A method and system for controlling the charging of a power battery

Abstract

The present invention belongs to the technical field of battery charging control, and particularly relates to a power battery charging control method and system. The method includes: after the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state; the battery management system sends a charging instruction to the charger based on the obtained target charging current; the charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current; during the charging process, the battery management system monitors the state of the battery in real time; the battery management system determines whether a preset charging cut-off condition is reached according to the state of the battery; when the battery management system determines that the charging cut-off condition is reached, it sends a charging end instruction to the charger; or, when the battery management system receives a message for aborting charging sent by the charger, it sends a charging end instruction to the charger. The charging time is shortened and the charging efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery charging control, and particularly relates to a power battery charging control method and system. Background Art

[0002] With the increasing global awareness of environmental protection and the rapid development of technology, electric vehicles (EVs) and renewable energy storage systems (RESSs) are becoming popular at an unprecedented rate, becoming an important force in promoting green travel and achieving energy transformation. In this context, as the core component of these systems, the performance and safety reliability of power batteries are directly related to the effectiveness of the entire system and the user's trust. Power batteries must not only have basic attributes such as high energy density and long cycle life, but also be able to maintain stable performance under various working conditions, especially in the critical charging process.

[0003] Traditional charging methods, whether constant current charging or constant voltage charging, have certain limitations. They often ignore the dynamic changes of the battery during the charging process, which directly affect the charging efficiency and safety of the battery. Charging with fixed charging parameters may not only cause overcharging of the battery, increasing the risks of thermal runaway and short circuit, but also may prolong the charging time due to low charging efficiency, affecting the user experience. Summary of the Invention

[0004] Aiming at the problems that the traditional charging control method leads to overcharging of the battery, increasing the risks of thermal runaway and short circuit, and may also prolong the charging time due to low charging efficiency, the present invention provides a power battery charging control method and system.

[0005] In a first aspect, the technical solution of the present invention provides a power battery charging control method, which is applied to a control system. The control system includes a battery management system and a charger; the battery management system is used to manage the battery, and the method includes:

[0006] After the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state;

[0007] The battery management system sends a charging instruction to the charger based on the obtained target charging current;

[0008] The charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current;

[0009] During the charging process, the battery management system monitors the state of the battery in real time;

[0010] The battery management system determines whether a preset charging cut-off condition is reached according to the state of the battery;

[0011] When the battery management system determines that the charging cut-off condition is reached, it sends a charging end command to the charger; or, when the battery management system receives a message from the charger to abort charging, it sends a charging end command to the charger, and the charger responds to the charging end command to stop current output.

[0012] Preferably, before the step in which the battery management system obtains the current state of the battery and determines the target charging current according to the battery state, the method includes:

[0013] The battery management system divides the battery charging interval into different strategy stages, and the number of stages divided by each strategy is different;

[0014] The particle swarm optimization algorithm is used to calculate the optimal charging current for each stage;

[0015] Compare the charging time and the charged amount under each division strategy to obtain the optimal strategy; and generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy.

[0016] Preferably, the step of using the particle swarm optimization algorithm to calculate the optimal charging current for each stage includes:

[0017] According to the specified maximum charging current and the SOC interval of the battery, by dividing the SOC interval into stages, m groups of current sequences are generated, and the current sequences form an initial current population;

[0018] Set the fitness function and use the particle swarm optimization algorithm for parameter optimization;

[0019] After reaching the optimal point or reaching the set number of iterations, output the optimal charging current for each stage.

[0020] Preferably, the step of setting the fitness function and using the multi-objective particle swarm optimization algorithm for parameter optimization includes:

[0021] Initialize the particle population, where the velocity is zero, the population with the number of individuals being m, and the position of the i-th particle is The velocity of the i-th particle is ;

[0022] Set the fitness function to evaluate the fitness of each particle in the population;

[0023] Update the position and velocity of each particle;

[0024] Compare the fitness value of the current particle swarm with the fitness value of the previous particle swarm to perform convergence judgment.

[0025] Preferably, as a technical solution of the present invention, the steps of obtaining the optimal strategy by comparing the charging time and the charged power under each division strategy, and generating a relationship table between the charging intervals and the corresponding optimal charging currents under the optimal strategy include:

[0026] Obtain the charging time and the charged power under each division strategy;

[0027] Calculate the average charged power under each division strategy, and delete the division strategy with the smallest average charged power;

[0028] Calculate the average charging time under each of the remaining division strategies, and delete the division strategy with the longest average charging time;

[0029] Calculate the difference in the average charged power between adjacent stages in the currently remaining division strategies, and select the division strategy with the smaller average charged power among the two division strategies with the smallest difference as the optimal strategy;

[0030] Generate a relationship table between the charging intervals and the corresponding optimal charging currents under the optimal strategy.

