High-voltage energy storage power system and battery cluster state precise sensing method thereof

Abstract

The present invention provides a high-voltage energy storage power system and a battery cluster state precise sensing method thereof. The high-voltage energy storage power system comprises: a battery cluster, a start-up protection circuit, PCS units, a fault bypass circuit, a direct-current side filter inductor, a bus capacitor, PCS unit sub-controllers, and a BMS; direct-current side positive electrodes of the PCS units are sequentially connected in series to the direct-current side start-up protection circuit, the battery cluster, and the direct-current side filter inductor, and then connected to direct-current side negative electrodes of the PCS units; alternating-current side positive electrodes of the PCS units are sequentially connected in series; the bus capacitor is connected in parallel to two electrodes at a direct-current side of each PCS unit; the PCS unit sub-controllers are respectively connected to the fault bypass circuit, the PCS units, the start-up protection circuit, and the BMS; and the BMS is connected to the battery cluster.

Description

High-voltage energy storage power system and its battery cluster status accurate perception method Technical Field

[0001] The present invention relates to the technical field of electrical automation equipment, and more specifically, to a high-voltage energy storage power system and a method for accurately sensing the status of a battery cluster thereof. More specifically, it relates to a high-voltage energy storage power module capable of accurately sensing the status of a battery cluster and a method for combining and expanding the module. Background Art

[0002] Existing battery energy storage systems can be broadly categorized into low-voltage and high-voltage energy storage systems. Low-voltage battery energy storage systems consist of a battery stack, a battery management system (BMS), a low-voltage converter (PCS), and a step-up transformer. Increasing the capacity of a single unit can only be achieved by increasing the number of cells connected in series or parallel within the battery stack. However, this number is limited by technologies such as battery materials, battery grouping, and the BMS, resulting in a battery stack's economically safe capacity typically in the hundreds of kW range. Building a 100-MW energy storage power station typically requires connecting hundreds of devices in parallel on the low-voltage AC side. This results in complex power collection and information systems, difficult coordinated control, poor stability, and long command response times, making it difficult to meet future development needs.

[0003] High-voltage, high-capacity battery energy storage systems utilize a high-voltage direct-connected PCS (PCS) system to divide the system's massive battery cells into numerous battery clusters (composed of cells connected in series) for distributed connection to the DC side of the power modules. The AC side of these clusters is then cascaded to increase the PCS output voltage and individual unit capacity, enabling transformerless direct connection to high-voltage grids of 35kV and above. High-voltage energy storage systems feature a highly modular structure, theoretically enabling the large-capacity requirements of individual units. For example, a dual-star parallel configuration, when connected directly to a 35kV grid, could theoretically reach a maximum capacity of hundreds of megawatts, making them more adaptable to the large-scale development of renewable energy in the future. However, in practice, the parameters of the numerous battery cells in high-voltage energy storage systems continuously vary over time, increasing the degree of inconsistency and exacerbating the "barrel effect." This increases the risk of localized overcharge or over-discharge of individual cells. Overcharge, discharge, or failure of a single cell often leads to system shutdown, reducing the utilization and safety of the battery system (composed of distributed battery clusters). In addition, cell variation will directly lead to significant differences in parameters such as the effective capacity and internal resistance of each battery cluster. However, the current high-voltage direct-mounted PCS is mainly responsible for battery charge and discharge control, and the BMS is mainly responsible for cell balancing control and cell status management within the battery module. Both lack the ability to perceive the real-time status of the battery cluster throughout its life cycle. Therefore, they cannot achieve reasonable energy balancing control between battery clusters with inconsistent parameters, which is not conducive to the safe and efficient management and control of massive battery cells.

[0004] The premise for high-voltage direct-hung PCS to achieve safe and efficient energy balancing control of battery clusters at different levels is to be able to accurately obtain key parameters such as the internal resistance and effective capacity of numerous dispersed battery clusters in real time. Currently, PCS mainly obtains battery cluster parameters through BMS, but BMS cannot achieve online measurement of the internal resistance of the battery cluster. The acquisition of real-time internal resistance information is the prerequisite for accurate perception of key parameters such as effective capacity and SOC. Currently, the only information that BMS transmits to PCS for battery system energy balancing is SOC. This is the only control basis for PCS to achieve battery cluster energy balancing. This SOC balancing control method is only applicable to situations where battery parameters are relatively consistent in the early stages of service. However, as the service time increases, the variation of battery cell parameters leads to a gradual increase in the inconsistency of parameters such as the effective capacity, internal resistance, and voltage of the battery cluster. At this time, the control basis for energy balancing should comprehensively consider the above-mentioned inconsistent parameters.

