A multi-state battery hybrid dynamic control method and system

Abstract

The application discloses a kind of multi-state battery mixed use dynamic control method and system, belong to battery management technical field, its technical scheme key points include, through bidirectional conversion power supply, the charge-discharge data of each type of battery and the working parameter of bus side are collected;Discharge current limit and charging current limit are obtained based on the capacity remaining rate and internal resistance change rate of battery;When battery is in discharge operation, discharge current ratio is obtained based on discharge current limit and load demand through first genetic algorithm, and / or, when battery is in charging operation, charging current ratio is obtained according to charging current limit and preset charging priority through second genetic algorithm, the present application dynamically determines charge-discharge current limit based on charge-discharge data, and the optimal current ratio is obtained according to charge-discharge working condition through genetic algorithm, realize the full-condition dynamic accurate control of multi-type multi-state battery mixed use, improve system safety and the efficiency of energy utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a multi-state battery mixed use dynamic control method and system. BACKGROUND

[0002] With the rapid development of energy storage technology and new energy application, the mixed use of multiple types of batteries (such as lithium batteries and lead-acid batteries) is increasing, for example, in emergency power supply systems, high-energy-density lithium batteries and low-cost lead-acid batteries are often configured together, and when old equipment is upgraded, new / old lead-acid batteries and lithium batteries may coexist. However, the charging and discharging characteristics and loss states of multiple types of batteries are significantly different: lithium batteries have fast charging speed but are sensitive to overcharging and overdischarging, old lead-acid batteries have large internal resistance and serious capacity attenuation, and new lead-acid batteries are in the performance stable period.

[0003] The existing multi-type battery control method sets the charging and discharging limit based on the rated parameters of the battery, which may easily lead to overcurrent damage to old batteries or insufficient utilization of new batteries; the charging and discharging current ratio is usually allocated in a fixed manner or by using a simple priority strategy, and the optimization algorithm focuses on a single target, which may easily result in low load satisfaction or accelerated battery degradation, so the existing technology has defects. SUMMARY

[0004] In view of the defects of the existing technology, the purpose of the present application is to provide a multi-state battery mixed use dynamic control method and system, which calculates the capacity remaining rate and internal resistance change rate of the battery based on real-time collected battery charging and discharging data to dynamically obtain the charging and discharging current limit, optimizes the discharging current ratio by combining the discharging limit and load demand through a first genetic algorithm during discharging, optimizes the charging current ratio by combining the charging limit and charging priority through a second genetic algorithm during charging, solves the defects of parameter static and allocation strategy simplification in the existing battery mixed use control, prolongs the overall life of the battery while ensuring the stability of the load power supply, and improves the adaptability and energy utilization efficiency of the multi-type battery mixed use system.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] The present application provides a multi-state battery mixed use dynamic control method, comprising:

[0007] The charging and discharging data of each type of battery and the working parameters of the bus side are collected through a bidirectional conversion power supply;

[0008] Based on the capacity remaining rate and internal resistance change rate of each type of battery, the discharging current limit and the charging current limit of each type of battery are obtained, and the capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data;

[0009] when each type of battery is in discharging operation, a discharging current ratio of each type of battery is obtained by a first genetic algorithm based on the discharging current limit and a load demand, the load demand being obtained based on an operating parameter of the bus side;

[0010] and / or;

[0011] when each type of battery is in charging operation, a charging current ratio of each type of battery is obtained by a second genetic algorithm according to the charging current limit and a preset charging priority, the preset charging priority being obtained based on charging and discharging data of each type of battery.

[0012] As a further improvement of the present application, the discharging current limit and the charging current limit of each type of battery are obtained based on a capacity remaining rate and a internal resistance change rate of each type of battery, comprising:

[0013] the capacity remaining rate and the internal resistance change rate are obtained based on the charging and discharging data of each type of battery;

[0014] the discharging current limit is obtained by correcting the rated discharging limit of each type of battery under discharging operation based on the capacity remaining rate and the internal resistance change rate;

[0015] the charging current limit is obtained based on the type and the capacity remaining rate and the internal resistance change rate of each type of battery under charging operation.

[0016] As a further improvement of the present application, the capacity remaining rate and the internal resistance change rate are obtained based on the charging and discharging data of each type of battery, comprising:

[0017] the historical charging and discharging cycle times, the actual battery capacity and the internal resistance of each type of battery are obtained based on the charging and discharging data;

[0018] the capacity decay rate and the capacity remaining rate of each type of battery are calculated based on the actual battery capacity and a rated capacity of each type of battery, the rated capacity being obtained according to a battery specification;

[0019] the internal resistance growth rate is calculated based on the internal resistance and an initial internal resistance of each type of battery;

[0020] the battery health state and the internal resistance change rate of each type of battery are obtained by weighted summation based on the capacity remaining rate, the internal resistance growth rate and the cycle times.

[0021] As a further improvement of the present application, the discharging current ratio of each type of battery is obtained by the first genetic algorithm based on the discharging current limit and the load demand when each type of battery is in discharging operation, comprising:

[0022] obtaining the discharge state of charge of each type of battery by ampere-hour integration method based on the first discharge current of each type of battery;

[0023] obtaining the discharge internal resistance of each type of battery by direct current discharge method;

[0024] obtaining the discharge current ratio of each type of battery by first genetic algorithm according to the discharge current limit, the discharge state of charge and the discharge internal resistance of each type of battery;

[0025] obtaining the second discharge current of each type of battery by proportional distribution based on the discharge current ratio and the load current of bus side.

[0026] As a further improvement of the present application, the obtaining of the discharge current ratio of each type of battery by first genetic algorithm comprises:

[0027] determining the objective function and the constraint condition of the first genetic algorithm based on the discharge efficiency, the discharge state of charge, the discharge internal resistance and the load demand of each type of battery at the current moment;

[0028] obtaining the initial population according to the discharge current limit of each type of battery at the current moment;

[0029] calculating the fitness value of each individual by substituting the current ratio combination in the initial population into the objective function;

[0030] updating the initial population based on the fitness value by selection, crossover and mutation operations to obtain a new generation of candidate current ratio combination;

[0031] obtaining the optimal discharge current ratio of each type of battery by selecting the individual with the highest fitness value from the updated initial population through iteration.

[0032] As a further improvement of the present application, when each type of battery is in charging operation, the charging current ratio of each type of battery is obtained by second genetic algorithm according to the charging current limit and the preset charging priority, comprising:

[0033] obtaining the charging state of charge of each type of battery by ampere-hour integration method based on the first charging current of each type of battery, and obtaining the charging internal resistance and the effective full state of charge of each type of battery by Ohm's law;

[0034] obtaining the priority constraint according to the charging scenario by preset charging priority, and obtaining the total charging current constraint according to the maximum current of the bidirectional conversion power supply;

[0035] obtaining, by a second genetic algorithm, optimal charging current proportions of the batteries of each type based on the charging current limits, the charging state of charge and the charging internal resistance of the batteries of each type, the priority constraint and the total charging current constraint;

[0036] obtaining, by proportional distribution, second charging currents of the batteries of each type based on the charging current proportions and the input current of the bus side.

[0037] As a further improvement of the application, the priority constraint is obtained according to the charging scenario through a preset charging priority, which comprises:

[0038] obtaining the preset charging priority based on the charging sequence and the corresponding priority order weight of each type of battery preset in advance based on historical charging scenarios;

[0039] obtaining the priority constraint and the actual corresponding priority order weight of each type of battery according to the charging scenario by calling the preset charging priority through pattern recognition.

