Hybrid vehicle energy management strategy based on engine dynamic characteristic prediction
By constructing an engine dynamic characteristic prediction model and the DADMM method, the energy management strategy of hybrid vehicles in extreme environments was improved, the fuel economy and power output problems caused by changes in engine dynamic characteristics were solved, and stable operation in high-altitude environments was achieved.
Patent Information
- Application Number
- CN202511741286.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-25
- Publication Date
- 2025-12-26
AI Technical Summary
Existing energy management strategies fail to effectively consider the dynamic characteristics of engines under different environmental conditions, resulting in reduced fuel economy, impaired power output, and unstable operation of series hybrid electric vehicles in high-altitude environments.
The hybrid vehicle energy management strategy based on engine dynamic characteristic prediction constructs an engine transient load capacity prediction model and a transient fuel consumption calculation model, and combines the DADMM method to efficiently solve the energy management problem, update the constraints in real time, and improve control accuracy and response speed.
It improves the control accuracy and real-time power performance of hybrid vehicles in extreme environments, solves the convergence problem of nonlinear multi-objective optimization, and ensures stable operation of vehicles in high-altitude environments.
Smart Images
Figure CN121201019A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to energy matching technology, in particular to a hybrid vehicle energy management strategy based on engine dynamic characteristic prediction. BACKGROUND
[0002] With the development of science and technology, series hybrid electric vehicles are paid attention by many fields at home and abroad. There are multiple energy sources in series hybrid electric vehicles, so the energy management strategy plays a key role in coordinating the power distribution between the engine-generator set and the battery and ensuring the efficient and stable operation of the vehicle. What needs to be considered in particular is the plateau heavy series hybrid electric vehicle, which will face significant changes in altitude during long-distance transportation, accompanied by changes in atmospheric pressure. Changes in atmospheric pressure can cause significant changes in engine characteristics in plateau heavy series hybrid electric vehicles, thereby greatly reducing the effectiveness of energy management strategies.
[0003] At present, energy management strategies are divided into three categories: rule-based energy management strategies, learning-based energy management strategies, and optimization-based energy management strategies. Although these three types of energy management strategies have achieved certain results, they all have limitations. A common defect of these three types of energy management strategies is that they do not consider the dynamic characteristic changes of the engine under different environmental conditions, which can cause the series hybrid electric vehicle to have problems such as reduced fuel economy, damaged power output, and unstable operation when running. In particular, when the plateau heavy series hybrid electric vehicle runs in the plateau environment, the low air pressure and thin oxygen in this extreme condition can significantly reduce the engine intake performance, reduce the efficiency of the turbocharger, and affect the combustion stability, ultimately increasing the risk of operation instability and power output deficiency.
[0004] Therefore, in the prior art, there is no energy management strategy that considers the influence of external environment on the dynamic characteristics of the generator, has high control accuracy, fast response, and strong real-time performance. SUMMARY
[0005] Therefore, the main purpose of the present application is to provide a hybrid vehicle energy management strategy based on engine dynamic characteristic prediction, which considers the influence of external environment on the dynamic characteristics of the generator, has high control accuracy, strong real-time performance, and fast response.
[0006] In order to achieve the above purpose, the technical solution provided by the present application is as follows:
[0007] The hybrid vehicle energy management strategy based on engine dynamic characteristic prediction comprises the following steps:
[0008] Step 1, obtain the power characteristics and fuel consumption characteristics of the internal engine of the series hybrid vehicle under extreme low pressure environment and the change rule of atmospheric pressure.
[0009] Step 2, constructing engine transient load capacity prediction model and engine transient fuel consumption calculation model.
[0010] Step 3, obtaining the energy transmission matching rule between the front power chain and the rear power chain of the series hybrid vehicle, including: transmission efficiency and dynamic coordination characteristics between the front power chain and the rear power chain; the efficiency and dynamic response characteristics of the generator for converting mechanical torque into electric energy; the dynamic characteristics of the battery state of charge with the change of charging and discharging power; the efficiency and torque-speed response characteristics of the engine for converting electric energy into mechanical energy; the power demand characteristics of each auxiliary device; the real-time driving power demand characteristics based on driving intention.