[0005] Summary of the Invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide a high-voltage energy storage power system and a method for accurately sensing the status of its battery cluster.

[0007] According to the present invention, a high-voltage energy storage power system includes: a battery cluster, a startup protection circuit, a PCS unit, a fault bypass circuit, a DC side filter inductor, a bus capacitor, a PCS unit sub-controller, and a BMS;

[0008] The DC side positive electrode of the PCS unit is connected in series with the DC side starting protection circuit, the battery cluster, and the DC side filter inductor, and then connected to the DC side negative electrode of the PCS unit; the AC side positive electrodes of the PCS units are connected in series in sequence; the bus capacitor is connected in parallel with the two DC side electrodes of the PCS unit;

[0009] The PCS unit sub-controller is connected to the fault bypass circuit, the PCS unit, the startup protection circuit and the BSM respectively;

[0010] The BSM is connected to the battery cluster.

[0011] Preferably, the PCS unit is implemented using a half-bridge or full-bridge topology.

[0012] Preferably, the battery cluster is formed by connecting multiple battery cells in series.

[0013] Preferably, considering the application scenario, single-machine capacity and the influencing factors of the selected battery type, star chain, delta chain and double star parallel chain are selected to realize the AC side cascade combination expansion of the high-voltage energy storage power system.

[0014] According to the present invention, a method for accurately sensing the status of a battery cluster is provided, which utilizes the above-mentioned high-voltage energy storage power system to achieve:

[0015] Step S1: The PCS unit sub-controller implements the aortic power injection of the PCS unit into the battery cluster by superimposing different frequency harmonic voltages on the PCS unit modulation wave;

[0016] Step S2: collecting battery pulsating voltage and current information, identifying battery internal resistance model parameters based on the collected battery pulsating voltage and current information, and transmitting the identified battery internal resistance model parameters to the BMS;

[0017] Step S3: The BMS uses the identified battery internal resistance model parameters and the PCS unit sub-controller to jointly perform online estimation of the effective capacity of the battery cluster.

[0018] Preferably, the step S1 adopts: determining a harmonic voltage frequency sequence injected into the AC port of the PCS unit that does not affect its normal operation; the PCS unit sub-controller realizes the PCS unit's aortic power injection into the battery cluster by superimposing harmonic voltages of different frequencies on the PCS unit modulation wave.

[0019] Preferably, the step S2 adopts:

[0020] Step S2.1: Collecting battery pulsating voltage and current information, performing Fourier decomposition on the battery pulsating voltage and current to obtain the voltage and current components of the battery at different pulsating frequencies;

[0021] Step S2.2: Calculate the impedance amplitude and phase angle at different pulsation frequencies;

[0022] Step S2.3: Calculate the battery internal resistance model parameters based on the impedance amplitude and phase angle at different pulsation frequencies, and transmit the battery internal resistance model parameters to the BMS.

[0023] Preferably, step S3 adopts: the BMS uses the extended Kalman filter algorithm to online estimate the polarization voltage drop on the RC parallel branch and the open circuit voltage of the battery cluster according to the battery internal resistance model parameters, and estimates the SOC based on the open circuit voltage at the end of charge and discharge according to the OCV-SOC curve measured offline, and at the same time, estimates the effective capacity of the battery cluster online at the end of charge and discharge by the transfer charge method.

[0024] Preferably, the energy state of the battery cluster is characterized according to the estimated effective capacity, terminal voltage and SOC parameters of the battery cluster.

[0025] Preferably, the OCV-SOC curves of the battery clusters under different working conditions are normalized by considering the differences in the effective capacity of the lithium batteries and the ambient temperature, so that only one OCV-SOC test is required for any battery cluster to obtain its OCV-SOC curve under any state.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. A method for accurately sensing battery system parameters online, using a high-voltage direct-mount PCS and BMS, is proposed. The PCS uses active broadband pulsating power injection to identify the battery cluster's internal resistance online. The BMS and PCS leverage this real-time internal resistance information to accurately estimate the battery cluster's effective capacity and SOC. This deep integration overcomes the difficulty of accurately estimating battery status when relying solely on the BMS in the presence of varying cell parameters.

