Multi-objective optimization control method suitable for electrochemical energy storage system and related device

Abstract

The invention provides a multi-objective optimization control method suitable for an electrochemical energy storage system and a related device. The multi-objective optimization control method comprises the following steps: acquiring state data of the electrochemical energy storage system for measuring battery performance, economic and safe states at the current moment, and calculating index data; generating a plurality of groups of battery control strategy data containing different charge and discharge control parameter combinations; calculating a plurality of states of the system at the next moment by using a pre-constructed state transfer function based on the index data and the strategy data; determining a group of better battery control strategies by using a multi-objective optimization algorithm; and according to the attention degree of the system to different indexes at the next moment, selecting a battery control strategy which enables the system state to most accord with the attention degree from the better strategies. According to the method, the battery quantification accuracy is improved by introducing the battery economic state, and the optimization efficiency is improved by adopting the multi-objective optimization algorithm; and meanwhile, the control strategy is dynamically selected to meet the lithium battery control target in different states in the actual process, so that a more excellent battery control scheme is obtained.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of electrochemical energy storage calculation optimization, and particularly relates to a multi-objective optimization control method suitable for an electrochemical energy storage system and a related device. BACKGROUND

[0002] In today's era, electrochemical energy storage technology is in a stage of rapid development. Energy storage technology plays a crucial role in the stable operation and efficient use of modern energy systems, and its application scenarios are widespread, covering smart grids, distributed energy storage, electric vehicles and other fields. With the large-scale access of new energy and the continuous growth of electricity demand, higher requirements are placed on the performance, economy and safety of energy storage systems.

[0003] In an electrochemical energy storage system, lithium batteries have become one of the most widely used energy storage devices due to their high energy density and low self-discharge rate. In order to achieve efficient management of lithium batteries, the management system needs to obtain key state data such as battery capacity data and internal resistance data, and then develop a reasonable battery use strategy to adjust the charging rate, control the temperature and voltage range, and other operations to optimize the use of the battery and economy while ensuring the safety of the battery. The core of this series of operations is to develop an appropriate battery control strategy for the management system.

[0004] Although existing lithium battery control methods can develop battery use strategies while considering battery state and safety, they have many shortcomings. On the one hand, existing methods cannot fully consider the cost factors in the battery use process; on the other hand, they cannot adjust the battery use plan according to the different stages of the battery, resulting in low battery use efficiency. In addition, the optimization efficiency of these methods is low, and the actual application performance is poor, making it difficult to meet the efficiency and practicality requirements of battery control strategies in actual application scenarios. SUMMARY

[0005] Therefore, the present application provides a multi-objective optimization control method suitable for an electrochemical energy storage system and a related device, aiming to achieve a more realistic battery state analysis, improve optimization efficiency and the actual application performance of optimization solutions, and thus obtain a better battery control plan, thereby overcoming the above-mentioned shortcomings of existing lithium battery control methods.

[0006] To achieve the above-mentioned purpose, the technical solutions provided by the present application are as follows:

[0007] In a first aspect, the present application provides a multi-objective optimization control method suitable for an electrochemical energy storage system, comprising the following steps:

[0008] Collecting state data for measuring battery performance state, economic state and safety state in the current time of electrochemical energy storage system and calculating corresponding index data;

[0009] Generating several groups of battery control strategy data containing different combinations of charging and discharging control parameters;

[0010] Based on the index data and several groups of battery control strategy data, using pre-constructed state transition function, calculating several states of the system at the next time; each state is represented by index data of battery performance state, economic state and safety state;

[0011] Using multi-objective optimization algorithm to evaluate the multi-objective optimization results of several states of the system, and determining a group of better battery control strategies according to the evaluated multi-objective optimization results;

[0012] According to the degree of attention of the system to different indicators at the next time, selecting a strategy from a group of better battery control strategies that makes the system state most consistent with the degree of attention as the optimal battery control strategy at the next time.

