Battery management method and system integrating dc-dc converter with bms

Abstract

The application provides a battery management method and system integrating a DC-DC converter and a BMS, relates to the technical field of battery management, and obtains real-time battery states of a target battery pack through dynamic monitoring of the BMS and analyzes to obtain a real-time state index; whether the real-time driving state of a target automobile meets a predetermined energy recovery constraint is judged; if not, the real-time control parameters of the DC-DC converter are controlled and optimized by calling a conversion control plan to obtain first optimal control parameters; and the DC-DC converter is controlled according to the first optimal control parameters. The application solves the technical problem that, due to the low coupling degree of the BMS and the DC-DC converter control system, the dynamic joint control based on the battery state and the driving state cannot be realized, the battery management response is delayed, and the energy recovery and power supply efficiency are affected, and achieves the technical effect of improving the battery energy utilization efficiency and the conversion response speed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a battery management method and system integrating a DC-DC converter and a BMS. BACKGROUND

[0002] With the rapid development of electric vehicles, battery energy management has become a key technology to improve vehicle range, prolong battery life, and optimize vehicle performance. The battery energy management system (BMS) and the DC-DC converter are the core components of battery energy management, respectively responsible for battery state monitoring and protection, and voltage conversion and energy distribution. Existing automobile battery energy management methods usually adopt a modular control architecture, that is, the BMS and the DC-DC converter operate independently, and exchange information and adjust energy through preset control strategies. Although this independent operation can meet the basic energy management needs, it lags behind in response when facing complex working conditions and dynamic load changes, and it is difficult to achieve real-time control optimization based on battery state. Especially in energy recovery conditions, there are problems such as low energy recovery efficiency or excessive intervention of control strategies, which affect the performance of the whole vehicle and aggravate the load of the battery. SUMMARY

[0003] The present application provides a battery management method and system integrating a DC-DC converter and a BMS, which solves the technical problem that the existing technology cannot realize dynamic joint control based on battery state and driving state due to low coupling degree of BMS and DC-DC converter control system, resulting in lag of battery management response, thereby affecting energy recovery and energy supply efficiency, and achieves the technical effect of realizing adaptive energy scheduling under all working conditions and improving battery energy utilization efficiency and conversion response speed.

[0004] In view of the above problems, on the one hand, the present application provides a battery management method integrating a DC-DC converter and a BMS, the method comprising: obtaining real-time battery state of a target battery pack through dynamic monitoring of the BMS, and analyzing to obtain a real-time state index, wherein the target battery pack refers to a battery pack loaded on a target vehicle; judging whether the real-time driving state of the target vehicle meets a predetermined energy recovery constraint; if not, calling a conversion control plan to control and optimize the real-time control parameters of the DC-DC converter to obtain first optimal control parameters, wherein the DC-DC converter is carried on the target battery pack; and performing conversion control on the DC-DC converter according to the first optimal control parameters.

[0005] In another aspect, the application also provides a battery management system integrating a DC-DC converter and a BMS, comprising: a real-time state monitoring module for dynamically monitoring the real-time battery state of a target battery pack through the BMS and analyzing a real-time state index, wherein the target battery pack refers to a battery pack loaded on a target vehicle; an energy recovery judgment module for judging whether the real-time driving state of the target vehicle meets a predetermined energy recovery constraint; a control optimization module for, if not, calling a conversion control plan to control and optimize the real-time control parameters of the DC-DC converter, to obtain first optimal control parameters, wherein the DC-DC converter is mounted on the target battery pack; and a conversion control module for performing conversion control on the DC-DC converter according to the first optimal control parameters.

[0006] The one or more technical solutions provided in the application have at least the following beneficial effects:

[0007] Through real-time monitoring of the target battery pack by the BMS, accurate battery state information is obtained, and a real-time state index is analyzed, which provides a key basis for subsequent control decisions and a quantitative basis for subsequent control strategies. By judging whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint, the judgment logic of the vehicle working condition is introduced, avoiding energy recovery at inappropriate times and improving the rationality and efficiency of energy recovery. When the driving state does not meet the energy recovery constraint, the conversion control plan is called to control and optimize the real-time control parameters of the DC-DC converter to obtain first optimal control parameters. Through the control method based on the plan and the optimization algorithm, the control parameters of the DC-DC converter can be dynamically adjusted according to different working conditions and battery states to achieve optimal conversion control and ensure the efficiency and accuracy of battery energy management. According to the first optimal control parameters, the DC-DC converter is controlled and converted, the optimization result is executed, and the DC-DC converter is driven to perform optimized energy conversion operations, forming a closed-loop control process from state perception, judgment, optimization to execution, and improving the response and regulation accuracy of battery energy management.

[0008] In summary, the application builds a closed-loop collaborative control mechanism between the BMS and the DC-DC converter, relies on real-time state perception and state index analysis of the battery pack by the BMS, combines dynamic discrimination of the vehicle driving state and energy recovery constraint, triggers online optimization and real-time adjustment of the control parameters of the DC-DC converter, realizes efficient energy scheduling and adaptive control of the battery management system under complex working conditions, and achieves the technical effects of improving the efficiency of battery energy recovery and control conversion response speed, providing strong support for efficient battery energy recovery and performance improvement of electric vehicles.

