be estimated at multiple data collection moments and the preset current threshold. The preset current threshold, the first preset rest time threshold, the second preset rest time threshold, the maximum operating voltage, the minimum operating voltage, etc. can be pre-configured in the battery management system. The specific values ​​of the above quantities can be determined according to actual needs, and the present disclosure does not make specific restrictions on this. For example, the preset current threshold is 0.5mA, etc.

[0146] The platform voltage of the battery to be estimated refers to the relatively stable operating voltage range of the battery during the charge and discharge process. For different types of batteries, the platform voltage range is different. For example, the standard voltage platform of lithium iron phosphate battery is 3.2V, and the voltage range corresponding to the 80%-90% voltage platform is 2.56V~2.88V, while the nominal voltage of the single cell of the ternary lithium battery is 3.7V and the charging voltage is 4.2V. Within this range, the battery performs well and can provide relatively stable power output.

[0147] In the embodiments of the present disclosure, the continuous standing time is determined based on the relationship between the current value of the battery to be estimated and the preset current threshold, and based on the relationship between the continuous standing time and the preset standing time threshold, as well as the relationship between the voltage value of the battery to be estimated and the platform voltage and the operating voltage, it is determined whether the corresponding correction conditions are met, thereby improving the effectiveness and reliability of the determination of the current state of the battery and providing a reliable data basis for correcting the SoC through the corresponding correction method.

[0148] Figure 8 FIG. 2 is a flow chart showing a method for determining charging and discharging data of a target charging and discharging process provided in an embodiment of the present disclosure. Figure 8 As shown, in one embodiment, the step of determining the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence in S204 above includes:

[0149] S802, determining a charging process or a discharging process with the deepest charging and discharging depth of the battery in the battery state of charge sequence as a target charging and discharging process;

[0150] S804. Determine the charge and discharge data of the target charge and discharge process, wherein the charge and discharge data includes the start data collection time of the target charge and discharge process, the historical battery health status corresponding to the start data collection time, the end data collection time, and the end battery charge state corresponding to the end data collection time.

[0151] The target charge and discharge process may be a charge process or a discharge process with the deepest charge and discharge depth in the battery state of charge sequence. The charge and discharge depth includes the discharge depth and the charge depth.

[0152] Depth of discharge refers to the ratio of the amount of electricity consumed by a battery during use to the total amount of electricity in the battery. For example, a battery with a total capacity of 200A·h has 80A·h of electricity remaining after discharge, and 120A·h of electricity is actually discharged. The depth of discharge of the battery is 60%.

[0153] The depth of charge refers to the ratio between the amount of electricity received by the battery from the external circuit during the charging process and the amount of electricity when the battery reaches a fully charged state. For example, if a battery receives 120A·h of electricity and reaches a fully charged state of 200A·h, the depth of charge of the battery is 60%.

[0154] In one embodiment, the battery state of charge sequence may include at least one charging process and may also include at least one discharging process. The charging depth of each charging process and the discharging depth corresponding to each discharging process are calculated respectively, and the charging process corresponding to the maximum charging depth is regarded as the charging process with the deepest charging and discharging depth, and the discharging process corresponding to the maximum discharge depth is regarded as the discharging process with the deepest charging and discharging depth.

[0155] The above-mentioned one charging process or the above-mentioned one discharging process can be selected as the target charging and discharging process, and the larger one of the charging depth of the above-mentioned one charging process and the discharging depth of the above-mentioned one discharging process can also be selected as the target charging and discharging process.

[0156] The charge and discharge data of the target charge and discharge process may include the starting data collection time T of the target charge and discharge process. m and the end time of data collection T n , and the starting data collection time T m Corresponding battery state of charge SoC m The end time of data collection is T n Corresponding battery state of charge SoC n .

