Multi-conditional constraint energy storage battery discharge control system and method

Abstract

The invention relates to the technical field of ship energy storage battery management, and particularly discloses a multi-conditional constraint energy storage battery discharge control system and method, which are used for solving the problems of poor power source coordination, power resource waste, accelerated equipment aging, low energy consumption and the like caused by lack of effective management of an energy storage battery in marine ship multi-power source energy supply. The system comprises a discharging system unit, a data and instruction processing unit and an energy storage battery discharging central processing module unit, a dynamic prediction module collects and processes real-time data from the data and instruction processing unit, and the energy storage battery discharging central processing module unit is connected with the data and instruction processing unit. The predicted system state is transmitted to a discharge optimization prediction control module and a multi-condition constraint analysis module; through dynamic prediction, optimization control and multi-condition constraint analysis, it is ensured that the energy storage battery supplies energy efficiently and safely in a complex environment, the service life of the battery is prolonged, and the stability and response capacity of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship energy storage battery control, and more specifically, to a multi-condition constrained energy storage battery discharge control system and method. Background Art

[0002] When a ship at sea uses multiple power sources for energy supply, the energy storage battery is usually directly connected to the energy consumption system. Although this direct connection method simplifies the system structure, it will lead to the singleness of the functional system and lack of in-depth and more complex energy supply control capabilities. Under this framework, the energy storage battery cannot effectively manage and optimize the power supply process, especially when multiple electric power sources coexist. The lack of effective coordination and management mechanism makes it impossible to dynamically balance the discharge power of each power source. This incoordination will lead to excessive use of some power sources and under-utilization of other power sources, resulting in waste of electric power resources and accelerated aging of power sources. Due to the lack of comprehensive monitoring and diagnostic functions in the system, especially in the face of rapidly changing power demands, the system cannot obtain key status data of energy storage batteries and peripheral equipment in a timely manner, resulting in the inability to quickly adjust the discharge strategy. This delayed response will This causes excessive current fluctuations, damages battery life, and even causes system instability. The current system lacks in-depth energy supply control capabilities and fails to fully consider the various constraints of energy storage batteries and their peripheral systems. This will cause the system to ignore key constraints when distributing discharge power, resulting in a mismatch between the discharge strategy and actual needs, thereby reducing the overall efficiency and safety of the system. In a multi-power source system, the impact of the aging status of each power source on the discharge strategy has not been fully considered, which will lead to failure to adjust the discharge strategy in a timely manner, thereby accelerating the aging of the power source and affecting the long-term reliability and economy of the system. In the complex environment of maritime navigation, the dynamic changes in external conditions have a greater impact on energy storage batteries and their peripheral systems. The lack of an effective environmental adaptability and stability assurance mechanism will cause the system to oscillate or become unstable under complex conditions, affecting the normal navigation of the ship. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a multi-condition constrained energy storage battery discharge control system and method, which ensures that the energy storage battery can efficiently and safely supply energy in a complex environment through dynamic prediction, optimization control and multi-condition constraint analysis, thereby extending the battery life and improving the system stability and responsiveness.

[0004] In sea areas where typhoons and severe weather frequently occur, emergency rescue ships are used to perform emergency rescue missions and are equipped with multiple power sources such as batteries, diesel generators, wind turbines and supercapacitors to ensure a stable power supply in emergency situations. When starting radars, communication systems, life-saving equipment and medical equipment, rescue ships require instantaneous high power, which is usually powered by diesel generators, but batteries and supercapacitors need to intervene quickly at critical moments. However, the existing system lacks an effective coordination mechanism, resulting in excessive use of batteries or rapid exhaustion of supercapacitors during peak demand, affecting system stability. In addition, low temperature, high humidity and wave impact under severe sea conditions will affect the performance of the power source, such as increasing the internal resistance of the battery, reducing the discharge efficiency or causing short circuit risks. Since the existing system cannot monitor and adjust the power source usage strategy in real time, it may lead to unstable power supply, affecting equipment operation and the execution of rescue missions. The power source gradually ages during frequent use, performance declines, and the risk of failure increases. The system lacks an effective monitoring and adjustment mechanism, and fails to reduce the load or switch the power source in time, further affecting the continuous execution of the rescue mission.

[0005] In order to address the technical problems raised by the background technology in the above application scenarios and achieve the above objectives, the present invention provides the following technical solutions:

[0006] A multi-condition constrained energy storage battery discharge control system comprises a discharge system unit, a data and instruction processing unit, and an energy storage battery discharge central processing module unit. The discharge system unit is connected to the data and instruction processing unit and the energy storage battery discharge central processing module unit. The energy storage battery discharge central processing module comprises a dynamic prediction module, a discharge optimization prediction control module, a multi-condition constraint analysis module, and a control execution and feedback module. The dynamic prediction module collects and processes real-time data from the data and instruction processing unit, and transmits the predicted system state to the discharge optimization prediction control module and the multi-condition constraint analysis module. The discharge optimization prediction control module receives the predicted data of the dynamic prediction module, calculates the optimal discharge strategy, and transmits the predicted data to the discharge optimization prediction control module and the multi-condition constraint analysis module. The preliminary optimization results are sent to the multi-condition constraint analysis module, which analyzes the preliminary strategy provided by the discharge optimization prediction control module to ensure that all condition constraints are met, and then generates the final discharge strategy, and then passes the strategy to the control execution and feedback module. The control execution and feedback module executes the control command and monitors the system response in real time according to the final strategy generated by the multi-condition constraint analysis module, and returns the feedback information to the dynamic prediction module for further optimization. In the dynamic prediction module, the predicted value of the future state is generated by combining the collected input data with the prediction model algorithm formula, and the prediction result is passed to the discharge optimization prediction control module and the multi-condition constraint analysis module. The prediction model algorithm formula is:

[0007]

[0008] y(t)=E·x(t)+F·u(t)

[0009] Where: t is the current time, k is the number of future time steps, is the system state vector predicted at the current time t for the next k time steps, A is the state matrix obtained by the system identification method, A k is the state matrix of the next k time steps, x(t) is the system state vector at the current time t, obtained from the real-time collected data, j is the future cumulative time step index, A j is the state matrix of the future,th time step, B is the input matrix, u(t+j) is the control input vector at the future time step (t+j), D is the disturbance matrix, d(t+j) is the disturbance vector at the future time step (t+j), y(t) is the system output vector, E is the output matrix, F is the influence matrix from input to output, and u(t) is the input vector of the system.