[0009] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application, the contents of the specification can be implemented, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 The flowchart of the battery management method of the integrated DC-DC converter and BMS provided by the embodiment of the present application is shown.

[0011] Figure 2 The flowchart of determining whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint in the battery management method of the integrated DC-DC converter and BMS provided by the embodiment of the present application is shown.

[0012] Figure 3 The structural diagram of the battery management system of the integrated DC-DC converter and BMS provided by the embodiment of the present application is shown.

[0013] Explanation of reference signs: real-time state monitoring module 10, energy recovery judgment module 20, control optimization module 30, conversion control module 40. DETAILED DESCRIPTION

[0014] The embodiment of the present application provides a battery management method and system integrating DC-DC converter and BMS, which solves the technical problem that the existing technology cannot realize dynamic joint control based on battery state and driving state due to low coupling degree of BMS and DC-DC converter control system, resulting in lag of battery management response and affecting energy recovery and power supply efficiency, and achieves the technical effect of realizing adaptive energy scheduling under all working conditions and improving battery energy utilization efficiency and conversion response speed.

[0015] Embodiment one, as shown in the figure, the embodiment of the present application provides a battery management method integrating DC-DC converter and BMS, which comprises: Figure 1

[0016] Step S1: obtaining the real-time battery state of the target battery pack by dynamic monitoring of BMS, and analyzing to obtain the real-time state index, wherein the target battery pack refers to the battery pack loaded on the target vehicle.

[0017] ​Specifically, the BMS collects the voltage, current, temperature, load, etc. of the target vehicle battery pack in real time using sensors and monitoring circuits to determine the real-time battery state of the battery pack. These sensors are installed inside or near the battery pack and can collect data at a frequency of milliseconds. Further, by statistically analyzing the collected battery data, the voltage, current, temperature, load deviation, etc. of the battery pack are fused into a quantitative index, and a real-time state index is obtained to represent the current running health and performance state of the battery pack, providing accurate and quantitative input basis for subsequent control strategies.

[0018] Step S2: Determine whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint.

[0019] Specifically, the energy recovery constraint is a preset limit condition for vehicle energy recovery, and it is determined whether to enable brake energy recovery. For example, energy recovery cannot be performed when cruising at high speed. Through the vehicle CAN bus interface, the real-time driving state data of the target vehicle transmitted by the ECU (Electronic Control Unit) is obtained, including vehicle speed, acceleration, braking state, etc. Compare these real-time driving state data with the pre-set energy recovery constraint conditions to determine whether the driving state meets the energy recovery constraint. The result provides a basis for subsequent control optimization of the DC-DC converter, avoiding starting energy recovery in inappropriate scenarios, and improving vehicle handling stability and energy utilization efficiency.

[0020] Step S3: If it does not meet, retrieve the conversion control plan to control the real-time control parameters of the DC-DC converter for control optimization, and obtain the first optimal control parameter, wherein the DC-DC converter is mounted on the target battery pack.

[0021] Specifically, when the real-time driving state of the target vehicle does not meet the predetermined energy recovery constraint, the DC-DC converter candidate control strategy matching the current driving state and battery state index is called from the control plan library stored in the BMS or ECU, and the optimization algorithm based on the objective function (such as genetic algorithm, gradient descent, particle swarm optimization, etc.) is used to optimize these candidate control strategies, and the current best DC-DC converter parameter is selected to obtain the first optimal control parameter. The conversion control plan is a pre-prepared control scheme that includes strategies for adjusting the control parameters of the DC-DC converter under different driving states and different battery states. In the case where the energy recovery constraint is not met, the control parameters of the DC-DC converter are optimized to ensure that the DC-DC converter operates in the optimal energy efficiency zone, improving energy utilization efficiency and avoiding energy waste caused by fixed parameters.

[0022] Step S4: Perform conversion control on the DC-DC converter according to the first optimal control parameter.

[0023] Specifically, the first optimal control parameter obtained by optimization is issued to the DC-DC control unit. Through the execution of the control logic by the digital controller (such as DSP or MCU), the switching device (such as MOSFET or IGBT) is driven to realize the actual voltage and current conversion process. During the control process, the output voltage and current of the DC-DC converter are monitored in real time to ensure that they operate within the expected range. According to the monitoring results, necessary feedback adjustments are made to ensure the stability and accuracy of the conversion process. By implementing the theoretical optimization results, a closed-loop control chain of "state driving-strategy optimization-control execution" is established, realizing precise control of the DC-DC converter, optimizing battery energy management, and significantly improving energy utilization efficiency and vehicle running stability.

[0024] Further, the real-time battery state of the target battery pack obtained by dynamic monitoring through the BMS in step S1 of the embodiments of the present application includes:

[0025] Step S11: Assemble a battery monomer set of the target battery pack, wherein the battery monomer set includes a first monomer.