[0157] It should be noted that the deeper the charge and discharge depth of the battery, the shorter the battery life. For example, when the battery discharge depth reaches 80%, the battery life will be significantly shortened. Specifically, when the battery is discharged to SoC close to 0, the battery capacity decays faster and the chemical substances inside the battery will be more damaged.

[0158] In the embodiment of the present disclosure, by selecting the charging and discharging process with the deepest charging and discharging depth of the battery, the damage of the charging and discharging process to the internal chemical substances of the battery can be effectively measured, the impact on the battery life can be fully considered, and the accuracy of the battery health status estimation can be effectively improved.

[0159] In one embodiment, the exponential smoothing method is used in S206 to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity, including:

[0160] S902, calculating the initial battery capacity according to the charge and discharge data of the target charge and discharge process;

[0161] S904, smoothing the initial battery capacity and the historical battery capacity using a smoothing exponential method to obtain a current battery capacity;

[0162] The above S208 calculates the first battery health status according to the current battery capacity, including:

[0163] S906: Calculate a ratio of the current battery capacity to the rated battery capacity, and determine the ratio as a first battery health state.

[0164] In S902, the initial battery capacity It can be calculated by the following formula:

[0165]

[0166] Among them, T m and Tn They are the start and end data collection time of the target charge and discharge process, SoC m is the starting time of data collection T m Corresponding battery state of charge, SoC n The time T at which data collection ends n The corresponding battery state of charge, η is the battery charge and discharge efficiency, and I is the battery charge and discharge current.

[0167] In S904, the historical battery capacity is the battery capacity calculated last time. If it does not exist, the historical battery capacity is the battery rated capacity Q e .

[0168] Current battery capacity value Q c,i It can be calculated by the following formula:

[0169]

[0170] Where β is the smoothing coefficient, which can be determined according to actual needs. For example, β = 0.5, Q c,i-1 is the historical battery capacity.

[0171] In S906, the first battery health state SoH i The calculation is done using the following formula:

[0172]

[0173] In an embodiment of the present disclosure, the initial battery capacity is calculated based on the charge and discharge data of the target charge and discharge process, the current battery capacity is calculated using the exponential smoothing method based on the initial battery capacity and the historical battery capacity, and the first battery health state is calculated based on the current battery capacity and the battery rated capacity, which can improve the accuracy of the battery health state.

[0174] Fig.10 1 shows a flow chart provided in an embodiment of the present disclosure. Fig.10 As shown, in one embodiment, after calculating the first battery health state according to the current battery capacity in S208, the method further includes:

[0175] S1002, if the battery state of charge sequence has a full charge to full discharge condition or a full discharge to full charge condition, calculate a second battery health state according to the battery charge and discharge efficiency, the charge and discharge current and the battery rated capacity; calculate an average of the first battery health state and the second battery health state as the battery health state of the battery;

[0176] S1004: If the battery state of charge sequence does not include a full charge to full discharge condition or a full discharge to full charge condition, taking the first battery health state as the battery health state of the battery.

[0177] In S1002, the above-mentioned full charge to full discharge operation refers to the process of using the battery from a fully charged state to a fully exhausted state, and the full discharge to full charge operation refers to the process of charging the battery from a fully exhausted state to a fully charged state.

[0178] Full charge means charging the battery to 100% capacity, that is, the battery voltage reaches the maximum charging voltage and the charging current is reduced to C / 10. Full discharge means discharging the battery to 0% capacity, that is, the battery voltage is less than or equal to the minimum discharge voltage.

[0179] Second Battery Health Status SoH i ′ It can be calculated by the following formula:

[0180]

[0181] The final battery health state of the battery is the average of the first battery health state and the second battery health state, expressed as

[0182]

[0183] It should be noted that the battery state of charge can be estimated and updated point by point based on the sensor's collection frequency, while the battery health status can be corrected according to longer correction cycles such as months or quarters.

[0184] In the embodiment of the present disclosure, by judging whether there is a full charge to full discharge operation or a full discharge to full charge operation in the battery state of charge sequence, it is determined whether to correct the SoH, thereby effectively improving the accuracy of the battery health state estimation.