[0031] Preferably, as a technical solution of the present invention, the steps for the battery management system to obtain the current state of the battery and determine the target charging current according to the battery state include:

[0032] The battery management system obtains the current state of the battery; the state includes the battery SOC;

[0033] Judge the charging interval of the current battery under the optimal division strategy according to the obtained SOC;

[0034] Obtain the corresponding optimal charging current as the target charging current in the optimal charging current relationship table according to the charging interval.

[0035] Preferably, as a technical solution of the present invention, after the step of the battery management system monitoring the state of the battery in real time during the charging process, it includes:

[0036] Judge the charging state of the current battery in the last stage according to the battery state;

[0037] If so, execute the step: the battery management system judges whether the preset charging cut-off condition is reached according to the state of the battery;

[0038] If not, when it is judged according to the battery state that the battery needs to enter the next stage, execute the step: judge the charging interval of the current battery under the optimal division strategy according to the obtained SOC.

[0039] Preferably, as a technical solution of the present invention, the state of the battery further includes the battery voltage, the battery temperature and the charging time; the steps for the battery management system to judge whether the preset charging cut-off condition is reached according to the state of the battery include:

[0040] When the battery voltage reaches the cut-off voltage, or the battery temperature reaches the preset temperature, or the charging time reaches the preset maximum charging time, the charging cut-off condition is satisfied; otherwise, the charging cut-off condition is not satisfied. This is to prevent overcharging of the battery, as overcharging can cause the battery to overheat, expand, and be damaged. For example, for a lithium-ion battery, when the voltage of a single cell reaches 4.2V or 4.25V (the specific value may vary depending on the battery type and BMS design), the constant current or constant voltage charging state will be terminated immediately.

[0041] High temperature can cause battery degradation, so the BMS will limit or terminate charging to keep the battery temperature within the allowable range. For example, during constant current charging, when the battery temperature reaches 60°C, the constant current charging state should be terminated immediately. This helps protect the battery from damage caused by high temperature and extends its service life.

[0042] To ensure safe charging of the battery, in addition to setting the maximum voltage, maximum temperature, and minimum charging current, the maximum charging time should also be set. In specific situations (such as when temperature and voltage detection fails), the maximum charging time can be used as an additional safety measure.

[0043] Preferably, before the step of the battery management system obtaining the current state of the battery and determining the target charging current according to the battery state in the technical solution of the present invention, it includes:

[0044] The battery management system obtains the record of the battery's deep discharge;

[0045] Judge whether the number of times of the battery's deep discharge reaches the set number of times;

[0046] If so, output a prompt message; and output a constant current charging or constant voltage charging instruction to the charger;

[0047] If not, execute the steps: the battery management system obtains the current state of the battery and determines the target charging current according to the battery state.

[0048] Some BMSs can adjust the charging termination condition according to the battery's health status and usage history. For example, if the battery has been used for a long time or has experienced multiple deep discharges, the BMS may control the charging process more strictly to prevent further degradation of the battery.

[0049] In a second aspect, the technical solution of the present invention provides a power battery charging control system, including a battery management system and a charger; after the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state; the battery management system sends a charging instruction to the charger based on the obtained target charging current; during the charging process, the battery management system monitors the state of the battery in real time; the battery management system determines whether the preset charging cut-off condition is reached according to the state of the battery; when the battery management system determines that the charging cut-off condition is reached, it sends a charging end instruction to the charger; or, when the battery management system receives a message for aborting charging sent by the charger, it sends a charging end instruction to the charger;

[0050] The charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current; the charger stops the current output in response to the charging end instruction.

[0051] Preferably, as a technical solution of the present invention, the battery management system divides the battery charging interval into different strategy stages, and the number of stages divided by each strategy is different; the particle swarm algorithm is used to calculate the optimal charging current for each stage; the charging time and the charged power under each division strategy are compared to obtain the optimal strategy; and a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy is generated.