[0040] As a further improvement of the application, the optimal charging current proportions of the batteries of each type are obtained by a second genetic algorithm, which comprises:

[0041] obtaining a target function of the second genetic algorithm according to different targets under the charging condition based on the charging current limits, the effective full state of charge and the charging internal resistance of the batteries of each type, the target function comprising a first target function and a second target function;

[0042] determining a constraint condition of the second genetic algorithm according to the total charging current constraint and the charging current limit of each type of battery;

[0043] obtaining an initial population of the second genetic algorithm by randomly generating charging current proportion combinations satisfying the constraint condition, the initial population comprising a first initial population and a second initial population;

[0044] obtaining a first individual and a second individual by calculating the fitness values of individuals in the first initial population and the second initial population based on the target function, and obtaining an updated first population according to a preset migration period;

[0045] obtaining a new generation of candidate charging current proportion combinations by selecting, crossing and mutating the updated first population;

[0046] obtaining the optimal charging current proportions of the batteries of each type by selecting the individual with the highest fitness from the updated first population through iteration.

[0047] As a further improvement of the present application, the first individual and the second individual are obtained by calculating the fitness values of the individuals in the first initial population and the second initial population based on the target function, and the updated first population is obtained according to a preset migration period, comprising:

[0048] The first fitness value of the individual in the first initial population is calculated according to the first target function, and the first individual is obtained.

[0049] The second fitness value of the individual in the second initial population is calculated according to the second target function, and the second individual is obtained.

[0050] According to the preset migration period, the first individual of the first initial population is replaced by the second individual of the second initial population, and the updated first population is obtained.

[0051] The present application provides a multi-state battery mixed use dynamic control system, comprising:

[0052] The data acquisition module acquires the charge and discharge data of each type of battery and the operating parameters of the bus side through a bidirectional conversion power supply.

[0053] The parameter calculation module obtains the discharge current limit and the charge current limit of each type of battery based on the capacity remaining rate and the internal resistance change rate of each type of battery, and the capacity remaining rate and the internal resistance change rate are obtained according to the charge and discharge data.

[0054] The discharge control module obtains the discharge current proportion of each type of battery through a first genetic algorithm based on the discharge current limit and the load demand when each type of battery is in discharge operation, and the load demand is obtained based on the operating parameters of the bus side.

[0055] And / or

[0056] The charge control module obtains the charge current proportion of each type of battery through a second genetic algorithm according to the charge current limit and the preset charge priority when each type of battery is in charge operation, and the preset charge priority is obtained based on the charge and discharge data of each type of battery.

[0057] The application calculates the capacity remaining rate and internal resistance change rate of the battery based on the charging and discharging data, obtains the charging and discharging current limit suitable for the actual state of the battery, and respectively adopts the first and second genetic algorithms for different operating states of charging and discharging, and optimizes and distributes the current proportion in combination with the load demand or the preset charging priority, solves the problems of parameter setting solidification and simple current distribution strategy in the prior art, realizes the precision and dynamic control in the charging and discharging working condition of the multi-type battery mixed use system, improves the energy utilization efficiency and operation reliability of the system, prolongs the overall life of the battery and guarantees the load power supply stability. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A step flow chart of a multi-state battery mixed use dynamic control method provided by the application is shown in the figure.

[0059] Figure 2 A step flow chart for determining the discharge current proportion under the discharge working condition is shown in the figure.

[0060] Figure 3 A step flow chart for determining the charging current proportion under the charging working condition is shown in the figure.

[0061] Figure 4 A schematic diagram for determining the charging current proportion under the emergency energy storage scene of the charging working condition is shown in the figure.

[0062] Figure 5 A step flow chart of a protection mechanism is shown in the figure.

[0063] Figure 6 A structural schematic diagram of a multi-state battery mixed use dynamic control system provided by the application is shown in the figure. DETAILED DESCRIPTION

[0064] The technical scheme of the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical scheme of the application, rather than limitations of the technical scheme of the application.

[0065] Wherein the same parts are indicated by the same reference numerals. It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "bottom surface" and "top surface", "inner" and "outer" refer to the directions towards or away from the geometric center of a particular part.

[0066] The term "and / or" in the following description only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " generally represents that the front and rear associated objects have an "or" relationship.

[0067] As Figure 1 shown, a multi-state battery mixed use dynamic control method provided by the application comprises:

[0068] The charging and discharging data of each type of battery and the working parameters of the bus side are collected through the bidirectional conversion power supply;

[0069] The discharge current limit and the charging current limit of each type of battery are obtained based on the capacity remaining rate and the internal resistance change rate of each type of battery, and the capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data;

[0070] When each type of battery is in discharging operation, the discharge current proportion of each type of battery is obtained through the first genetic algorithm based on the discharge current limit and the load demand, and the load demand is obtained based on the operating parameters of the bus side;

[0071] When each type of battery is in charging operation, the charging current proportion of each type of battery is obtained through the second genetic algorithm according to the charging current limit and the preset charging priority, and the preset charging priority is obtained based on the charging and discharging data of each type of battery.

[0072] Among them, the bidirectional conversion power supply is the SSCD5CL048100 bidirectional conversion power supply, the current and voltage flow bidirectionally between the battery side and the bus side, realizing the dynamic switching of the two modes of charging and discharging; each type of battery includes old lead-acid battery, new lead-acid battery and lithium battery; the charging and discharging data includes the first charging current, the first charging voltage and the first charging temperature under the charging condition, the first discharging current, the first discharging voltage and the first discharging temperature under the discharging condition, and the cycle number, wherein the charging and discharging data is collected in real time through the bidirectional conversion power supply under the charging or discharging condition; the working parameters of the bus side include the load current and the load voltage during discharging, and the input voltage and the input current during charging; the charging and discharging data and the working parameters of the bus side are collected in real time through the built-in detection module of the bidirectional conversion power supply according to the preset sampling frequency, then transmitted to the control unit through CAN communication, and the internal resistance and the real-time battery capacity of each type of battery are obtained according to the charging and discharging data; the bus side working parameters are collected through the bus sensor; the first genetic algorithm is an algorithm for obtaining the optimal discharging current proportion through population iteration optimization based on the discharge current limit, the discharge state of charge, the discharge internal resistance and the load demand when the battery is discharging; the second genetic algorithm is an algorithm for obtaining the optimal charging current proportion through population iteration optimization based on the charging current limit, the charging state of charge, the charging internal resistance and the preset charging priority, combined with the total charging current constraint, when the battery is charging.

[0073] The embodiment collects charging and discharging data of multiple types of batteries and bus side working parameters through a bidirectional conversion power supply, dynamically corrects charging and discharging current limits based on the charging and discharging data, respectively adopts a first genetic algorithm considering load satisfaction and discharge balance and a second genetic algorithm optimizing charging efficiency and effective full charge state balance according to charging and discharging conditions to obtain optimal current proportion, realizes dynamic control in a mixed use scene of multiple types of batteries in multiple states, improves the matching degree of current distribution and real-time performance of the batteries, the adaptation accuracy of the algorithm to scene requirements and the defense ability of the system to abnormal risks, and improves the safety, energy utilization efficiency and long-term stability of system operation, and prolongs the service life of the batteries.

[0074] Further, the embodiment provides a step of obtaining a discharging current limit and a charging current limit of each type of battery based on a capacity remaining rate and a resistance change rate of each type of battery, including:

[0075] obtaining the capacity remaining rate and the resistance change rate according to the charging and discharging data of each type of battery;

[0076] correcting the rated discharging limit of each type of battery under the discharging condition based on the capacity remaining rate and the resistance change rate to obtain the discharging current limit;

[0077] obtaining the charging current limit according to the type of the battery, the capacity remaining rate SOH and the resistance change rate under the charging condition.