[0185] In order to deepen the understanding of the battery health status estimation method disclosed in the present invention, a detailed description is given below with reference to specific examples.

[0186] The battery health status estimation method disclosed in the present invention mainly includes the following steps:

[0187] Step 1: Collect the voltage and current values ​​of the battery at a collection frequency of 10 seconds, record the data collection time, and store it in a data table as shown in Table 1. Affected by hardware or delay, the time interval between adjacent data points may exceed 10 seconds.

[0188] Table 1. The performance data of the battery collected

[0189]

[0190]

[0191] Step 2: Known battery rated capacity Q e=330Ah, the ohmic internal resistance R of this type of battery is measured by time delay o ≈0.0002Ω, polarization internal resistance R p ≈0.0003Ω, polarization capacitance C p ≈10 / R p Since the battery is used for the first time, SOH = 1, then the actual capacity of the battery is Q c Q c =SoH×Q e =330 (Ah), calculate the time interval Δt between adjacent data sampling times, and summarize the time intervals in the data table to obtain Table 2.

[0192] Table 2 Data table after adding time interval

[0193]

[0194]

[0195] Step 3: When k=1, 2, 3, the battery current value I=0, and the battery is in a static state. At this time, there is no need to estimate and correct the SoC through the extended Kalman filter algorithm, and the SoC does not change, that is, SoC1=SoC2=SoC3.

[0196] Step 4: Calculate the continuous static time t static , add the continuous static time of each data collection moment to Table 2, corresponding to t static The numeric value of the column.

[0197] For open circuit voltage correction, set the continuous static time threshold T static =1800, then when k=3, the continuous static time t static =1800≥T static , and the starting voltage of the battery platform voltage of this model is U left =3.288V, and U t,3 =3.158 left , so the open circuit voltage correction condition is met at this time, and the correction SoC3=f -1 (U t,3 )=4.99%.

[0198] For full charge and discharge correction, the battery has not reached the maximum operating voltage U max =3.65 or minimum working voltage U min =2.6, which does not meet the full charge and discharge correction condition.

[0199] Return to step 3 to complete the subsequent SoC estimation and correction.

[0200] ​When k=4, since the time interval is 0, the battery has not been charged yet, so SoC4=SoC3=4.99%.

[0201] When k=5, the battery is in working state, and the SoC is estimated and corrected by the extended Kalman filter algorithm. The estimation and correction operations are as follows:

[0202] The estimation error matrix P of the extended Kalman filter algorithm, the error matrix Q of the state equation, and the measurement equation error r are set as follows:

[0203]

[0204] r=1.

[0205] The parameters for initializing the extended Kalman filter algorithm include:

[0206]

[0207] y5=U t,5 =3.2165;

[0208]

[0209] D = -R0 = -0.0002;

[0210] u4=I4=-147.692;

[0211] u5=I5=-149.992.

[0212] Substituting the above parameters into Formula 6 to Formula 12, we can obtain:

[0213]

[0214] Here, Uncorrected

[0215]

[0216]

[0217] The corrected

[0218] By repeating steps 3 and 4 until the SoC estimation and correction are completed for all data collection moments, retain two decimal places and add them to Table 2. For example, the value corresponding to the SoC column is the battery state of charge sequence corrected at each data collection moment.

[0219] Step 5: After receiving the battery state of charge sequence, select a charging process or discharging process with the deepest charge and discharge depth in the battery state of charge sequence, and record the start data collection time as T m SoC is SoC m The end time of data collection is T n SoC is SoC n , let the battery's charge and discharge efficiency be η=1, as follows:

[0220] T m =T3=2024 / 1 / 1 05:22:28;

[0221] T n =T 775 =2024 / 1 / 1 08:00:33;

[0222] SoC m =4.99%;

[0223] SoC n =99.84%;

[0224] Current estimated battery capacity for:

[0225]

[0226] Set the smoothing coefficient β = 0.5, and the smoothed battery capacity Q c,i :

[0227]

[0228] Then the SOH of the battery is:

[0229]

[0230] In a feasible implementation, the relevant parameters of the battery under the Thevenin equivalent circuit can be dynamically optimized by online identification. Dynamic identification of ohmic internal resistance R ′ o , Polarization internal resistance R ′ p , polarization capacitance C p ′ , exponential smoothing method can be used to reduce the impact of abnormal noise on online recognition.