[0052] Preferably, as a technical solution of the present invention, the battery management system divides the SOC interval into stages according to the specified maximum charging current and the SOC interval of the battery to generate m groups of current sequences, and the current sequences form an initial current population; a fitness function is set, and the particle swarm optimization algorithm is used for parameter optimization; after reaching the optimal point or reaching the set number of iterations, the optimal charging current for each stage is output.

[0053] Preferably, as a technical solution of the present invention, the battery management system obtains the charging time and the charged power under each division strategy; calculates the average charged power under each division strategy and deletes the division strategy with the smallest average charged power; calculates the average charging time under each remaining division strategy and deletes the division strategy with the longest average charging time; calculates the difference in the average charged power between adjacent stages in the current remaining division strategies, and selects the division strategy with the smaller average charged power among the two division strategies with the smallest difference as the optimal strategy;

[0054] Generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy.

[0055] Preferably, as a technical solution of the present invention, the battery management system obtains the current state of the battery; the state includes the battery SOC; determines the charging interval of the current battery under the optimal charging strategy according to the obtained SOC; and obtains the corresponding optimal charging current as the target charging current from the optimal charging current relationship table according to the charging interval.

[0056] Preferably, as a technical solution of the present invention, the battery management system obtains the record of the battery's deep discharge; determines whether the number of times of the battery's deep discharge reaches the set number of times; if so, outputs a prompt message; and outputs a constant current charging or constant voltage charging instruction to the charger.

[0057] Some BMSs can adjust the charging termination conditions according to the battery's health status and usage history records. For example, if the battery has been used for a long time or has experienced multiple deep discharges, the BMS may control the charging process more strictly to prevent further degradation of the battery.

[0058] It can be seen from the above technical solutions that the present invention has the following advantages: By real-time monitoring the battery state and determining the target charging current according to the battery state, the charging capacity of the battery can be maximally utilized, the charging time can be shortened, and the charging efficiency can be improved. During the charging process, the battery management system real-time monitors the state of the battery and determines whether the preset charging cut-off condition is reached according to the state of the battery. This can effectively avoid safety hazards such as battery overheating and overcharging, and protect the safety of the battery.

[0059] The method of the present invention allows the battery management system to adopt different charging strategies according to different states of the battery, and calculates the optimal charging current through the particle swarm algorithm. This enhances the flexibility of the system, enabling it to adapt to different types of batteries and different charging requirements. By precisely controlling the charging process, the method of the present invention can provide users with a more stable and reliable charging service. At the same time, due to the shortening of the charging time and the improvement of the charging efficiency, the user experience will also be significantly improved. Optimizing the charging process of power batteries can not only improve the utilization rate of the battery, but also reduce energy waste. This is of great significance for promoting the development of electric vehicles and renewable energy storage systems.

[0060] Through such a control system, not only can the overcharging of the battery be effectively avoided, the risk of thermal runaway be reduced, but also the charging efficiency can be significantly improved, the charging time can be shortened, thereby prolonging the service life of the battery and reducing the maintenance cost. In addition, the system can also continuously optimize the charging strategy according to the individual differences of the battery and the historical charging data, providing a more personalized charging service and further enhancing the user experience. Description of the Drawings

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0062] Figure 1 is a schematic flowchart of a method according to an embodiment of the present invention.

[0063] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention. Detailed implementation manners

[0064] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0065] As Figure 1 shown, an embodiment of the present invention provides a method for controlling the charging of a power battery. The method is applied to a control system, and the control system includes a battery management system and a charger; the battery management system is used to manage the battery, and the method includes:

[0066] Step 1: After the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state;

[0067] Step 2: The battery management system sends a charging instruction to the charger based on the obtained target charging current;

[0068] Step 3: The charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current;

[0069] Step 4: During the charging process, the battery management system monitors the state of the battery in real time;

[0070] Step 5: The battery management system determines whether a preset charging cut-off condition is reached according to the state of the battery;

[0071] Step 6: When the battery management system determines that the charging cut-off condition is reached, it sends a charging end instruction to the charger; or, when the battery management system receives a message for aborting charging sent by the charger, it sends a charging end instruction to the charger, and the charger stops the current output in response to the charging end instruction.

[0072] In some embodiments, the battery management system obtains the current state of the battery, and before the step of determining the target charging current according to the battery state includes:

[0073] S01: The battery management system divides the battery charging interval into stages with different strategies, and each strategy has a different number of stages;

[0074] S02: Particle swarm algorithm is used to calculate the optimal charging current in each stage;

[0075] S03: Compare the charging time and the charged power under each division strategy to obtain the optimal strategy; and generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy.

