Remote discharge method and system combining load current and inverter load discharge

Abstract

The present invention provides a remote discharge method and system combining load current and inverter load discharge, which relates to the field of discharge control technology, including collecting battery pack parameters through a remote monitoring system, evaluating the health status and dischargeable capacity of the battery pack based on a fuzzy control algorithm, and calculating the optimal discharge power and duration according to a prediction correction algorithm to generate load and inverter discharge power curves. When the battery pack is healthy and the dischargeable capacity is sufficient, the load and inverter discharge units are remotely controlled to control the load switch duty cycle and the inverter switching frequency and output voltage according to their respective power curves to form two discharge loops. The discharge current and battery pack parameters are collected in real time, and the discharge is stopped when the battery pack charge state reaches a preset threshold. The present invention realizes efficient and safe remote discharge and extends the life of the battery pack by combining load current and inverter load discharge and performing intelligent control based on the battery state.

Description

Technical Field

[0001] The present invention relates to a discharge control technology, and in particular to a remote discharge method and system combining load current with inverter load discharge. Background Art

[0002] Energy storage technology plays an increasingly important role in the fields of renewable energy grid connection, grid peak and frequency regulation, and distributed energy systems. In order to ensure the safe and reliable operation of the energy storage system and extend the life of the energy storage unit, it is necessary to effectively manage the charging and discharging of the energy storage unit, especially in the scenario of remote monitoring and management. At present, the discharge methods of energy storage units mainly include constant current discharge, constant power discharge, and dynamic discharge according to changes in load demand.

[0003] The existing remote discharge methods mainly have the following defects and shortcomings:

[0004] 1. Some remote discharge methods only consider the state of charge (SOC) of the battery pack, but ignore key parameters such as the health status, internal resistance, and temperature of the battery pack, resulting in an inadequate discharge strategy, which may cause the battery pack to be over-discharged or over-charged, affecting the battery life.

[0005] 2. Some remote discharge methods use a single discharge circuit, such as discharging only through an inverter, or only connecting a fixed load for discharge. This method cannot flexibly adjust the discharge power, is difficult to adapt to different application scenarios, and may result in low discharge efficiency.

[0006] 3. Existing remote discharge control strategies often lack real-time monitoring and adjustment of the battery pack discharge process, making it difficult to cope with the impact of load changes and environmental factors, and thus unable to ensure the safety and stability of the discharge process. For example, during the discharge process, the internal resistance and temperature of the battery pack will change. If the discharge parameters are not adjusted in time, the battery pack may overheat or be damaged. Summary of the invention

[0007] The embodiments of the present invention provide a remote discharge method and system combining load current with inverter load discharge, which can solve the problems in the prior art.

[0008] According to a first aspect of the embodiments of the present invention,

[0009] Provides a remote discharge method combining load current and inverter load discharge, including:

[0010] The battery pack parameter information of the energy storage unit is collected through a remote monitoring system, wherein the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack; a battery pack state evaluation model is constructed based on a fuzzy control algorithm, and the battery pack parameter information is input into the battery pack state evaluation model to obtain the health status index and the dischargeable capacity of the battery pack; the optimal discharge power and discharge duration of the battery pack are calculated according to the prediction and correction algorithm, and a load discharge power curve and an inverter discharge power curve are generated;

[0011] When the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, wherein the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop;

[0012] The load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack are collected in real time. When the charge state of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working, thereby completing the discharge process.

[0013] A battery pack status evaluation model is constructed based on a fuzzy control algorithm, and the battery pack parameter information is input into the battery pack status evaluation model to obtain a health status index and a dischargeable capacity of the battery pack, including:

[0014] The battery pack parameter information is subjected to noise elimination through a Kalman filter to obtain filtered battery pack parameter information; the state of charge of the battery pack is calculated based on an improved coulomb counting method, the improved coulomb counting method introduces a temperature compensation mechanism, a temperature influence coefficient is calculated according to the temperature information, and the temperature influence coefficient is combined with the current efficiency and nominal capacity of the energy storage unit obtained in advance to obtain a real-time state of charge;

[0015] Inputting the battery pack parameter information into a pre-constructed fuzzy membership function to obtain fuzzified data, establishing an IF-THEN rule base including voltage state, current state, temperature state and internal resistance state based on the fuzzified data, calculating the Euclidean distance of each rule in the IF-THEN rule base and determining the rule weight corresponding to each rule based on the Euclidean distance;

[0016] A multi-parameter fused feature vector is established, wherein the feature vector includes the battery pack parameter information and its derivative items, and the feature vector is weightedly fused according to the rule weight to obtain the health status index of the battery pack; a capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction to obtain the actual dischargeable capacity of the battery pack.

[0017] A capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction, so that the actual dischargeable capacity of the battery pack is obtained, including:

[0018] Obtain the number of cycles, actual temperature and health status index of the battery pack, establish a double exponential decay basic model, and calculate the basic capacity of the battery pack according to the number of cycles, wherein the double exponential decay basic model includes an initial capacity, a weight coefficient and a decay rate, and the basic capacity is obtained by a weighted combination of two exponential functions;

[0019] A temperature correction term is constructed based on the difference between the actual temperature and the preset reference temperature, wherein the temperature correction term includes a first-order temperature coefficient and a second-order temperature coefficient, and the temperature correction term is multiplied by the basic capacity to obtain a battery pack capacity after temperature correction;

[0020] Establishing a health status influence function based on the ratio of the health status index to the reference health status, introducing a nonlinear adjustment coefficient into the health status influence function, and multiplying the health status influence function by the temperature correction term to obtain a comprehensive correction coefficient;

[0021] Calculating the change in the health status index at adjacent moments, and constructing an adaptive weight function based on the change, wherein the adaptive weight function includes a sensitivity parameter, and the sensitivity parameter is used to adjust the responsiveness of the weight to the change in health status;

[0022] The comprehensive correction coefficient, the adaptive weight function and the temperature-corrected battery pack capacity are multiplied to obtain the actual dischargeable capacity of the battery pack.