[0078] The loss state of each type of battery is reflected by the capacity remaining rate SOH, the resistance change rate and the cycle number. The capacity remaining rate SOH is obtained by the current capacity and the rated capacity of each type of battery. The resistance change rate is obtained based on the internal resistance of each type of battery. The rated discharging limit of each type of battery is obtained by the product specification.

[0079] Specifically, first, the capacity remaining rate SOH is calculated based on the rated capacity and the real-time capacity of each battery by wherein is the rated capacity of the battery in a brand-new state, which is obtained according to the product specification, is the current capacity of the battery at the sampling moment, which is obtained by the rated capacity of the battery and the change of the charge amount of the battery; the internal resistance of the battery at the current moment is obtained based on the voltage and current of each type of battery, and the resistance change rate is obtained by wherein is the rated internal resistance of the battery, which is obtained according to the product specification, is the internal resistance of the battery at the current moment, which is obtained by the change rate of the voltage and current curve;

[0080] Then, the rated capacity of the battery is ​and discharge rate obtaining the rated maximum discharge current of the battery , the discharge current limit is obtained by correcting the preliminary maximum discharge current of the battery according to the capacity remaining rate and the internal resistance change rate , the calculation formula is:

[0081] ;

[0082] wherein, is the capacity remaining rate, are the temperature influence coefficient and the safety coefficient respectively, which are obtained by experiment fitting;

[0083] Then, according to the battery type, the battery is divided into lithium battery and lead-acid battery, the rated maximum charging current is obtained according to the product specification of the battery, and then the charging current limit of different battery types is obtained based on the capacity remaining rate and the internal resistance change rate of the battery, and the calculation formula is:

[0084] ;

[0085] ;

[0086] wherein is the charging current limit of the lead-acid battery under the charging condition, is the rated maximum charging current of the lead-acid battery, is the cycle number, is the charging current limit of the lithium battery under the charging condition, is the rated maximum charging current of the lithium battery, are fitting coefficients, the value range is , and are obtained by the product specification; the charging current limit formula of the lead-acid battery and the lithium battery is based on the rated maximum charging current of the battery, by introducing the capacity remaining rate and the internal resistance growth rate as quantitative parameters, and additionally introducing the negative exponential term based on the cycle number for the lead-acid battery to adapt to its nonlinear cycle attenuation characteristics, the lithium battery does not need the negative exponential term; at the same time, the fitting coefficients in the interval [0, 1] are combined for weighted calculation, to determine the charging current limit of the lead-acid battery and the lithium battery under the charging condition respectively, to realize the dynamic current limiting adaptation based on the battery loss state parameters.

[0087] Further, the embodiment provides a step of obtaining the capacity remaining rate and the internal resistance change rate according to the charging and discharging data of each type of battery, comprising:

[0088] obtaining the cycle number, the actual battery capacity and the internal resistance of each type of battery according to the charging and discharging data;

[0089] The capacity attenuation rate and the capacity remaining rate of each type of battery are calculated based on the actual battery capacity and the rated capacity of each type of battery, and the rated capacity is obtained according to the battery specification book;

[0090] The internal resistance growth rate is calculated based on the internal resistance and the initial internal resistance of each type of battery;

[0091] The battery state of health and the internal resistance change rate of each type of battery are obtained by weighted summation based on the capacity remaining rate, the internal resistance growth rate and the historical number of charge and discharge cycles.

[0092] The capacity remaining rate and the internal resistance change rate reflecting the loss state of the battery aging level are calculated based on the charge and discharge data of each type of battery, and the rated discharge limit under the discharge condition is dynamically corrected according to the capacity remaining rate and the internal resistance change rate to obtain the discharge current limit, and the charging current limit is determined in combination with the battery type characteristics and the loss state under the charging condition, which effectively solves the problem that the existing technology only relies on the rated parameters of the battery to set a fixed current limit and cannot adapt to the capacity attenuation and internal resistance growth of the battery during use, realizes the deep coupling of the current limit and the real-time performance state of the battery, improves the accuracy of the charge and discharge control in the mixed use scene of multiple types and multiple states of batteries, and avoids the overcurrent damage of old batteries or the waste of new battery performance due to the mismatch between the limit and the actual state.

[0093] Further, the embodiment provides a step of obtaining the discharge current proportion of each type of battery through a first genetic algorithm based on the discharge current limit and the load demand when each type of battery is in discharge operation, and the load demand is obtained based on the operating parameters of the bus side, including:

[0094] The discharge state of charge of each type of battery is obtained by ampere-hour integration method based on the first discharge current of each type of battery;

[0095] The discharge internal resistance of each type of battery is obtained by direct current discharge method;

[0096] The discharge current proportion of each type of battery is obtained by the first genetic algorithm according to the discharge current limit, the discharge state of charge and the discharge internal resistance of each type of battery;

[0097] The second discharge current of each type of battery is obtained by proportional distribution based on the discharge current proportion and the load current of the bus side.

[0098] The first discharge current refers to the actual discharge current of each type of battery at the current moment, which is acquired in real time by the built-in current sensor of the bidirectional converter; the state of discharge refers to the ratio of the battery's remaining capacity to its rated capacity at the current moment, which is obtained based on the first discharge current using the ampere-hour integration method; the current discharge internal resistance is the battery's discharge resistance at the current moment, which is obtained using the DC discharge method; the load current on the bus side is the sum of the first discharge currents of each type of battery, which is the total discharge current; the discharge current ratio refers to the ratio of the discharge current of each type of battery to the load current on the bus side, which is obtained using the first genetic algorithm; the second discharge current is the final discharge current of each type of battery obtained by proportionally allocating the discharge current ratio of each type of battery obtained based on the first genetic algorithm with the load current on the bus side.

[0099] Specifically, such as Figure 2 As shown, first collect the first Historical discharge data for each battery, including historical discharge current. and historical discharge temperature Based on the first discharge current The current time is obtained by the ampere-hour integration method. The The state of charge of each battery :

[0100] ;

[0101] in This represents the charge state at the initial stage of discharge. For the first The rated battery capacity of each battery, For the first The first discharge current of the next sample, The sampling interval time is used to calculate the State of Charge (SOC). The formula for calculating the SOC is based on the charge state at the initial discharge state. The total discharge charge is obtained by accumulating the product of the first discharge current and the sampling interval time. Then, the ampere-hour integration method is used in combination with the battery's rated capacity to finally obtain the battery's SOC at the current moment, providing basic SOC data for discharge control.

[0102] Then, the current moment during the discharge process is obtained using the DC discharge method. The The discharge internal resistance of each battery ,in and For the current moment The The current and voltage of each battery;

[0103] After that, according to the discharge current limit, the discharge state of charge and the discharge internal resistance of each type of battery, the discharge current proportion of each type of battery is obtained by the first genetic algorithm The objective function of the first genetic algorithm is set to maximize the satisfaction degree of the total discharge current of the bus side , while ensuring that the discharge current of each type of battery does not exceed its discharge current limit , and the discharge process is as uniform as possible to avoid excessive discharge or charge of a certain battery, thereby prolonging the service life of the battery pack, is the discharge current limit corresponding to the i-th battery. The constraint conditions include that the sum of the discharge current proportions of each type of battery is 1, and the discharge current of each battery does not exceed its discharge current limit; through the iterative process of the first genetic algorithm, individuals with high fitness are continuously selected and individuals with low fitness are eliminated, and the optimal solution is gradually obtained; the fitness value is calculated according to the objective function, which reflects the comprehensive performance of the current individual in meeting the load demand, ensuring the safety of the battery, prolonging the service life of the battery, etc.