[0231] R o,t+1 =β+(1-β)R ′ o,t+1

[0232] R p,t+1 =βR p,t +(1-β)R′ p,t+1

[0233] C p,t+1 =βC p,t +(1-β)C′ p,t+1

[0234] For example, referring to the above data, for the ohmic internal resistance R ′ o , by the point calculation method we can get:

[0235]

[0236] Then the ohmic internal resistance is updated by exponential smoothing method:

[0237] R o,t+1 =0.5×0.0002+0.5×0.000248=0.000224Ω

[0238] The polarization internal resistance and polarization capacitance are similar and will not be elaborated in this disclosure.

[0239] Based on the same inventive concept, the present disclosure also provides a battery health status estimation device, as described in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0240] Fig.11 A schematic diagram of a battery health status estimation device according to an embodiment of the present disclosure is shown. Fig.11 As shown, the device includes a sequence acquisition module 1110 , a data determination module 1120 , a capacity calculation module 1130 and a health status estimation module 1140 .

[0241] The sequence acquisition module 1110 is used to acquire the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset period;

[0242] A data determination module 1120, configured to determine the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence;

[0243] A capacity calculation module 1130 is used to calculate the current battery capacity based on the charge and discharge data and the historical battery capacity using an exponential smoothing method;

[0244] The health state estimation module 1140 is used to calculate a first battery health state according to the current battery capacity.

[0245] It should be noted that the above sequence acquisition module 1110, data determination module 1120, capacity calculation module 1130 and health status estimation module 1140 correspond to S202 to S208 in the method embodiment, and the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above method embodiment. It should be noted that the above modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0246] In one embodiment, the data determination module 1120 is used to determine the charging process or discharging process with the deepest battery charging and discharging depth in the battery charge state sequence as the target charging and discharging process; determine the charging and discharging data of the target charging and discharging process, wherein the charging and discharging data includes the starting data collection time of the target charging and discharging process, the historical battery health status corresponding to the starting data collection time, the ending data collection time, and the ending battery charge state corresponding to the ending data collection time.

[0247] In one embodiment, the capacity calculation module 1130 is used to calculate the initial battery capacity according to the charge and discharge data of the target charge and discharge process; the initial battery capacity and the historical battery capacity are smoothed by a smoothing exponential method to obtain the current battery capacity; the health status estimation module 1140 is used to calculate the ratio of the current battery capacity to the rated capacity of the battery, and determine the ratio as the first battery health status.

[0248] In one embodiment, the health state estimation module 1140 is used to calculate the second battery health state according to the battery charge and discharge efficiency, the charge and discharge current and the battery rated capacity if the battery state of charge sequence has a full charge to full discharge condition or a full discharge to full charge condition; calculate the average of the first battery health state and the second battery health state as the battery health state of the battery; if the battery state of charge sequence does not have a full charge to full discharge condition or a full discharge to full charge condition, use the first battery health state as the battery health state of the battery.

[0249] In one embodiment, the data determination module 1120 is used to obtain a battery state of charge sequence within a preset time period in the following manner, obtain the historical battery health status and the current value and voltage value of the battery to be estimated at multiple data collection moments within the preset time period; use an extended Kalman filter algorithm to process the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection moment within the preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated, determine the battery state of charge at each data collection moment, and obtain a battery state of charge sequence.