[0011] Step 4, according to the energy transmission matching rule obtained in step 3, constructing the energy management strategy of the hybrid vehicle under the model predictive control framework: designing the total cost function based on the engine transient fuel consumption calculation model established in step 2, and generating the constraint condition sequence in the rolling optimization time domain under the predictive control framework based on the engine transient load capacity prediction model.
[0012] Step 5, solving the energy management problem in the rolling time domain under the predictive control framework by using the DADMM method, and updating the constraint sequence in real time.
[0013] In summary, the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction firstly obtains the power characteristics and fuel consumption characteristics of the internal engine of the series hybrid vehicle under the extreme low pressure environment and the change rule of the atmospheric pressure, introduces the external extreme low pressure environment factor into the power characteristics and fuel consumption characteristics, so that the control accuracy of the series hybrid vehicle is greatly improved; especially for heavy series hybrid vehicles, the improvement of the control accuracy is more significant. On this basis, the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction constructs the engine transient load capacity prediction model and the engine transient fuel consumption calculation model, converts the nonlinear multi-objective problem into a convex form, and solves the problem of uncertain convergence. The hybrid vehicle energy management strategy based on engine dynamic characteristic prediction also constructs the energy management strategy of the hybrid vehicle under the model predictive control framework according to the energy transmission matching rule between the front power chain and the rear power chain of the series hybrid vehicle, and solves the energy management problem efficiently by using the damping alternating direction multiplier method, improves the real-time performance and effectiveness of the hybrid vehicle, and the response is relatively fast, which is worth popularization and application. BRIEF DESCRIPTION OF DRAWINGS
[0014] Figure 1 It is the overall flowchart of the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction. DETAILED DESCRIPTION
[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and specific embodiments.
[0016] Figure 1 The overall flowchart of the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction is shown in the figure. As shown in the figure, the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction comprises the following steps: Figure 1
[0017] Step 1, obtaining the power characteristic and fuel consumption characteristic of the internal engine in the series hybrid vehicle under the extreme low pressure environment and the variation law of the atmospheric pressure.
[0018] Step 2, constructing the engine transient load capacity prediction model and the engine transient fuel consumption calculation model.
[0019] Step 3, obtaining the energy transmission matching law between the front power chain and the rear power chain of the series hybrid vehicle, including: the transmission efficiency and dynamic coordination characteristic between the front power chain and the rear power chain; the efficiency and dynamic response characteristic of the generator for converting mechanical torque into electric energy; the dynamic characteristic of the state of charge of the battery with the variation of the charging and discharging power; the efficiency and torque-speed response characteristic of the engine for converting electric energy into mechanical energy; the power demand characteristic of each auxiliary device; the real-time driving power demand characteristic based on the driving intention.
[0020] Step 4, according to the energy transmission matching law obtained in step 3, constructing the energy management strategy of the hybrid vehicle under the model predictive control framework: designing the total cost function based on the engine transient fuel consumption calculation model established in step 2, and simultaneously, predicting and generating the constraint condition sequence in the rolling optimization time domain under the predictive control framework based on the engine transient load capacity prediction model.
[0021] Step 5, using the DADMM (damped alternating direction method of multipliers) method to solve the energy management problem in the rolling time domain under the predictive control framework, and updating the constraint sequence in real time.
[0022] In summary, the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction firstly obtains the power characteristic and fuel consumption characteristic of the internal engine of the series hybrid vehicle under the extreme low pressure environment, and the external extreme low pressure environment factor is introduced into the power characteristic and fuel consumption characteristic, so that the control precision of the series hybrid vehicle is greatly improved; in particular, for heavy series hybrid vehicles, the improvement of the control precision is more significant. On this basis, the hybrid vehicle energy management strategy based on engine dynamic characteristic prediction constructs an engine transient load capacity prediction model and an engine transient fuel consumption calculation model, converts the nonlinear multi-objective optimization problem into a convex form, and solves the problem of uncertain convergence. The hybrid vehicle energy management strategy based on engine dynamic characteristic prediction also obtains the energy transmission matching rule between the front power chain and the rear power chain of the series hybrid vehicle, constructs the energy management strategy of the hybrid vehicle under the model predictive control framework, and solves the energy management problem efficiently by using the damping alternating direction multiplier method, improves the power real-time performance and effectiveness of the hybrid vehicle, and the response is relatively fast, and is worth popularization and application.