[0028] 2. Accurate perception of the battery cluster status can achieve safe and efficient utilization of the battery cluster throughout its life cycle.

[0029] 2. The AC cascade combination expansion method of the high-voltage energy storage power module can greatly increase the capacity of the system's single unit, and the theoretical capacity of the single unit can reach hundreds of MW. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0031] FIG1 is a schematic diagram of a high-voltage energy storage power module with the ability to accurately sense the state of a battery cluster provided by the present invention;

[0032] FIG2 is a technical roadmap for online precise sensing of key battery system parameters in collaboration with a high-voltage direct-mounted PCS and a BMS, provided by the present invention;

[0033] FIG3 is a structural diagram of a combined expansion system of high-voltage energy storage power modules with the ability to accurately sense the status of battery clusters provided by the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0035] Example 1

[0036] According to the present invention, a high-voltage energy storage power system includes: a battery cluster, a startup protection circuit, a PCS unit, a fault bypass circuit, a DC side filter inductor, a bus capacitor, a PCS unit sub-controller, and a BMS;

[0037] The DC side positive electrode of the PCS unit is connected in series with the DC side starting protection circuit, the battery cluster, and the DC side filter inductor, and then connected to the DC side negative electrode of the PCS unit; the AC side positive electrodes of the PCS units are connected in series in sequence; the bus capacitor is connected in parallel with the two DC side electrodes of the PCS unit;

[0038] The PCS unit sub-controller is connected to the fault bypass circuit, the PCS unit, the startup protection circuit and the BSM respectively;

[0039] The BSM is connected to the battery cluster.

[0040] Specifically, the high-voltage energy storage power system externally presents an AC power port and an information communication port

[0041] Specifically, the PCS unit is implemented using a half-bridge or full-bridge topology.

[0042] Specifically, the battery cluster is formed by connecting multiple battery cells in series.

[0043] Specifically, taking into account the influencing factors of application scenarios, single-machine capacity and selected battery types, star chain, delta chain and double star parallel chain are selected to achieve the AC side cascade combination expansion of the high-voltage energy storage power system.

[0044] According to the present invention, a method for accurately sensing the status of a battery cluster is provided, characterized in that the high-voltage energy storage power system described above is used to achieve:

[0045] Step S1: The PCS unit sub-controller implements the aortic power injection of the PCS unit into the battery cluster by superimposing different frequency harmonic voltages on the PCS unit modulation wave;

[0046] Step S2: collecting battery pulsating voltage and current information, identifying battery internal resistance model parameters based on the collected battery pulsating voltage and current information, and transmitting the identified battery internal resistance model parameters to the BMS;

[0047] Step S3: The BMS uses the identified battery internal resistance model parameters and the PCS unit sub-controller to jointly perform online estimation of the effective capacity of the battery cluster.

[0048] Specifically, step S1 adopts: determining a harmonic voltage frequency sequence injected into the PCS unit AC port that does not affect its normal operation; the PCS unit sub-controller realizes the PCS unit's aortic power injection into the battery cluster by superimposing different frequency harmonic voltages on the PCS unit modulation wave.

[0049] Specifically, the step S2 adopts:

[0050] Step S2.1: Collecting battery pulsating voltage and current information, performing Fourier decomposition on the battery pulsating voltage and current to obtain the voltage and current components of the battery at different pulsating frequencies;

[0051] Step S2.2: Calculate the impedance amplitude and phase angle at different pulsation frequencies;

[0052] Step S2.3: Calculate the battery internal resistance model parameters based on the impedance amplitude and phase angle at different pulsation frequencies and transmit them to the BMS. Based on the number of equivalent circuit model parameters to be calculated, solve the internal resistance equivalent circuit model parameters by simultaneously solving 2m+1 impedance equations.

[0053] Specifically, step S3 adopts: the BMS uses the extended Kalman filter algorithm to online estimate the polarization voltage drop on the RC parallel branch and the open circuit voltage of the battery cluster based on the battery internal resistance model parameters, and estimates the SOC based on the open circuit voltage at the end of charge and discharge based on the OCV-SOC curve measured offline, and at the same time, estimates the effective capacity of the battery cluster online at the end of charge and discharge through the transfer charge method.

[0054] Specifically, the energy state of the battery cluster is characterized according to the estimated effective capacity, terminal voltage, and SOC parameters of the battery cluster.