[0023] Calculating the optimal discharge power and discharge duration of the battery pack according to the prediction correction algorithm, and generating a load discharge power curve and an inverter discharge power curve include:

[0024] Acquire the real-time voltage, discharge current and temperature information of the battery pack, establish a discharge power model according to the real-time voltage, the product of the discharge current and the discharge efficiency, build a capacity loss model based on the discharge depth, the square term of the discharge current and the temperature information, and obtain an estimated value of the initial discharge power and capacity loss;

[0025] Taking the initial discharge power and the estimated value of capacity loss as system state vectors, constructing a state prediction equation including a state transfer matrix and a control matrix, and a measurement update equation including an observation matrix, calculating the Kalman gain based on the prediction error covariance and the measurement noise covariance, and correcting the initial discharge power and the estimated value of capacity loss;

[0026] Based on the corrected discharge power, an objective function is established, wherein the objective function is composed of a square term of the deviation between the load power and the discharge power and a weighted term of the corrected capacity loss estimate. Under the constraints of power limit, current limit, temperature limit and state of charge limit, a dynamic programming algorithm is used to solve the optimal discharge power trajectory;

[0027] According to the optimal discharge power trajectory, a basic discharge duration is calculated in combination with the available capacity and discharge efficiency of the battery pack, and the basic discharge duration is multiplied by the health status index and temperature coefficient monitored in real time, respectively, to obtain a corrected discharge duration that takes into account the health status and temperature effects; based on the corrected discharge duration, a time interval is divided, and the load power data is fitted using Fourier series to obtain a continuous load power curve, and the optimal discharge power trajectory is multiplied by the dynamic inverter efficiency to generate an initial inverter discharge power curve;

[0028] The power deviation between the load power curve and the initial inverter discharge power curve at the same coordinate point is calculated in real time, and the power deviation is input into an adaptive proportional-integral controller, wherein the proportional coefficient and the integral coefficient are automatically adjusted according to the deviation change rate to obtain a power compensation amount, and the power compensation amount is superimposed on the optimal discharge power trajectory to update the inverter discharge power curve.

[0029] The load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop, including:

[0030] Calculate the reference duty cycle of the load switch according to the ratio of the load discharge power curve to the bus voltage of the battery pack, establish a voltage drop compensation model based on the equivalent inductance and equivalent resistance of the load circuit, superimpose the compensation voltage with the reference duty cycle of the load switch to obtain a corrected duty cycle, and use the corrected duty cycle to control the on-off timing of the load switch to form a first discharge circuit;

[0031] The inverter is controlled by adopting a sinusoidal pulse width modulation method, the inverter discharge power curve is converted into an expected output voltage value, the modulation depth is determined based on the amplitude ratio of the expected output voltage value to the triangular carrier of the battery pack, the inverter switching frequency and the output voltage effective value are calculated according to the modulation depth, and the switching timing of the inverter is controlled to form a second discharge loop;

[0032] The power ratio of the load discharge power curve to the inverter discharge power curve at multiple same moments is calculated to obtain a dynamic power allocation coefficient, and the corrected duty cycle and the modulation depth are adjusted in real time according to the power allocation coefficient to achieve dynamic power balance between the first discharge circuit and the second discharge circuit.

[0033] The method further comprises:

[0034] Taking the load current and the inverter current as state variables, taking the measured load power and the measured inverter power as observation variables, establishing a state space equation including process noise and observation noise, using a Kalman filter algorithm to recursively estimate the state variables, and taking the estimated state variables as correction inputs for power prediction;

[0035] Calculate the prediction errors of the load discharge power curve and the inverter discharge power curve relative to the measured load power and the measured inverter power, set the error accumulation window length, perform exponential weighted summation on the historical prediction errors within the window length to obtain an error compensation term, and correct the power prediction value at the current moment according to the product of the error compensation term and the compensation coefficient, wherein the compensation coefficient increases as the prediction error increases;

[0036] A multi-objective function with the square term of the prediction error and the square term of the power change rate as optimization targets is constructed, and the optimal solution of the multi-objective function is solved by a dynamic programming algorithm. The power allocation ratio of the first discharge circuit and the second discharge circuit is determined according to the optimal solution to achieve coordinated optimization control of the dual-circuit discharge power.

[0037] A second aspect of an embodiment of the present invention provides a remote discharge system combining load current and inverter load discharge, comprising:

[0038] The first unit is used to collect the battery pack parameter information of the energy storage unit through the remote monitoring system, the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack, and construct a battery pack state evaluation model based on the fuzzy control algorithm, input the battery pack parameter information into the battery pack state evaluation model, and obtain the health status index and dischargeable capacity of the battery pack; calculate the optimal discharge power and discharge time of the battery pack according to the prediction and correction algorithm, and generate a load discharge power curve and an inverter discharge power curve;

[0039] The second unit is used for, when the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop;

[0040] The third unit is used to collect the load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack in real time. When the charge state of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working to complete the discharge process.

[0041] A third aspect of the embodiments of the present invention

[0042] An electronic device is provided, comprising:

[0043] processor;

[0044] a memory for storing processor-executable instructions;

[0045] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0046] According to a fourth aspect of the embodiments of the present invention,

[0047] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0048] The beneficial effects of this application are as follows:

[0049] 1. Intelligent discharge management of battery packs has been realized: through the battery pack status assessment model and prediction and correction algorithm, the discharge power and discharge time can be dynamically adjusted according to the real-time status of the battery pack to avoid over-discharge or under-discharge and extend the service life of the battery pack.

[0050] 2. Improved discharge efficiency: Combining load discharge and inverter discharge can more flexibly control the discharge process, make full use of the remaining capacity of the battery pack, and improve discharge efficiency.