[0076] The battery management system (BMS) first divides the battery's charging range from a low power state (such as SOC 20%) to a high power state (such as SOC 80% or 95%) into multiple stages based on the battery's chemical characteristics, historical charging data, and the charging goals set by the user (such as fast charging, normal charging, or protecting battery life).

[0077] The number of stages can be different under different strategies. For example, strategy A may simply divide the charging process into two stages (fast charging stage and constant voltage charging stage), while strategy B may be more detailed and divided into five stages (pre-charging, fast charging, gentle charging, constant voltage charging and floating charging) to more finely control the charging process.

[0078] For each stage under each strategy, the BMS uses a particle swarm algorithm (PSO) for optimization calculations. PSO is an optimization algorithm based on swarm intelligence that simulates the foraging behavior of bird flocks to find the optimal solution to the problem. In each stage, a set of particles is defined, each particle represents a possible charging current value. The position (i.e., charging current value) and speed (i.e., the adjustment direction of the charging current) of the particles are updated iteratively to minimize the objective function (such as charging time, battery temperature rise, charging efficiency, etc.). After multiple iterations, the algorithm converges to the optimal solution, that is, the optimal charging current for each stage.

[0079] BMS calculates the total charging time and charged power under each division strategy, as well as other possible performance indicators (such as battery temperature, charging efficiency, etc.). By comprehensively comparing these indicators, BMS selects the optimal charging strategy, which is a strategy that can charge more power in a shorter time while keeping the battery temperature within a safe range and with higher charging efficiency. Based on the selected optimal strategy, BMS generates a relationship table between the charging range and the corresponding optimal charging current. This table lists in detail each stage from low power to high power, as well as the optimal charging current that should be used in each stage.

[0080] Improving charging efficiency and safety: By precisely controlling the charging current, the BMS can dynamically adjust the charging strategy according to the real-time state of the battery, avoiding overcharging and undercharging, thereby improving the charging efficiency and the safety of the battery. By optimizing the charging process, the cyclic stress on the battery is reduced, which helps to extend the battery's service life and lower the user's maintenance cost. According to the charging target set by the user, the BMS can provide more personalized charging services, such as fast charging or a charging mode that protects the battery life, enhancing the user's satisfaction. The BMS can learn the charging history and characteristics of the battery, continuously optimizing the charging strategy to make it more intelligent and adaptable to different battery states and charging environments.

[0081] Specifically, the steps of using the particle swarm algorithm to calculate the optimal charging current for each stage include:

[0082] S021: According to the specified maximum charging current and the SOC range of the battery, by dividing the SOC range into stages, m groups of current sequences are generated, and the current sequences form an initial current population;

[0083] Based on the current state of the battery (such as SOC, i.e., the percentage of remaining charge) and the target charging state, the SOC range is divided into multiple stages. For example, the range of SOC from 20% to 80% can be divided into 4 stages, and each stage corresponds to a certain SOC range (such as 20% - 40%, 40% - 60%, 60% - 80%). o For each stage, according to the specified maximum charging current and the chemical characteristics of the battery (such as the maximum acceptable charging rate, battery temperature, etc.), a sequence of possible charging current values within this stage is generated. This sequence will serve as the initial population in the particle swarm algorithm. Assuming that n charging current values are generated for each stage, then for m stages, a total of m groups of current sequences will be generated, with each group containing n current values.

[0084] S022: Set the fitness function and use the particle swarm optimization algorithm for parameter optimization;

[0085] In this step, the particle population is initialized, where the velocity is zero, the population size is m, and the position of the i-th particle is The velocity of the i-th particle is ; Set the fitness function to evaluate the fitness of each particle in the population; Update the position and velocity of each particle; Compare the fitness value of the current particle swarm with the fitness value of the previous particle swarm for convergence judgment.

[0086] In the particle swarm algorithm, each particle represents a potential solution. Therefore, each particle can be initialized as a vector containing m charging current values, where each value corresponds to the optimal charging current candidate for a stage. The position and velocity of the particle also need to be initialized. The position represents the current combination of charging current values, and the velocity represents the direction and magnitude of the adjustment of the charging current values.

[0087] The fitness function is used to evaluate the quality of each particle. In the battery charging optimization problem, the fitness function can be based on multiple metrics, such as charging time, battery temperature, charging efficiency, etc.