[0055] Specifically, the OCV-SOC curves of battery clusters under different working conditions are normalized by considering the differences in the effective capacity of lithium batteries and ambient temperature, so that only one OCV-SOC test needs to be performed on any battery cluster to obtain its OCV-SOC curve under any state.

[0056] Example 2

[0057] Example 2 is a preferred example of Example 1

[0058] Referring to Figure 1 , the present invention discloses a high-voltage energy storage power system capable of accurately sensing the status of a battery cluster. The internal structure of the high-voltage energy storage power system includes: a battery cluster, a power conversion unit, a bus capacitor, a DC-side filter inductor, a power buffer, a fault bypass circuit, a startup circuit, a power conversion unit sub-controller, a BMS, etc. The DC-side positive pole of the H-bridge or half-bridge power unit is sequentially connected in series with a DC-side pre-charging device, a DC fuse, a battery cluster, and a DC-side filter inductor, and then connected to the DC-side negative pole of the H-bridge or half-bridge power unit. The AC-side positive poles of the H-bridge or half-bridge power unit are sequentially connected in series, and the bus capacitor is connected in parallel to the DC-side poles of the H-bridge or half-bridge power unit. Based on the above-mentioned standard submodules, configured by the upper-level main control system, high-voltage, large-capacity energy storage equipment can be easily constructed to meet different requirements. The high-voltage energy storage power system displays an AC power port and an information communication port externally. The high-voltage energy storage power system implements deep collaboration between the PCS unit and the BMS to accurately identify the battery status. The PCS unit identifies the internal resistance of the battery cluster through aortic power injection. The BMS uses the measured real-time internal resistance information and the PCS unit to jointly perform online estimation of the effective capacity of the battery cluster. Referring to Figure 2, this is a technical roadmap for online accurate perception of key parameters of the battery system in collaboration with the high-voltage direct-hung PCS and BMS provided by the present invention. The specific implementation is carried out in four steps, namely:

[0059] Step S0: determining a frequency sequence of pulsating power injected into the battery cluster by the power conversion unit;

[0060] First, determine the harmonic voltage frequency sequence that does not affect the normal operation of the PCS unit after injection into the AC port, and analyze the relationship between the injected harmonic voltage frequency and the pulsating power frequency entering the battery cluster. The injection control of the above-mentioned wide-band pulsating power and the solution of internal resistance information are mainly completed by the sub-controller of the PCS unit. Each PCS unit contains a sub-controller. Assuming that a harmonic voltage with a frequency of f0 is injected into the battery cluster, generating a pulsating power with a frequency of f1, a pulsating component with a frequency of f1 will be generated in the voltage and current of the battery cluster. By detecting the amplitude and phase angle of the above voltage and current components, the impedance amplitude Z of the battery cluster at this frequency can be calculated. f1 and phase angle θ f1 As shown in formula (1), two internal resistance parameter solution equations can be established under the pulsating power with a frequency of f1. The above equations can also be established under the pulsating power of other frequencies.

[0061] Step S1: Establishing a battery cluster equivalent circuit model and solving a set of equations for model parameters;

[0062] Online detection of the internal resistance of the battery cluster is the prerequisite for accurate online perception of effective capacity and SOC. Taking into account the model accuracy and modeling complexity, a second-order equivalent circuit model is used as an example to illustrate the process of online detection of the internal resistance of the battery cluster by high-voltage direct-hook PCS. b is the equivalent ohmic internal resistance of the battery cluster, which is the ohmic internal resistance of all series-connected cells R s and the connecting wire resistance R L R1 and R2 are polarization resistors, C1 and C2 are polarization capacitors. The parallel circuit of R1 and C1 represents the electrochemical polarization phenomenon, and the parallel circuit of R2 and C2 represents the concentration polarization phenomenon. u1 and u2 are polarization voltage drops, u R is the ohmic internal resistance voltage drop, u b and i b is the battery cluster operating voltage and current, Uocv is the battery cluster open circuit voltage. The model parameters that need to be tested are R b, R1, R2, C1, C2. At least five different equations must be established to solve five different internal resistance parameters, so at least three different frequencies of pulsating power must be injected into the battery cluster. To improve the accuracy of parameter identification, the frequency of the injected pulsating power can be increased, and the corresponding internal resistance parameter solution equations will also increase. At this time, the internal resistance solution results under different sets of equations can be cross-validated and averaged to reduce measurement errors. The DC side of the half-bridge power module itself has fundamental frequency and double frequency pulsating power. Pulsating power of other frequencies can be generated by injecting harmonic voltage of a specific frequency on the AC side of the module as needed. The current and terminal voltage of the battery cluster must be Fourier decomposed to obtain the amplitude and phase of the pulsating voltage and current at different frequencies, and based on this, the impedance amplitude and phase equations at different frequencies are established. The impedance amplitude and phase equations at different frequencies are combined to establish an equivalent circuit model parameter solution equation group to obtain the battery cluster internal resistance under real-time working conditions. At a specific frequency f1, the amplitude and phase equations established are:

[0063] Step S2: Based on the extended Kalman filter algorithm, the discrete state and observation equations of the battery cluster are established to perform online estimation of the battery cluster SOC and SOH.