[0051] 3. Remote monitoring and control are realized: The remote monitoring system can monitor the status of the battery pack in real time and remotely control the discharge process, which is convenient and fast and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a remote discharge method combining load current and inverter load discharge according to an embodiment of the present invention;

[0053] Figure 2 The structure diagram of the remote discharge system combining load current and inverter load discharge according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0056] Figure 1 FIG. 1 is a flow chart of a remote discharge method combining load current and inverter load discharge according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0057] S101. Collect the battery pack parameter information of the energy storage unit through the remote monitoring system, the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack, build a battery pack state evaluation model based on the fuzzy control algorithm, input the battery pack parameter information into the battery pack state evaluation model, and obtain the health status index and dischargeable capacity of the battery pack; calculate the optimal discharge power and discharge time of the battery pack according to the prediction and correction algorithm, and generate a load discharge power curve and an inverter discharge power curve;

[0058] S102. When the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, wherein the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop;

[0059] S103. Collect the load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack in real time. When the charge state of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working, thereby completing the discharge process.

[0060] In an optional implementation, a battery pack state evaluation model is constructed based on a fuzzy control algorithm, and the battery pack parameter information is input into the battery pack state evaluation model to obtain a health status index and a dischargeable capacity of the battery pack, including:

[0061] The battery pack parameter information is subjected to noise elimination through a Kalman filter to obtain filtered battery pack parameter information; the state of charge of the battery pack is calculated based on an improved coulomb counting method, the improved coulomb counting method introduces a temperature compensation mechanism, a temperature influence coefficient is calculated according to the temperature information, and the temperature influence coefficient is combined with the current efficiency and nominal capacity of the energy storage unit obtained in advance to obtain a real-time state of charge;

[0062] Inputting the battery pack parameter information into a pre-constructed fuzzy membership function to obtain fuzzified data, establishing an IF-THEN rule base including voltage state, current state, temperature state and internal resistance state based on the fuzzified data, calculating the Euclidean distance of each rule in the IF-THEN rule base and determining the rule weight corresponding to each rule based on the Euclidean distance;

[0063] A multi-parameter fused feature vector is established, wherein the feature vector includes the battery pack parameter information and its derivative items, and the feature vector is weightedly fused according to the rule weight to obtain the health status index of the battery pack; a capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction to obtain the actual dischargeable capacity of the battery pack.

[0064] The battery pack status assessment method aims to accurately assess the health status and remaining capacity of the battery pack. The method is based on a fuzzy control algorithm, integrates multiple battery pack parameters, and combines Kalman filtering and improved coulomb counting method to achieve a comprehensive assessment of the battery pack status.

[0065] First, collect various parameter information of the battery pack, including voltage, current, temperature and internal resistance. These parameter information are usually affected by sensor noise, so noise elimination is required. Use Kalman filter to filter the collected battery pack parameter information to obtain more accurate battery pack parameter information. For example, the collected voltage value is 3.7V. After Kalman filtering, the voltage value is corrected to 3.72V.

[0066] Next, the state of charge (SOC) of the battery pack is calculated. An improved coulomb counting method is used, which introduces a temperature compensation mechanism. First, the temperature influence coefficient is calculated based on the collected temperature information, such as 25 degrees Celsius. Assume that the temperature influence coefficient is 0.001. Then, the calculated temperature influence coefficient is combined with the pre-acquired current efficiency of the energy storage unit, such as 98%, and the nominal capacity, such as 2000mAh, to calculate the real-time state of charge. For example, the SOC calculated at the current moment is 80%.

[0067] The filtered battery pack parameter information is input into the pre-built fuzzy membership function to obtain fuzzy data. For example, the fuzzy membership function value corresponding to the voltage of 3.72V is 0.8, the fuzzy membership function value corresponding to the current of 1A is 0.5, the fuzzy membership function value corresponding to the temperature of 25 degrees Celsius is 0.7, and the fuzzy membership function value corresponding to the internal resistance of 0.02Ω is 0.9.

[0068] Based on the fuzzified data, an IF-THEN rule base containing voltage state, current state, temperature state and internal resistance state is established. For example, a rule can be: if the voltage is high, the current is low, the temperature is moderate, and the internal resistance is low, then the battery health state is good. The Euclidean distance of each rule in the IF-THEN rule base is calculated, and the rule weight corresponding to each rule is determined based on the Euclidean distance. For example, the Euclidean distance of the first rule is calculated to be 0.2, and the corresponding rule weight is 0.9.

[0069] A multi-parameter fusion feature vector is established, which contains the battery pack parameter information and its derivative items, such as the rate of change of voltage. The feature vector is weighted and fused according to the calculated rule weights to obtain the health status index of the battery pack. For example, the health status index finally calculated is 0.95.

[0070] Based on nonlinear fitting, a capacity decay model is established, and the calculated health status index and actual temperature are input into the capacity decay model for dynamic correction to obtain the actual discharge capacity of the battery pack. For example, at 25 degrees Celsius, a battery pack with a health status index of 0.95 has an actual discharge capacity of 1900mAh.

[0071] The beneficial effects of this method are reflected in three aspects:

[0072] First, the accuracy of battery pack status assessment is improved. Kalman filtering eliminates noise interference, and the improved Coulomb counting method is combined to accurately calculate SOC, which improves the reliability of input data. The application of fuzzy control algorithm can effectively handle the nonlinear characteristics of battery pack parameters, thereby improving the accuracy of status assessment.

[0073] Second, a comprehensive evaluation of the battery pack status is achieved. This method comprehensively considers multiple parameters such as voltage, current, temperature, internal resistance, etc., and through the feature vector of multi-parameter fusion, a comprehensive evaluation of the battery pack health status and dischargeable capacity is achieved.

[0074] Third, the adaptability of the battery management system is enhanced. This method establishes a capacity decay model based on nonlinear fitting and performs dynamic correction based on the actual temperature. It can adapt to the battery pack status evaluation under different temperature conditions and improve the robustness and adaptability of the battery management system.