[0010] As a further solution of the present invention, in the multi-condition constraint analysis module, the system state vector at the current time t is decomposed into the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current time. The system state vector expression at the current time t is:

[0011] x(t)=[x1(t)x2(t)x3(t)x4(t)x5(t)x6(t)x7(t)x8(t)] T

[0012] Among them: x1(t), x2(t), x3(t), x4(t), x5(t), x6(t), x7(t), x8(t) are respectively the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current moment, and T is the transposition symbol.

[0013] As a further solution of the present invention, in the multi-condition constraint analysis module, the specific composition of the system state vector decomposition vector is: x1(t)=[V b1 (t)V b2 (t)...V bm (t)R int (t)] T

[0014] Where: V b1 (t), V b2 (t),…,V bm(t) is the output voltage of each energy storage battery pack, m is the number of energy storage battery packs, R int (t) is the internal resistance of the energy storage battery;

[0015] x2(t)=[I b1 (t)I b2 (t)...I bm (t)I load (t)] T

[0016] Where: I b1 (t), I b2 (t),…,I bm (t) is the discharge current of each energy storage battery pack, I load (t) is the total load current of the system;

[0017] x3(t)=[T b1 (t)T b2 (t)...T bm (t)T env (t)] T

[0018] Where: T b1 (t), T b2 (t),…,T bm (t) is the temperature of each energy storage battery pack, T env (t) is the ambient temperature;

[0019] x4(t)=[S b1 (t)S b2 (t)...S bm (t)] T

[0020] Where: S b1 (t), S b2 (t),…,Sb m( t) The state of charge of each energy storage battery pack;

[0021] x5(t)=[R ins (t)V s (t)] T

[0022] Where: R ins (t) is the insulation resistance of the power supply busbar, V s (t) is the voltage stability index of the power supply busbar;

[0023] x6(t)=[R int (t)N cycle (t)F hist ] T

[0024] Where: N cycle (t) is the number of charge and discharge cycles of the energy storage battery, F hist Historical fault records of the battery;

[0025] x7(t)=[P demand (t)P avg (t)] T

[0026] Where: P demand (t) is the instantaneous required power, P avg (t) is the average required power;

[0027] x8(t)=[T env (t)H env (t)V env (t)] T

[0028] Where: H env (t) is the ambient humidity, V env (t) is the ambient vibration state.

[0029] As a further solution of the present invention, in the multi-condition constraint analysis module, the preliminary discharge strategy is received from the discharge optimization prediction control module, and the constraint conditions are analyzed using a preset constraint formula to ensure that the preliminary discharge strategy meets the system requirements under all constraint conditions. The preset constraint formula is:

[0030]

[0031] Where i is the index of the constraint condition, z is the index of the decomposition variable of each system state variable, and F i (u) is the function of the i-th constraint condition, representing multiple decomposition state variables The combination result of the control input u, m i is the number of state variables associated with the ith constraint, λ iz is the weight coefficient of the zth variable in the ith constraint to the constraint, is the zth state variable Mathematical function related to control input u, C i is the limit value of the i-th constraint.

[0032] As a further solution of the present invention, in the multi-condition constraint analysis module, when used to describe the effect of temperature on the internal resistance of the energy storage battery, The formula is:

[0033]

[0034] Where: a iz 、b iz 、c iz For adjustment The adjustment parameters of the function shape and amplitude are set according to the requirements;

[0035] When used to describe the impact of load fluctuations and environmental factors on system status, The formula is:

[0036]

[0037] Where: d iz , g iz is the amplitude adjustment coefficient, e iz 、f iz h is the parameter used to control the frequency and phase of the periodic influence of the state variable. iz is a parameter for adjusting the periodic effect of the control input on the adjustment frequency;

[0038] When used to describe the gradual decrease in discharge efficiency during the life decay of energy storage batteries, The formula is:

[0039]

[0040] Where: v iz ,ω iz , iz , To adjust the parameters, set them according to actual needs.

[0041] As a further solution of the present invention, in the discharge optimization prediction control module, according to the prediction data provided by the dynamic prediction module, based on the set discharge optimization objective function, the discharge strategy of the energy storage battery is calculated and optimized to minimize the discharge error and the change of the control input, while meeting the system requirements, maximizing the efficiency and life of the energy storage battery, generating a preliminary discharge strategy, and then passing the preliminary discharge strategy to the multi-condition constraint analysis module. The formula of the discharge optimization objective function is:

[0042]

[0043] Where: u is the discharge strategy of the energy storage battery, J MPC (u) is the objective function of predictive control, which represents the overall evaluation value of the discharge strategy u, N is the number of future time steps, is the system output vector predicted at time t for the next k time steps, y ref (t+k) is the reference output value of the system in the next k time steps, Q is the weight matrix of the output variable, To predict the output at the future time step (t+k) With reference output y ref (t+k), Δu(t+k-1) is the weighted square error between the control input and the control input at the future time step (t+k-1), R is the weight matrix for adjusting the relative importance of different control input variables, which is set according to the requirements. It is the weighted square value of the control input variable Δu(t+k-1).

[0044] As a further solution of the present invention, the discharge system unit includes a discharge switch, a battery management system, an energy storage battery, a fuse and a power supply busbar. The upstream of the energy storage battery is connected to the discharge switch through the battery management system, and the downstream of the energy storage battery is connected to the fuse and the power supply busbar through the discharge switch. The battery management system is connected to the energy storage battery discharge central processing module.