[0013] Further, the multi-objective optimization algorithm is NSGA-II algorithm based on Pareto optimal solution;

[0014] Using multi-objective optimization algorithm to evaluate the multi-objective optimization results of several states of the system, and determining a group of better battery control strategies according to the evaluated multi-objective optimization results, including:

[0015] Taking the system state corresponding to each group of battery control strategies as an individual to form an initial population;

[0016] Based on the initial population, using the objective function of NSGA-II algorithm to evaluate the optimization results of each individual for various indicators, and constructing a Pareto solution set;

[0017] Eliminating solutions in the Pareto solution set that do not meet the safety constraints, and taking the battery control strategies corresponding to the remaining solutions as a group of better battery control strategies.

[0018] Further, the mathematical expression of the objective function is as follows:

[0019]

[0020] In the formula, represents the candidate battery control strategy, is the target vector for evaluating the candidate strategy, represents the expected calculation, , and respectively represent performance degradation cost, economic cost and safety cost, and are used to quantify the optimization results of the battery performance state, economic state and safety state indicators; the objective of the objective function is to minimize the sum of the performance degradation cost, economic cost and safety cost.

[0021] Further, according to the degree of attention of the system to different indicators at the next moment, one of a group of optimal battery control strategies is selected as the optimal battery control strategy at the next moment, which makes the system state most consistent with the degree of attention, including:

[0022] determining the weight values of the indicators at the next moment to quantify the degree of attention of the system to the indicators;

[0023] using the weight values of the indicators and the optimization results of the indicators under each of the optimal battery control strategies, calculating the synthetic optimization result values of each of the optimal battery control strategies;

[0024] selecting the strategy corresponding to the minimum synthetic optimization result value as the optimal battery control strategy at the next moment.

[0025] Further, the weight values of the indicators at each moment are further updated, and the update formula is:

[0026]

[0027] In the formula, represents a weight vector composed of the indicators; is a normalization function; , and are parameter vectors; represents a performance state indicator of the battery; represents a safety state indicator of the battery, and subscript t represents a moment.

[0028] Further, the mathematical expression of the safety constraint is:

[0029]

[0030] In the formula, represents the safety cost of the system at t moment, represents a safety cost threshold, represents a risk tolerance.

[0031] Further, the mathematical expression of the state transition function is:

[0032]

[0033] In the formula, and respectively represent the system state at time t and time t+1, containing index data of battery performance state, economic state and safety state; represents the battery control strategy at time t; represents the disturbance term at time t.

[0034] In a second aspect, the present application provides a multi-objective optimization control device suitable for an electrochemical energy storage system, comprising:

[0035] a data acquisition module, configured to acquire state data of the electrochemical energy storage system at a current time for measuring battery performance state, economic state and safety state, and to calculate corresponding index data;

[0036] a strategy generation module, configured to generate a plurality of sets of battery control strategy data containing different combinations of charging and discharging control parameters;

[0037] a calculation module, configured to calculate a plurality of states of the system at a next time based on the index data and the plurality of sets of battery control strategy data, using a pre-constructed state transition function; each state is represented by index data of battery performance state, economic state and safety state;

[0038] a multi-objective optimization module, configured to evaluate multi-objective optimization results of the plurality of states of the system using a multi-objective optimization algorithm, and to determine a set of more optimal battery control strategies according to the evaluated multi-objective optimization results;

[0039] a strategy selection module, configured to select a strategy that makes the system state most consistent with the degree of attention from a set of more optimal battery control strategies according to the degree of attention of the system to different indexes at the next time, as the optimal battery control strategy at the next time.

[0040] In a third aspect, the present application provides a computer device, comprising a processor and a memory:

[0041] The memory is configured to store a computer program and send instructions of the computer program to the processor;

[0042] The processor executes a multi-objective optimization control method suitable for an electrochemical energy storage system according to instructions of the computer program.

[0043] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement a multi-objective optimization control method suitable for an electrochemical energy storage system according to the first aspect.