[0075] In an optional implementation, a capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction to obtain the actual dischargeable capacity of the battery pack, which includes:

[0076] Obtain the number of cycles, actual temperature and health status index of the battery pack, establish a double exponential decay basic model, and calculate the basic capacity of the battery pack according to the number of cycles, wherein the double exponential decay basic model includes an initial capacity, a weight coefficient and a decay rate, and the basic capacity is obtained by a weighted combination of two exponential functions;

[0077] A temperature correction term is constructed based on the difference between the actual temperature and the preset reference temperature, wherein the temperature correction term includes a first-order temperature coefficient and a second-order temperature coefficient, and the temperature correction term is multiplied by the basic capacity to obtain a battery pack capacity after temperature correction;

[0078] Establishing a health status influence function based on the ratio of the health status index to the reference health status, introducing a nonlinear adjustment coefficient into the health status influence function, and multiplying the health status influence function by the temperature correction term to obtain a comprehensive correction coefficient;

[0079] Calculating the change in the health status index at adjacent moments, and constructing an adaptive weight function based on the change, wherein the adaptive weight function includes a sensitivity parameter, and the sensitivity parameter is used to adjust the responsiveness of the weight to the change in health status;

[0080] The comprehensive correction coefficient, the adaptive weight function and the temperature-corrected battery pack capacity are multiplied to obtain the actual dischargeable capacity of the battery pack.

[0081] Get the number of cycles, actual temperature, and health status index of the battery pack. Assume that at a certain moment, the number of cycles of the battery pack is 500, the actual temperature is 25 degrees Celsius, and the health status index is 0.9.

[0082] A double exponential decay basic model is established. The model contains parameters such as initial capacity, weight coefficient and decay rate. The initial capacity is set to 100Ah, the weight coefficients are set to 0.6 and 0.4, and the decay rates are set to 0.0002 and 0.0005 respectively. The basic capacity of the battery pack is calculated according to the number of cycles. The basic capacity is obtained by a weighted combination of two exponential functions. The first exponential function is an exponential function with a decay rate of 0.0002 and 500 cycles as the independent variable, and the result is multiplied by the initial capacity of 100Ah and the weight coefficient of 0.6. The second exponential function is an exponential function with a decay rate of 0.0005 and 500 cycles as the independent variable, and the result is multiplied by the initial capacity of 100Ah and the weight coefficient of 0.4. Finally, the two results are added to get the basic capacity, for example, the calculated result is 85Ah.

[0083] Construct a temperature correction term. The preset reference temperature is 20 degrees Celsius, the first-order temperature coefficient is set to 0.001, and the second-order temperature coefficient is set to -0.00001. The difference between the actual temperature and the reference temperature is 5 degrees Celsius. The temperature correction term is calculated as follows: First, the difference 5 is multiplied by the first-order temperature coefficient 0.001, then the square of the difference (i.e. 25) is multiplied by the second-order temperature coefficient -0.00001, and finally the two results are added together to obtain the temperature correction term, for example, the calculated result is 0.00475. Multiply the temperature correction term by the base capacity to obtain the temperature-corrected battery pack capacity. For example, 85Ah multiplied by 1.00475 is approximately 85.4Ah.

[0084] Establish a health status impact function. The baseline health status is set to 1. The ratio of the health status index to the baseline health status is 0.9. The nonlinear adjustment coefficient is set to 0.8. The health status impact function is calculated by subtracting the 0.8 power of the ratio of the health status index to the baseline health status from 1. For example, 1 minus 0.9 to the 0.8 power is approximately 0.087. Multiply the health status impact function by the temperature correction term to obtain a comprehensive correction coefficient, for example, 0.087 multiplied by 1.00475 is approximately 0.0874.

[0085] Construct an adaptive weight function. Assume that the health index at the previous moment is 0.92. The change in the health index between the current moment and the previous moment is -0.02. The sensitivity parameter is set to 50. The adaptive weight function is calculated by: 1 plus the product of the change in the health index and the sensitivity parameter. For example, 1 plus -0.02 times 50 equals 0.

[0086] Multiply the comprehensive correction coefficient, the adaptive weight function and the temperature-corrected battery pack capacity to get the actual dischargeable capacity of the battery pack. For example, 85.4Ah multiplied by 0.0874 and then multiplied by 0, the final result is 0Ah. In this case, the adaptive weight function calculated the result to be 0, resulting in a final capacity of 0, which means that the parameter settings need to be adjusted to suit the actual situation. Under normal circumstances, the adaptive weight function should be close to 1. Beneficial Effects

[0087] Improve the accuracy of battery capacity estimation. By comprehensively considering multiple factors such as cycle number, temperature and health status, and introducing methods such as nonlinear fitting and adaptive weighting, the actual dischargeable capacity of the battery pack can be estimated more accurately and the estimation error can be reduced.

[0088] Extend battery life. Accurate capacity estimation helps avoid overcharging or over-discharging of the battery, thereby effectively extending the battery life and reducing battery loss.

[0089] Optimize the battery management system. More accurate capacity estimation results can provide more reliable data support for the battery management system, help optimize battery charging and discharging strategies, and improve the overall performance and efficiency of the battery system.

[0090] In an optional implementation, calculating the optimal discharge power and discharge duration of the battery pack according to the prediction correction algorithm, and generating the load discharge power curve and the inverter discharge power curve includes:

[0091] Acquire the real-time voltage, discharge current and temperature information of the battery pack, establish a discharge power model according to the real-time voltage, the product of the discharge current and the discharge efficiency, build a capacity loss model based on the discharge depth, the square term of the discharge current and the temperature information, and obtain an estimated value of the initial discharge power and capacity loss;

[0092] Taking the initial discharge power and the estimated value of capacity loss as system state vectors, constructing a state prediction equation including a state transfer matrix and a control matrix, and a measurement update equation including an observation matrix, calculating the Kalman gain based on the prediction error covariance and the measurement noise covariance, and correcting the initial discharge power and the estimated value of capacity loss;

[0093] Based on the corrected discharge power, an objective function is established, wherein the objective function is composed of a square term of the deviation between the load power and the discharge power and a weighted term of the corrected capacity loss estimate. Under the constraints of power limit, current limit, temperature limit and state of charge limit, a dynamic programming algorithm is used to solve the optimal discharge power trajectory;

[0094] According to the optimal discharge power trajectory, a basic discharge duration is calculated in combination with the available capacity and discharge efficiency of the battery pack, and the basic discharge duration is multiplied by the health status index and temperature coefficient monitored in real time, respectively, to obtain a corrected discharge duration that takes into account the health status and temperature effects; based on the corrected discharge duration, a time interval is divided, and the load power data is fitted using Fourier series to obtain a continuous load power curve, and the optimal discharge power trajectory is multiplied by the dynamic inverter efficiency to generate an initial inverter discharge power curve;

[0095] The power deviation between the load power curve and the initial inverter discharge power curve at the same coordinate point is calculated in real time, and the power deviation is input into an adaptive proportional-integral controller, wherein the proportional coefficient and the integral coefficient are automatically adjusted according to the deviation change rate to obtain a power compensation amount, and the power compensation amount is superimposed on the optimal discharge power trajectory to update the inverter discharge power curve.