[0026] Step S12: Take the mean value of the first real-time voltage of the first monomer as the real-time battery voltage.

[0027] Step S13: Take the mean value of the first real-time current of the first monomer as the real-time battery current.

[0028] Step S14: Take the mean value of the first real-time temperature of the first monomer as the real-time battery temperature.

[0029] Step S15: Obtain the first real-time load of the first monomer and filter to obtain the maximum load and the minimum load.

[0030] Step S16: Compare the maximum load with the minimum load to obtain the real-time battery load deviation.

[0031] Step S17: Based on the real-time battery voltage, the real-time battery current, the real-time battery temperature, and the real-time battery load deviation, the real-time battery state is composed.

[0032] Specifically, the battery monomer is the smallest unit that constitutes the battery pack. A plurality of battery monomers are combined together in series or parallel to form a battery pack. For example, for a lithium ion battery pack composed of a plurality of 18650 type lithium ion cells, each 18650 lithium ion cell is a battery monomer. Using the sensors and control units of the BMS, each monomer in the battery pack is identified and labeled to form a battery monomer set. Each battery monomer in the battery monomer set is labeled as a first monomer.

[0033] The voltage sensor of the BMS is used to collect the real-time voltage of the plurality of first monomers, obtain a plurality of first real-time voltages, and then calculate the average value of all the first real-time voltages as the real-time battery voltage of the target battery.

[0034] The Hall current sensor or shunt resistor of the BMS is used to collect the real-time current of the plurality of first monomers, obtain a plurality of first real-time currents, and then calculate the average value of all the first real-time currents as the real-time battery current of the target battery.

[0035] The thermocouple or thermistor sensor of the BMS is used to collect the real-time temperature of the plurality of first monomers, obtain a plurality of first real-time temperatures, and then calculate the average value of all the first real-time temperatures as the real-time battery temperature of the target battery.

[0036] The real-time load data of the first monomers is obtained by the load monitoring device, and then the maximum and minimum values are found among the collected plurality of real-time load data to obtain the maximum load and the minimum load. The maximum load and the minimum load are then calculated by difference to obtain the real-time battery load deviation. The real-time load deviation refers to the real-time load power deviation, which represents the fluctuation range of the output power between the battery monomers.

[0037] The real-time battery voltage, current, temperature, and load deviation are integrated into a data structure as the real-time battery state output for subsequent steps. For example, the first real-time voltage, first real-time current, first real-time temperature, and first real-time load data of the first monomers collected are shown in Table 1. It should be noted that this example is only for the purpose of clearly describing the calculation process of the real-time battery state, and therefore only data of 5 battery monomers are selected.

[0038] Table 1 Real-time data of first monomers

[0039]

[0040] According to Table 1, the real-time battery voltage, real-time battery current, real-time battery temperature, and real-time load deviation are calculated as follows: the real-time voltage average V = (3.60 + 3.62 + 3.58 + 3.59 + 3.61) / 5 = 3.60 V. The real-time current average I = 30.2 A. The real-time temperature average T = (34.5 + 35.0 + 34.2 + 35.1 + 34.8) / 5 = 34.72℃. The real-time load deviation = 118-106 = 12 W. The final real-time battery state is (3.60 V, 30.2 A, 34.72℃, 12 W).

[0041] Further, step S1 further comprises:

[0042] Step S18: Obtain the predetermined battery state and compare the real-time battery state with the predetermined battery state to obtain the state deviation.

[0043] Step S19: Perform normalized weighted analysis on the state deviation to obtain the real-time state index.

[0044] Specifically, a set of predefined operating standards for the target battery under normal and safe operating conditions, i.e., predetermined battery states, includes voltage, current, temperature, load deviation, etc. For example: ideal voltage 3.75V, ideal current: 30A, ideal temperature: 35℃, ideal load deviation: 10W. The real-time battery state is compared with the predetermined battery state item by item, and the deviation value of each parameter is calculated to form the state deviation. For example, based on the real-time battery state in the above example, the state deviation is calculated as follows: voltage deviation ΔV = |3.60-3.75| = 0.15V, current deviation ΔI = |30.2-30.00| = 0.2A, temperature deviation ΔT = |34.72-35.00| = 0.28℃, load deviation ΔP = |12-10| = 2W.

[0045] A weighted coefficient of variation (VCS) is used to normalize and weight the state deviations, removing the influence of dimensions to obtain the real-time state index. Specifically, the voltage deviation, current deviation, temperature deviation, and load deviation collected at the current moment are constructed into a deviation set. The average and standard deviation of this deviation set are calculated, and then the weighted VCS is used as the real-time state index. For example, the weights are set according to expert experience as follows: voltage deviation weight ω1 = 0.3, current deviation weight ω2 = 0.3, temperature deviation weight ω3 = 0.2, and load deviation weight ω4 = 0.2. A deviation set (ΔV, ΔI, ΔT, ΔP) = (0.15V, 0.2A, 0.28℃, 2W) is constructed based on the voltage deviation, current deviation, temperature deviation, and load deviation. The weighted average of the deviation set μ = ω1×ΔV + ω2×ΔI + ω3×ΔT + ω4×ΔP = 0.561 is calculated, and then the weighted standard deviation is calculated. Then, the weighted coefficient of variation (CV) = μ / σ ≈ 0.75 is calculated, and this weighted coefficient of variation is used as the real-time state index. This index can comprehensively and quantitatively reflect the degree of deviation between the real-time state and the predetermined state of the battery, facilitating a rapid assessment of the overall battery state and enabling reasonable battery management decisions.