[0044] In summary, the application provides a multi-objective optimization control method and related device suitable for an electrochemical energy storage system, which collects state data for measuring battery performance state, economic state and safety state in the electrochemical energy storage system at the current time and calculates corresponding index data; generates several groups of battery control strategy data containing different combinations of charging and discharging control parameters; based on the index data and the several groups of battery control strategy data, calculates several states of the system at the next time using a pre-constructed state transition function; each state is represented by index data of the battery performance state, economic state and safety state; evaluates multi-objective optimization results of the several states of the system using a multi-objective optimization algorithm, and determines a group of better battery control strategies according to the evaluated multi-objective optimization results; according to the attention degree of the system to different indexes at the next time, selects a strategy that makes the system state most consistent with the attention degree from the group of better battery control strategies as the optimal battery control strategy at the next time. The application improves battery quantification accuracy by introducing the battery economic state, and improves optimization efficiency by using a multi-objective optimization algorithm; at the same time, dynamically selects a control strategy to meet the lithium battery control target under different states in the actual process, so as to obtain a better battery control scheme. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0046] Figure 1 A flowchart of a multi-objective optimization control method suitable for an electrochemical energy storage system provided by the embodiment of the present application;

[0047] Figure 2 A composition block diagram of a multi-objective optimization control device suitable for an electrochemical energy storage system provided by the embodiment of the present application;

[0048] Figure 3 A composition block diagram of a computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the objectives, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] Referring to Figure 1 The embodiment of the present application provides a multi-objective optimization control method suitable for an electrochemical energy storage system, comprising the following steps:

[0051] S11: Collect state data for measuring battery performance state, economic state and safety state in the electrochemical energy storage system at the current time and calculate corresponding index data.

[0052] It should be noted that the electrochemical energy storage system is a system for storing and releasing electrical energy by using electrochemical principles, and common examples include lithium battery energy storage systems, which are used as control objects in this embodiment.

[0053] The battery performance state reflects the state of the performance of the battery during use such as charging and discharging, for example, the state reflected by related parameters such as battery capacity and internal resistance. The economic state is related to the state of the use cost of the battery, for example, the state reflected by factors such as charging and discharging price cost. The safety state is related to the safety of the use of the battery, for example, the state reflected by factors such as battery temperature and overcharging and overdischarging.

[0054] The state data is the relevant data for measuring the performance, economic and safety states of the battery, such as specific values such as voltage, current, temperature and charging and discharging price. The index data is quantitative data that can more directly reflect the characteristics of each state after the state data is calculated, for example, performance index values converted from performance state data such as battery capacity.

[0055] This step can collect various state data related to the performance, economic and safety states of the battery in the electrochemical energy storage system at the current time by using sensors and other devices, such as measuring data such as the voltage, current and temperature of the battery, and obtaining economic data such as the current charging and discharging price. Then, according to a pre-set calculation method, the state data is converted into corresponding index data for subsequent analysis.

[0056] S12: Generate a plurality of sets of battery control strategy data containing different combinations of charging and discharging control parameters.

[0057] It should be noted that the charging and discharging control parameters are parameters for controlling the charging and discharging process of the battery, such as the size of the charging current and the discharging cutoff voltage.

[0058] This step generates a plurality of different combinations of charging and discharging control parameters, and each combination constitutes a battery control strategy data. For example, different combinations of parameters such as charging current values and discharging cutoff voltage values are set.

[0059] S13: Based on the index data and the several groups of battery control strategy data, the pre-constructed state transition function is used to calculate the several states of the system at the next time; each state is represented by the index data of the battery performance state, economic state and safety state.

[0060] It should be noted that the state transition function is a pre-constructed mathematical function used to describe the relationship between the current system state and the next time system state based on a certain control strategy (i.e. combination of charging and discharging control parameters).

[0061] This step substitutes the index data obtained in step S11 and the several groups of battery control strategy data generated in step S12 into the pre-constructed state transition function. According to these inputs, the function calculates the several states that the system may have at the next time, and each state is represented by the index data of the battery performance, economic and safety states.

[0062] S14: The multi-objective optimization algorithm is used to evaluate the multi-objective optimization results of the several states of the system, and according to the evaluated multi-objective optimization results, a group of better battery control strategies is determined.

[0063] It should be noted that the multi-objective optimization algorithm is an algorithm that can optimize multiple conflicting objectives simultaneously, and in this embodiment, it is used to comprehensively consider multiple objectives such as battery performance, economy and safety to evaluate the system state.

