DC-DC converter and BMS integrated battery management method and system

Abstract

The invention provides a battery management method and system integrating a DC-DC converter and a BMS, and relates to the technical field of battery management, the real-time battery state of a target battery pack is obtained through dynamic monitoring of the BMS, and a real-time state index is obtained through analysis; judging whether the real-time driving state of the target automobile accords with a preset energy recovery constraint or not; if not, calling a conversion control plan to perform control optimization on the real-time control parameter of the DC-DC converter to obtain a first optimal control parameter; and performing conversion control on the DC-DC converter according to the first optimal control parameter. The technical problems that in the prior art, due to the fact that the coupling degree of a BMS and a DC-DC converter control system is low, dynamic combined control based on the battery state and the driving state cannot be achieved, battery management response lags behind, and the energy recovery and energy supply efficiency is affected are solved, and the technical effect of improving the battery energy utilization efficiency and the conversion response speed is achieved.

Description

Technical Field

[0001] This application relates to the technical field of battery management, and particularly to a battery management method and system integrating a DC-DC converter and a BMS. Background Art

[0002] With the rapid development of electric vehicles, battery energy management has become a key technology for improving vehicle driving range, extending battery life, and optimizing the overall vehicle performance. The battery energy management system (BMS) and the DC-DC converter are the core components of battery energy management, responsible for battery state monitoring and protection, and voltage conversion and energy distribution respectively. Existing automotive battery energy management methods usually adopt a modular control architecture, that is, the BMS and the DC-DC converter operate independently, and information exchange and energy regulation are carried out through preset control strategies. Although this independent operation mode can meet the basic energy management requirements, it has a lag in response when facing complex working conditions and dynamic load changes, and it is difficult to achieve real-time control optimization based on the battery state. Especially in the energy recovery working condition, problems such as low energy recovery efficiency or excessive intervention of control strategies are likely to occur, affecting the overall vehicle performance and increasing the battery load. Summary of the Invention

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

[0004] In view of the above problems, on the one hand, this application provides a battery management method integrating a DC-DC converter and a BMS, and the method includes: dynamically monitoring the real-time battery state of a target battery pack through the BMS and analyzing to obtain a real-time state index, where the target battery pack refers to the battery pack loaded on a target vehicle; determining whether the real-time driving state of the target vehicle meets a predetermined energy recovery constraint; if not, retrieving a conversion control plan to perform control optimization on the real-time control parameters of the DC-DC converter to obtain a first optimal control parameter, where the DC-DC converter is mounted on the target battery pack; performing conversion control on the DC-DC converter according to the first optimal control parameter.

[0005] On the other hand, the present application also provides a battery management system integrating a DC-DC converter and a BMS. The system includes: a real-time status monitoring module for dynamically monitoring the real-time battery status of a target battery pack through the BMS and analyzing to obtain a real-time status index, where the target battery pack refers to the battery pack installed on a target vehicle; an energy recovery judgment module for judging whether the real-time driving status of the target vehicle meets a predetermined energy recovery constraint; a control optimization module for, if not meeting the constraint, retrieving a conversion control plan to perform control optimization on the real-time control parameters of the DC-DC converter to obtain a first optimal control parameter, where 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 parameter.

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

[0007] Through the real-time monitoring of the target battery pack by the BMS, accurate battery status information is obtained, and a real-time status index is analyzed, providing a key basis for subsequent control decisions and a quantitative basis for subsequent control strategies. By judging whether the real-time driving status of the target vehicle meets the predetermined energy recovery constraint, the judgment logic of the vehicle operating conditions is introduced, avoiding energy recovery at inappropriate times and improving the rationality and efficiency of energy recovery. When the driving status does not meet the energy recovery constraint, a conversion control plan is retrieved to perform control optimization on the real-time control parameters of the DC-DC converter to obtain a first optimal control parameter. 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 statuses to achieve optimal conversion control, ensuring the high efficiency and accuracy of battery energy management. Performing conversion control on the DC-DC converter according to the first optimal control parameter, implementing the optimization result, driving the DC-DC converter to perform the optimized energy conversion operation, and forming a closed-loop control process from state perception, judgment, optimization to execution, improving the response and regulation accuracy of battery energy management.

[0008] In summary, the present application constructs a closed-loop collaborative control mechanism between the BMS and the DC-DC converter, relying on the real-time status perception and status index analysis of the battery pack by the BMS, combining the dynamic discrimination of the vehicle driving status and the energy recovery constraint, triggering the online optimization and real-time adjustment of the control parameters of the DC-DC converter, realizing the efficient energy scheduling and adaptive control of the battery management system under complex operating conditions, achieving the technical effects of improving the energy recovery and utilization efficiency of the battery and the control conversion response speed, and providing strong support for the efficient battery energy recovery and utilization and performance improvement of electric vehicles.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically exemplified. Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of the battery management method integrating a DC-DC converter and a BMS provided by an embodiment of this application.