[0046] Furthermore, such as Figure 2 As shown, step S2 includes:

[0047] Step S21: Extract the real-time speed from the real-time driving state.

[0048] Step S22: judging whether the real-time speed is within the predetermined speed limit in the predetermined energy recovery constraint.

[0049] Step S23: if yes, extracting the real-time acceleration in the real-time driving state.

[0050] Step S24: establishing an acceleration time sequence according to the correspondence between the real-time acceleration and the real-time time, and analyzing to obtain an acceleration change coefficient.

[0051] Step S25: judging whether the acceleration change coefficient is within the predetermined acceleration change threshold in the predetermined energy recovery constraint.

[0052] Step S26: if no, the real-time driving state does not meet the predetermined energy recovery constraint.

[0053] Specifically, through the vehicle-mounted CAN bus interface, the real-time driving state data of the target vehicle transmitted by the ECU (Electronic Control Unit) is obtained, and the real-time speed information is extracted therefrom. The real-time speed is compared with the predetermined speed limit value in the predetermined energy recovery constraint stored in advance, to judge whether the real-time speed is within the allowed range. This process can be performed by a comparison module in the control system. The predetermined speed limit value is the upper limit of the speed allowed for energy recovery. Through speed judgment, high-speed running scenarios unsuitable for energy recovery can be filtered out, avoiding false triggering of current backflow or reducing battery power efficiency at high speed.

[0054] If the real-time speed is within the predetermined speed limit value, the real-time acceleration information in the real-time driving state is extracted. With time as the horizontal axis and real-time acceleration as the vertical axis, the acceleration values corresponding to different times are plotted to form an acceleration time sequence. Then, through mathematical analysis method, the change rate or variance of acceleration is calculated to obtain the acceleration change coefficient. For example, the first-order difference of the acceleration time sequence can be calculated, and the average value of these differences is obtained to obtain the acceleration change coefficient. The larger the acceleration change coefficient, the more intense the acceleration change.

[0055] The acceleration change coefficient is compared with the predetermined acceleration change threshold in the predetermined energy recovery constraint. It is judged whether the acceleration change coefficient is within the predetermined acceleration change threshold. The predetermined acceleration change threshold is a pre-set upper limit of the acceleration change coefficient, and exceeding this threshold indicates that the vehicle state fluctuates greatly and is not suitable for energy recovery. For example, the acceleration change threshold can be set to 0.5 m / s. If the acceleration change coefficient is not within the predetermined acceleration change threshold, it is determined that the real-time driving state does not meet the predetermined energy recovery constraint. Through acceleration change trend, the unfavorable scenario of "short low speed but frequent acceleration and deceleration" is filtered out, to ensure the stability and safety of energy recovery operation.

[0056] Further, if the real-time speed is not within the predetermined speed limit, then the real-time driving state does not meet the predetermined energy recovery constraint.

[0057] Specifically, if the real-time speed is not within the predetermined speed limit, it is directly determined that the real-time driving state does not meet the predetermined energy recovery constraint, and no subsequent acceleration data extraction and analysis process is performed. Through the judgment of the two dimensions of real-time speed and acceleration change trend, it is intelligently identified whether it is suitable to perform energy recovery based on the current running state of the vehicle, so as to ensure the rationality and efficiency of the energy recovery process.

[0058] Further, step S3 comprises:

[0059] Step S31: constructing a first conversion control space with the real-time control parameters as constraints.

[0060] Step S32: randomly extracting a first control parameter in the first conversion control space, and obtaining a first control record under the first control parameter.

[0061] Step S33: traversing in the first control record based on a first predetermined conversion feature, and analyzing to obtain a first conversion fitness.

[0062] Step S34: obtaining the first optimal control parameter by optimizing the first conversion fitness maximum as the target.

[0063] Specifically, according to the specifications and real-time running state of the DC-DC converter, in order to ensure stable output power, the value range of each control parameter is set based on the current load demand, battery SOC, voltage upper and lower limits, etc., including duty ratio, voltage control level, current limit level, etc. A multi-dimensional parameter space is constructed according to the value range of each control parameter, each dimension corresponds to a control parameter, and a first conversion control space is obtained. Among the control parameters, the duty ratio is the core control variable, which controls the on and off time ratio of the power switch tube in the DC-DC converter, thereby determining the dynamic response of the output voltage and current. In order to realize the dynamic optimization of the DC-DC control strategy under different load conditions and battery working states, the voltage control level and the current limit level are included in the control parameter space design as the target constraint dimension of optimization search. The voltage control level Vtarget sets the level of the target output voltage (such as 3.6V, 3.7V, 3.8V, etc.), which is used to limit the expected level of the output behavior of the control strategy; the current limit level sets the maximum range of the output current (such as 10A-50A), which is used to constrain the output load range and ensure the safe execution of the control strategy.