[0064] This step uses the multi-objective optimization algorithm to evaluate the several states of the system obtained in step S13. The algorithm considers multiple objectives such as battery performance, economy and safety to calculate the multi-objective optimization results of each state. According to these results, a group of better battery control strategies is selected from the battery control strategies corresponding to the numerous states.

[0065] S15: According to the attention degree of the system to different indicators at the next time, a strategy that makes the system state most consistent with the attention degree is selected from the group of better battery control strategies as the optimal battery control strategy at the next time.

[0066] It should be noted that considering that the attention degree of the system to different indicators (performance, economy, safety, etc.) may be different at the next time, according to this attention degree, a strategy that makes the system state most consistent with the attention degree is selected from the group of better battery control strategies determined in step S14, and it is determined as the optimal battery control strategy at the next time.

[0067] This embodiment provides a multi-objective optimization control method suitable for electrochemical energy storage systems. The method first collects state data and converts it into index data to comprehensively understand the current battery state. Then, it generates multiple battery control strategy data and uses a state transition function to predict the system's state at the next moment under different strategies. Next, a multi-objective optimization algorithm is used to comprehensively evaluate these states and select a group of superior strategies. Finally, based on the system's focus on different indicators at the next moment, the optimal strategy is determined from the group of superior strategies, achieving dynamic, multi-objective optimization control of the electrochemical energy storage system battery. Unlike traditional methods that only focus on battery performance and safety, this method comprehensively considers battery performance, economic, and safety states simultaneously, making the battery control strategy more comprehensive and practical, better adaptable to actual application needs. Furthermore, by dynamically adjusting and determining the optimal battery control strategy according to changes in the system's focus on different indicators at different times, it can more flexibly adapt to system operating conditions compared to traditional fixed control strategies, improving battery utilization efficiency and optimization efficiency.

[0068] The health, economics, and safety of electrochemical energy storage systems are not static but continuously evolve with changes in charging and discharging behavior, operating environment, and control strategies. Each decision affects the next state. For example, using high-rate charging in hot weather may cause a rapid decline in SOH (State of Health), increasing safety risks and, in the long run, economic costs. Therefore, in one embodiment of this invention, a set of state transition functions is proposed to characterize this time-varying mechanism, representing the state of the entire system. As time changes, it is subject to the current control strategy. The influence, and its evolution, can be formally represented as:

[0069]

[0070] In the formula, and These represent the system states at time t and time t+1, respectively, and include indicator data from three dimensions: battery performance state, economic state, and safety state. This represents the battery control strategy at time t, such as charging rate, temperature setting, and current limit. This represents the disturbance term at time t, such as changes in ambient temperature or fluctuations in power grid load. It is a state transition function pre-constructed based on historical data, which represents how "current state + control strategy + disturbance" is mapped to the state at the next moment, reflecting the dynamic evolution law of the system.

[0071] For example, for the state in the above state transition function This can be represented by a three-dimensional vector:

[0072]

[0073] Among them:

[0074] Battery performance state. SOH (State of Health) represents the health of the battery, reflecting whether it is close to retirement. It is mainly measured by two indicators:

[0075] Capacity decay rate ( ): indicates the degree of decline of the current battery capacity relative to the initial capacity at the factory;

[0076] Internal resistance growth rate ( ): the internal resistance of the battery increases with the use of time, and the increase of internal resistance will cause the increase of heat and the decrease of efficiency.

[0077] Economic state. Measure the cost of using the battery, use LCOS (Levelized Cost of Storage, Levelized Cost of Storage), unit "yuan / kWh", representing the average cost of per kilowatt-hour of energy storage, including equipment purchase, maintenance, energy consumption, etc.

[0078] Safety state. Combined with the following two key indicators to describe the potential risks of the battery:

[0079] Thermal runaway probability : the risk probability of fire or explosion caused by overheating of the battery;

[0080] Capacity drop rate : whether the battery capacity suddenly drops in a short time, often related to internal failure or material problems.

[0081] For example, for the control variables (regulatable strategy variables) in the above state transition function, they can include:

[0082] Charge-discharge rate ( ): the proportion of the capacity charged or discharged per hour to the rated capacity of the battery. High rate will accelerate aging;

[0083] Current and voltage set value: by limiting the current peak or voltage threshold, avoid excessive stress damage to the battery;

[0084] Temperature control strategy: for example, cooling system adjustment, environmental control, etc., to avoid thermal runaway.