[0045] As a further solution of the present invention, the data and instruction processing unit includes a power supply busbar diagnosis collection unit, a battery health diagnosis collection unit, an energy management joint control signal unit, and a system start and stop instruction unit. The power supply busbar diagnosis collection unit collects relevant data of the power supply busbar and transmits the collected data to the energy storage battery discharge central processing module. The battery health diagnosis collection unit is connected to the battery management system of the discharge system unit through the bus communication module to collect data related to the output voltage, discharge current, temperature, state of charge and health status of the energy storage battery, and transmits the collected data to the energy storage battery discharge central processing module. The energy management joint control signal unit is connected to the energy management system of the energy consumption system through the bus communication module to receive real-time power demand data and transmit these signals to the energy storage battery discharge central processing module unit. The system start and stop instruction unit receives start and stop instructions from the energy consumption system and transmits these instructions to the energy storage battery discharge central processing module unit.

[0046] A multi-condition constrained energy storage battery discharge control method, using the above-mentioned multi-condition constrained energy storage battery discharge control system, comprises the following steps:

[0047] Step 1, data collection: when the system starts, initialize each module in the system and collect the initial state data of the system in real time;

[0048] Step 2, dynamic prediction and state monitoring: predict the system state within the future set time step based on the real-time collected data, and pass the predicted system state vector to the discharge optimization prediction control module and the multi-condition constraint analysis module;

[0049] Step 3, calculate the discharge optimization strategy: the discharge optimization prediction control module calculates the preliminary discharge strategy based on the prediction data provided by the dynamic prediction module, maximizes the efficiency and life of the energy storage battery while meeting the system requirements, generates a preliminary discharge strategy, and passes the preliminary discharge strategy to the multi-condition constraint analysis module;

[0050] Step 4, multi-condition constraint analysis and strategy adjustment: The multi-condition constraint analysis module conducts a detailed analysis of the preliminary discharge strategy to ensure that the preliminary discharge strategy meets all the constraints set by the system. If the preliminary discharge strategy does not meet the constraints, the strategy is adjusted until all the constraints are met, and the final generated discharge strategy is passed to the control execution and feedback module;

[0051] Step 5, strategy execution and feedback adjustment: The control execution and feedback module executes the discharge strategy according to the final strategy generated by the multi-condition constraint analysis module, adjusts the operating status of the discharge switch and the battery management system, monitors the system status data in real time, ensures that the system operates according to the set strategy, and transmits the system's real-time feedback information back to the dynamic prediction module to further optimize the next discharge strategy;

[0052] Step 6, loop iteration and system optimization: loop through steps 1 to 5, and after each time step, dynamically adjust and optimize the discharge strategy based on new real-time data and feedback information;

[0053] Step 7, exception handling and safety protection: When an abnormal situation is detected, the system triggers the safety protection mechanism, immediately disconnects the abnormal circuit through the fuse and discharge switch, protects the energy storage battery, sends alarm information to the system operator and monitoring platform, and records the fault data.

[0054] As a further solution of the present invention, the abnormal situation and protection mechanism in step 7 include:

[0055] If the insulation value of the power supply busbar is greater than the safety insulation value, the discharge switch is allowed to close, otherwise the discharge switch is disconnected;

[0056] If the power supply busbar voltage is within the allowable discharge voltage range provided by the energy storage battery manufacturer, the discharge switch is allowed to be connected to the energy storage battery, otherwise the discharge switch is disconnected from the energy storage battery;

[0057] If the discharge voltage, discharge current, resistance and temperature of the energy storage battery are abnormal, the discharge switch and the energy storage battery are disconnected;

[0058] If the charging and discharging status of the energy storage battery is abnormal, the path between the discharge switch and the energy storage battery is disconnected;

[0059] If the health status of the energy storage battery is abnormal, the discharge switch and the energy storage battery are disconnected;

[0060] If the power of the energy storage battery is lower than the power value set for discharge, the discharge switch and the energy storage battery are disconnected. The power of the energy storage battery = rated capacity of the battery pack × current battery voltage / rated voltage;

[0061] If the start / stop command of the diagnostic system is invalid, the path between the discharge switch and the energy storage battery is disconnected;

[0062] If the diagnostic energy management joint control signal is invalid, the path between the discharge switch and the energy storage battery is disconnected.

[0063] Compared with the prior art, the technical effects of the present invention are as follows: the present invention predicts the future system state based on real-time data through a dynamic prediction module, and provides an accurate discharge strategy reference; the discharge optimization prediction control module generates the optimal discharge strategy through an optimization algorithm, ensuring that the battery life is maximized while the power resources are efficiently utilized; the multi-condition constraint analysis module ensures that the discharge strategy can meet the system requirements in various environments through strict condition constraint analysis, avoiding excessive discharge or waste of resources; finally, the control execution and feedback module dynamically adjusts the discharge strategy through real-time monitoring and feedback, ensuring the stability and reliability of the system in complex environments; overall, the system significantly improves the energy supply efficiency and safety of energy storage batteries in complex and changeable environments, extends the life of the equipment, and improves the overall stability and responsiveness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a system block diagram of the present invention;

[0065] Figure 2 The present invention is a flowchart of the method. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the present invention to clearly and completely describe the technical solutions in the present invention. Obviously, the described content is only a part of the present invention rather than the whole. Based on the content of the present invention, all other technical solutions obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] like Figure 1As shown, a multi-condition constrained energy storage battery discharge control system proposed by the present invention includes a discharge system unit, a data and instruction processing unit, and an energy storage battery discharge central processing module unit. The discharge system unit is connected to the data and instruction processing unit and the energy storage battery discharge central processing module unit. The energy storage battery discharge central processing module includes a dynamic prediction module, a discharge optimization prediction control module, a multi-condition constraint analysis module and a control execution and feedback module. The dynamic prediction module collects and processes real-time data from the data and instruction processing unit, and transmits the predicted system state to the discharge optimization prediction control module and the multi-condition constraint analysis module. The discharge optimization prediction control module receives the predicted data of the dynamic prediction module and calculates the optimal discharge strategy. The preliminary optimization results are sent to the multi-condition constraint analysis module. The multi-condition constraint analysis module analyzes the preliminary strategy provided by the discharge optimization prediction control module to ensure that all condition constraints are met, and then generates the final discharge strategy, and then passes the strategy to the control execution and feedback module. The control execution and feedback module executes the control command and monitors the system response in real time according to the final strategy generated by the multi-condition constraint analysis module, and returns the feedback information to the dynamic prediction module for further optimization. In the dynamic prediction module, the predicted value of the future state is generated by combining the collected input data with the prediction model algorithm formula, and the prediction result is passed to the discharge optimization prediction control module and the multi-condition constraint analysis module. The prediction model algorithm formula is:

[0068]

[0069] y(t)=E·x(t)+F·u(t)

[0070] Where: t is the current time, k is the number of future time steps, is the system state vector predicted at the current time t for the next k time steps, A is the state matrix obtained by the system identification method, A k is the state matrix of the next k time steps, x(t) is the system state vector at the current time t, obtained from the real-time collected data, j is the future cumulative time step index, A j is the state matrix of the jth time step in the future, B is the input matrix, u(t+j) is the control input vector at the future time step (t+j), D is the disturbance matrix, d(t+j) is the disturbance vector at the future time step (t+j), y(t) is the system output vector, E is the output matrix, F is the input to output influence matrix, and u(t) is the input vector of the system.

[0071] The present invention uses a dynamic prediction module to predict the future system state based on real-time data and provide an accurate discharge strategy reference. The discharge optimization prediction control module generates the optimal discharge strategy through an optimization algorithm to ensure that the battery life is maximized while the power resources are efficiently utilized. The multi-condition constraint analysis module uses strict condition constraint analysis to ensure that the discharge strategy can meet the system requirements in various environments and avoid excessive discharge or waste of resources. Finally, the control execution and feedback module dynamically adjusts the discharge strategy through real-time monitoring and feedback to ensure the stability and reliability of the system in complex environments. Overall, the system significantly improves the energy supply efficiency and safety of energy storage batteries in complex and changeable environments, extends the life of the equipment, and improves the overall stability and responsiveness of the system.

[0072] It should be noted that in the multi-condition constraint analysis module, the system state vector at the current time t is decomposed into the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current time. The system state vector expression at the current time t is:

[0073] x(t)=[x1(t)x2(t)x3(t)x4(t)x5(t)x6(t)x7(t)x8(t)] T

[0074] Among them: x1(t), x2(t), x3(t), x4(t), x5(t), x6(t), x7(t), x8(t) are respectively the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current moment, and T is the transposition symbol.

[0075] In the multi-condition constraint analysis module, the current system state vector is decomposed into specific energy storage battery output voltage, discharge current, temperature, state of charge, power bus status, health status, real-time power demand and environmental status, so that the system can more accurately evaluate and monitor each key parameter. This fine-grained state decomposition can more effectively identify potential risks, optimize discharge strategies, and ensure that in a changing environment, the system can dynamically adjust and meet all constraints to avoid over-discharge, waste of resources and equipment damage, thereby improving the reliability, efficiency and safety of the system.

[0076] It should be noted that in the multi-condition constraint analysis module, the specific composition of the system state vector decomposition vector is: x1(t) = [V b1 (t)V b2 (t)...V bm (t)R int (t)]T

[0077] Where: V b1 (t), V b2 (t),…,V bm (t) is the output voltage of each energy storage battery pack, m is the number of energy storage battery packs, R int (t) is the internal resistance of the energy storage battery;

[0078] x2(t)=[I b1 (t)I b2 (t)...I bm (t)I load (t)] T

[0079] Where: I b1 (t), I b2 (t),…,I bm (t) is the discharge current of each energy storage battery pack, I load (t) is the total load current of the system;

[0080] x3(t)=[T b1 (t)T b2 (t)...T bm (t)T env (t)] T

[0081] Where: T b1 (t), T b2 (t),…,T bm (t) is the temperature of each energy storage battery pack, T env (t) is the ambient temperature;

[0082] x4(t)=[S b1 (t)S b2 (t)...S bm (t)] T

[0083] Where: S b1 (t), S b2 (t),…,S bm (t) The state of charge of each energy storage battery pack;

[0084] x5(t)=[R ins (t)V s (t)] T

[0085] Where: R ins (t) is the insulation resistance of the power supply busbar, V s (t) is the voltage stability index of the power supply busbar;

[0086] x6(t)=[R int (t)N cycle (t)F hist ] T

[0087] Where: N cycle (t) is the number of charge and discharge cycles of the energy storage battery, F hist Historical fault records of the battery;

[0088] x7(t)=[P demand (t)P avg (t)] T

[0089] Where: P demand (t) is the instantaneous required power, P avg (t) is the average required power;

[0090] x8(t)=[T env (t)H env (t)V env (t)] T

[0091] Where: H env (t) is the ambient humidity, V env (t) is the ambient vibration state.

[0092] In the multi-condition constraint analysis module, the system state vector is subdivided into specific fine-grained variables such as voltage, current, temperature, state of charge, power busbar status, health status, power demand and environmental conditions, which helps to comprehensively and accurately monitor the operating status of energy storage batteries and their surrounding systems. This refined state decomposition enables the system to identify and evaluate the specific performance of each energy storage battery pack, such as voltage imbalance, temperature anomaly, state of charge difference, etc., and respond to these changes in a timely manner to optimize the discharge strategy. By monitoring the insulation resistance and voltage stability of the power busbar, the system can detect and prevent potential electrical faults in advance. In addition, considering health parameters such as the number of charge and discharge cycles and historical fault records can help extend battery life and prevent excessive aging and unexpected failures. The decomposition analysis of instantaneous demand power and average power can better match actual demand with power supply capacity and avoid system instability caused by power fluctuations. Monitoring of external environmental conditions such as temperature, humidity and vibration allows the system to dynamically adjust the discharge strategy to cope with the impact of external conditions on battery performance. Overall, this decomposition and detailed analysis of the state vector significantly improves the system's resilience and control accuracy, and improves the operating efficiency, safety, and reliability of the energy storage battery system in complex and changing environments.