[0096] Get real-time information of the battery pack and build a model: First, use sensors to collect information such as the voltage, discharge current, and temperature of the battery pack in real time. Then, based on the real-time voltage and the product of the discharge current and the discharge efficiency, a discharge power model is established, which can calculate the current discharge power based on real-time data. At the same time, based on the depth of discharge (the ratio of the remaining capacity of the battery to the total capacity), the square of the discharge current, and temperature information, a capacity loss model is constructed to estimate the capacity loss of the battery under the current operating conditions. For example, if the battery voltage is 50V, the discharge current is 10A, and the discharge efficiency is 90%, the discharge power is 50V * 10A * 90% = 450W. Assuming that the current depth of discharge is 20% and the temperature is 25°C, the estimated capacity loss calculated by the capacity loss model is 0.01%.

[0097] Correct the initial discharge power and capacity loss estimates: Use the initial discharge power and capacity loss estimates obtained in the previous step as the system state vector. Construct a state prediction equation that contains the state transfer matrix and the control matrix to predict the system state at the next moment. At the same time, construct a measurement update equation that contains the observation matrix to incorporate new sensor measurements into the state estimate. Use the prediction error covariance and the measurement noise covariance to calculate the Kalman gain, and use the Kalman gain to correct the initial discharge power and capacity loss estimates to obtain more accurate estimates. For example, the discharge power at the next moment is predicted to be 460W and the capacity loss is 0.011%. After Kalman filter correction, the corrected discharge power is 455W and the capacity loss is 0.0105%.

[0098] Solve the optimal discharge power trajectory: Based on the corrected discharge power, establish an objective function. The objective function consists of two parts: the squared term of the deviation between the load power and the discharge power, and the weighted term of the corrected capacity loss estimate. The goal is to minimize the deviation between the load power and the discharge power while taking into account the battery capacity loss. Under the constraints of power limit, current limit, temperature limit and state of charge limit, a dynamic programming algorithm is used to solve the optimal discharge power trajectory, which describes the optimal discharge power change of the battery pack over a period of time in the future. For example, in the next hour, the optimal discharge power trajectory is [455W, 460W, 465W, ..., 480W].

[0099] Calculate the corrected discharge time and generate the discharge power curve: According to the optimal discharge power trajectory, the basic discharge time is calculated in combination with the available capacity and discharge efficiency of the battery pack. Multiply the basic discharge time with the real-time monitored battery health status index and temperature coefficient to obtain the corrected discharge time that takes into account the influence of health status and temperature. For example, if the basic discharge time is 2 hours, the health status index is 0.98, and the temperature coefficient is 1.01, then the corrected discharge time is 2 * 0.98 * 1.01 = 1.98 hours. Divide the time interval based on the corrected discharge time, and use Fourier series to fit the load power data to obtain a continuous load power curve. Multiply the optimal discharge power trajectory with the dynamic inverter efficiency to generate the initial inverter discharge power curve.

[0100] Update the inverter discharge power curve: Calculate the power deviation between the load power curve and the initial inverter discharge power curve at the same coordinate point in real time. Input the power deviation into the adaptive proportional-integral controller, where the proportional coefficient and integral coefficient are automatically adjusted according to the deviation change rate to obtain the power compensation amount. Superimpose the power compensation amount on the optimal discharge power trajectory, update the inverter discharge power curve, and make the power output of the inverter closer to the load demand. For example, at a certain moment, the load power is 470W, the initial inverter discharge power is 465W, and the power deviation is 5W. After calculation by the proportional-integral controller, the power compensation amount is 2W, and the updated inverter discharge power is 467W.

[0101] Beneficial effects:

[0102] 1. Extend battery life: By taking capacity loss into consideration for power control, the capacity decay of the battery is effectively slowed down, thus extending the battery life.

[0103] 2. Improve discharge efficiency: The application of dynamic programming algorithm and adaptive proportional integral controller enables the discharge power to better match the load demand, thereby improving the discharge efficiency and reducing energy waste.

[0104] 3. Improve system stability: Accurate power prediction and control strategies enhance system stability and avoid system failures caused by power fluctuations.

[0105] In an optional implementation, the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop, including:

[0106] Calculate the reference duty cycle of the load switch according to the ratio of the load discharge power curve to the bus voltage of the battery pack, establish a voltage drop compensation model based on the equivalent inductance and equivalent resistance of the load circuit, superimpose the compensation voltage with the reference duty cycle of the load switch to obtain a corrected duty cycle, and use the corrected duty cycle to control the on-off timing of the load switch to form a first discharge circuit;

[0107] The inverter is controlled by adopting a sinusoidal pulse width modulation method, the inverter discharge power curve is converted into an expected output voltage value, the modulation depth is determined based on the amplitude ratio of the expected output voltage value to the triangular carrier of the battery pack, the inverter switching frequency and the output voltage effective value are calculated according to the modulation depth, and the switching timing of the inverter is controlled to form a second discharge loop;

[0108] The power ratio of the load discharge power curve to the inverter discharge power curve at multiple same moments is calculated to obtain a dynamic power allocation coefficient, and the corrected duty cycle and the modulation depth are adjusted in real time according to the power allocation coefficient to achieve dynamic power balance between the first discharge circuit and the second discharge circuit.