[0085] These states and control variables together constitute the basis of the system operation.

[0086] Since the three targets (performance, economy, and safety) often contradict each other, one control strategy can benefit in one aspect but cause damage in another. For example, low-rate charging is safer and less wasteful, but it takes a long time and has a high scheduling cost; aggressive discharging strategies can improve instantaneous benefits, but significantly increase thermal risk and life consumption; extreme temperature control can reduce the probability of thermal runaway, but the operation and maintenance cost increases accordingly. Therefore, in one embodiment of the present application, a multi-objective optimization algorithm is proposed to find the optimal compromise solution between different targets. The multi-objective optimization algorithm is the NSGA-II algorithm based on Pareto optimal solution.

[0087] The multi-objective optimization results of several states of the system are evaluated using the multi-objective optimization algorithm, and a set of better battery control strategies is determined according to the evaluated multi-objective optimization results, including:

[0088] S21: The system state corresponding to each set of battery control strategies is taken as an individual to form an initial population;

[0089] S22: Based on the initial population, the optimization results of each individual in various indicators are evaluated using the objective function of the NSGA-II algorithm, and a Pareto solution set is constructed;

[0090] S23: Eliminate solutions in the Pareto solution set that do not meet the safety constraints, and take the battery control strategies corresponding to the remaining solutions as a set of better battery control strategies.

[0091] In this embodiment, the Pareto optimal solution is a set of solutions in a multi-objective optimization problem. If there is no other solution that is better than it in all targets, and at least one target is better than it, then this set of solutions is called a Pareto optimal solution.

[0092] The NSGA-II algorithm is a classical multi-objective optimization algorithm that searches for Pareto optimal solutions in the solution space by simulating selection, crossover, and mutation operations in the genetic process. The running process of the NSGA-II algorithm includes initialization of the population, non-dominated sorting, calculation of crowding degree, selection operation, crossover and mutation operation, and repeated iteration until the Pareto optimal solution is obtained after meeting the preset termination condition.

[0093] The embodiment proposes a NSGA-II multi-objective optimization algorithm based on Pareto optimal solution to find the optimal compromise solution. The system state corresponding to each group of battery control strategies is regarded as an individual to form an initial population. The algorithm objective function is used to evaluate the optimization results of individual indicators and build a Pareto solution set. Finally, the solutions that do not meet the safety constraints are removed, and the remaining solutions correspond to a group of better battery control strategies. In this way, the reasonable strategy of the system is balanced between contradictory objectives.

[0094] In order to realize the balance of the electrochemical energy storage system in the three aspects of "performance optimization", "economic efficiency" and "safety and reliability", the system needs to have decision-making ability to select the most reasonable scheme from a large number of candidate strategies. This process is called "multi-objective optimization". The battery multi-objective optimization control method must consider multiple indicators: the battery cannot degrade too fast, the capacity and efficiency need to be continuously maintained (from the performance angle); the operation and maintenance cannot cost too high, otherwise the cost benefit ratio is too low (from the economic angle); the running process must avoid out of control or failure to ensure that there is no high risk situation (from the safety angle).

[0095] The three aspects of the target often conflict. For example, selecting low-rate discharge can prolong the life, but the available power of the battery is reduced, and the operation value is reduced. Therefore, the system should balance between multiple objectives. The optimization algorithm evaluates the pros and cons of different control strategies through a set of "objective functions". In one embodiment of the invention, an objective function is proposed, which includes optimization considering multiple indicators at the same time. Specifically, during the entire running period to The following three types of cost objectives are optimized at the same time, which are defined as follows:

[0096] (1) Performance degradation cost : measure the degree of capacity decline and internal resistance rise;

[0097] This part measures the "intangible loss" cost of battery performance degradation due to use:

[0098]

[0099] Wherein:

[0100] : the remaining capacity of the battery at the moment;

[0101] : the internal resistance of the battery at the moment;

[0102] : the capacity and internal resistance at the initial factory;

[0103] : Weighting factor, indicating the importance of capacity and internal resistance in overall performance.