[0064] The BMS uses the real-time internal resistance parameters obtained through the extended Kalman filter algorithm to identify the average values ​​of the polarization voltage pulsation period U1 and U2. Under the extended Kalman filter algorithm, the discrete state space equation of the battery cluster is established as follows:

[0065] Among them, x k =[U 1k U 2k SOC k ] T is the state variable of the system at time k, is the state transfer matrix, T s is the average value of the battery cluster current cycle I bk The sampling period of τ1=R1C1 and τ2=R2C2 are the time constants of the two RC parallel circuits. k =I bk is the input of the system at time k, B k =[T s / C1 T s / C2 -ηT s / Q] T is the system input matrix, η is the coulombic efficiency of the battery, and Q is the battery cluster capacity. k =ΔU k is the system output at time k, C k =[1 1 0] is the system output matrix, D k =R bis the feedforward matrix of the system. k is the process noise of the system, v k is the observation noise affected by sensor accuracy and external factors. Both noises are Gaussian white noise with a mean of 0 and are independent of each other. Then the open circuit voltage of the battery cluster at time k can be estimated as U OCVk =U b -I bk R b -ΔU k =U b -I bk R b -U1-U2 (4)

[0066] The open circuit voltage estimation uses the real-time internal resistance information obtained through PCS active testing, which can ensure the accuracy of the open circuit voltage estimation.

[0067] After the estimation of the open circuit voltage of the battery cluster is completed, the OCV-SOC curve measured offline can be used to estimate the battery cluster SOC according to the open circuit voltage at the end of the battery cluster charge and discharge, and the effective capacity Q of the battery cluster can be estimated based on the state of charge transfer method. e , as shown in formula (5). Since the accuracy of SOC estimation based on the OCV-SOC curve at the end of charge and discharge is high, the accuracy of effective capacity estimation can also be guaranteed.

[0068] PCS normalizes the OCV-SOC curve based on the estimated effective capacity, that is, the actual effective capacity estimated online is used as the calibration basis for the SOC of each battery cluster, and the effective capacity is adjusted according to the ambient temperature, as shown in formula (6), where λ T is the proportional coefficient of the effective capacity of the battery cluster at different ambient temperatures, which can be obtained by fitting the static capacity test data.

[0069] By using the OCV-SOC curve normalization method, for any battery module, given its effective capacity and a certain ambient temperature, a single OCV test is required to obtain its OCV-SOC curve under any conditions. The relevant research results can also be applied to OCV-SOC curve testing of battery clusters, simplifying the OCV-SOC curve testing process for battery clusters of varying aging levels and improving the OCV-SOC curve's adaptability to variations in battery cluster parameters.

[0070] Step S3: Characterize the energy states of battery clusters at different levels based on the battery cluster state parameters obtained through online estimation.

[0071] After completing the online perception of battery cluster parameters, in order to achieve safe and efficient coordinated control of battery clusters with inconsistent parameters, the energy state of the battery cluster must be characterized based on the identification parameters as the basis for real-time power allocation of battery clusters at each level. After the capacity baseline value of each battery cluster changes, SOC as the only variable can no longer accurately reflect the remaining capacity of each battery cluster. Therefore, it is necessary to use SOC and actual effective capacity to measure the remaining capacity. In addition, the voltage of the battery cluster will also have a certain degree of discreteness, and it is also necessary to include the battery cluster voltage in the energy state characterization parameter. Therefore, the energy state characterization factor E shown in formula (7) is introduced m It is reasonable to represent the real-time energy status of the battery cluster.