[0011] Figure 2 It is a schematic flowchart of determining whether the real-time driving state of a target vehicle conforms to a predetermined energy recovery constraint in the battery management method integrating a DC-DC converter and a BMS provided by an embodiment of this application.

[0012] Figure 3 It is a schematic structural diagram of the battery management system integrating a DC-DC converter and a BMS provided by an embodiment of this application.

[0013] Description of the reference numerals: Real-time state monitoring module 10, energy recovery judgment module 20, control optimization module 30, conversion control module 40. Detailed Embodiments

[0014] By providing a battery management method and system integrating a DC-DC converter and a BMS in an embodiment of this application, the technical problem in the prior art that due to the low coupling degree between the BMS and the DC-DC converter control system, it is impossible to achieve dynamic joint control based on the battery state and the driving state, resulting in a lag in battery management response, thereby affecting the energy recovery and power supply efficiency is solved, and the technical effect of realizing adaptive energy scheduling under all working conditions and improving the battery energy utilization efficiency and conversion response speed is achieved.

[0015] Embodiment 1, as Figure 1 shown, an embodiment of this application provides a battery management method integrating a DC-DC converter and a BMS, and the method includes:

[0016] Step S1: Dynamically monitor the real-time battery state of the target battery pack through the BMS, and analyze to obtain a real-time state index, where the target battery pack refers to the battery pack installed on the target vehicle.

[0017] Specifically, the BMS uses sensors and monitoring circuits to collect parameters such as voltage, current, temperature, and load of the target vehicle's battery pack in real time, and determines 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, parameters such as voltage deviation, current deviation, temperature deviation, and load deviation of the battery pack are fused into a quantitative index to obtain a real-time state index, which characterizes the current operating health status and performance state of the battery pack, providing an 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 limitation condition for vehicle energy recovery, which is used to determine whether it is suitable to enable braking energy recovery. For example, energy recovery cannot be performed during high-speed cruising. Through the in-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. These real-time driving state data are compared with the pre-set energy recovery constraint conditions to determine whether the driving state meets the energy recovery constraint. The judgment result provides a basis for the control optimization of the subsequent DC-DC converter, avoiding the activation of energy recovery in inappropriate scenarios and improving the handling stability and energy utilization efficiency of the whole vehicle.

[0020] Step S3: If not, retrieve the conversion control plan to optimize the real-time control parameters of the DC-DC converter to obtain the first optimal control parameters, where 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 candidate control strategies of the DC-DC converter that match the current driving state and battery state index are retrieved from the control plan library stored in the BMS or ECU, and an optimization algorithm based on the objective function (such as genetic algorithm, gradient descent, particle swarm optimization, etc.) is used to optimize among these candidate control strategies to select the current best DC-DC converter parameters and obtain the first optimal control parameters. Among them, the conversion control plan is a set of pre-developed control schemes, which contain strategies for adjusting the control parameters of the DC-DC converter under different driving states and different battery states. In the case of not meeting the energy recovery constraint, by optimizing the control parameters of the DC-DC converter, it can be ensured that the DC-DC converter operates in the optimal energy efficiency area, improving the 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 parameters.

[0023] Specifically, the first optimal control parameters obtained through optimization are sent to the DC-DC control unit. The control logic is executed by a digital controller (such as a DSP or MCU) to drive a switching device (such as a MOSFET or IGBT) to achieve 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-driven - policy optimization - control execution" is constructed, achieving precise control of the DC-DC converter, optimizing battery energy management, and significantly improving energy utilization efficiency and vehicle operation stability.

[0024] Furthermore, in step S1 of the embodiment of the present application, the real-time battery state of the target battery pack is dynamically monitored by the BMS, including:

[0025] Step S11: Form a battery cell set of the target battery pack, where the battery cell set includes a first cell.

[0026] Step S12: Take the average value of the first real-time voltages of the first cells as the real-time battery voltage.

[0027] Step S13: Take the average value of the first real-time currents of the first cells as the real-time battery current.

[0028] Step S14: Take the average value of the first real-time temperatures of the first cells as the real-time battery temperature.

[0029] Step S15: Obtain the first real-time load of the first cells and screen 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, form the real-time battery state.

[0032] Specifically, a battery cell is the smallest unit that makes up a battery pack. Multiple battery cells are combined together in series or parallel to form a battery pack. For example, for a lithium-ion battery pack composed of several 18650 lithium-ion battery cells, each 18650 lithium-ion battery cell is a battery cell. Using the sensors and control unit of the BMS, each cell in the battery pack is identified and marked to form a battery cell set. Each cell in the battery cell set is marked as the first cell.

[0033] The real-time voltages of multiple first monomers are collected by using the voltage sensors of the BMS to obtain several first real-time voltages, and then the average value of all the first real-time voltages is calculated as the real-time battery voltage of the target battery.