[0104] (2) Economic cost : Calculate the average cost per unit of energy;

[0105] Measure the average energy cost generated during the use of the battery system:

[0106]

[0107] Where:

[0108] : Initial investment cost of equipment;

[0109] : Operating and maintenance costs (such as replacement of parts, labor, etc.);

[0110] : Cost caused by energy loss, such as heat loss, loss of electrochemical efficiency;

[0111] : The actual effective electric energy (kWh) released during this period.

[0112] This index can directly reflect the economy of the battery in actual use.

[0113] (3) Safety cost : Indicates the risk level of abnormality or thermal runaway during operation.

[0114] Measure the cost risk brought by potential safety hazards:

[0115]

[0116] Where: and are weighting factors;

[0117] : Thermal runaway probability, defined as:

[0118]

[0119] This function evaluates the possibility of battery thermal runaway by combining temperature fluctuations and voltage deviation, is a Sigmoid function that limits the risk value to the interval; is the voltage deviation degree at time t;

[0120] : Capacity drop rate, defined as:

[0121]

[0122] representing the capacity of the battery to drop sharply in the future time window .

[0123] This item can be used to guide the dispatching system to avoid high-risk operating states.

[0124] The objective of the objective function is to minimize the sum or weighted sum of the three types of costs.

[0125] Since there is no strategy that is optimal on all objectives simultaneously, a set of "non-dominated solutions", i.e. the so-called Pareto front, is constructed. In these solutions, if one objective is improved, another objective must be sacrificed. This embodiment further proposes that each candidate strategy is evaluated by the following objective vector:

[0126]

[0127] The algorithm will continuously evolve and select a series of solutions that achieve different balances between the three objectives from the initial candidate set.

[0128] Although there are multiple "better" solutions, one of them still needs to be selected for execution in actual scheduling, so in a further embodiment of the application, according to the degree of attention of the system to different indicators at the next moment, one of the optimal battery control strategies is selected as the optimal battery control strategy at the next moment, which makes the system state most consistent with the degree of attention, including:

[0129] S31: Determine the weight value of each type of indicator to quantify the degree of attention of the system to each type of indicator.

[0130] S32: Calculate the synthetic optimization result value of each optimal battery control strategy using the weight value of each type of indicator and the optimization result of each type of indicator under each optimal battery control strategy.

[0131] S33: Select the strategy corresponding to the smallest synthetic optimization result value as the optimal battery control strategy at the next moment.

[0132] For example, a synthetic objective function is designed to select according to the principle of this embodiment. The mathematical expression of the synthetic objective function is as follows:

[0133]

[0134] Where, is the degree of attention to each objective at the current stage, is the objective function value for each target. For example, more attention can be paid to economy at the early stage of battery life; while at the end of life or when the safety risk increases, the safety target should be prioritized.

[0135] In further embodiments of the application, for the weight value of each type of indicator at each time, an adaptive adjustment function is proposed to ensure that the system makes reasonable choices in different states. The adaptive adjustment function of the weight factor (i.e. the weight value) is as follows:

[0136]

[0137] wherein, represents a vector composed of the weights of each type of indicator; is a normalization function; , and are parameter vectors; represents the performance state indicator of the battery; represents the safety state indicator of the battery, and subscript t represents the time.

[0138] In an embodiment of the application, in order to enhance the robustness of the system, a safety constraint in the form of probability is introduced in the optimization process to ensure that the dangerous boundary will not be crossed even in an uncertain environment:

[0139]

[0140] wherein is the maximum allowable safety cost, is the risk probability (such as 1%) that the system can tolerate.

[0141] When approximately obeys a normal distribution , the constraint can be converted to:

[0142]

[0143] This ensures that the system operates within a controllable risk range in most cases.

[0144] Based on the above embodiments, the process of determining the optimal control strategy using a multi-objective optimization algorithm is as follows:

[0145] Step 1: Generate multiple charge and discharge control strategies;

[0146] Step 2: Evaluate the performance, cost and safety of each strategy using a multi-objective optimization algorithm;

[0147] Step 3: Construct a Pareto solution set and eliminate solutions that do not meet the safety constraint;

[0148] Step 4: Calculate the comprehensive weighted objective value of each solution ;

[0149] Step 5: Select the strategy with the minimum as the current scheduling decision;

[0150] Step 6: Roll over the state over time, re-evaluate and optimize.