[0072] Referring to Figure 3, the present invention provides an AC-connected cascaded expansion method for high-voltage energy storage power modules with precise battery cluster status sensing. Three expansion schemes are presented: star-chain, delta-chain, and double-star parallel. The selection of a specific expansion scheme should comprehensively consider factors such as the application scenario, unit capacity, and selected battery type. Under each combined expansion scheme, the submodules within the system are all cascaded on the AC side to form a single-phase bridge arm. The difference lies in the connection relationship between the bridge arms.

[0073] Those skilled in the art will appreciate that, in addition to implementing the system, device, and various modules provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like by logically programming the method steps. Therefore, the system, device, and various modules provided by the present invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; the modules for implementing various functions can also be considered both software programs for implementing the method and structures within the hardware component.

[0074] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A high voltage energy storage power system, characterized in that: include: Battery cluster, startup protection circuit, PCS unit, fault bypass circuit, DC side filter inductor, bus capacitor, PCS unit sub-controller and BMS; The DC side positive electrode of the PCS unit is connected in series with the DC side startup protection circuit, the battery cluster, and the DC side filter inductor in sequence, and then connected to the DC side negative electrode of the PCS unit; the AC side positive electrodes of the PCS units are connected in series in sequence; the bus capacitor is connected in parallel with the two DC side electrodes of the PCS unit; The PCS unit sub-controller is respectively connected to the fault bypass circuit, the PCS unit, the startup protection circuit and the BSM; The BSM is connected to the battery cluster.

2. The high-voltage energy storage power system according to claim 1, characterized in that: The PCS unit is implemented using a half-bridge or full-bridge topology.

3. The high-voltage energy storage power system according to claim 1, characterized in that: The battery cluster is formed by connecting a plurality of battery cells in series.

4. The high-voltage energy storage power system according to claim 1, characterized in that: Taking into account the influencing factors of application scenarios, single-machine capacity and selected battery type, star chain, delta chain and double star parallel chain are selected to realize the AC side cascade combination expansion of the high-voltage energy storage power system.

5. A method for accurately sensing the status of a battery cluster, characterized in that: The high-voltage energy storage power system according to any one of claims 1 to 4 is used to achieve: Step S1: The PCS unit sub-controller realizes the aortic power injection of the PCS unit to the battery cluster by superimposing different frequency harmonic voltages in the PCS unit modulation wave; Step S2: collecting battery pulsating voltage and current information, identifying battery internal resistance model parameters based on the collected battery pulsating voltage and current information, and transmitting the identified battery internal resistance model parameters to the BMS; Step S3: The BMS uses the identified battery internal resistance model parameters and the PCS unit sub-controller to jointly perform online estimation of the effective capacity of the battery cluster.

6. The method for accurately sensing the battery cluster status according to claim 5, characterized in that: The step S1 adopts: determining a harmonic voltage frequency sequence injected into the AC port of the PCS unit that does not affect its normal operation; the PCS unit sub-controller realizes the PCS unit's aortic power injection into the battery cluster by superimposing harmonic voltages of different frequencies in the PCS unit modulation wave.

7. The method for accurately sensing the battery cluster status according to claim 5, characterized in that: The step S2 adopts: Step S2.1: Collecting battery pulsating voltage and current information, performing Fourier decomposition on the battery pulsating voltage and current to obtain voltage and current components of the battery at different pulsating frequencies; Step S2.2: Calculate the impedance amplitude and phase angle at different pulsation frequencies; Step S2.3: Calculate the battery internal resistance model parameters based on the impedance amplitude and phase angle at different pulsation frequencies, and pass the battery internal resistance model parameters to the BMS.

8. The method for accurately sensing the battery cluster status according to claim 5, characterized in that: The step S3 adopts: the BMS uses the extended Kalman filter algorithm to estimate the polarization voltage drop on the RC parallel branch and the open circuit voltage of the battery cluster online according to the battery internal resistance model parameters, and estimates the SOC based on the open circuit voltage at the end of charge and discharge according to the OCV-SOC curve measured offline, and at the same time estimates the effective capacity of the battery cluster online at the end of charge and discharge through the charge transfer method.

9. The method for accurately sensing the battery cluster status according to claim 8, characterized in that: The energy state of the battery cluster is characterized by the estimated effective capacity, terminal voltage and SOC parameters of the battery cluster.

10. The method for accurately sensing the battery cluster status according to claim 8, characterized in that: The OCV-SOC curves of the battery clusters under different working conditions are normalized by considering the differences in the effective capacity of the lithium battery and the ambient temperature, so that only one OCV-SOC test is required for any battery cluster to obtain its OCV-SOC curve under any state.

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