[0088] For example, the fitness function can be defined as: Fitness = w1 * (1 / ChargingTime) + w2 * (1 / BatteryTemperature) + w3 * ChargingEfficiency, where w1, w2, and w3 are weight coefficients used to balance the importance of different metrics. According to the fitness function, the fitness value of each particle is calculated. The personal best position (pBest) and global best position (gBest) of each particle are updated. The personal best position is the best position found by the particle during the iteration process, and the global best position is the best position found in the population. According to the update rules, the velocity and position of the particle are adjusted. This usually involves the calculation of the current position, velocity, personal best position, and global best position of the particle. The above process is repeated until the set number of iterations is reached or a certain stopping condition is met (such as the fitness value no longer improves significantly).

[0089] Here, the update formulas are as follows:

[0090]

[0091]

[0092] v n ( k ) is the velocity vector of the n th particle, x n ( k ) is the position vector of the n th particle, w ( k ) is the inertia weight, is the self-cognition learning factor, is the social-cognition learning factor, r 1 and r 2 are random values in the range [0, 1], is the best-performing particle in the current population;

[0093]

[0094]

[0095]

[0096] Among them, y(k) is the actual operating state of the k-th iteration, and y*(k) is the set operating state of the k-th iteration. is the maximum value of the operating state. is the minimum value of the operating state.

[0097] S023: After reaching the optimal point or the set number of iterations, output the optimal charging current for each stage.

[0098] When the particle swarm algorithm reaches the optimal point (i.e., the fitness value of the global best position no longer improves significantly) or the set number of iterations, the algorithm stops. Extract the optimal charging current values for each stage from the global best position. These values form part of the optimal charging strategy. Output the optimal charging current values for each stage to the BMS system for use during the actual charging process. At the same time, these values can also be saved to the database for subsequent analysis and optimization. Through the above implementation process, the particle swarm algorithm can effectively find the optimal charging current values for each stage, thereby optimizing the battery charging process.

[0099] In some embodiments, the steps of comparing the charging time and the charged amount under each partitioning strategy to obtain the optimal strategy; and generating a relationship table between the charging intervals and the corresponding optimal charging currents under the optimal strategy include:

[0100] S031: Obtain the charging time and the charged amount under each partitioning strategy;

[0101] For each partitioning strategy of the battery charging interval, record and obtain the total charging time and the total charged amount under this strategy through actual charging tests or simulated charging processes.

[0102] S032: Preliminary screening - delete the partitioning strategy with the minimum average charged amount;

[0103] Calculate the average charged amount under each partitioning strategy (total charged amount divided by the number of stages of the charging interval partitioning). Compare the average charged amounts of all partitioning strategies and delete the partitioning strategy with the minimum average charged amount because this means that the amount of electricity charged under this strategy in the same time is the least and the efficiency is low.

[0104] S033: Further screening - delete the partitioning strategy with the longest average charging time;

[0105] For the remaining partitioning strategies, calculate the average charging time under each strategy (total charging time divided by the number of stages of the charging interval partitioning. However, it is more common here to directly compare the total charging time because the number of stages is already different). Compare the average charging times of the remaining strategies (or directly compare the total charging times), and delete the partitioning strategy with the longest average charging time (or the longest total time in the direct comparison) because long charging times may increase the thermal stress and aging rate of the battery.

[0106] S034: Fine screening - Select the optimal strategy;

[0107] For the currently remaining partitioning strategies, calculate the difference in the average charge amount between adjacent stages. This helps evaluate the smoothness of the strategy during charging. The smaller the difference, the smoother the charging process and the better the protection of the battery. Among the two partitioning strategies with the smallest difference, select the strategy with the smaller average charge amount as the optimal strategy. This is because, when the charging smoothness is similar, choosing a strategy with a slightly smaller charge amount may mean that the strategy does better in protecting the battery life and reducing thermal stress. Especially when the battery is close to full charge, a smaller charging current helps reduce battery degradation.

[0108] S035: Generate a charging relationship table under the optimal strategy;

[0109] According to the selected optimal strategy, record in detail the optimal charging current value corresponding to each charging interval (stage) to generate a relationship table between the charging interval and the optimal charging current. This table will be used as the basis for the BMS system to control the charging current during the actual charging process.