[0064] In the first conversion control space, a set of parameters is randomly extracted as the first control parameters by using a random number generation algorithm. Then, the first control parameters are used as indexes to search in the historical conversion control data to obtain the control result records of the DC-DC converter under the first control parameters, including output voltage, current fluctuation, conversion efficiency, etc., to form the first control record.

[0065] The first predetermined conversion feature is a specific feature type preset for evaluating the conversion control effect of the DC-DC converter, such as conversion efficiency, conversion load balancing degree, etc. According to the requirements of the first predetermined conversion feature, each data in the first control record is analyzed, and the corresponding feature index is calculated. The feature indexes are weighted and calculated to obtain the first conversion fitness to measure the advantages and disadvantages of the first control parameters. With the goal of maximum fitness, genetic algorithm, particle swarm optimization and other algorithms are used to perform multiple iterations in the first conversion control space. Each iteration selects a set of first control parameters, and then calculates the first conversion fitness under the control parameters according to the foregoing method, and compares it with the previous first conversion fitness, and keeps the larger value, and performs multiple rounds of iteration until the preset iteration number is met. Then, the first control parameter corresponding to the maximum first conversion fitness is selected as the optimal solution, and the output is the first optimal control parameter.

[0066] For example, the duty cycle D ∈ [0.2, 0.9], the voltage control level Vtarget ∈ {3.6V, 3.7V, 3.8V}, and the current limit level ∈ [10A, 50A] are used to construct a three-dimensional parameter space C1 to obtain the first conversion control space. A set of first control parameters (D = 0.5, Vtarget = 3.7V, I = 30A) is randomly extracted from C1. According to the first control parameters, the corresponding control record is indexed in the historical control database to form the first control record, as shown in Table 2.

[0067] Table 2 First control record

[0068] Time Output voltage (V) Output current (A) Input current (A) Input voltage (V) t1 3.68 28.5 31.2 4.20 t2 3.70 29.1 32.0 4.15 t3 3.71 30.2 33.3 4.10

[0069] According to the first control record, the first conversion fitness is calculated. First, the fitness function is constructed as follows: fitness = γ * η + (1-γ) * L, where η is the average conversion efficiency, L is the conversion load balancing degree, and γ is the conversion efficiency weight.

[0070] For the average conversion efficiency, the conversion efficiency at each time is first calculated, which is the ratio of output power to input power; wherein the output power is the product of output current and output voltage, and the input power is the product of input current and input voltage, and the conversion efficiency at each time is calculated as (0.80, 0.81, 0.82). Then, the average value of the conversion efficiency at each time is obtained as η = 0.81.

[0071] For the conversion load balancing degree L, in combination with the output current standard deviation and the extreme value of the output current, the index is constructed as follows: Wherein, σ I is the output current standard deviation; σ ref is the preset maximum allowable current fluctuation value (for example: 5A); I max is the maximum value of the output current; I min is the minimum value of the output current; I avg is the average value of the output current; α and β are weight coefficients, and α is preset as 0.6 and β is preset as 0.4. The load balancing degree index combines the standard deviation and the range ratio, and takes into account the average fluctuation and the influence of extreme value, so that the stable response capability of the control parameter to the load change can be more comprehensively and quantitatively reflected. According to the index calculation formula, the conversion load balancing degree L is 0.8928.

[0072] Setting γ = 0.7, the first fitness is obtained = 0.7 * η + 0.3 * ≈ 0.83. The particle swarm optimization algorithm is used to search different combinations in C1, the fitness value corresponding to each set of control parameters is recorded, and the control parameters corresponding to the current optimal fitness are retained, until the preset iteration number is reached, and finally the control parameters with the maximum fitness are output as the first optimal control parameters.

[0073] By constructing the conversion control space, based on the control record extraction and feature evaluation of the historical data, the adaptive optimization selection of the DC-DC converter control parameters is realized. Under the premise of maintaining safety constraints, the optimal conversion strategy can be dynamically selected to improve the energy transmission efficiency and the battery operation stability.

[0074] Further, the step S32 comprises:

[0075] Step S321: obtaining a conversion control database of the DC-DC converter.

[0076] Step S322: traversing the first control parameters in the conversion control database to obtain a first historical control data set.

[0077] Step S323: extracting a first historical control record in the first historical control data set as the first control record.

[0078] Specifically, the data access interface is used to obtain the database of the historical operation records and performance data of the DC-DC converter under different control parameters, i.e. the conversion control database, from the storage device (such as the hard disk or flash memory in the automobile control system).

[0079] In the conversion control database, a search is performed using a traversal algorithm to search for all historical control records corresponding to the first control parameter or a parameter close to the first control parameter, to obtain a first historical control data set.