[0109] The dual-circuit discharge control method of a battery pack comprises the following steps:

[0110] First, obtain the bus voltage value of the battery pack. This voltage value can be measured in real time by a voltage sensor. For example, the currently measured bus voltage value of the battery pack is 500V. At the same time, pre-set the load discharge power curve and the inverter discharge power curve. For example, the load discharge power curve is set to discharge slowly with a small power in the initial stage, then the power gradually increases, and finally slowly decreases; the inverter discharge power curve is set to have a large power in the initial stage, then the power gradually decreases, and finally remains at a small stable value.

[0111] Next, calculate the load switch reference duty cycle. The duty cycle is the ratio of the load discharge power curve to the battery pack bus voltage. For example, assuming that at a certain moment, the set value of the load discharge power curve is 250kW and the battery pack bus voltage is 500V, the load switch reference duty cycle is 50%.

[0112] Then, a voltage drop compensation model is established. This model is based on the equivalent inductance and equivalent resistance of the load loop and is used to compensate for the voltage drop on the load loop. For example, assuming that the equivalent inductance of the load loop is 0.1mH and the equivalent resistance is 0.01Ω, the compensation voltage calculated by the model is 5V.

[0113] The compensation voltage is added to the load switch reference duty cycle to obtain the corrected duty cycle. For example, if the reference duty cycle is 50%, the compensation voltage is 5V, and the corresponding battery pack bus voltage of 500V is converted into a duty cycle of 1%, then the corrected duty cycle is 51%.

[0114] The first discharge loop is formed by controlling the on-off timing of the load switch using the modified duty cycle. For example, pulse width modulation (PWM) technology is used to control the on-off of the load switch according to the calculated 51% duty cycle to achieve discharge control of the load.

[0115] At the same time, the inverter is controlled by sinusoidal pulse width modulation. The inverter discharge power curve is converted into the expected output voltage value. For example, assuming that at a certain moment, the set value of the inverter discharge power curve is 200kW, it is converted into the corresponding expected output voltage value, such as 400V, according to the parameters of the inverter.

[0116] The modulation depth is determined based on the ratio of the expected output voltage to the amplitude of the battery pack triangular carrier. For example, assuming the amplitude of the battery pack triangular carrier is 500V and the expected output voltage is 400V, the modulation depth is 80%.

[0117] The inverter switching frequency and output voltage RMS are calculated based on the modulation depth. For example, based on a modulation depth of 80%, the inverter switching frequency is calculated to be 10kHz and the output voltage RMS is 400V.

[0118] The switching timing of the inverter is controlled to form a second discharge loop. For example, a sinusoidal pulse width modulation (SPWM) technique is used to control the switching of the inverter according to the calculated switching frequency and output voltage effective value to achieve discharge control of the load.

[0119] The power ratio of the load discharge power curve and the inverter discharge power curve at multiple same moments is calculated to obtain the dynamic power allocation coefficient. For example, at a certain moment, the load discharge power is 250kW and the inverter discharge power is 200kW, then the power allocation coefficient is 1.25.

[0120] The duty cycle and modulation depth are adjusted in real time according to the power allocation coefficient to achieve dynamic power balance between the first discharge loop and the second discharge loop. For example, according to the power allocation coefficient of 1.25, the duty cycle can be adjusted to 51%*1.25 = 63.75%, and the modulation depth can be adjusted to 80% / 1.25 = 64%, thereby achieving dynamic power balance between the two discharge loops.

[0121] The beneficial effects of the dual-circuit discharge control method of the battery pack can be summarized into the following three parts:

[0122] 1. Improve discharge efficiency: By precisely controlling the switching timing of the load switch and inverter, and dynamically adjusting power distribution, energy loss can be minimized and discharge efficiency can be improved.

[0123] 2. Extend battery life: By avoiding overcharge and over discharge and controlling the stability of discharge current, the service life of the battery pack can be effectively extended.

[0124] 3. Enhance system stability: Dual-circuit discharge design and dynamic power balance control can improve system stability and reliability and avoid abnormal situations such as overload.

[0125] In an optional implementation, the method further includes:

[0126] Taking the load current and the inverter current as state variables, taking the measured load power and the measured inverter power as observation variables, establishing a state space equation including process noise and observation noise, using a Kalman filter algorithm to recursively estimate the state variables, and taking the estimated state variables as correction inputs for power prediction;

[0127] Calculate the prediction errors of the load discharge power curve and the inverter discharge power curve relative to the measured load power and the measured inverter power, set the error accumulation window length, perform exponential weighted summation on the historical prediction errors within the window length to obtain an error compensation term, and correct the power prediction value at the current moment according to the product of the error compensation term and the compensation coefficient, wherein the compensation coefficient increases as the prediction error increases;

[0128] A multi-objective function with the square term of the prediction error and the square term of the power change rate as optimization targets is constructed, and the optimal solution of the multi-objective function is solved by a dynamic programming algorithm. The power allocation ratio of the first discharge circuit and the second discharge circuit is determined according to the optimal solution to achieve coordinated optimization control of the dual-circuit discharge power.

[0129] Get the real-time measured values ​​of load current and inverter current. These current values ​​will be used as state variables to describe the current state of the system. At the same time, get the measured load power and inverter power. These power values ​​will be used as observation variables to reflect the output of the system.

[0130] Based on the acquired state variables (load current and inverter current) and observed variables (measured load power and measured inverter power), the state space equation is established. This equation describes the dynamic process of the system state evolution over time, and takes into account the influence of process noise and observation noise. Process noise represents the unmodeled dynamic factors inside the system, while observation noise represents the error in the measurement process. For example, it can be assumed that process noise and observation noise follow Gaussian distribution, and their variances are set to 0.1 and 0.05 respectively.

[0131] The Kalman filter algorithm is used to recursively estimate the state variables. The Kalman filter algorithm is an optimization algorithm based on Bayesian estimation theory. It can estimate the system state in real time based on the system's state space equations and observations, and effectively suppress the influence of noise. The estimated load current and inverter current are used as correction inputs for power prediction to improve prediction accuracy. For example, assuming that the initial state estimates are 10A and 5A, the Kalman filter algorithm continuously updates the state estimates based on the latest measurements and state space equations.