[0104] Finally, based on the discharge current proportion and the total discharge current , the second discharge current of the i-th battery is obtained by proportional distribution , wherein .

[0105] Further, the embodiment provides a step of obtaining the discharge current proportion of each type of battery by the first genetic algorithm, comprising:

[0106] Based on the discharge efficiency, the discharge state of charge, the discharge internal resistance and the load demand of each type of battery at the current time, the objective function and the constraint condition of the first genetic algorithm are determined;

[0107] According to the discharge current limit of each type of battery at the current time, an initial population is obtained;

[0108] By substituting the current proportion combination in the initial population into the objective function, the fitness value of each individual is calculated;

[0109] Based on the fitness value, the initial population is updated by selection, crossover and mutation operations to obtain a new generation of candidate current proportion combinations;

[0110] Through iteration, the individual with the highest fitness value is selected from the updated initial population to obtain the optimal discharge current proportion of each type of battery.

[0111] The objective function is a mathematical expression for measuring the pros and cons of the discharge current proportion combination, used to guide the algorithm to iterate to the optimal solution that meets the priority; the constraint condition is a rule for limiting the value range of the discharge current proportion; the initial population is a starting solution set of the first genetic algorithm, composed of randomly generated discharge current proportion combinations that meet the constraint condition; the fitness value is an index for evaluating the pros and cons of each individual in the population, i.e., a group of discharge current proportions, and the higher the fitness value, the better the proportion combination; selection, crossover and mutation are operations of the first genetic algorithm, selection is used to retain high-quality individuals that meet the priority, crossover is used to fuse the excellent characteristics of different individuals to obtain new individuals, and mutation is used to introduce randomness to avoid local optimum; iteration refers to the process of repeatedly performing selection, crossover and mutation operations to optimize the population until the optimal individual is obtained; the optimal discharge current proportion is the individual with the highest fitness value selected from the population after iteration, i.e., the proportion of the discharge current of each type of battery in the total load current.

[0112] Specifically, first, the discharge current limit of the first battery is determined according to the type of the battery ; then the objective function of the first genetic algorithm is determined by multi-objective weighting according to the discharge state of charge and the discharge internal resistance of the first battery

[0113] ;

[0114] wherein, is the value of the objective function of the first genetic algorithm, is the coefficient of the corresponding parameter, is the average value of , and is the first discharge current of the first battery; thereafter, the constraint condition of the first genetic algorithm is determined according to the discharge current limit of the first battery , ; the first genetic algorithm objective function formula is optimized to minimize the objective function, and the weighted sum of the deviation sum of the load current and the discharge current of each battery, the deviation sum of the discharge state of charge of each battery and the average value, and the product sum of the square of the discharge current limit of each battery and the discharge internal resistance is performed by the weight coefficient, while the discharge current of each battery does not exceed its limit and the total discharge current matches the load demand as the constraint condition, a multi-objective optimization model under the discharge condition is constructed;

[0115] Further, the initial discharge current proportion is encoded into a chromosome , wherein ,​​​​​​​ is the number of batteries; then randomly generate an initial population of individuals satisfying the constraint condition, wherein the initial population satisfies that the sum of the current proportions is 1 and the discharge current limit of each type of battery is not exceeded; then the current proportion combination in the initial population is substituted into the objective function to calculate the fitness value of each individual , is the objective function value of the first genetic algorithm corresponding to the i-th battery; based on the fitness value , the population is updated through selection, crossover and mutation operations to obtain a new generation of candidate current proportion combinations; finally, the optimal individual after iterations is taken as the final discharge current proportion , The specific numerical value is set by a person skilled in the art according to the actual situation.

[0116] The embodiment solves the multi-objective optimization of the discharge current proportion through the first genetic algorithm, taking into account the load satisfaction degree and the battery SOC balance, and overcomes the defects of low load satisfaction degree and unbalanced battery attenuation caused by the fixed current distribution or simple priority strategy in the prior art, realizes the optimal balance among the multiple objectives of meeting the load demand, ensuring the safety of the battery and prolonging the life of the battery, and significantly improves the synergy and energy utilization efficiency of the discharge process of multiple types of batteries.

[0117] Further, the embodiment provides a step of obtaining the charging current proportion of each type of battery when each type of battery is in a charging operation according to the charging current limit and the preset charging priority through the second genetic algorithm, comprising:

[0118] Based on the first charging current of each type of battery, the charge state of charge of each type of battery is obtained through the ampere-hour integration method, and the charging internal resistance and the effective full charge state of each type of battery are obtained through Ohm's law;

[0119] According to the charging scenario, the priority constraint is obtained through the preset charging priority, and the total charging current constraint is obtained according to the maximum current of the bidirectional conversion power supply;

[0120] Based on the charging current limit, the charge state of charge and the charging internal resistance of each type of battery, and in combination with the priority constraint and the total charging current constraint, the optimal charging current proportion of each type of battery is obtained through the second genetic algorithm;

[0121] Based on the charging current proportion and the input current of the bus side, the second charging current of each type of battery is obtained through proportional distribution.

[0122] Among them, the first charging current refers to the actual charging current of each type of battery at the current moment; the state of charge is the ratio of the battery's current remaining capacity to its rated capacity; the charging internal resistance refers to the internal resistance of the battery in the charging state; the effective full charge state is a comprehensive quantitative index of the battery obtained based on the actual charging state and charging internal resistance, used to quantify the effective full charge capability of different types of batteries; the preset charging priority is the battery charging sequence pre-set according to the charging scenario, which includes emergency energy storage and equalization maintenance, used to obtain priority constraints based on the actual charging scenario; the maximum current of the bidirectional converter is the maximum output current that the bidirectional converter can provide in charging mode, which is the upper limit of the total charging current; the total charging current constraint refers to the limit that the total charging current must not exceed, which is the sum of the charging currents of each type of battery; the total input current on the bus side is the total charging current actually input on the bidirectional converter bus side, which cannot exceed the total input current constraint; the charging current ratio refers to the optimal ratio of the charging current of each type of battery to the total charging current; the second charging current is the final charging current of each type of battery obtained by proportional allocation based on the charging current ratio of each type of battery obtained by the second genetic algorithm and the bus side input current.

[0123] Specifically, such as Figure 3 As shown, first collect the first Historical charging data for each battery, including historical charging current. and historical charging temperature Based on the first charging current The current time is obtained using the ampere-hour integration method. The The state of charge of each battery :

[0124] ;

[0125] in This represents the charge state at the initial charging stage. For the first The rated capacity of each battery, For the first The first discharge current of the next sample, The sampling interval time is used for the calculation of the state of charge (SOC). Based on the initial charge state, the total charge is obtained by summing the product of the first charging current and the sampling interval time. Combined with the battery's rated capacity, the ampere-hour integration method is used to obtain the current SOC parameter, providing support for charging control. Then, Ohm's law is used to obtain the current SOC parameter during the charging process. The The charging internal resistance of each battery ,in and is the current time is the current time is the current time is the current time is the current time is the current time is the current time is the current time is the current time is the current time

[0126] is the current time is the current time

[0127] is the current time is the current time

[0128] is the current time is the current time is the current time The mutation probability executes a random mutation operation to obtain a new generation of population; every interval of a preset migration period, the highest fitness equilibrium elite individual in the shadow population is migrated to the main population to replace the lowest fitness individual in the main population; the evolution and migration process is repeated until the fitness change rate is less than a preset fitness threshold, and finally the individual with the highest fitness in the main population is obtained, and the corresponding charging current proportion combination is the optimal charging current proportion; the optimal charging current proportion is subjected to charging current limit checking, if the charging current of a certain battery exceeds the charging current limit thereof, the current is adjusted to the charging current limit thereof, and the remaining current is redistributed according to the priority proportion of the remaining batteries, to ensure that the final output optimal charging current proportion meets the safe operation requirements of each type of battery;

[0129] Finally, according to the charging current proportion and the total input current of the bus side , the second charging current of each type of battery is calculated by proportional distribution , wherein . is the charging current limit of the first battery.