[0080] From the first historical control data set, a representative first historical control record is selected according to a certain filtering rule. The filtering rule can be to select the latest historical record or the historical record with the best data integrity. The first historical control record is output as a first control record for subsequent analysis and calculation based on the first conversion fitness, to provide high-credibility performance feedback support for real-time control parameter optimization, and effectively avoid blind trial and error or inefficient parameter tuning.

[0081] Further, the first predetermined conversion feature at least includes conversion efficiency and conversion load balancing degree.

[0082] Specifically, in the evaluation of the first control parameter, the value of the conversion efficiency can directly reflect the energy utilization of the DC-DC converter under the first control parameter. Higher conversion efficiency means less energy loss. Conversion efficiency is expressed as the ratio of electrical energy output to input, and the unit is percentage (%). The conversion load balancing degree can reflect the ability of the DC-DC converter to cope with load changes under the control parameter. It can be measured by standard deviation, maximum / minimum current ratio, etc.

[0083] Further, step S2 further includes: if it is consistent, retrieving the conversion control plan to control and optimize the real-time control parameter to obtain a second optimal control parameter. Wherein, retrieving the conversion control plan to control and optimize the real-time control parameter to obtain the second optimal control parameter includes:

[0084] Step A: constructing a second conversion control space with the real-time control parameter as a constraint.

[0085] Step B: randomly extracting a second control parameter in the second conversion control space, and obtaining a second control record under the second control parameter.

[0086] Step C: traversing in the second control record based on a second predetermined conversion feature, and analyzing to obtain a second conversion fitness.

[0087] Step D: optimizing the second optimal control parameter with the maximum second conversion fitness as the target.

[0088] Specifically, when the vehicle is in low speed or standby, etc. to meet the predetermined conditions of energy recovery, the battery energy management is no longer simply maintained by the existing control strategy, but further optimized by calling the conversion control plan. At this time, a new optimization path is formed to improve the energy recovery efficiency, a second conversion control space is constructed based on the real-time control parameters, and the second optimal control parameters are found based on the historical data under the current working condition to maximize the energy recovery efficiency.

[0089] Similar to the construction of the first conversion control space, the second conversion control space is a parameter search space constructed based on the current battery acceptance capacity, recharge channel constraints, thermal limitations, etc. with the real-time parameters provided by the current BMS as constraints (such as current battery SOC, output limit, voltage range, etc.).

[0090] In the second conversion control space, a set of parameters are randomly extracted as the second control parameters using a random number generation algorithm. Then, the second control parameters are used as indexes to search in the historical conversion control data to obtain the control result records of the DC-DC converter under the second control parameters to form the second control records.

[0091] The second predetermined conversion feature is a specific feature type used to evaluate the conversion control effect of the DC-DC converter in the energy recovery configuration, such as recharge conversion efficiency, conversion load balancing degree, etc. According to the requirements of the second predetermined conversion feature, each data in the second control record is analyzed and the corresponding feature index is calculated. The feature indexes are weighted and calculated to obtain the second conversion fitness to measure the advantages and disadvantages of the second control parameters.

[0092] With the goal of maximizing fitness, genetic algorithms, particle swarm optimization, etc. are used to perform multiple iterations in the second conversion control space. Each iteration selects a set of second control parameters, calculates the second conversion fitness under the control parameters, and performs multiple iterations until the preset number of iterations is met. Then, the second control parameters corresponding to the maximum second conversion fitness are selected as the optimal solution, and the output is the second optimal control parameters. The optimization process here is similar to the optimization process of the first optimal control parameters, and the specific process can be referred to the foregoing description.

[0093] The second optimal control parameters are obtained by optimizing the maximum second conversion fitness, which realizes the optimal adaptive control configuration selection in the energy recovery scenario and maximizes the recharge energy utilization rate.

[0094] In summary, the battery management method provided by the embodiments of the present application has the following beneficial effects:

[0095] By real-time monitoring of the target battery pack through the BMS, accurate battery state information is obtained, and a real-time state index is analyzed to provide a key basis for subsequent control decisions and a quantitative basis for subsequent control strategies. By judging whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint in this step, the judgment logic of the vehicle operating condition is introduced to avoid energy recovery at inappropriate times and improve the rationality and efficiency of energy recovery. When the driving state does not meet the energy recovery constraint, the real-time control parameters of the DC-DC converter are controlled and optimized by calling the conversion control plan to obtain the first optimal control parameters. If the energy recovery constraint is met, a second conversion control space is constructed, and the second optimal control parameters are obtained by optimization. Through the control method based on the plan and the optimization algorithm, the control parameters of the DC-DC converter can be dynamically adjusted according to different operating conditions and battery states to achieve optimal conversion control in energy supply and energy recovery modes, ensuring the efficiency and accuracy of battery energy management. According to the optimal control parameters, the DC-DC converter is controlled, the optimization result is executed, and the DC-DC converter is driven to perform optimized energy conversion operations, forming a closed-loop control process from state perception, judgment, optimization to execution, and improving the response and regulation accuracy of battery energy management.