[0132] Calculate the prediction error of the load discharge power curve and the inverter discharge power curve relative to the measured load power and the measured inverter power. The prediction error refers to the difference between the predicted value and the actual measured value. For example, if the predicted load power is 100W and the measured load power is 95W, the prediction error is 5W.

[0133] Set an error accumulation window length, for example, set the window length to 10 time steps. Perform exponential weighted summation on the historical prediction errors within the window length to obtain the error compensation term. Exponential weighted summation means assigning different weights to historical data, with more recent data having a greater weight and more distant data having a smaller weight. For example, an exponential weighted summation method with an exponential decay factor of 0.9 can be used.

[0134] The power forecast value at the current moment is corrected according to the product of the error compensation term and the compensation coefficient. The compensation coefficient increases as the forecast error increases. This means that when the forecast error is large, a larger correction will be made. For example, the compensation coefficient can be set to be proportional to the absolute value of the forecast error, with a proportionality coefficient of 0.2.

[0135] A multi-objective function is constructed with the square term of the prediction error and the square term of the power change rate as optimization targets. The power change rate refers to the speed at which the power changes over time. For example, the weighted sum of the square term of the prediction error and the square term of the power change rate can be used as the multi-objective function, with weight coefficients of 0.8 and 0.2 respectively.

[0136] The dynamic programming algorithm is used to find the optimal solution of the multi-objective function. The dynamic programming algorithm is an algorithm that decomposes a complex problem into multiple sub-problems and obtains the optimal solution of the original problem by solving the sub-problems. Through the dynamic programming algorithm, the optimal solution that minimizes the value of the multi-objective function can be found.

[0137] The power allocation ratio of the first discharge circuit and the second discharge circuit is determined according to the optimal solution to achieve coordinated optimization control of the dual-circuit discharge power. For example, assuming that the optimal solution is to allocate 70% of the total power to the first discharge circuit and 30% to the second discharge circuit.

[0138] Beneficial effects:

[0139] Improve prediction accuracy: The Kalman filter algorithm is used to recursively estimate state variables and compensate for historical prediction errors, which can effectively improve the accuracy of power prediction.

[0140] Optimize power allocation: By solving the optimal solution of the multi-objective function through the dynamic programming algorithm, the coordinated optimization control of the dual-circuit discharge power can be achieved to improve energy utilization efficiency.

[0141] Enhance system stability: By comprehensively considering the prediction error and power change rate, power fluctuations can be effectively suppressed and system stability can be enhanced.

[0142] Figure 2 FIG. 1 is a schematic diagram of a remote discharge system combining load current and inverter load discharge according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0143] The first unit is used to collect the battery pack parameter information of the energy storage unit through the remote monitoring system, the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack, and construct a battery pack state evaluation model based on the fuzzy control algorithm, input the battery pack parameter information into the battery pack state evaluation model, and obtain the health status index and dischargeable capacity of the battery pack; calculate the optimal discharge power and discharge time of the battery pack according to the prediction and correction algorithm, and generate a load discharge power curve and an inverter discharge power curve;

[0144] The second unit is used for, when the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop;

[0145] The third unit is used to collect the load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack in real time. When the charge state of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working to complete the discharge process.

[0146] According to a third aspect of the embodiments of the present invention,

[0147] An electronic device is provided, comprising:

[0148] processor;

[0149] a memory for storing processor-executable instructions;

[0150] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0151] A fourth aspect of the embodiments of the present invention is:

[0152] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0153] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote discharge method combining load current and inverter load discharge, characterized in that: include: The battery pack parameter information of the energy storage unit is collected through a remote monitoring system, wherein the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack; a battery pack state evaluation model is constructed based on a fuzzy control algorithm, and the battery pack parameter information is input into the battery pack state evaluation model to obtain the health status index and the dischargeable capacity of the battery pack; the optimal discharge power and discharge duration of the battery pack are calculated according to the prediction and correction algorithm, and a load discharge power curve and an inverter discharge power curve are generated; When the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, wherein the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop; The load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack are collected in real time. When the state of charge of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working, thereby completing the discharge process; The battery pack parameter information is subjected to noise elimination through a Kalman filter to obtain filtered battery pack parameter information; the state of charge of the battery pack is calculated based on an improved coulomb counting method, the improved coulomb counting method introduces a temperature compensation mechanism, a temperature influence coefficient is calculated according to the temperature information, and the temperature influence coefficient is combined with the current efficiency and nominal capacity of the energy storage unit obtained in advance to obtain a real-time state of charge; Inputting the battery pack parameter information into a pre-constructed fuzzy membership function to obtain fuzzified data, establishing an IF-THEN rule base including voltage state, current state, temperature state and internal resistance state based on the fuzzified data, calculating the Euclidean distance of each rule in the IF-THEN rule base and determining the rule weight corresponding to each rule based on the Euclidean distance; A multi-parameter fused feature vector is established, wherein the feature vector includes the battery pack parameter information and its derivative items, and the feature vector is weightedly fused according to the rule weight to obtain the health status index of the battery pack; a capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction to obtain the actual dischargeable capacity of the battery pack.

2. The method according to claim 1, characterized in that A capacity decay model is established based on nonlinear fitting, and the health status index and the actual temperature are input into the capacity decay model for dynamic correction to obtain the actual dischargeable capacity of the battery pack, including: Obtain the number of cycles, actual temperature and health status index of the battery pack, establish a double exponential decay basic model, and calculate the basic capacity of the battery pack according to the number of cycles, wherein the double exponential decay basic model includes an initial capacity, a weight coefficient and a decay rate, and the basic capacity is obtained by a weighted combination of two exponential functions; A temperature correction term is constructed based on the difference between the actual temperature and the preset reference temperature, wherein the temperature correction term includes a first-order temperature coefficient and a second-order temperature coefficient, and the temperature correction term is multiplied by the basic capacity to obtain a battery pack capacity after temperature correction; Establishing a health status influence function based on the ratio of the health status index to the reference health status, introducing a nonlinear adjustment coefficient into the health status influence function, and multiplying the health status influence function by the temperature correction term to obtain a comprehensive correction coefficient; Calculating the change in the health status index at adjacent moments, and constructing an adaptive weight function based on the change, wherein the adaptive weight function includes a sensitivity parameter, and the sensitivity parameter is used to adjust the responsiveness of the weight to the change in health status; The comprehensive correction coefficient, the adaptive weight function and the temperature-corrected battery pack capacity are multiplied to obtain the actual dischargeable capacity of the battery pack.