[0023] In step 2 of the application, the engine transient fuel consumption characteristic calculation model is as follows:
[0024] ;
[0025] Wherein, is a nonlinear mapping derived from the trained back propagation neural network model; the fuel injection amount of each cylinder per cycle , is the engine speed, is the accelerator pedal position of the engine, is the first mapping relationship; the mass flow of the cylinder , indicates the cylinder volume, indicates the number of working cycles of the crankshaft rotation, indicates the engine speed, is the thermodynamic constant, and the volume efficiency , indicates the second mapping relationship, indicates the cylinder intake temperature, indicates the cylinder intake pressure.
[0026] In step 2 of the application, the engine transient load capacity prediction model is as follows:
[0027]
[0028] Wherein, indicates the dynamic fuel consumption, Indicates the engine's output power; , , For adaptive fitting parameters, and , , This indicates the external atmospheric pressure under extremely low pressure conditions. This indicates the external temperature under extremely low pressure conditions. This represents the least squares method.
[0029] In practical applications, the cylinder intake temperature The cylinder intake pressure The details are as follows:
[0030] ;
[0031] in, This represents a feedforward neural network; This is the predicted output of the feedforward neural network, and ; Let be the input vector of the feedforward neural network, and ; Indicates the first The cylinder intake temperature of the step, Indicates the first The cylinder intake pressure of the step, Indicates the first The cylinder intake temperature of the step, Indicates the first The cylinder intake pressure of the step; It is a natural number.
[0032] In practical applications, the actual output power of an engine during operation depends on its combustion state, which is directly determined by the state of the air-fuel mixture in the cylinder. The state of the air-fuel mixture is influenced by the engine's intake and injection states. Due to the compressibility of gases and the oscillations and friction within the intake system, there is a delay between the actual intake pressure and the target control pressure. This delay causes the intake pressure change to exhibit continuous convolutional characteristics and a nonlinear relationship, resulting in a certain coupling relationship between different states and increasing the difficulty of decoupling calculations. Therefore, the engine transient load capacity prediction model employs a data-driven method and uses a feedforward neural network to train a neural network model to predict the intake state of the engine cylinders, thus solving the aforementioned practical problems.
[0033] In step 3 of this invention, the transmission efficiency and dynamic coordination characteristics between the front power chain and the back power chain are as follows:
[0034] ;
[0035] wherein, represents the output power of the series hybrid vehicle generator, represents the output power of the battery at the th step, represents the power consumption of the series hybrid vehicle at the th step, is a natural number.
[0036] In step 3 of the present application, the dynamic characteristics of the battery state of charge varying with the charge and discharge power are specifically as follows:
[0037] ;
[0038] wherein, , represents the rolling time domain; represents the state of the battery at the th step, represents the state of the battery at the th step, represents the open circuit voltage of the battery, represents the internal equivalent resistance of the battery.
[0039] In step 4 of the present application, the total cost function is specifically as follows:
[0040] ;
[0041] wherein, the dynamic fuel consumption cost function , the electrochemical power of the series hybrid vehicle at the th step ; satisfies , is the battery power at the th step, is the lower limit of the battery power , is the upper limit of the battery power ; represents a mapping function, represents the inverse function of , and ; is a first positive weight factor, is a third mapping relationship; the electrochemical energy deviation cost function ; is a first positive weight factor, the electrochemical energy of the battery at the th step ; , the set , Indicates the desired range of battery energy regulation; Self-given value , , These correspond to the initial electrochemical energy and the final electrochemical energy of the battery. Indicates that the series hybrid vehicle is in the first The distance traveled in a step, from the first step Step to the first Predicted driving distance , This indicates the length of the prediction time domain, and For natural numbers; series hybrid vehicles; series hybrid vehicles use long short-term memory neural networks in the first... Predicting speed of steps , This indicates that the long short-term memory neural network is in the first stage. The hidden state of the step, This represents the weight matrix of a long short-term memory neural network. This represents the bias vector of a long short-term memory neural network.