[0096] Overall, the embodiments of the present application construct a closed-loop cooperative control mechanism between the BMS and the DC-DC converter, rely on real-time state perception and state index analysis of the battery pack by the BMS, combine dynamic discrimination of the vehicle driving state and energy recovery constraint, trigger online optimization and real-time adjustment of the control parameters of the DC-DC converter, achieve efficient energy scheduling and adaptive control of the battery management system under complex operating conditions, and achieve the technical effects of improving the efficiency of battery energy recovery and control conversion response speed, providing strong support for efficient battery energy recovery and performance improvement of electric vehicles.

[0097] Embodiment two, as shown in Figure 3 The battery management system integrating the DC-DC converter and the BMS provided by the embodiments of the present application comprises:

[0098] The real-time state monitoring module 10 is used for obtaining the real-time battery state of the target battery pack through dynamic monitoring by the BMS and analyzing the real-time state index, wherein the target battery pack refers to the battery pack loaded on the target vehicle.

[0099] The energy recovery judgment module 20 is used for judging whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint.

[0100] The control optimization module 30 is configured to, if the real-time state does not meet the predetermined energy recovery constraint, call the conversion control plan to control and optimize the real-time control parameter of the DC-DC converter to obtain a first optimal control parameter, wherein the DC-DC converter is mounted on the target battery pack.

[0101] The conversion control module 40 is configured to perform conversion control on the DC-DC converter according to the first optimal control parameter.

[0102] Further, the real-time state monitoring module 10 is further configured to perform the following steps:

[0103] The battery monomer set of the target battery pack is assembled, wherein the battery monomer set includes a first monomer; the average of the first real-time voltage of the first monomer is taken as the real-time battery voltage; the average of the first real-time current of the first monomer is taken as the real-time battery current; the average of the first real-time temperature of the first monomer is taken as the real-time battery temperature; the first real-time load of the first monomer is obtained, and the maximum load and the minimum load are screened; the real-time battery load deviation is obtained by comparing the maximum load and the minimum load; and the real-time battery state is composed based on the real-time battery voltage, the real-time battery current, the real-time battery temperature, and the real-time battery load deviation.

[0104] Further, the real-time state monitoring module 10 is further configured to perform the following steps:

[0105] The predetermined battery state is obtained, and the state deviation is obtained by comparing the real-time battery state and the predetermined battery state; and the real-time state index is obtained by performing normalized weighted analysis on the state deviation.

[0106] Further, the energy recovery judgment module 20 is further configured to perform the following steps:

[0107] The real-time speed in the real-time driving state is extracted; it is judged whether the real-time speed is in the predetermined speed limit value in the predetermined energy recovery constraint; if it is in, the real-time acceleration in the real-time driving state is extracted; the acceleration time sequence is established according to the corresponding relationship between the real-time acceleration and the real-time time, and the acceleration change coefficient is analyzed and obtained; it is judged whether the acceleration change coefficient is in the predetermined acceleration change threshold value in the predetermined energy recovery constraint; if it is not in, the real-time driving state does not meet the predetermined energy recovery constraint.

[0108] Further, if the real-time speed is not in the predetermined speed limit value, the real-time driving state does not meet the predetermined energy recovery constraint.

[0109] Further, the control optimization module 30 is further configured to perform the following steps:

[0110] constructing a first conversion control space with the real-time control parameter as a constraint; randomly extracting a first control parameter in the first conversion control space, and obtaining a first control record under the first control parameter; traversing in the first control record based on a first predetermined conversion feature, and analyzing to obtain a first conversion fitness; and obtaining the first optimal control parameter by optimizing the first conversion fitness as a target.

[0111] Further, the control optimization module 30 is further used to execute the following steps:

[0112] obtaining a conversion control database of the DC-DC converter; traversing the first control parameter in the conversion control database to obtain a first historical control data set; and extracting a first historical control record in the first historical control data set as the first control record.

[0113] Further, the first predetermined conversion feature at least includes conversion efficiency and conversion load balancing degree.

[0114] Further, after judging whether the real-time driving state of the target automobile meets the predetermined energy recovery constraint, the control optimization module 30 is further used to execute the following steps:

[0115] If yes, the conversion control plan is called to perform control optimization on the real-time control parameter to obtain a second optimal control parameter; wherein, calling the conversion control plan to perform control optimization on the real-time control parameter to obtain the second optimal control parameter includes: constructing a second conversion control space with the real-time control parameter as a constraint; randomly extracting a second control parameter in the second conversion control space, and obtaining a second control record under the second control parameter; traversing in the second control record based on a second predetermined conversion feature, and analyzing to obtain a second conversion fitness; and obtaining the second optimal control parameter by optimizing the second conversion fitness as a target.

[0116] Through the foregoing detailed description of the battery management method integrating the DC-DC converter and the BMS, those skilled in the art can clearly know the battery management system integrating the DC-DC converter and the BMS in the embodiment. For the system disclosed in the second embodiment, since it corresponds to the method disclosed in the first embodiment, it has corresponding functional modules and beneficial effects, and the related parts are referred to the method part description.