3. The method according to claim 1, characterized in that Calculating the optimal discharge power and discharge duration of the battery pack according to the prediction correction algorithm, generating a load discharge power curve and an inverter discharge power curve includes: Acquire the real-time voltage, discharge current and temperature information of the battery pack, establish a discharge power model according to the real-time voltage, the product of the discharge current and the discharge efficiency, build a capacity loss model based on the discharge depth, the square term of the discharge current and the temperature information, and obtain an estimated value of the initial discharge power and capacity loss; Taking the initial discharge power and the estimated value of capacity loss as system state vectors, constructing a state prediction equation including a state transfer matrix and a control matrix, and a measurement update equation including an observation matrix, calculating the Kalman gain based on the prediction error covariance and the measurement noise covariance, and correcting the initial discharge power and the estimated value of capacity loss; Based on the corrected discharge power, an objective function is established, wherein the objective function is composed of a square term of the deviation between the load power and the discharge power and a weighted term of the corrected capacity loss estimate. Under the constraints of power limit, current limit, temperature limit and state of charge limit, a dynamic programming algorithm is used to solve the optimal discharge power trajectory; According to the optimal discharge power trajectory, a basic discharge duration is calculated in combination with the available capacity and discharge efficiency of the battery pack, and the basic discharge duration is multiplied by the health status index and temperature coefficient monitored in real time, respectively, to obtain a corrected discharge duration that takes into account the health status and temperature effects; based on the corrected discharge duration, a time interval is divided, and the load power data is fitted using Fourier series to obtain a continuous load power curve, and the optimal discharge power trajectory is multiplied by the dynamic inverter efficiency to generate an initial inverter discharge power curve; The power deviation between the load power curve and the initial inverter discharge power curve at the same coordinate point is calculated in real time, and the power deviation is input into an adaptive proportional-integral controller, wherein the proportional coefficient and the integral coefficient are automatically adjusted according to the deviation change rate to obtain a power compensation amount, and the power compensation amount is superimposed on the optimal discharge power trajectory to update the inverter discharge power curve.

4. The method according to claim 1, characterized in that: The load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop, including: Calculate the reference duty cycle of the load switch according to the ratio of the load discharge power curve to the bus voltage of the battery pack, establish a voltage drop compensation model based on the equivalent inductance and equivalent resistance of the load circuit, superimpose the compensation voltage with the reference duty cycle of the load switch to obtain a corrected duty cycle, and use the corrected duty cycle to control the on-off timing of the load switch to form a first discharge circuit; The inverter is controlled by adopting a sinusoidal pulse width modulation method, the inverter discharge power curve is converted into an expected output voltage value, the modulation depth is determined based on the amplitude ratio of the expected output voltage value to the triangular carrier of the battery pack, the inverter switching frequency and the output voltage effective value are calculated according to the modulation depth, and the switching timing of the inverter is controlled to form a second discharge loop; The power ratio of the load discharge power curve to the inverter discharge power curve at multiple same moments is calculated to obtain a dynamic power allocation coefficient, and the corrected duty cycle and the modulation depth are adjusted in real time according to the power allocation coefficient to achieve dynamic power balance between the first discharge circuit and the second discharge circuit.

5. The method according to claim 1, characterized in that: The method further comprises: Taking the load current and the inverter current as state variables, taking the measured load power and the measured inverter power as observation variables, establishing a state space equation including process noise and observation noise, using a Kalman filter algorithm to recursively estimate the state variables, and taking the estimated state variables as correction inputs for power prediction; Calculate the prediction errors of the load discharge power curve and the inverter discharge power curve relative to the measured load power and the measured inverter power, set the error accumulation window length, perform exponential weighted summation on the historical prediction errors within the window length to obtain an error compensation term, and correct the power prediction value at the current moment according to the product of the error compensation term and the compensation coefficient, wherein the compensation coefficient increases as the prediction error increases; A multi-objective function with the square term of the prediction error and the square term of the power change rate as optimization targets is constructed, and the optimal solution of the multi-objective function is solved by a dynamic programming algorithm. The power allocation ratio of the first discharge circuit and the second discharge circuit is determined according to the optimal solution to achieve coordinated optimization control of the dual-circuit discharge power.

6. A remote discharge system combining load current and inverter load discharge, used to implement the method according to any one of claims 1 to 5, characterized in that: include: The first unit is used to collect the battery pack parameter information of the energy storage unit through the remote monitoring system, the battery pack parameter information includes the state of charge of the battery pack, the total voltage of the battery pack, the total current of the battery pack, the temperature information of the battery pack, and the internal resistance information of the battery pack, and construct a battery pack state evaluation model based on the fuzzy control algorithm, input the battery pack parameter information into the battery pack state evaluation model, and obtain the health status index and dischargeable capacity of the battery pack; calculate the optimal discharge power and discharge time of the battery pack according to the prediction and correction algorithm, and generate a load discharge power curve and an inverter discharge power curve; The second unit is used for, when the health status index of the battery pack is higher than a preset health threshold and the dischargeable capacity is greater than a preset capacity threshold, the remote monitoring system sends a discharge instruction to the load discharge control unit and the inverter discharge control unit, the discharge instruction includes the load discharge power curve and the inverter discharge power curve; the load discharge control unit controls the duty cycle of the load switch according to the load discharge power curve to form a first discharge loop; the inverter discharge control unit controls the switching frequency and the output voltage effective value of the inverter according to the inverter discharge power curve to form a second discharge loop; The third unit is used to collect the load current of the first discharge circuit, the inverter current of the second discharge circuit and the parameter information of the battery pack in real time. When the charge state of the battery pack drops to a preset discharge termination threshold, the load discharge control unit controls the load switch to disconnect, and the inverter discharge control unit controls the inverter to stop working to complete the discharge process.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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