[0093] It should be noted that in the multi-condition constraint analysis module, the preliminary discharge strategy is received from the discharge optimization prediction control module, and the constraint conditions are analyzed using the preset constraint formula to ensure that the preliminary discharge strategy meets the system requirements under all constraint conditions. The preset constraint formula is:

[0094]

[0095] Where i is the index of the constraint condition, z is the index of the decomposition variable of each system state variable, and F i (u) is the function of the i-th constraint condition, representing multiple decomposition state variables The combination result of the control input u, m i is the number of state variables associated with the ith constraint, λ iz is the weight coefficient of the zth variable in the ith constraint to the constraint, is the zth state variable Mathematical function related to control input u, C i is the limit value of the i-th constraint.

[0096] In the multi-condition constraint analysis module, the preliminary discharge strategy is received from the discharge optimization prediction control module and analyzed using the preset constraint formula to ensure that the preliminary discharge strategy meets the system requirements under all constraint conditions. This method brings significant technical effects. Specifically, by using the preset constraint formula, the system can fully consider the multiple constraints related to each state variable. The weight coefficient λ in the formula iz and state variables The combination of enables the system to flexibly adjust the degree of emphasis on different constraints, ensuring that in complex and changing environments, the system can more accurately control the discharge strategy of each power source, ensuring that the discharge strategy not only meets the current power demand, but also takes into account future environmental changes and power source status, and optimizes the overall performance by dynamically adjusting each constraint. Preset mathematical functions It can describe complex physical and electrical relationships, allowing the system to more accurately predict possible abnormal situations and make adjustments in advance to avoid problems such as excessive battery discharge and overheating. In general, this multi-condition constraint analysis can significantly improve the robustness and responsiveness of the system, ensuring efficient and stable operation under extreme conditions. At the same time, it extends the battery life, reduces unnecessary resource consumption, and improves the overall safety and reliability of the system by optimizing the discharge strategy.

[0097] It should be noted that in the multi-condition constraint analysis module, when used to describe the effect of temperature on the internal resistance of the energy storage battery, The formula is:

[0098]

[0099] Where: a iz 、b iz 、c iz For adjustment The adjustment parameters of the function shape and amplitude are set according to the requirements;

[0100] When used to describe the impact of load fluctuations and environmental factors on system status, The formula is:

[0101]

[0102] Where: d iz , g iz is the amplitude adjustment coefficient, e iz 、f iz h is the parameter used to control the frequency and phase of the periodic influence of the state variable. iz is a parameter for adjusting the periodic effect of the control input on the adjustment frequency;

[0103] When used to describe the gradual decrease in discharge efficiency during the life decay of energy storage batteries, The formula is:

[0104]

[0105] Where: v iz ,ω iz , iz , To adjust the parameters, set them according to actual needs.

[0106] The exponential function is used to accurately describe the effect of temperature changes on the internal resistance of the energy storage battery. As the temperature changes, the internal resistance may increase or decrease significantly, directly affecting the discharge efficiency and life of the battery. By introducing this formula, the system can dynamically adjust the discharge strategy to avoid over-discharge or reduced efficiency due to excessive internal resistance, thereby extending the battery life and maintaining the stability of the system. By using sine and cosine functions, the formula can capture the periodic effects of load fluctuations and environmental factors (such as humidity, vibration, etc.) on the system state. The system can predict and adjust the discharge strategy based on these functions to reduce the instability caused by load fluctuations and environmental changes, thereby improving the response speed and adaptability of the system and ensuring stable power supply in complex environments. Through the logarithmic function, the formula effectively describes the process of gradually reducing the discharge efficiency as the energy storage battery life decays. This function form can reflect the nonlinear effect of battery aging on discharge performance, enabling the system to predict the performance degradation of the battery in advance, optimize the discharge strategy, prevent excessive use of aging batteries, and further improve the reliability and economy of the system.

[0107] It should be noted that in the discharge optimization prediction control module, according to the prediction data provided by the dynamic prediction module, based on the set discharge optimization objective function, the discharge strategy of the energy storage battery is calculated and optimized to minimize the discharge error and the change of the control input. While meeting the system requirements, the efficiency and life of the energy storage battery are maximized, and a preliminary discharge strategy is generated. The preliminary discharge strategy is then passed to the multi-condition constraint analysis module. The formula of the discharge optimization objective function is:

[0108]

[0109] Where: u is the discharge strategy of the energy storage battery, J MPC (u) is the objective function of predictive control, which represents the overall evaluation value of the discharge strategy u, N is the number of future time steps, is the system output vector predicted at time t for the next k time steps, y ref (t+k) is the reference output value of the system in the next k time steps, Q is the weight matrix of the output variable, To predict the output at the future time step (t+k) With reference output y ref (t+k), Δu(t+k-1) is the weighted square error between the control inputs at the future time step (t+k-1), and R is the weight matrix for adjusting the relative importance of different control input variables. It is set according to the requirements. It is the weighted square value of the control input variable Δu(t+k-1).

[0110] In the discharge optimization prediction control module, based on the set discharge optimization objective function J MPC (u) is used to calculate and optimize the discharge strategy of the energy storage battery. This method has brought significant technical results. The objective function comprehensively considers the error between the system output and the reference value in the future time step and the change in the control input, with the goal of minimizing the discharge error and the change in the control input, thereby achieving more accurate discharge control. Specifically, the weighted square error term in the objective function is By adjusting the weights of the Q matrix, certain key output variables can be prioritized to ensure that the system output can closely track the reference value, thereby improving the accuracy of the discharge strategy and the flexibility of the system response. By adjusting the weights of the R matrix, the large fluctuations in the control input can be suppressed, the system instability caused by frequent or drastic adjustments can be avoided, and the service life of the energy storage battery and related equipment can be extended. This optimization method not only meets the real-time power requirements of the system, but also maximizes the efficiency and life of the energy storage battery, ensuring stable operation in various complex environments. By generating a preliminary discharge strategy and passing it to the multi-condition constraint analysis module, the system can be further optimized under multi-condition constraints, ultimately achieving safer and more efficient power management.