[0250] In one embodiment, the device also includes a state of charge correction module not shown in the accompanying drawings. The state of charge correction module is used to, after determining the battery state of charge at each data collection moment, if the current value and voltage value of the battery to be estimated meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition, then use the corresponding correction strategy to correct the battery state of charge at the current data collection moment, obtain the target battery state of charge at the current data collection moment, and use the target battery state of charge to update the battery state of charge sequence.

[0251] In one embodiment, the state of charge correction module is used to determine the continuous standing time of the battery to be estimated at the current data collection moment according to the relationship between the current value of the battery to be estimated at multiple data collection moments and the preset current threshold; if the continuous standing time of the battery to be estimated at the current data collection moment is greater than or equal to the first preset standing time threshold, and the voltage value of the battery to be estimated at the current data collection moment is less than or equal to the starting voltage value of the battery platform voltage, or the voltage value of the battery to be estimated at the current data collection moment is greater than or equal to the ending voltage value of the battery platform voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition; if the continuous standing time of the battery to be estimated at the current data collection moment is greater than the second preset standing time threshold, and the voltage value of the battery to be estimated at the current data collection moment reaches the maximum working voltage or reaches the minimum working voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the full charge and discharge correction condition.

[0252] In one embodiment, the state of charge correction module is used to determine the target battery state of charge at the current data collection moment as a value corresponding to the inverse function of the battery state of charge-open circuit voltage curve and the voltage value of the battery to be estimated when the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition.

[0253] In one embodiment, the state of charge correction module is used to determine the target battery state of charge to be 110% when the current value and voltage value of the battery to be estimated meet the full charge and discharge correction conditions, if the voltage value of the battery to be estimated reaches the maximum operating voltage; if the voltage value of the battery to be estimated reaches the minimum operating voltage, then the target battery state of charge is determined to be 0%.

[0254] In one embodiment, the state of charge correction module is used to not update the battery state of charge sequence if the current value and voltage value of the battery to be estimated at the current data collection moment do not meet the preset open circuit voltage correction condition or the preset full charge and discharge correction condition.

[0255] In one embodiment, the state of charge correction module is used to use the current value, voltage value and model parameters of the equivalent circuit model of the battery to be estimated as the input of the extended Kalman filter algorithm, and the battery state of charge as the output, to construct the state equation and measurement equation of the extended Kalman filter algorithm; initialize the parameters of the extended Kalman filter algorithm, and calculate the battery state of charge, polarization voltage and covariance matrix at the current data collection moment; use the polarization voltage and covariance matrix at the current data collection moment as the initialization parameters of the extended Kalman filter algorithm at the next data collection moment, calculate the battery state of charge polarization voltage and covariance matrix at the next data collection moment, and iterate in sequence until the battery state of charge at multiple data collection moments is obtained.

[0256] In the embodiment of the present disclosure, the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period are obtained, and the charge and discharge data of the target charge and discharge process of the battery to be estimated are determined according to the battery state of charge sequence; the current battery capacity is calculated according to the charge and discharge data and the historical battery capacity using the exponential smoothing method, and the first battery health state is calculated according to the current battery capacity. The method disclosed in the present disclosure for estimating the battery SOH by using the historical battery capacity and the charge state of charge sequence within a preset time period is simple, can reduce the demand for data and computing power consumption, improve the estimation efficiency and estimation accuracy of the battery health state, and has a wider range of applications.

[0257] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0258] Refer to the following Fig.12 1200 according to this embodiment of the present disclosure is described. Fig.12 The electronic device 1200 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0259] like Fig.12 As shown, the electronic device 1200 is in the form of a general computing device. The components of the electronic device 1200 may include but are not limited to: at least one processing unit 1210, at least one storage unit 1220, and a bus 1230 connecting different system components (including the storage unit 1220 and the processing unit 1210).