[0110] In some embodiments, the steps for the battery management system to obtain the current state of the battery and determine the target charging current based on the battery state include:

[0111] Step 11: The battery management system obtains the current state of the battery; the state includes the battery SOC;

[0112] The battery management system (BMS) collects the current state data of the battery in real time through built-in sensors or an externally connected sensor network. These data include, but are not limited to, the voltage, current, temperature, and remaining charge (State of Charge, abbreviated as SOC) of the battery. The BMS preprocesses the collected raw data, including data cleaning, denoising, calibration, etc., to ensure the accuracy and reliability of the data. In particular, the calculation of SOC may involve complex algorithms such as the Coulomb counting method, open circuit voltage method, etc., to accurately estimate the remaining charge of the battery.

[0113] Step 12: Determine the charging interval of the current battery under the optimal partitioning strategy based on the obtained SOC;

[0114] The BMS divides the battery according to preset optimal division strategies, which may be formulated based on factors such as the battery's chemical type, capacity, usage history, user preferences, etc. Each strategy defines different charging interval divisions, and these intervals reflect the charging characteristics and requirements of the battery at different SOCs. The BMS compares the current SOC of the battery with the charging intervals in the optimal division strategy to determine the charging interval where the battery is currently located. This step is the basis for subsequent selection of the optimal charging current.

[0115] Step 13: Obtain the corresponding optimal charging current in the optimal charging current relationship table according to the charging interval as the target charging current.

[0116] Optimal charging current relationship table: There is an optimal charging current relationship table stored inside the BMS. This table lists the optimal charging current values corresponding to each charging interval according to the optimal division strategy. These optimal values may be pre-calculated through optimization techniques such as particle swarm optimization algorithm and genetic algorithm, aiming to balance multiple performance indicators such as charging speed, battery life, and charging efficiency. Once the charging interval of the current battery is determined, the BMS will look up the optimal charging current value corresponding to this interval in the optimal charging current relationship table. This value will be set as the current target charging current to guide the subsequent charging process.

[0117] During the actual charging process, the BMS may also need to fine-tune the target charging current according to the real-time state of the battery (such as temperature and voltage changes) to ensure the safety and efficiency of the charging process. These adjustments may be based on preset control logic or more complex real-time optimization algorithms, which will not be elaborated here.

[0118] In some embodiments, after the step of the battery management system real-time monitoring the state of the battery during the charging process, it includes:

[0119] Judge whether the current battery is in the last stage of the charging state according to the battery state;

[0120] If so, execute the step: The battery management system judges whether the preset charging cut-off condition is reached according to the state of the battery.

[0121] If not, when it is judged according to the battery state that the battery needs to enter the next stage, execute the step: Judge the charging interval of the current battery under the optimal division strategy according to the obtained SOC.

[0122] In some embodiments, the state of the battery also includes battery voltage, battery temperature, and charging time; the step of the battery management system judging whether the preset charging cut-off condition is reached according to the state of the battery includes:

[0123] When the battery voltage reaches the cut-off voltage, or the battery temperature reaches the preset temperature, or the charging time reaches the preset maximum charging time, the charging cut-off condition is met; otherwise, the charging cut-off condition is not met. This is to prevent overcharging of the battery, as overcharging can cause the battery to overheat, expand, and be damaged. For example, for a lithium-ion battery, when the voltage of a single cell reaches 4.2V or 4.25V (the specific value may vary depending on the battery type and BMS design), the constant current or constant voltage charging state will be terminated immediately.

[0124] High temperature can cause battery degradation, so the BMS will limit or terminate charging to keep the battery temperature within the allowable range. For example, during constant current charging, when the battery temperature reaches 60°C, the constant current charging state should be terminated immediately. This helps protect the battery from damage caused by high temperature and extends its service life.

[0125] To ensure safe charging of the battery, in addition to setting the maximum voltage, maximum temperature, and minimum charging current, the maximum charging time should also be set. In specific situations (such as when temperature and voltage detection fails), the maximum charging time can be used as an additional safety measure.

[0126] In some embodiments, before the step of the battery management system obtaining the current state of the battery and determining the target charging current according to the battery state, it includes:

[0127] The battery management system obtains the record of the battery's deep discharge;

[0128] Determine whether the number of times of the battery's deep discharge reaches the set number of times;

[0129] If so, output a prompt message; and output a constant current charging or constant voltage charging instruction to the charger;

[0130] If not, execute the steps: The battery management system obtains the current state of the battery and determines the target charging current according to the battery state.