[0111] It should be noted that the discharge system unit includes a discharge switch, a battery management system, an energy storage battery, a fuse and a power supply busbar. The upstream of the energy storage battery is connected to the discharge switch through the battery management system, and the downstream of the energy storage battery is connected to the fuse and the power supply busbar through the discharge switch. The battery management system is connected to the energy storage battery discharge central processing module.

[0112] It should be noted that the data and instruction processing unit includes a power supply busbar diagnosis collection unit, a battery health diagnosis collection unit, an energy management joint control signal unit, and a system start and stop instruction unit. The power supply busbar diagnosis collection unit collects relevant data of the power supply busbar, and transmits the collected data to the energy storage battery discharge central processing module. The battery health diagnosis collection unit is connected to the battery management system of the discharge system unit through the bus communication module, collects data related to the output voltage of the energy storage battery, the discharge current of the energy storage battery, the temperature of the energy storage battery, the state of charge of the energy storage battery, and the health status of the energy storage battery, and transmits the collected data to the energy storage battery discharge central processing module. The energy management joint control signal unit is connected to the energy management system of the energy consumption system through the bus communication module, receives real-time power demand data, and then transmits these signals to the energy storage battery discharge central processing module unit. The system start and stop instruction unit receives start and stop instructions from the energy consumption system, and transmits these instructions to the energy storage battery discharge central processing module unit.

[0113] like Figure 2 The present invention provides a multi-condition constrained energy storage battery discharge control method, comprising the following steps:

[0114] Step 1, data collection: when the system starts, initialize each module in the system and collect the initial state data of the system in real time;

[0115] Step 2, dynamic prediction and state monitoring: predict the system state within the future set time step based on the real-time collected data, and pass the predicted system state vector to the discharge optimization prediction control module and the multi-condition constraint analysis module;

[0116] Step 3, calculate the discharge optimization strategy: the discharge optimization prediction control module calculates the preliminary discharge strategy based on the prediction data provided by the dynamic prediction module, maximizes the efficiency and life of the energy storage battery while meeting the system requirements, generates a preliminary discharge strategy, and passes the preliminary discharge strategy to the multi-condition constraint analysis module;

[0117] Step 4, multi-condition constraint analysis and strategy adjustment: The multi-condition constraint analysis module conducts a detailed analysis of the preliminary discharge strategy to ensure that the preliminary discharge strategy meets all the constraints set by the system. If the preliminary discharge strategy does not meet the constraints, the strategy is adjusted until all the constraints are met, and the final generated discharge strategy is passed to the control execution and feedback module;

[0118] Step 5, strategy execution and feedback adjustment: The control execution and feedback module executes the discharge strategy according to the final strategy generated by the multi-condition constraint analysis module, adjusts the operating status of the discharge switch and the battery management system, monitors the system status data in real time, ensures that the system operates according to the set strategy, and transmits the system's real-time feedback information back to the dynamic prediction module to further optimize the next discharge strategy;

[0119] Step 6, loop iteration and system optimization: loop through steps 1 to 5, and after each time step, dynamically adjust and optimize the discharge strategy based on new real-time data and feedback information;

[0120] Step 7, exception handling and safety protection: When an abnormal situation is detected, the system triggers the safety protection mechanism, immediately disconnects the abnormal circuit through the fuse and discharge switch, protects the energy storage battery, sends alarm information to the system operator and monitoring platform, and records the fault data.

[0121] This multi-condition constrained energy storage battery discharge control method significantly improves the stability and responsiveness of the system through a series of steps, including data acquisition, dynamic prediction, optimization strategy calculation, constraint analysis and adjustment, strategy execution and feedback adjustment, loop iteration, exception handling and safety protection. Based on real-time monitoring and prediction, this method dynamically optimizes the discharge strategy to ensure that the energy storage battery operates efficiently and safely in a complex environment. Through multi-condition constraint analysis, it is ensured that the discharge strategy meets all system requirements and maximizes battery efficiency and life. The introduction of the exception handling mechanism provides important safety protection for the system, preventing fault expansion and protecting key equipment.

[0122] It should be noted that the abnormal situations and protection mechanisms in step 7 include:

[0123] If the insulation value of the power supply busbar is greater than the safety insulation value, the discharge switch is allowed to close, otherwise the discharge switch is disconnected;

[0124] If the power supply busbar voltage is within the allowable discharge voltage range provided by the energy storage battery manufacturer, the discharge switch is allowed to be connected to the energy storage battery, otherwise the discharge switch is disconnected from the energy storage battery;

[0125] If the discharge voltage, discharge current, resistance and temperature of the energy storage battery are abnormal, the discharge switch and the energy storage battery are disconnected;

[0126] If the charging and discharging status of the energy storage battery is abnormal, the path between the discharge switch and the energy storage battery is disconnected;

[0127] If the health status of the energy storage battery is abnormal, the discharge switch and the energy storage battery are disconnected;

[0128] If the power of the energy storage battery is lower than the power value set for discharge, the discharge switch and the energy storage battery are disconnected. The power of the energy storage battery = rated capacity of the battery pack × current battery voltage / rated voltage;

[0129] If the start / stop command of the diagnostic system is invalid, the path between the discharge switch and the energy storage battery is disconnected;

[0130] If the diagnostic energy management joint control signal is invalid, the path between the discharge switch and the energy storage battery is disconnected.