[0260] The storage unit stores a program code, and the program code can be executed by the processing unit 1210, so that the processing unit 1210 executes the steps described in the "Exemplary Method" section of the specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 1210 can execute the following steps of the above method embodiment: obtaining the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period; determining the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence; calculating the current battery capacity according to the charge and discharge data and the historical battery capacity using an exponential smoothing method; and calculating the first battery health state according to the current battery capacity.

[0261] The storage unit 1220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 12201 and / or a cache storage unit 12202 , and may further include a read-only storage unit (ROM) 12203 .

[0262] The storage unit 1220 may also include a program / utility 12204 having a set (at least one) of program modules 12205, such program modules 12205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0263] Bus 1230 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0264] The electronic device 1200 may also communicate with one or more external devices 1240 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 1200, and / or any device that enables the electronic device 1200 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1250. Furthermore, the electronic device 1200 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1260. Fig.12As shown, the network adapter 1260 communicates with other modules of the electronic device 1200 via the bus 1230. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0265] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0266] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer program product, which includes: a computer program, which implements the above-mentioned battery health state estimation method when executed by a processor.

[0267] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0268] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0269] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0270] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0271] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0272] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0273] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0274] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0275] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for estimating a battery health state, characterized in that: include: Obtaining a historical battery capacity of a battery to be estimated and a battery state of charge sequence within a preset period; Determining charging and discharging data of the target charging and discharging process of the battery to be estimated according to the battery state of charge sequence; Calculate the current battery capacity according to the charge and discharge data and the historical battery capacity using an exponential smoothing method; A first battery health state is calculated according to the current battery capacity.

2. The battery health status estimation method according to claim 1, characterized in that: The step of determining the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence includes: Determine the charging process or discharging process with the deepest charging and discharging depth of the battery in the battery state of charge sequence as the target charging and discharging process; Determine the charge and discharge data of the target charge and discharge process, wherein the charge and discharge data include the start data collection time of the target charge and discharge process, the historical battery health status corresponding to the start data collection time, the end data collection time, and the end battery charge state corresponding to the end data collection time.

3. The battery health status estimation method according to claim 2, characterized in that: The exponential smoothing method is used to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity, including: Calculating the initial battery capacity according to the charge and discharge data of the target charge and discharge process; Smoothing the initial battery capacity and the historical battery capacity using a smoothing exponential method to obtain the current battery capacity; The calculating a first battery health state according to the current battery capacity includes: A ratio of the current battery capacity to the rated battery capacity is calculated, and the ratio is determined as the first battery health state.

4. The battery health status estimation method according to claim 2, characterized in that: The calculating a first battery health state according to the current battery capacity includes: If the battery state of charge sequence has a full charge to full discharge condition or a full discharge to full charge condition, calculating a second battery health state according to the battery charge and discharge efficiency, the charge and discharge current and the battery rated capacity; calculating an average of the first battery health state and the second battery health state as a battery health state of the battery; If the battery state of charge sequence does not include a full charge to full discharge condition or a full discharge to full charge condition, the first battery health state is used as the battery health state of the estimated battery.

5. The battery health status estimation method according to claim 1, characterized in that: The battery state of charge sequence within the preset time period is obtained by the following method, including: Acquire the historical battery health status and the current value and voltage value of the battery to be estimated at multiple data collection moments within the preset time period; An extended Kalman filter algorithm is used to process the historical battery health status, the current value and voltage value of the battery to be estimated at each data collection moment within the preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated, determine the battery charge state at each data collection moment, and obtain the battery charge state sequence.

6. The battery health status estimation method according to claim 5, characterized in that: After determining the battery charge state at each data collection moment, the method further includes: If the current value and voltage value of the battery to be estimated meet the preset open circuit voltage correction condition and / or the preset full charge and discharge correction condition, the corresponding correction strategy is used to correct the battery state of charge at the current data collection moment, and the target battery state of charge at the current data collection moment is obtained, and the target battery state of charge is used to update the battery state of charge sequence.