[0131] Some BMSs can adjust the charging termination condition according to the battery's health status and usage history. For example, if the battery has been used for a long time or has experienced multiple deep discharges, the BMS may control the charging process more strictly to prevent further degradation of the battery.

[0132] Such as Figure 2As shown in the figure, an embodiment of the present invention provides a power battery charging control system, including a battery management system and a charger; after the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state; the battery management system sends a charging instruction to the charger based on the obtained target charging current; during the charging process, the battery management system monitors the state of the battery in real time; the battery management system determines whether the preset charging cut-off condition is reached according to the state of the battery; when the battery management system determines that the charging cut-off condition is reached, it sends a charging end instruction to the charger; or, when the battery management system receives a message from the charger to abort the charging, it sends a charging end instruction to the charger;

[0133] The charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current; the charger stops the current output in response to the charging end instruction.

[0134] In some embodiments, the battery management system divides the battery charging interval into different strategy stages, and the number of stages divided by each strategy is different; the particle swarm optimization algorithm is used to calculate the optimal charging current for each stage; the charging time and the charged power under each division strategy are compared to obtain the optimal strategy; and a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy is generated.

[0135] In some embodiments, the battery management system generates m groups of current sequences by dividing the SOC interval according to the specified maximum charging current and the SOC interval of the battery, and the current sequences form an initial current population; a fitness function is set, and the particle swarm optimization algorithm is used for parameter optimization; after reaching the optimal point or reaching the set number of iterations, the optimal charging current for each stage is output.

[0136] In some embodiments, the battery management system obtains the charging time and the charged power under each division strategy; calculates the average charged power under each division strategy, and deletes the division strategy with the smallest average charged power; calculates the average charging time under each remaining division strategy, and deletes the division strategy with the longest average charging time; calculates the difference in the average charged power between adjacent stages in the current remaining division strategies, and selects the division strategy with the smaller average charged power among the two division strategies with the smallest difference as the optimal strategy;

[0137] Generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy.

[0138] In some embodiments, the battery management system obtains the current state of the battery; the state includes the battery SOC; determines the charging interval of the current battery under the optimal division strategy according to the obtained SOC; and obtains the corresponding optimal charging current as the target charging current in the optimal charging current relationship table according to the charging interval.

[0139] In some embodiments, the battery management system obtains records of deep discharge of the battery; determines whether the number of times of deep discharge of the battery reaches a set number of times; if so, outputs a prompt message; and outputs a constant current charging or constant voltage charging instruction to the charger.

[0140] Some BMSs can adjust the charging termination condition according to the health status and usage history of the battery. For example, if the battery has been used for a long time or has experienced multiple deep discharges, the BMS may control the charging process more strictly to prevent further degradation of the battery.

[0141] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0142] An embodiment of the power battery charging control system provided by the embodiment of the present invention belongs to the same inventive concept as the power battery charging control method in the above embodiments. Details not described in detail in the embodiment of the power battery charging control system can refer to the embodiment of the power battery charging control method.

[0143] Although the present invention has been described in detail by referring to the drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should be within the scope of the present invention / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for controlling the charging of a power battery, characterized in that, The method is applied to a control system, which includes a battery management system and a charger; the battery management system is used to manage the battery, and the method includes: After the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state; The battery management system sends a charging instruction to the charger based on the obtained target charging current; The charger adjusts the charging current to the target current according to the received charging instruction and charges the battery with the adjusted charging current; During the charging process, the battery management system monitors the state of the battery in real time; The battery management system determines whether the preset charging cut-off condition is reached according to the state of the battery; When the battery management system determines that the charging cut-off condition is reached, it sends a charging end instruction to the charger; or, when the battery management system receives a message for aborting charging sent by the charger, it sends a charging end instruction to the charger, and the charger stops current output in response to the charging end instruction; Among them, before the step that the battery management system obtains the current state of the battery and determines the target charging current according to the battery state, it includes: The battery management system divides the battery charging interval into different strategy stages, and the number of stages divided by each strategy is different; The particle swarm algorithm is used to calculate the optimal charging current for each stage; Compare the charging time and the charged amount under each division strategy to obtain the optimal strategy; and generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy, including: Obtain the charging time and the charged amount under each division strategy; Calculate the average charged amount under each division strategy, and delete the division strategy with the smallest average charged amount; Calculate the average charging time under each remaining division strategy, and delete the division strategy with the longest average charging time; Calculate the difference in the average charged amount between adjacent stages in the current remaining division strategies, and select the division strategy with the smaller average charged amount among the two division strategies with the smallest difference as the optimal strategy; Generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy.