[0131] In the abnormal situation and protection mechanism of step 7, by real-time monitoring and diagnosis of key parameters such as the insulation value of the power supply busbar, voltage range, energy storage battery charge and discharge status, health status, power, system start and stop instructions, and energy management signals, the system can immediately take protective measures when abnormal situations occur. These measures include cutting off the connection between the energy storage battery and the discharge circuit to ensure that discharge operations are prevented under any unsafe conditions, thereby avoiding equipment damage, system failures or safety accidents. Through this mechanism, the system significantly improves the safety and stability of operation, ensuring that the energy storage battery can work reliably and efficiently in a complex and changing environment, while extending the battery life and reducing potential waste of power resources and the occurrence of unexpected failures.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0133] Finally: The above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-condition constrained energy storage battery discharge control system, characterized in that: It includes a discharge system unit, a data and instruction processing unit, and a storage battery discharge central processing module unit. The discharge system unit is connected to the data and instruction processing unit and the storage battery discharge central processing module unit. The storage battery discharge central processing module includes a dynamic prediction module, a discharge optimization prediction control module, a multi-condition constraint analysis module, and a control execution and feedback module. The dynamic prediction module collects and processes real-time data from the data and instruction processing unit, and passes the predicted system state to the discharge optimization prediction control module and the multi-condition constraint analysis module. The discharge optimization prediction control module receives the predicted data of the dynamic prediction module, calculates the optimal discharge strategy, and sends the preliminary optimization results to the control execution and feedback module. The multi-condition constraint analysis module analyzes the preliminary strategy provided by the discharge optimization prediction control module, ensures that all condition constraints are met, generates the final discharge strategy, and then passes the strategy to the control execution and feedback module. The control execution and feedback module executes the control command and monitors the system response in real time according to the final strategy generated by the multi-condition constraint analysis module, and returns the feedback information to the dynamic prediction module for further optimization. In the dynamic prediction module, the predicted value of the future state is generated by combining the collected input data with the prediction model algorithm formula, and the prediction result is passed to the discharge optimization prediction control module and the multi-condition constraint analysis module. The prediction model algorithm formula is: y(t)=E·x(t)+F·u(t) Where: t is the current time, k is the number of future time steps, is the system state vector predicted at the current time t for the next k time steps, A is the state matrix obtained by the system identification method, A k is the state matrix of the next k time steps, x(t) is the system state vector at the current time t, obtained from the real-time collected data, j is the future cumulative time step index, A j is the state matrix of the jth time step in the future, B is the input matrix, u(t+j) is the control input vector at the future time step (t+j), D is the disturbance matrix, d(t+j) is the disturbance vector at the future time step (t+j), y(t) is the system output vector, E is the output matrix, F is the input to output influence matrix, and u(t) is the input vector of the system.

2. A multi-condition constrained energy storage battery discharge control system according to claim 1, characterized in that: In the multi-condition constraint analysis module, the system state vector at the current time t is decomposed into the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current time. The system state vector expression at the current time t is: x(t)=[x1(t)x2(t)x3(t)x4(t)x5(t)x6(t)x7(t)x8(t)] T Among them: x1(t), x2(t), x3(t), x4(t), x5(t), x6(t), x7(t), x8(t) are respectively the energy storage battery output voltage, energy storage battery discharge current, energy storage battery temperature, energy storage battery charge state, power supply busbar state, energy storage battery health state, real-time power demand and real-time environmental state at the current moment, and T is the transposition symbol.

3. A multi-condition constrained energy storage battery discharge control system according to claim 2, characterized in that: In the multi-condition constraint analysis module, the specific composition of the system state vector decomposition vector is: x1(t)=[V b1 (t) V b2 (t) ... V bm (t) R int (t)] T Where: V b1 (t), V b2 (t),…,V bm (t) is the output voltage of each energy storage battery pack, m is the number of energy storage battery packs, R int (t) is the internal resistance of the energy storage battery; x2(t)=[I b1 (t) I b2 (t) ... I bm (t) I load (t)] T Where: I b1 (t), I b2 (t),…,I bm (t) is the discharge current of each energy storage battery pack, I load (t) is the total load current of the system; x3(t)=[T b1 (t) T b2 (t) ... T bm (t) T env (t)] T Where: T b1 (t), T b2 (t),…,T bm (t) is the temperature of each energy storage battery pack, T env (t) is the ambient temperature; x4(t)=[S b1 (t) S b2 (t) ... S bm (t)] T Where: S b1 (t), S b2 (t),…,S bm (t) The state of charge of each energy storage battery pack; x5(t)=[R ins (t) V s (t)] T Where: R ins (t) is the insulation resistance of the power supply busbar, V s (t) is the voltage stability index of the power supply busbar; x6(t)=[R int (t) N cycle (t) F hist ] T Where: N cycle (t) is the number of charge and discharge cycles of the energy storage battery, F hist Historical fault records of the battery; x7(t)=[P demand (t) P avg (t)] T Where: P demand (t) is the instantaneous required power, P avg (t) is the average required power; x8(t)=[T env (t) H env (t) V env (t)] T Where: H env (t) is the ambient humidity, V env (t) is the ambient vibration state.

4. A multi-condition constrained energy storage battery discharge control system according to claim 3, characterized in that: In the multi-condition constraint analysis module, the preliminary discharge strategy is received from the discharge optimization prediction control module, and the constraint conditions are analyzed using the preset constraint formula to ensure that the preliminary discharge strategy meets the system requirements under all constraint conditions. The preset constraint formula is: Where i is the index of the constraint condition, z is the index of the decomposition variable of each system state variable, and F i (u) is the function of the i-th constraint condition, representing multiple decomposition state variables The combination result of the control input u, m i is the number of state variables associated with the ith constraint, λ iz is the weight coefficient of the zth variable in the ith constraint to the constraint, is the zth state variable Mathematical function related to control input u, C i is the limit value of the i-th constraint.

5. A multi-condition constrained energy storage battery discharge control system according to claim 4, characterized in that: In the multi-condition constraint analysis module, when used to describe the effect of temperature on the internal resistance of the energy storage battery, The formula is: Where: a iz , b iz 、c iz For adjustment The adjustment parameters of the function shape and amplitude are set according to the requirements; When used to describe the impact of load fluctuations and environmental factors on system status, The formula is: Where: d iz , g iz is the amplitude adjustment coefficient, e iz 、f iz h is the parameter used to control the frequency and phase of the periodic influence of the state variable. iz is a parameter for adjusting the periodic effect of the control input on the adjustment frequency; When used to describe the gradual decrease in discharge efficiency during the life decay of energy storage batteries, The formula is: Where: v iz ,ω iz , iz , To adjust the parameters, set them according to actual needs.