[0042] here, ,and , , Indicates the first Electrochemical energy of a step-by-step battery The lower limit, Indicates the first Electrochemical energy of a step-by-step battery The upper limit, Indicates the output power of the generator in a series hybrid vehicle. The lower limit, Indicates the output power of the generator in a series hybrid vehicle. The upper limit.
[0043] In step 4 of this invention, the sequence of constraint conditions in the rolling optimization time domain under the model prediction control framework is as follows:
[0044] ;
[0045] in, Indicates the engine power at the 1st Dynamic constraints of the step, This indicates the fourth mapping relationship; the cylinder is in the first... Step intake temperature The cylinder is in the first Step intake pressure , Indicates cylinder from the first Step to the Step target power sequence.
[0046] In practical application, the total cost function as the basis of energy management strategy, can realize the calculation of complex multi-objective optimization problem, so as to accurately control the energy distribution in hybrid vehicle. The total cost function includes dynamic fuel consumption cost function and battery chemical energy deviation cost function.
[0047] In practical application, since the battery state of charge is nonlinear, the electrochemical power of series hybrid vehicle is introduced in the dynamic characteristics of the battery state of charge with charge and discharge power change As a control variable, and The relationship between Can be defined as Specifically:
[0048]
[0049] In order to study the nature of the above formula, the following discussion and analysis are carried out:
[0050]
[0051] From the above discussion and analysis results Is convex, twice differentiable, monotone non-decreasing, and single valued mapping. Therefore, the inverse of the mapping function Is obtained as follows:
[0052]
[0053] The above inverse function is discussed and analyzed, and it is found that Is concave, twice differentiable, monotone non-decreasing, and single valued mapping. Therefore, the dynamic fuel consumption cost function In Is convex and monotonically increasing, and since the third mapping relationship Is convex and monotonically increasing, the dynamic fuel consumption cost function Is convex and monotonically increasing. Through the above processing, the energy management strategy of the hybrid vehicle based on engine dynamic characteristics prediction of the present application solves the problem of convergence and divergence uncertainty, and ensures the convergence of the nonlinear multi-objective optimization problem.
[0054] In practical applications, the constraints play a vital role in the optimization of energy management strategy, which determines the safety boundary and feasibility conditions of the hybrid vehicle operation to ensure that the optimization results meet the engineering requirements and physical limitations. Therefore, the hybrid vehicle energy management strategy based on engine dynamic characteristics prediction in the present application updates the operating state of the engine through the control variable state equation on one hand; on the other hand, the updated state variable determines the real-time power constraint boundary through the above-mentioned model. By dynamically adjusting the boundary range of the feasible region in real time, the feasibility and stability of the hybrid vehicle operation are ensured.
[0055] In the present application, the step 5 specifically comprises the following steps:
[0056] Step 51, the first optimization of the energy management problem in the rolling time domain under the model predictive control framework is carried out: ;
[0057] Among them, ; ; ; wherein, is a natural number.
[0058] Step 52, on the basis of the first optimization in step 51, the second optimization is continued, as follows:
[0059] ;
[0060] Among them, the fourth mapping relationship ; the fifth mapping relationship ; the intermediate parameter ; the control variable matrix ; the state variable matrix ; the first coefficient matrix , the second coefficient matrix , the third coefficient matrix , and satisfy ; wherein, denotes the unit matrix, denotes the lower triangular matrix, denotes the upper triangular matrix; the auxiliary matrix is a full 1 matrix, denotes the number of rows and is a natural number.
[0061] In practical applications, is a decomposition variable.
[0062] Step 53, based on step 52, step 53, the following Lagrange function is established :
[0063] ;
[0064] wherein, represents a penalty coefficient; a Lagrange coefficient matrix , , , respectively represent a first Lagrange parameter, a second Lagrange parameter, a third Lagrange parameter; represents a 2-norm of , represents a square of a 2-norm of .
[0065] Step 54, the Lagrange function in step 3 is solved by using a DADMM method and iteratively updated to obtain: a control variable update equation ; a state variable update equation ; and a constraint relationship update equation between the state variable and the control variable . .