[0117] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A battery management method integrating a DC-DC converter and a BMS, characterized in that, The application comprises: Real-time battery state of target battery pack is obtained through dynamic monitoring by BMS, and real-time state index is obtained through analysis, wherein the target battery pack refers to the battery pack loaded on the target vehicle; It is judged whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint; If not, the real-time control parameter of the DC-DC converter is controlled and optimized by calling the conversion control plan, and the first optimal control parameter is obtained, wherein the DC-DC converter is mounted on the target battery pack; The DC-DC converter is controlled and converted according to the first optimal control parameter; It is judged whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint, comprising: The real-time speed in the real-time driving state is extracted; It is judged whether the real-time speed is in the predetermined speed limit value in the predetermined energy recovery constraint; If yes, the real-time acceleration in the real-time driving state is extracted; The acceleration time sequence is established according to the corresponding relationship between the real-time acceleration and the real-time time, and the acceleration change coefficient is obtained through analysis; It is judged whether the acceleration change coefficient is in the predetermined acceleration change threshold in the predetermined energy recovery constraint; If not, the real-time driving state does not meet the predetermined energy recovery constraint; If not, the real-time control parameter of the DC-DC converter is controlled and optimized by calling the conversion control plan, and the first optimal control parameter is obtained, comprising: The first conversion control space is constructed with the real-time control parameter as the constraint; The first control parameter in the first conversion control space is randomly extracted, and the first control record under the first control parameter is obtained; The first conversion fitness is obtained through analysis based on the first predetermined conversion characteristic in the first control record; The first optimal control parameter is obtained through optimization with the maximum first conversion fitness as the target.

2. The battery management method of claim 1, wherein, Real-time battery state of target battery pack is obtained through dynamic monitoring by BMS, comprising: The battery monomer set of the target battery pack is established, wherein the battery monomer set comprises a first monomer; The mean value of the first real-time voltage of the first monomer is taken as the real-time battery voltage; The mean value of the first real-time current of the first monomer is taken as the real-time battery current; The mean value of the first real-time temperature of the first monomer is taken as the real-time battery temperature; The first real-time load of the first monomer is obtained, and the maximum load and the minimum load are obtained through screening; The real-time battery load deviation is obtained by comparing the maximum load and the minimum load; The real-time battery state is composed based on the real-time battery voltage, the real-time battery current, the real-time battery temperature and the real-time battery load deviation.

3. The battery management method of claim 2, wherein, Real-time battery state of target battery pack is obtained through dynamic monitoring by BMS, and real-time state index is obtained through analysis, comprising: The predetermined battery state is obtained, and the state deviation is obtained by comparing the real-time battery state and the predetermined battery state; The state deviation is normalized and weighted analyzed to obtain the real-time state index.

4. The battery management method of claim 1, wherein, The application comprises: The real-time speed is not in the predetermined speed limit value, and the real-time driving state does not meet the predetermined energy recovery constraint.

5. The battery management method of claim 1, wherein, Randomly extracting a first control parameter in the first conversion control space, and obtaining a first control record under the first control parameter, comprising: Obtaining a conversion control database of the DC-DC converter; Traversing the first control parameter in the conversion control database to obtain a first historical control data set; Extracting a first historical control record in the first historical control data set as the first control record.

6. The battery management method of claim 1, wherein, The first predetermined conversion feature at least includes conversion efficiency and conversion load balancing degree.

7. The battery management method of claim 5, wherein, After judging whether the real-time driving state of the target automobile meets the predetermined energy recovery constraint, further comprising: If yes, calling the conversion control plan to control and optimize the real-time control parameter to obtain a second optimal control parameter; Wherein, calling the conversion control plan to control and optimize the real-time control parameter to obtain a second optimal control parameter, comprising: Building a second conversion control space with the real-time control parameter as a constraint; Randomly extracting a second control parameter in the second conversion control space, and obtaining a second control record under the second control parameter; Based on the second predetermined conversion feature, traversing the second control record and analyzing to obtain a second conversion fitness; Taking the maximum second conversion fitness as the target to optimize to obtain the second optimal control parameter.

8. A battery management system integrating a DC-DC converter with a BMS, characterized in that, The system is used to execute the battery management method of the integrated DC-DC converter and BMS according to any one of claims 1-7, comprising: A real-time state monitoring module for obtaining the real-time battery state of the target battery pack through dynamic monitoring by the BMS, and analyzing to obtain a real-time state index, wherein the target battery pack refers to the battery pack loaded on the target automobile; An energy recovery judgment module for judging whether the real-time driving state of the target automobile meets the predetermined energy recovery constraint; A control optimization module for calling the conversion control plan to control and optimize the real-time control parameter of the DC-DC converter if not, to obtain a first optimal control parameter, wherein the DC-DC converter is mounted on the target battery pack; A conversion control module for performing conversion control on the DC-DC converter according to the first optimal control parameter.

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