[0151] Based on the above embodiments, the battery economic state is introduced to improve the accuracy of battery quantification, and the non-dominated sorting genetic algorithm is selected to improve the optimization efficiency; at the same time, the dynamic target weight is used to meet the lithium battery control target in different states in the actual process. The addition of probabilistic safety constraints enhances the robustness of the control system, and the introduction of the state transition function takes into account the dynamic evolution of the system state. Compared with the prior art, the present application can realize accurate data expression of the actual state and control target of the battery, and take into account the long-term impact of safety constraints and control strategies on the battery state, and obtain a better battery control scheme.

[0152] Based on the same inventive concept, the embodiments of the present application also provide a multi-objective optimization control device for an electrochemical energy storage system for implementing the multi-objective optimization control method for an electrochemical energy storage system as described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in the following multi-objective optimization control device embodiments for an electrochemical energy storage system can be referred to the limitations of the multi-objective optimization control method for an electrochemical energy storage system described above, which will not be repeated here.

[0153] Please refer to Figure 2 , the embodiments of the present application provide a multi-objective optimization control device for an electrochemical energy storage system, comprising:

[0154] A data acquisition module is configured to acquire state data for measuring battery performance state, economic state and safety state in the electrochemical energy storage system at the current time and calculate corresponding index data;

[0155] A strategy generation module is configured to generate a plurality of sets of battery control strategy data containing different combinations of charging and discharging control parameters;

[0156] A calculation module is configured to calculate a plurality of states of the system at the next time based on the index data and the plurality of sets of battery control strategy data, using a pre-constructed state transition function; each state is represented by index data of battery performance state, economic state and safety state;

[0157] a multi-objective optimization module configured to evaluate multi-objective optimization results of a plurality of states of the system using a multi-objective optimization algorithm, and determine a set of better battery control strategies according to the evaluated multi-objective optimization results;

[0158] a strategy selection module configured to select a strategy that makes the state of the system most consistent with the degree of attention from the set of better battery control strategies as the optimal battery control strategy for the next time according to the degree of attention of the system to different indicators at the next time.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of the functional units and modules are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0160] Reference Figure 3 The embodiments of the present application also provide a computer device, comprising a memory and a processor and a computer program stored in the memory, when the computer program is executed on the processor, the multi-objective optimization control method for the electrochemical energy storage system is realized.

[0161] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that, Figure 3 It is only an example of the computer device, and does not constitute a limitation on the computer device, and can include more or fewer components than the illustration, or combine certain components, or different components, for example, it can also include input and output devices, network access devices and the like.

[0162] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0163] The memory can be an internal storage unit of the computer device in some embodiments, for example, a hard disk or a memory of the computer device. The memory can also be an external storage device of the computer device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, for example, program codes of the computer program, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0164] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the method for multi-target optimization control of an electrochemical energy storage system is implemented.

[0165] In the embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-described embodiment methods through a computer program to instruct relevant hardware to complete, and the computer program can be stored in a computer readable storage medium. The computer program is executed by a processor, and can implement the steps of each method embodiment described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0166] The embodiment of the present application provides a computer program product, comprising a computer program, which is executed by a processor to implement the multi-objective optimization control method suitable for an electrochemical energy storage system according to any one of the above methods.

[0167] In the above-described embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0168] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0169] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other manners. For example, the described apparatus / terminal device embodiments are merely schematic. For example, the division of the modules or units is merely logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0170] The foregoing embodiments are merely intended for describing the technical solutions of the present application, but not for limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-objective optimization control method suitable for electrochemical energy storage systems, characterized in that, The method comprises the following steps: Collecting state data of an electrochemical energy storage system at a current time for measuring battery performance state, economic state and safety state, and calculating corresponding index data; Generating a plurality of sets of battery control strategy data containing different combinations of charging and discharging control parameters; Based on the index data and the plurality of sets of battery control strategy data, calculating a plurality of states of the system at a next time using a pre-constructed state transition function; each state is represented by index data of battery performance state, economic state and safety state; Evaluating multi-objective optimization results of the plurality of states of the system using a multi-objective optimization algorithm, and determining a set of better battery control strategies according to the evaluated multi-objective optimization results; According to the degree of attention of the system to different indexes at the next time, selecting a strategy that makes the system state most consistent with the degree of attention from the set of better battery control strategies as the optimal battery control strategy at the next time.

2. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 1, characterized in that, The multi-objective optimization algorithm is NSGA-II algorithm based on Pareto optimal solution; Evaluating multi-objective optimization results of the plurality of states of the system using a multi-objective optimization algorithm, and determining a set of better battery control strategies according to the evaluated multi-objective optimization results, comprising: Constructing an initial population by taking the system state corresponding to each set of battery control strategies as an individual; Based on the initial population, evaluating the optimization results of each type of index of each individual using the objective function of the NSGA-II algorithm, and constructing a Pareto solution set; Removing solutions in the Pareto solution set that do not satisfy the safety constraint, and taking the battery control strategies corresponding to the remaining solutions as a set of better battery control strategies.

3. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 2, characterized in that, The mathematical expression of the objective function is as follows: wherein, represents a candidate battery control strategy, is a target vector for evaluating the candidate strategy, represents a desired calculation, , and respectively represent a performance degradation cost, an economic cost and a safety cost for quantifying the optimization results of the battery performance state, economic state and safety state indicators; the objective of the objective function is to minimize the sum of the performance degradation cost, economic cost and safety cost.

4. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 2, characterized in that, According to the degree of attention of the system to different indexes at the next time, selecting a strategy that makes the system state most consistent with the degree of attention from the set of better battery control strategies as the optimal battery control strategy at the next time, comprising: Determining the weight values of each type of index to quantify the degree of attention of the system to each type of index; Calculating the synthetic optimization result values of each better battery control strategy using the weight values of each type of index and the optimization results of each type of index under each better battery control strategy; Selecting the strategy corresponding to the smallest synthetic optimization result value as the optimal battery control strategy at the next time.

5. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 4, characterized in that, Further comprising updating the weight values of each type of index at each time, and the update formula is: wherein is a vector representing the weight composition of each type of indicator; is a normalization function; , and are parameter vectors; represents a performance state indicator of the battery; represents a safety state indicator of the battery, with the subscript t indicating the time.

6. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 2, characterized in that, The mathematical expression of the safety constraint is: wherein represents the security cost of the system at time t, represents the security cost threshold, represents the risk tolerance.

7. The multi-objective optimization control method suitable for electrochemical energy storage systems according to claim 1, characterized in that, The mathematical expression of the state transition function is: In the formula, and respectively represent the system state at time t and time t+1, containing index data of battery performance state, economic state and safety state; represents the battery control strategy at time t; represents the disturbance term at time t.

8. A multi-objective optimization control device for an electrochemical energy storage system, characterized by, Comprising: A data collection module for collecting state data of an electrochemical energy storage system at a current time for measuring battery performance state, economic state and safety state, and calculating corresponding index data; A strategy generation module for generating a plurality of sets of battery control strategy data containing different combinations of charging and discharging control parameters; A calculation module for calculating a plurality of states of the system at a next time based on the index data and the plurality of sets of battery control strategy data using a pre-constructed state transition function; each state is represented by index data of battery performance state, economic state and safety state; A calculation module for calculating a plurality of states of the system at a next time based on the index data and the plurality of sets of battery control strategy data using a pre-constructed state transition function; each state is represented by index data of battery performance state, economic state and safety state; The multi-objective optimization module is configured to evaluate multi-objective optimization results of a plurality of states of the system by using a multi-objective optimization algorithm, and determine a set of better battery control strategies according to the evaluated multi-objective optimization results; The strategy selection module is configured to select a strategy that makes the state of the system most consistent with the degree of attention of the system to different indexes from the set of better battery control strategies as the optimal battery control strategy at the next moment according to the degree of attention of the system to different indexes at the next moment.

9. A computer device, comprising: The device comprises a processor and a memory: The memory is configured to store a computer program and send instructions of the computer program to the processor; The processor executes the method according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method.

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