6. A multi-condition constrained energy storage battery discharge control system according to claim 4, characterized in that: In the discharge optimization prediction control module, according to the prediction data provided by the dynamic prediction module, based on the set discharge optimization objective function, the discharge strategy of the energy storage battery is calculated and optimized to minimize the discharge error and the change of the control input. While meeting the system requirements, the efficiency and life of the energy storage battery are maximized, and a preliminary discharge strategy is generated. Then, the preliminary discharge strategy is passed to the multi-condition constraint analysis module. The formula of the discharge optimization objective function is: Where: u is the discharge strategy of the energy storage battery, J MPc (u) is the objective function of predictive control, which represents the overall evaluation value of the discharge strategy u, N is the number of future time steps, is the system output vector predicted at time t for the next k time steps, y ref (t+k) is the reference output value of the system in the next k time steps, Q is the weight matrix of the output variable, To predict the output at the future time step (t+k) With reference output y ref (t+k), Δu(t+k-1) is the weighted square error between the control inputs at the future time step (t+k-1), and R is the weight matrix for adjusting the relative importance of different control input variables. It is set according to the requirements. It is the weighted square value of the control input variable Δu(t+k-1).

7. The multi-condition constrained energy storage battery discharge control system according to claim 1, characterized in that: The discharge system unit includes a discharge switch, a battery management system, an energy storage battery, a fuse and a power supply busbar. The upstream of the energy storage battery is connected to the discharge switch through the battery management system, and the downstream of the energy storage battery is connected to the fuse and the power supply busbar through the discharge switch. The battery management system is connected to the energy storage battery discharge central processing module.

8. The multi-condition constrained energy storage battery discharge control system according to claim 1, characterized in that: The data and instruction processing unit includes a power supply busbar diagnosis collection unit, a battery health diagnosis collection unit, an energy management joint control signal unit, and a system start and stop instruction unit. The power supply busbar diagnosis collection unit collects relevant data of the power supply busbar and transmits the collected data to the energy storage battery discharge central processing module. The battery health diagnosis collection unit is connected to the battery management system of the discharge system unit through the bus communication module to collect data related to the output voltage, discharge current, temperature, state of charge and health status of the energy storage battery, and transmits the collected data to the energy storage battery discharge central processing module. The energy management joint control signal unit is connected to the energy management system of the energy consumption system through the bus communication module to receive real-time power demand data and then transmit these signals to the energy storage battery discharge central processing module unit. The system start and stop instruction unit receives start and stop instructions from the energy consumption system and transmits these instructions to the energy storage battery discharge central processing module unit.

9. A multi-condition constrained energy storage battery discharge control method, which is applied to implement a multi-condition constrained energy storage battery discharge control system as described in any one of claims 1 to 8, characterized in that: The steps include: Step 1, data collection: when the system starts, initialize each module in the system and collect the initial state data of the system in real time; Step 2, dynamic prediction and state monitoring: predict the system state within the future set time step based on the real-time collected data, and pass the predicted system state vector to the discharge optimization prediction control module and the multi-condition constraint analysis module; Step 3, calculate the discharge optimization strategy: the discharge optimization prediction control module calculates the preliminary discharge strategy based on the prediction data provided by the dynamic prediction module, maximizes the efficiency and life of the energy storage battery while meeting the system requirements, generates a preliminary discharge strategy, and passes the preliminary discharge strategy to the multi-condition constraint analysis module; Step 4, multi-condition constraint analysis and strategy adjustment: The multi-condition constraint analysis module conducts a detailed analysis of the preliminary discharge strategy to ensure that the preliminary discharge strategy meets all the constraints set by the system. If the preliminary discharge strategy does not meet the constraints, the strategy is adjusted until all the constraints are met, and the final generated discharge strategy is passed to the control execution and feedback module; Step 5, strategy execution and feedback adjustment: The control execution and feedback module executes the discharge strategy according to the final strategy generated by the multi-condition constraint analysis module, adjusts the operating status of the discharge switch and the battery management system, monitors the system status data in real time, ensures that the system operates according to the set strategy, and transmits the system's real-time feedback information back to the dynamic prediction module to further optimize the next discharge strategy; Step 6, loop iteration and system optimization: loop through steps 1 to 5, and after each time step, dynamically adjust and optimize the discharge strategy based on new real-time data and feedback information; Step 7, exception handling and safety protection: When an abnormal situation is detected, the system triggers the safety protection mechanism, immediately disconnects the abnormal circuit through the fuse and discharge switch, protects the energy storage battery, sends alarm information to the system operator and monitoring platform, and records the fault data.

10. The multi-condition constrained energy storage battery discharge control system according to claim 9, characterized in that: The abnormal situations and protection mechanisms in step 7 include: If the insulation value of the power supply busbar is greater than the safety insulation value, the discharge switch is allowed to close, otherwise the discharge switch is disconnected; If the power supply busbar voltage is within the allowable discharge voltage range provided by the energy storage battery manufacturer, the discharge switch is allowed to be connected to the energy storage battery, otherwise the discharge switch is disconnected from the energy storage battery; If the discharge voltage, discharge current, resistance and temperature of the energy storage battery are abnormal, the discharge switch and the energy storage battery are disconnected; If the charging and discharging status of the energy storage battery is abnormal, the path between the discharge switch and the energy storage battery is disconnected; If the health status of the energy storage battery is abnormal, the discharge switch and the energy storage battery are disconnected; If the power of the energy storage battery is lower than the power value set for discharge, the discharge switch and the energy storage battery are disconnected. The power of the energy storage battery = rated capacity of the battery pack × current battery voltage / rated voltage; If the start / stop command of the diagnostic system is invalid, the path between the discharge switch and the energy storage battery is disconnected; If the diagnostic energy management joint control signal is invalid, the path between the discharge switch and the energy storage battery is disconnected.

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