[0130] For example, as shown in the figure, the embodiment provides a system including a lithium battery M, a use time Figure 4 , a new lead-acid battery N, a use time , an old lead-acid battery L, a use time , the parameters of each battery are as follows: the first charging current , the sampling interval , the battery state of charge at the initial state of charging is , the rated battery capacity is ; the charging internal resistance measured by Ohm's law is , the historical maximum charging internal resistance is , the correlation coefficient is , the maximum current of the bidirectional conversion power supply is , the input current of the bus side is ; the charging current limit , the preset minimum proportion difference is . When the mode recognition module determines that it is an emergency energy storage scene, the priority is set based on the charging speed and energy density priority principle , wherein the priority ordering weight is , wherein , and are the quantization values of the priority ordering weights corresponding to the lithium battery M, the new lead-acid battery N and the old lead-acid battery L respectively. In the implementation process, the three battery charging states of charge are first calculated, and the effective full state of charge is obtained in combination with the charging internal resistance ; then determine the total charging current constraint as the sum of the second charging currents corresponding to the lithium battery M, the new lead-acid battery N, and the old lead-acid battery L being less than or equal to ; then obtain the optimal charging current ratio through the second genetic algorithm ; finally, the second charging current is calculated , satisfying all constraints and the preset charging priority. When calculating the second charging current, if , it is adjusted to , wherein is the second charging current corresponding to the old lead-acid battery L that meets the constraint condition, obtained through the charging current limit check, and the remaining current is redistributed according to , finally achieving a significant reduction in the effective full state-of-charge equalization error.

[0131] The embodiment is based on accurate quantification of battery state, dynamic adaptation of scene constraints, and double-population collaborative optimization. Through the integration of ampere-hour integral method and equivalent circuit model calculation parameters, and the individual migration between the two populations of the second genetic algorithm, multi-objective optimization is achieved. This not only solves the problem of traditional single-objective optimization that cannot balance charging efficiency and equalization, but also adapts the effective full state-of-charge to the battery health state, reduces the charging risk of aging batteries, and does not require additional hardware devices, making it suitable for multiple scenarios and multiple types of battery packs. This significantly improves the safety, efficiency, and scene versatility of the charging system.

[0132] Further, the embodiment provides a step of obtaining a priority constraint according to a charging scene through a preset charging priority, comprising:

[0133] previously setting the charging order and the corresponding priority ordering weight of each type of battery based on historical charging scenes to obtain the preset charging priority;

[0134] obtaining the actual charging scene through pattern recognition, calling the preset charging priority according to the charging scene, and obtaining the priority constraint and the priority ordering weight corresponding to each type of battery.

[0135] The preset charging priority is the battery charging order previously set according to the historical charging scene, used to clarify the priority level of different types of batteries in the charging current allocation, and is previously set through the association between the scene and the battery characteristics. The charging scene is the application background during the battery charging process, including the emergency energy storage scene and the equalization maintenance scene. The emergency energy storage scene is a charging situation to cope with sudden power supply demand, used to accumulate enough emergency energy in a short time. The equalization maintenance scene is a charging situation for stable operation of the system as a whole, used to balance the battery state and prolong the service life of the battery. The priority ordering weight is a quantitative indicator reflecting the priority level of the battery, and the higher the charging priority, the greater the corresponding priority ordering weight.

[0136] Specifically, first, the charging priority rules are set according to the charging sequence and quantitative weight of the current distribution of the batteries of different types and different loss states in the historical charging scene, to limit the charging current proportion relationship and weight proportion of each type of battery; in the emergency energy storage scene, based on the target of quickly reserving energy, the charging priority rules are set as follows: the priority is set in the order from high to low according to the charging speed and the state of charge, and the priority ranking weight is distributed according to the rules that the faster the charging speed, the better the state of charge, and the greater the weight; this embodiment assumes that the charging current proportion of a certain lithium battery is , the priority ranking weight is , the charging current proportion of another new lead-acid battery is , the priority ranking weight is , the charging current proportion of another old lead-acid battery is , the priority ranking weight is , the constraint condition is and , and is satisfied; to ensure the effectiveness of the priority order, the minimum proportion difference is set, that is, , ; and in the balanced maintenance scene, based on the target of maintaining the stability of the battery system, the charging priority rules are set as follows: the priority is set in the order of the lowest state of charge and the longest use time, and the priority ranking weight is distributed according to the rules that the lower the state of charge, the longer the use time, and the greater the weight; another embodiment of the application assumes that the charging current proportion of the battery with the lowest state of charge and the longest use time is , the priority ranking weight is , the charging current proportion of the other batteries is , the priority ranking weight is , the constraint condition is and , and is satisfied; the charging priority and the corresponding priority ranking weight are pre-configured through the association between the scene and the battery characteristics, and are stored in the system control module; the system determines the current charging scene through the mode recognition module, determines that it is an emergency energy storage scene when receiving an external emergency signal such as a power grid outage warning; when it is detected that the system is in a stable running state, it is determined to be a balanced maintenance scene; according to the identified charging scene, the control module calls the corresponding charging priority constraint condition and priority ranking weight from the storage unit, inputs them into the constraint condition set and the objective function of the second genetic algorithm, and the priority constraint, the battery charging current limit, and the total charging current constraint jointly constitute the total constraint condition.

[0137] An example is provided, which includes a lithium battery A (state of charge 90%), a new lead-acid battery B (use time 4 months, state of charge 60%), an old lead-acid battery C (use time 10 months, state of charge 40%), and the total charging current is , the minimum ratio difference . When the system identifies the charging scenario as an emergency energy storage scenario, the second genetic algorithm needs to meet , and , wherein are the initial charging current ratios of the lithium battery A, the new lead-acid battery B, and the old lead-acid battery C based on the constraint condition, are the corresponding weight quantization values of the lithium battery A, the new lead-acid battery B, and the old lead-acid battery C. Assuming that the optimal ratio obtained after iteration is , the corresponding charging currents are , which meet the preset charging priority and weight constraints; when the system identifies as a balancing maintenance scenario, the constraint condition is , and , and the final optimal ratio is , the corresponding charging currents are , which meet the charging priority and weight requirements of the balancing maintenance scenario.

[0138] Further, the embodiment provides a step of obtaining the optimal charging current ratio of each type of battery by the second genetic algorithm, comprising:

[0139] Based on the charging current limit, the effective full charge state, and the charging internal resistance of each type of battery, the objective function of the second genetic algorithm is obtained according to different targets under the charging working condition, and the objective function includes a first objective function and a second objective function;

[0140] According to the total charging current constraint and the charging current limit of each type of battery, the constraint condition of the second genetic algorithm is determined;

[0141] The initial population of the second genetic algorithm is obtained by randomly generating a charging current ratio combination that satisfies the constraint condition, and the initial population includes a first initial population and a second initial population;

[0142] The fitness values of the individuals in the first initial population and the second initial population are calculated based on the objective function to obtain a first individual and a second individual, and the updated first population is obtained according to a preset migration period;

[0143] The new generation of candidate charging current ratio combinations are obtained by selecting, crossing, and mutating the updated first population;

[0144] The optimal charging current proportion of each type of battery is obtained by selecting the individual with the highest fitness from the updated first population through iteration.