7. The battery health status estimation method according to claim 6, characterized in that: The method further comprises: Determine the continuous static time length of the battery to be estimated at the current data collection moment according to the relationship between the current values ​​of the battery to be estimated at the multiple data collection moments and the preset current threshold; If the continuous rest time of the battery to be estimated at the current data collection moment is greater than or equal to the first preset rest time threshold, and the voltage value of the battery to be estimated at the current data collection moment is less than or equal to the starting voltage value of the battery platform voltage, or the voltage value of the battery to be estimated at the current data collection moment is greater than or equal to the ending voltage value of the battery platform voltage, then it is determined that the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition; If the continuous static time of the battery to be estimated at the current data collection moment is greater than the second preset static time threshold, and the voltage value of the battery to be estimated at the current data collection moment reaches the maximum operating voltage or the minimum operating voltage, it is determined that the current value and voltage value of the battery to be estimated meet the full charging and discharging correction condition.

8. The battery health status estimation method according to claim 7, characterized in that: The adopting a corresponding correction strategy to correct the battery state of charge at the current data collection moment to obtain a target battery state of charge at the current data collection moment includes: When the current value and voltage value of the battery to be estimated meet the open circuit voltage correction condition, the target battery state of charge at the current data collection moment is determined to be a value corresponding to the inverse function of the battery state of charge-open circuit voltage curve and the voltage value of the battery to be estimated.

9. The battery health status estimation method according to claim 7, characterized in that: The adopting a corresponding correction strategy to correct the battery state of charge at the current data collection moment to obtain a target battery state of charge at the current data collection moment includes: When the current value and voltage value of the battery to be estimated meet the full charge and discharge correction condition, if the voltage value of the battery to be estimated reaches the maximum operating voltage, the target battery state of charge is determined to be 100%; if the voltage value of the battery to be estimated reaches the minimum operating voltage, the target battery state of charge is determined to be 0%.

10. The battery health status estimation method according to claim 6, characterized in that: The method further comprises: If the current value and the voltage value of the battery to be estimated at the current data collection moment do not satisfy a preset open circuit voltage correction condition or a preset full charge / discharge correction condition, the battery state of charge sequence is not updated.

11. The battery health status estimation method according to claim 5, characterized in that: The extended Kalman filter algorithm is used to process the historical battery health status, the current value and the voltage value of the battery to be estimated at each data collection moment within the preset time period, and the model parameters of the equivalent circuit model of the battery to be estimated to determine the battery charge state at each data collection moment, including: The current value, voltage value and model parameters of the equivalent circuit model of the battery to be estimated are used as input quantities of the extended Kalman filter algorithm, and the battery state of charge is used as output quantity to construct the state equation and measurement equation of the extended Kalman filter algorithm; Initializing the parameters of the extended Kalman filter algorithm, and calculating the battery state of charge, polarization voltage and covariance matrix at the current data collection moment; The polarization voltage and covariance matrix at the current data collection moment are used as initialization parameters for the extended Kalman filter algorithm at the next data collection moment, and the polarization voltage and covariance matrix of the battery state of charge at the next data collection moment are calculated, and iterated in sequence until the battery state of charge at the multiple data collection moments is obtained.

12. A battery health status estimation device, characterized in that: include: A sequence acquisition module, used to acquire the historical battery capacity of the battery to be estimated and the battery state of charge sequence within a preset time period; A data determination module, used to determine the charge and discharge data of the target charge and discharge process of the battery to be estimated according to the battery state of charge sequence; A capacity calculation module, used to calculate the current battery capacity according to the charge and discharge data and the historical battery capacity by using an exponential smoothing method; The health state estimation module is used to calculate a first battery health state according to the current battery capacity.

13. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the battery health state estimation method according to any one of claims 1 to 11 by executing the executable instructions.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery health state estimation method according to any one of claims 1 to 11 is implemented.

15. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the battery health state estimation method according to any one of claims 1 to 11.

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