2. The method for controlling the charging of a power battery according to claim 1, wherein The step of using the particle swarm algorithm to calculate the optimal charging current for each stage includes: According to the specified maximum charging current and the SOC interval of the battery, by dividing the SOC interval into stages, m groups of current sequences are generated, and the current sequences form an initial current population; Set the fitness function and use the particle swarm optimization algorithm for parameter optimization; After reaching the optimal point or reaching the set number of iterations, output the optimal charging current for each stage.

3. The method for controlling the charging of a power battery according to claim 2, characterized in that, The step of setting the fitness function and using the multi-objective particle swarm optimization algorithm for parameter optimization includes: Initialize the particle swarm, where the velocity is zero, the number of individuals in the swarm is m, and the position of the i-th particle is x i The velocity of the i-th particle is v i ; Set the fitness function to evaluate the fitness of each particle in the population; Update the position and velocity of each particle; Compare the fitness value of the current particle swarm with the fitness value of the previous particle swarm to perform convergence judgment.

4. The power battery charging control method according to claim 1, wherein The step that the battery management system obtains the current state of the battery and determines the target charging current according to the battery state includes: The battery management system obtains the current state of the battery; the state includes the battery SOC; Judge the charging interval of the current battery under the optimal division strategy according to the obtained SOC; Obtain the corresponding optimal charging current in the optimal charging current relation table according to the charging interval as the target charging current.

5. The power battery charging control method according to claim 4, wherein, After the step of the battery management system monitoring the state of the battery in real time during the charging process, it includes: Judge whether the current battery is in the charging state of the last stage according to the battery state; If so, execute the step: the battery management system judges whether the preset charging cut-off condition is reached according to the state of the battery; If not, when it is judged according to the battery state that the battery needs to enter the next stage, execute the step: judge the charging interval of the current battery under the optimal division strategy according to the obtained SOC.

6. The power battery charging control method according to claim 5, wherein The state of the battery also includes the battery voltage, battery temperature and charging time; The step of the battery management system judging whether the preset charging cut-off condition is reached according to the state of the battery includes: When the battery voltage reaches the cut-off voltage or the battery temperature reaches the preset temperature or the charging time reaches the preset maximum charging time, the charging cut-off condition is satisfied; Otherwise, the charging cut-off condition is not satisfied.

7. The power battery charging control method according to claim 6, wherein Before the step of the battery management system obtaining the current state of the battery and determining the target charging current according to the battery state, it includes: The battery management system obtains the record of the battery's deep discharge; Judge whether the number of times of the battery's deep discharge reaches the set number of times; If so, output a prompt message; and output a constant current charging or constant voltage charging instruction to the charger; If not, execute the step: the battery management system obtains the current state of the battery and determines the target charging current according to the battery state.

8. A power battery charging control system, characterized in that, It includes a battery management system and a charger; after the battery is connected to the charger, the battery management system obtains the current state of the battery and determines the target charging current according to the battery state; the battery management system sends a charging instruction to the charger based on the obtained target charging current; during the charging process, the battery management system monitors the state of the battery in real time; The battery management system judges whether the preset charging cut-off condition is reached according to the state of the battery; After the battery management system judges that the charging cut-off condition is reached, send a charging end instruction to the charger; or, when the battery management system receives the message of aborting charging sent by the charger, send a charging end instruction to the charger; among them, before the battery management system obtains the current state of the battery and determines the target charging current according to the battery state, the battery management system divides the battery charging interval into different strategy stages, and the number of stages divided by each strategy is different; use the particle swarm algorithm to calculate the optimal charging current of each stage; compare the charging time and the charged power under each division strategy to obtain the optimal strategy; and generate a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy, including: obtaining the charging time and the charged power under each division strategy; calculating the average charged power under each division strategy, and deleting the division strategy with the smallest average charged power; calculating the average charging time under each remaining division strategy, and deleting the division strategy with the longest average charging time; calculating the difference in the average charged power between adjacent stages in the current remaining division strategy, and selecting the division strategy with the smaller average charged power among the two division strategies with the smallest difference as the optimal strategy; generating a relationship table between the charging interval and the corresponding optimal charging current under the optimal strategy; The charger adjusts the charging current to the target current according to the received charging instruction, and charges the battery with the adjusted charging current; the charger responds to the charging end instruction to stop the current output.

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