[0145] wherein the different targets under the charging condition include a charging efficiency maximization target and a battery state equalization target; the first objective function is an objective function of a main population for optimizing charging efficiency; the second objective function is an objective function of a shadow population for optimizing battery state equalization; the first initial population is the main population, including an initial individual set of the main population; the second initial population is the shadow population, including an initial individual set of the shadow population; the first individual is an individual with the smallest first fitness value calculated according to the first objective function in the first population; and the second individual is an individual with the largest second fitness value calculated according to the objective function of the second population in the second population.

[0146] Specifically, first, different targets under the charging condition are defined, the different targets under the charging condition include a charging efficiency maximization target and a battery state equalization target, the charging efficiency maximization target aims to shorten the overall charging time and match the efficiency-oriented scenario of emergency energy storage; and the battery group state equalization target aims to reduce the difference in the battery state of charge of each type of battery and match the health-oriented scenario of equalization maintenance.

[0147] Secondly, based on the charging current limit, the effective full state of charge and the charging internal resistance of each type of battery, a first objective function and a second objective function are constructed; the first objective function is the objective function of the main population, and the calculation formula is:

[0148] ;

[0149] wherein, is the first objective function value, according to total charging time , is the target state of charge of the first type of battery, set according to the actual scenario, is the rated capacity of the first battery, is the input current on the bus side, is the charging current proportion of the first battery, is the quantized value of the priority ranking weight of the first battery, is the charging current of the first battery, is the charging current reference allocation proportion of the first battery, and minimizing is the core target of shortening the charging time under the emergency energy storage scenario, is the target state of charge of the first Priority weight of each battery, g is the number of batteries; is a weight coefficient and satisfies , the efficiency and priority are optimized by weight distribution; the second objective function is the objective function of the shadow population, and the calculation formula is:

[0150] ;

[0151] The second objective function is constructed based on the mean square error minimization and energy loss optimization theory, wherein, is the second objective function value, is the average value of the effective full charge state, which is obtained according to the statistical mean calculation principle, is the effective full charge state, is the charging resistance of the first battery, is the charging current limit of the first battery, is a corresponding weight coefficient and satisfies , and the balance and loss are cooperatively controlled by weight distribution;

[0152] Then, the constraint condition of the second genetic algorithm is determined according to the charging current limit of the first battery and the total charging current constraint: and , wherein is the charging current limit of the first battery, is the maximum current of the bidirectional conversion power supply, and ;

[0153] Then, the initial population of the second genetic algorithm is obtained by randomly generating a charging current proportion combination satisfying the constraint condition, the initial population includes a first initial population and a second initial population, and the initial population satisfies and the constraint condition of the second genetic algorithm; the first initial population is a charging current proportion vector set meeting the scene priority constraint, and the first initial population is obtained by randomly generating a proportion vector and filtering individuals not meeting the constraint condition, so that the individuals in the first initial population meet the engineering actual operation boundary; the second initial population is a charging current proportion vector set meeting the effective full charge state guidance, and the second initial population is obtained by randomly generating a proportion vector and filtering individuals with an effective full charge state exceeding a preset effective full charge state threshold, so that the individuals in the second initial population have a balance optimization capability, wherein , is a population size proportion coefficient and is a population size proportion coefficient and ​​, for coordinating sizes of the first initial population and the second initial population;

[0154] After that, the fitness value of each individual is calculated according to the target function, and the first individual and the second individual are obtained; the first fitness value of each individual in the first initial population is calculated based on the first target function , the individual with the minimum fitness value in the first initial population is taken as the first individual; the second fitness value of each individual in the second initial population is calculated based on the second target function , the individual with the maximum fitness value in the second initial population is taken as the second individual; then, according to a preset migration period , the second individual is replaced to the first individual of the first initial population to obtain an updated first population, i.e., an updated main population;

[0155] Then, selection, crossover and mutation operations are performed on the updated first population, a parent individual is selected by using a tournament selection method, and a crossover probability is used to perform single-point crossover, and a mutation probability is used to perform random mutation, in which the proportion of the battery with high priority is increased by random disturbance, and the proportions of other batteries are simultaneously reduced to maintain the total sum of 1, so as to ensure that the priority order constraint is still met after mutation, and a new generation population is obtained; the fitness calculation, individual migration and genetic operation are repeatedly performed for times of iterations, and the individuals with top 10% fitness values are reserved in each generation to ensure that high-quality solutions are not lost, until the number of iterations reaches a preset total number of generations or the fitness change rate of consecutive generations is less than a preset fitness threshold; the individual with the highest fitness value is selected from the main population after the iterations, and the corresponding charging current proportion vector is the new generation candidate charging current proportion combination of each type of battery;

[0156] Finally, the individual with the highest fitness value in the final population after the iterations is selected, and the corresponding current proportion is the optimal charging current proportion that meets the preset charging priority .

[0157] The embodiment quantifies the battery health and the comprehensive state of charge based on the effective full charge state, realizes the collaborative optimization of the charging speed and the balance of the battery pack state of charge by constructing a double-population architecture of a main population for optimizing the charging efficiency and a shadow population for optimizing the balancing effect of the battery state of charge, and individual migration between populations, and combining the scenario-based priority constraint and the multi-objective weighted target function, that is, the final output charging current distribution proportion not only shortens the overall time consumption, but also avoids overcharging of the aging battery, which is suitable for different scenes such as emergency energy storage and balance maintenance.

[0158] Furthermore, this embodiment provides a step of obtaining a first individual and a second individual by calculating the fitness values ​​of individuals in a first initial population and a second initial population based on an objective function, and obtaining an updated first population according to a preset migration period, including:

[0159] The first fitness value of an individual in the first initial population is calculated based on the first objective function, thus obtaining the first individual;

[0160] The second fitness value of an individual in the second initial population is calculated based on the second objective function, thus obtaining the second individual;

[0161] According to the preset migration cycle, the first individual of the first initial population is replaced with the second individual of the second initial population to obtain the updated first population.

[0162] The first fitness value is a quantitative indicator of the degree of fit of individuals in the first initial population to the goal of maximizing charging efficiency, calculated based on the reciprocal of the first objective function value; the first individual is the individual with the highest first fitness value in the first initial population; the second fitness value is a quantitative indicator of the degree of fit of individuals in the second initial population to the goal of equalizing battery pack state, calculated based on the reciprocal of the second objective function value; the second individual is the individual with the highest second fitness value in the second initial population, i.e., the equalization elite individual; the preset migration period is the number of generations between the first and second populations for information interaction, set according to the population size and convergence speed requirements.

[0163] Specifically, firstly, the first initial population... Substituting each individual into the first objective function yields the objective function value for each individual. Based on the inverse relationship between fitness and the objective function, the value is obtained using the formula... The first fitness value is calculated, where The first fitness value, The first objective function value is used in this formula, which is based on the fitness principle in genetic algorithms: the smaller the objective function value, the higher the fitness. This ensures that individuals with higher efficiency obtain higher fitness, and the first fitness value is used to select individuals from the initial population. The largest individual is the first individual; then for the second initial population, the largest individual is the first individual. For each individual, substitute it into the second objective function to obtain the objective function value for that individual. Similarly, use the formula... The second fitness value is calculated, where This is the second fitness value. The second objective function value is used to ensure that individuals with better equilibrium performance obtain higher fitness, and these second fitness values ​​are used to enter the second initial population. The largest individual is used to obtain the second individual, i.e., the equilibrium elite individual; finally, when the number of iterations reaches the preset migration cycle... When the first individual of the first initial population is replaced by the second individual of the second initial population, the individual introduced into the first population is subjected to constraint verification, and if the charge current proportion vector thereof violates the priority or total current constraint, the proportion coefficient of the high-priority battery is fine-tuned to make it satisfy the constraint, and an updated first population is obtained.