[0066] In actual applications, the iterative update process of the control variable update equation is to fix the state variable and the Lagrange coefficient matrix obtained by the previous iteration update, to solve a sub-optimization problem about the control variable . The iterative update process of the state variable update equation is to fix the control variable and the Lagrange coefficient matrix obtained by the current iteration update, to solve a sub-optimization problem about the state variable . The iterative update process of the constraint relationship update equation between the state variable and the control variable is to obtain the constraint violation by using the control variable and the state variable obtained by the current iteration update; meanwhile, a momentum term is introduced to accelerate the update convergence. The numerical size of each Lagrange parameter reflects the cumulative violation of the constraint, which guides the entire iteration process to finally converge to an optimal solution that satisfies all constraint conditions.
[0067] The step 54 of the present application embodies the decomposition of an optimization problem into different parts for iterative updating: firstly, the control variable equation is solved by minimization and the control variable is updated iteratively; secondly, the state variable equation is solved by minimization and the state variable is updated iteratively; finally, the state variable, the control variable and the constraint relationship between the state variable and the control variable are updated iteratively, that is, the Lagrange coefficient matrix is updated iteratively. In the present application, the numerical value of each Lagrange parameter in the Lagrange coefficient matrix reflects the degree of violation of the constraint in the above updating process. That is, by introducing each Lagrange parameter, the optimal control variable obtained in the above iterative updating process does not exceed its instantaneous constraint range.
[0068] In the above content of the present application, the first mapping relationship , the second mapping relationship , the third mapping relationship , and the fourth mapping relationship are all prior art and will not be described here. In addition, the DADMM (Damped Alternating Direction Method of Multipliers) method is also prior art and will not be described here.
[0069] In summary, the above is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A hybrid vehicle energy management strategy based on engine dynamic characteristics prediction, characterized in that, The energy management strategy comprises the following steps: Step 1, obtaining the power characteristics and fuel consumption characteristics of the internal engine in the series hybrid vehicle under extremely low pressure environment and the variation law of atmospheric pressure; Step 2, constructing an engine transient load capacity prediction model and an engine transient fuel consumption calculation model; Step 3, obtaining the energy transmission matching law between the front power chain and the rear power chain of the series hybrid vehicle, including: the transmission efficiency and dynamic coordination characteristics between the front power chain and the rear power chain; the efficiency and dynamic response characteristics of the generator for converting mechanical torque into electric energy; the dynamic characteristics of the state of charge of the battery with the variation of the charging and discharging power; the efficiency and torque-speed response characteristics of the engine for converting electric energy into mechanical energy; the power demand characteristics of each auxiliary device; the real-time driving power demand characteristics based on the driving intention; Step 4, according to the energy transmission matching law obtained in step 3, constructing the energy management strategy of the hybrid vehicle under the model predictive control framework: designing the total cost function based on the engine transient fuel consumption calculation model established in step 2, and simultaneously, predicting and generating the constraint condition sequence in the rolling optimization time domain based on the engine transient load capacity prediction model; Step 5, using the DADMM method to solve the energy management problem in the rolling time domain under the model predictive control framework, and updating the constraint sequence in real time.
2. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 1, wherein, In step 2, the engine transient load capacity prediction model is as follows: ； wherein, is a non-linear mapping derived from a trained backpropagation neural network model; fuel injection quantity per cylinder per cycle , is an engine speed, is an engine accelerator pedal position, is a first mapping relationship; cylinder mass flow , denotes a cylinder volume, denotes a number of working cycles of a crankshaft rotation, denotes an engine speed, is a thermodynamic constant, volumetric efficiency , denotes a second mapping relationship, denotes a cylinder intake air temperature, denotes a cylinder intake air pressure.
3. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction according to claim 1 or 2, characterized in that, In step 2, the engine transient fuel consumption characteristic calculation model is as follows: ， wherein represents the dynamic fuel consumption, represents the output power of the engine; , , is an adaptive fitting parameter, and , , represents the external atmospheric pressure in an extreme low pressure environment, represents the external temperature in an extreme low pressure environment, represents the least square method.
4. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 2, wherein, The cylinder intake air temperature The cylinder intake air pressure In particular as follows: ， in, This represents a feedforward neural network; This is the predicted output of the feedforward neural network, and ; Let be the input vector of the feedforward neural network, and ; Indicates the first The cylinder intake temperature of the step, Indicates the first The cylinder intake pressure of the step, Indicates the first The cylinder intake temperature of the step, Indicates the first The cylinder intake pressure of the step; It is a natural number.
5. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 1, wherein, In step 3, the transmission efficiency and dynamic coordination characteristics between the front power chain and the rear power chain are as follows: ， wherein, represents the output power of the series hybrid vehicle generator, represents the output power of the battery at the step, represents the power consumption of the series hybrid vehicle at the step, is a natural number.
6. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 1, wherein, In step 3, the dynamic characteristics of the state of charge of the battery with the variation of the charging and discharging power are as follows: ， wherein, , represents a rolling time horizon; represents the state of the battery at step , represents the state of the battery at step , represents the open circuit voltage of the battery, represents the internal equivalent resistance of the battery.
7. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 1, wherein, In step 4, the total cost function is specified as follows: ， Among them, the dynamic fuel consumption cost function Series hybrid vehicles in the first electrochemical power of step ; satisfy , Indicates the first Step's battery power, Battery power The lower limit Battery power The upper limit; Represents a mapping function. for The inverse function of, and ; As the first positive weighting factor, The third mapping relationship; electrochemical energy deviation cost function ; As the first positive weighting factor, the... Electrochemical energy of a step-by-step battery ; ,gather , Indicates the desired range of battery energy regulation. express The 2-norm, express The square of the 2-norm; Self-given value , , These correspond to the initial electrochemical energy and the final electrochemical energy of the battery. Indicates that the series hybrid vehicle is in the first The distance traveled in a step, from the first step Step to the first Predicted driving distance , This indicates the length of the prediction time domain, and For natural numbers; series hybrid vehicles; series hybrid vehicles use long short-term memory neural networks in the first... Predicting speed of steps , This indicates that the long short-term memory neural network is in the first stage. The hidden state of the step, This represents the weight matrix of a long short-term memory neural network. This represents the bias vector of a long short-term memory neural network; here, ,and , , Indicates the first Electrochemical energy of a step-by-step battery The lower limit, Indicates the first Electrochemical energy of a step-by-step battery The upper limit, Indicates the output power of the generator in a series hybrid vehicle. The lower limit, Indicates the output power of the generator in a series hybrid vehicle. Above.
8. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction of claim 7, wherein, In step 4, the constraint condition sequence in the rolling optimization time domain is as follows: ， wherein, represents the engine power at the first step of the dynamic constraint, represents a third mapping relationship; the intake temperature of the cylinder at the first step of the dynamic constraint, represents the intake pressure of the cylinder at the first step of the dynamic constraint, , represents a target power sequence of the cylinder from the first step to the first step of the dynamic constraint.
9. The hybrid vehicle energy management strategy based on engine dynamic characteristics prediction according to claim 1 or 8, characterized in that, The step 5 specifically comprises the following steps: Step 51, performing the first optimization on the energy management problem in the rolling time domain under the model predictive control framework: ； wherein ; ; ; wherein is a natural number; Step 52, on the basis of the first optimization in step 51, continuing the second optimization, as follows: ； wherein the fourth mapping relationship ; the fifth mapping relationship ; the intermediate parameter ; the control variable matrix ; the state variable matrix ; the first coefficient matrix , the second coefficient matrix , the third coefficient matrix , and satisfy ; wherein denotes a unit matrix, denotes a lower triangular matrix, denotes an upper triangular matrix; the auxiliary matrix is a full one matrix, denotes the number of rows and is a natural number; Step 53, based on step 52, step 53, the following Lagrange function is established : ； in, Represents the penalty coefficient; Lagrange coefficient matrix ), , , These represent the first Lagrange parameter, the second Lagrange parameter, and the third Lagrange parameter, respectively. express The 2-norm, express The square of the 2-norm; Step 54, using the DADMM method to solve the Lagrangian function in step 3 Solving and iteratively updating, the control variable update equation is obtained ; the state variable update equation ; the constraint relationship update equation between the state variable and the control variable .