[0164] The embodiment is based on the double-population independent fitness evaluation and periodic information interaction mechanism. The main population matches the rapid energy supplement demand of the emergency energy storage scene by iteratively shortening the overall charging time, and the shadow population avoids the problems of overcharging of old batteries and insufficient charging of new batteries by reducing the state of charge deviation of each battery. By injecting the balancing elite individual with the optimal balancing effect in the shadow population into the main population, the main population obtains the balancing optimization gene while optimizing the charging speed, avoiding the problem of single-objective optimization falling into local optimum, effectively improving the algorithm's ability to cooperatively optimize the double objectives of charging efficiency and battery state of charge balancing, and making the output charging current proportion not only control the total charging time within the scene demand threshold, but also reduce the battery state of charge balancing error of the battery pack.

[0165] Further, as shown in Figure 5 The embodiment provides a step of stopping the bidirectional conversion power supply from charging and discharging and cutting off the energy flow between the battery and the bus side if an abnormal state occurs. The abnormal state types include overvoltage / undervoltage, overcurrent / short circuit, and overtemperature. First, the corresponding fault signal is obtained based on the abnormal state type, and then the energy flow between the battery and the bus side is cut off by the bidirectional conversion power supply according to the fault signal. If the abnormal state type is overtemperature, the heat dissipation module also needs to be activated. Then, system fault repair is performed, and when the abnormal state is eliminated, the system is restarted in a preset time interval to recover normal charging and discharging. If the continuous restart fails for 5 times, manual intervention is triggered and automatic restart is stopped. The preset interval and the number of consecutive restart failures in the embodiment are only examples, and a person skilled in the art can set them according to the actual situation, which is not limited in the embodiment.

[0166] The embodiment solves the problems of lack of bottom protection in the mixed use of multiple types of batteries in the prior art and easy system circulating current or thermal runaway caused by single battery failure through the multiple protection mechanism under abnormal state, realizes full coverage defense of abnormal risks in the whole life cycle of the battery mixed use system, greatly improves the anti-interference ability and operation reliability of the system under complex working conditions, and provides a key guarantee for long-term safe mixed use of multiple types and states of batteries.

[0167] Further, as shown in Figure 6 The embodiment provides a multi-state battery mixed use dynamic control system, which comprises:

[0168] The data acquisition module acquires the charging and discharging data of each type of battery and the working parameters of the bus side through the bidirectional conversion power supply;

[0169] The parameter calculation module obtains the discharging current limit and the charging current limit of each type of battery based on the capacity remaining rate and the internal resistance change rate of each type of battery, and the capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data;

[0170] The discharging control module obtains the discharging current proportion of each type of battery through the first genetic algorithm based on the discharging current limit and the load demand when each type of battery is in the discharging operation, and the load demand is obtained based on the operating parameters of the bus side.

[0171] The charging control module obtains the charging current proportion of each type of battery through the second genetic algorithm according to the charging current limit and the preset charging priority when each type of battery is in the charging operation, and the preset charging priority is obtained based on the charging and discharging data of each type of battery.

[0172] The multi-state battery mixed use dynamic control system provided by the embodiment can obtain the charging and discharging data of multiple types of batteries and the working parameters of the bus side in real time through the data acquisition module, and provide real-time and accurate basic data support for the parameter calculation module. The parameter calculation module obtains the capacity remaining rate, the internal resistance change rate and the corresponding charging and discharging current limit of each type of battery based on the above data, and provides dynamic constraint conditions for the discharging control module and / or the charging control module. The discharging control module obtains the discharging current proportion of each type of battery by using the first genetic algorithm, and the charging control module obtains the charging current proportion of each type of battery by using the second genetic algorithm. Through data transmission and synergistic action of each module, the precise dynamic control of the charging and discharging process in the multi-type and multi-state battery mixed use scene is realized, the stability of the load demand can be ensured, the current distribution can be optimized according to the actual state of the battery, the utilization efficiency of the battery is effectively improved, and the overall attenuation of the battery is delayed.

[0173] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0174] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0175] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0176] The above description is only preferred embodiments of the present application, the protection scope of the present application is not limited to the above-mentioned embodiments, any technical scheme falling within the idea of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for those skilled in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A dynamic control method for multi-state battery hybrid use, characterized in that, The method comprises: collecting charge and discharge data of each type of battery and operating parameters of a bus side through a bidirectional conversion power supply; obtaining discharge current limits and charge current limits of the each type of battery based on a capacity remaining rate and a resistance change rate of the each type of battery, the capacity remaining rate and the resistance change rate being obtained according to the charge and discharge data; obtaining a discharge current proportion of the each type of battery through a first genetic algorithm based on the discharge current limits and a load demand when the each type of battery is in a discharge operation, the load demand being obtained based on operating parameters of the bus side; and / or; obtaining a charge current proportion of the each type of battery through a second genetic algorithm according to the charge current limits and a preset charge priority when the each type of battery is in a charge operation, the preset charge priority being obtained based on the charge and discharge data of the each type of battery; wherein the obtaining of the discharge current limits and the charge current limits of the each type of battery based on the capacity remaining rate and the resistance change rate of the each type of battery comprises: obtaining a capacity remaining rate and a resistance change rate according to the charge and discharge data of the each type of battery; correcting the capacity remaining rate and the resistance change rate to obtain the discharge current limits based on rated discharge limits of the each type of battery under a discharge condition; obtaining the charge current limits according to the type, the capacity remaining rate and the resistance change rate of the battery under a charge condition; wherein the obtaining of the capacity remaining rate and the resistance change rate according to the charge and discharge data of the each type of battery comprises: obtaining a historical charge and discharge cycle number, an actual battery capacity and a resistance of the each type of battery according to the charge and discharge data; calculating a capacity attenuation rate and a capacity remaining rate of the each type of battery based on the actual battery capacity and a rated capacity of the each type of battery, the rated capacity being obtained according to a battery specification; calculating a resistance growth rate based on the resistance and an initial resistance of the each type of battery; calculating a battery health state and a resistance change rate of the each type of battery through weighted summation based on the capacity remaining rate, the resistance growth rate and the cycle number; wherein the obtaining of the charge current proportion of the each type of battery through the second genetic algorithm according to the charge current limits and the preset charge priority when the each type of battery is in the charge operation comprises: obtaining a charge state of charge of the each type of battery through ampere-hour integration based on a first charge current of the each type of battery, and obtaining a charge resistance and an effective full charge state of the each type of battery through Ohm's law; obtaining priority constraints according to a charge scenario through a preset charge priority, and obtaining a total charge current constraint according to a maximum current of the bidirectional conversion power supply; obtaining an optimal charge current proportion of the each type of battery through the second genetic algorithm based on the charge current limits, the charge state of charge and the charge resistance of the each type of battery, in combination with the priority constraints and the total charge current constraint. obtaining a second charging current of each type of battery through proportional distribution based on the charging current proportion and the input current of the bus side; wherein the optimal charging current proportion is solved by a second genetic algorithm, a double-population optimization model is constructed according to the charging current limit, the charging state of charge and the charging internal resistance of each battery, in combination with the determined priority constraint and the total charging current constraint; the optimization objective of the main population is to minimize the total charging time, and the target is constructed by fusing the total charging time parameter and the priority weight; the optimization objective of the shadow population is to minimize the error of the effective full state of charge, and the target is constructed by fusing the average value deviation of the effective full state of charge and the internal resistance power loss parameter; an initial population satisfying all constraint conditions is randomly generated, and the fitness of each individual in the two populations is calculated according to the objective function; the parent individual is screened by the tournament selection method, the single-point crossover operation is performed according to the crossover probability, and the random mutation operation is performed according to the mutation probability, to obtain a new generation population; the best balanced elite individual in the shadow population is migrated to the main population every interval preset migration period, to replace the individual with the lowest fitness in the main population; the evolution and migration process is repeated until the fitness change rate is less than the preset fitness threshold, and finally the individual with the highest fitness in the main population is obtained, and the corresponding charging current proportion combination is the optimal charging current proportion.

2. The multi-state battery hybrid dynamic control method of claim 1, wherein, When the each type of battery is in discharging operation, a discharging current proportion of the each type of battery is obtained through a first genetic algorithm based on the discharging current limit and the load demand, including: a discharging state of charge of the each type of battery is obtained through ampere-hour integration method based on the first discharging current of the each type of battery; a discharging internal resistance of the each type of battery is obtained through direct current discharging method; a discharging current proportion of the each type of battery is obtained through the first genetic algorithm according to the discharging current limit, the discharging state of charge and the discharging internal resistance of the each type of battery; a second discharging current of the each type of battery is obtained through proportional distribution based on the discharging current proportion and the load current of the bus side.

3. The multi-state battery hybrid dynamic control method of claim 2, wherein, The discharging current proportion of the each type of battery is obtained through the first genetic algorithm, including: a target function and constraint condition of the first genetic algorithm are determined based on the discharging efficiency, the discharging state of charge, the discharging internal resistance and the load demand of the each type of battery at the current moment; an initial population is obtained according to the discharging current limit of the each type of battery at the current moment; a fitness value of each individual is calculated by substituting the current proportion combination in the initial population into the target function; the initial population is updated based on the fitness value through selection, crossover and mutation operations to obtain a new generation of candidate current proportion combination; the individual with the highest fitness value is selected from the updated initial population through iteration to obtain the optimal discharging current proportion of the each type of battery.

4. The multi-state battery hybrid dynamic control method of claim 1, wherein, The priority constraint is obtained through the preset charging priority according to the charging scenario, including: a charging sequence and a corresponding priority order weight of each type of battery are preset based on historical charging scenarios to obtain a preset charging priority. The actual charging scenario is obtained through pattern recognition, the preset charging priority is called according to the charging scenario, and a priority constraint and a priority order weight corresponding to each type of battery are obtained.

5. The multi-state battery hybrid dynamic control method of claim 1, wherein, The optimal charging current ratio of each type of battery is obtained through the second genetic algorithm, and the method comprises the following steps: According to different targets under the charging working condition, a target function of the second genetic algorithm is obtained based on the charging current limit, the effective full charge state and the charging internal resistance of each type of battery, and the target function comprises a first target function and a second target function; According to the total charging current constraint and the charging current limit of each type of battery, a constraint condition of the second genetic algorithm is determined; An initial population of the second genetic algorithm is obtained by randomly generating a charging current ratio combination satisfying the constraint condition, and the initial population comprises a first initial population and a second initial population; Based on the target function, the fitness values of individuals in the first initial population and the second initial population are calculated to obtain a first individual and a second individual, and an updated first population is obtained according to a preset migration period; A new generation of candidate charging current ratio combinations is obtained through selection, crossover and mutation operations on the updated first population; The optimal charging current ratio of each type of battery is obtained by iteratively selecting the individual with the highest fitness from the updated first population.

6. The multi-state battery hybrid dynamic control method of claim 5, wherein, The method comprises the following steps: The first fitness value of the individual in the first initial population is calculated according to the first target function to obtain the first individual; The second fitness value of the individual in the second initial population is calculated according to the second target function to obtain the second individual; According to the preset migration period, the first individual of the first initial population is replaced by the second individual of the second initial population to obtain the updated first population.

7. A multi-state battery hybrid dynamic control system, characterized by, The method comprises the following steps: The data acquisition module acquires the charging and discharging data of each type of battery and the working parameters of the bus side through a bidirectional conversion power supply; The parameter calculation module obtains the discharging current limit and the charging current limit of each type of battery based on the capacity remaining rate and the internal resistance change rate of each type of battery, and the capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data; The method comprises the following steps: The capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data of each type of battery; The discharging current limit is obtained by correcting the rated discharging limit of each type of battery under the discharging working condition based on the capacity remaining rate and the internal resistance change rate; The charging current limit is obtained according to the type, the capacity remaining rate and the internal resistance change rate of the battery under the charging working condition; The method comprises the following steps: The capacity remaining rate and the internal resistance change rate are obtained according to the charging and discharging data of each type of battery. According to the charge-discharge data, the historical charge-discharge cycle number, the actual battery capacity and the internal resistance of each type of battery are obtained; Based on the actual battery capacity and the rated capacity of each type of battery, the capacity attenuation rate and the capacity remaining rate of each type of battery are calculated, and the rated capacity is obtained according to the battery specification book; Based on the internal resistance and the initial internal resistance of each type of battery, the internal resistance growth rate is calculated; Based on the capacity remaining rate, the internal resistance growth rate and the cycle number, the battery state of health and the internal resistance change rate of each type of battery are obtained by weighted summation; The discharge control module, when each type of battery is in discharge operation, based on the discharge current limit and the load demand, the discharge current proportion of each type of battery is obtained by the first genetic algorithm, and the load demand is obtained based on the operating parameters of the bus side; And / or; The charge control module, when each type of battery is in charging operation, according to the charging current limit and the preset charging priority, the charging current proportion of each type of battery is obtained by the second genetic algorithm, and the preset charging priority is obtained based on the charge-discharge data of each type of battery; Wherein, when each type of battery is in charging operation, according to the charging current limit and the preset charging priority, the charging current proportion of each type of battery is obtained by the second genetic algorithm, including: Based on the first charging current of each type of battery, the charge state of each type of battery is obtained by ampere-hour integration method, and the charging internal resistance and the effective full charge state of each type of battery are obtained by Ohm's law; According to the charging scene, the priority constraint is obtained by the preset charging priority, and the total charging current constraint is obtained according to the maximum current of the bidirectional conversion power supply; Based on the charging current limit, the charge state and the charging internal resistance of each type of battery, combined with the priority constraint and the total charging current constraint, the optimal charging current proportion of each type of battery is obtained by the second genetic algorithm; Based on the charging current proportion and the input current of the bus side, the second charging current of each type of battery is obtained by proportional distribution; The optimal charging current proportion is solved by the second genetic algorithm, a double-population optimization model is constructed according to the charging current limit, the charging state of charge and the charging internal resistance of each battery, in combination with the priority constraint and the total charging current constraint which have been determined; the optimization objective of the main population is to minimize the total charging time, and the total charging time parameter and the priority weight are fused when the objective is constructed; the optimization objective of the shadow population is to minimize the effective full state of charge error, and the average value deviation of the effective full state of charge and the internal resistance power loss parameter are fused when the objective is constructed; the initial population satisfying all constraint conditions is randomly generated, the fitness of each individual in the two populations is calculated according to the objective function; the parent individual is screened by adopting the tournament selection method, the single-point crossover operation is performed according to the crossover probability, and the random mutation operation is performed according to the mutation probability, so that the new generation population is obtained; the best balanced elite individual in the shadow population is migrated to the main population to replace the individual with the lowest fitness in the main population every interval preset migration period; the evolution and migration process is repeated until the fitness change rate is less than the preset fitness threshold, and finally the individual with the highest fitness in the main population is obtained, and the corresponding charging current proportion combination is the optimal charging current proportion.

Images