[0034] The real-time currents of multiple first monomers are collected by using the Hall current sensors or shunt resistors of the BMS to obtain several first real-time currents, and then the average value of all the first real-time currents is calculated as the real-time battery current of the target battery.

[0035] The real-time temperatures of multiple first monomers are collected by using the thermocouples or thermistor sensors of the BMS to obtain several first real-time temperatures, and then the average value of all the first real-time temperatures is calculated as the real-time battery temperature of the target battery.

[0036] The real-time load data of the first monomer is obtained through the load monitoring device, and then the collected multiple real-time load data are screened to find the maximum value and the minimum value among them, so as to obtain the maximum load and the minimum load. The difference between the obtained maximum load and minimum load is calculated to obtain the real-time battery load deviation. Among them, the real-time load deviation refers to the real-time load power deviation, which represents the fluctuation range of the output power between battery monomers.

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

[0038] Table 1 Real-time data of the first monomer

[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 average real-time voltage V = (3.60 + 3.62 + 3.58 + 3.59 + 3.61) / 5 = 3.60V. The average real-time current I = 30.2A. The average real-time temperature T = (34.5 + 35.0 + 34.2 + 35.1 + 34.8) / 5 = 34.72°C. The real-time load deviation = 118 - 106 = 12W. The finally obtained real-time battery state is (3.60V, 30.2A, 34.72°C, 12W).

[0041] Furthermore, step S1 further includes:

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

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

[0044] Specifically, a set of operating standards for a group of target batteries under normal and safe operating conditions, that is, the predetermined battery state, including voltage, current, temperature, load deviation, etc., are predefined. For example: the ideal voltage is 3.75V, the ideal current is 30A, the ideal temperature is 35°C, and the ideal load deviation is 10W. Compare the real-time battery state with the predetermined battery state item by item, calculate the deviation value of each parameter, and thus form a state deviation. Exemplarily, based on the real-time battery state of the foregoing example, calculate the state deviation: 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°C, load deviation ΔP = |12 - 10| = 2W.

[0045] Perform a normalized weighted analysis on the state deviation using the weighted coefficient of variation to remove the influence of dimension and obtain the real-time state index. Specifically, construct the voltage deviation, current deviation, temperature deviation, and load deviation collected at the current moment into a deviation set, calculate their average value and standard deviation, and then calculate the weighted coefficient of variation as the real-time state index. Exemplarily, set the weights according to expert experience as follows: the weight of voltage deviation ω1 = 0.3, the weight of current deviation ω2 = 0.3, the weight of temperature deviation ω3 = 0.2, and the weight of load deviation ω4 = 0.2. Construct a deviation set (ΔV, ΔI, ΔT, ΔP) = (0.15V, 0.2A, 0.28°C, 2W) according to the voltage deviation, current deviation, temperature deviation, and load deviation. Calculate the weighted average value μ = ω1×ΔV + ω2×ΔI + ω3×ΔT + ω4×ΔP = 0.561, and then calculate the weighted standard deviation Then, calculate the weighted coefficient of variation CV = μ / σ ≈ 0.75, and use the weighted coefficient of variation as the real-time state index. This index can comprehensively and quantitatively reflect the deviation degree of the real-time state of the battery from the predetermined state, which is convenient for quickly judging the overall state of the battery and then making reasonable battery management decisions.

[0046] Further, as Figure 2 shown, Step S2 includes:

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

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

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

[0050] Step S24: establishing an acceleration time series according to the corresponding relationship between the real-time acceleration and the real-time moment, and analyzing to obtain the acceleration variation coefficient.

[0051] Step S25: Determine whether the acceleration variation coefficient is within a predetermined acceleration variation threshold in the predetermined energy recovery constraint.

[0052] Step S26: If not, the real-time driving state does not meet the predetermined energy recovery constraint.

[0053] Specifically, the real-time driving status data of the target vehicle transmitted by the ECU (Electronic Control Unit) is obtained through the on-board CAN bus interface, and the real-time speed information is extracted from it. The real-time speed is compared with the predetermined speed limit in the pre-stored predetermined energy recovery constraints to determine whether the real-time speed is within the allowable range. This process can be performed by the comparison module in the control system. The predetermined speed limit is the upper limit of the speed allowed for energy recovery. Speed judgment can screen out high-speed operating scenarios that are not suitable for energy recovery, avoiding false triggering of current backflow at high speeds or reducing battery power supply efficiency.

[0054] If the real-time speed is within the predetermined speed limit, the real-time acceleration information is extracted from the real-time driving state. The acceleration values corresponding to different moments are plotted with time as the horizontal axis and the real-time acceleration as the vertical axis to form an acceleration time series. Mathematical analysis is then used to calculate the rate of change or variance of the acceleration to obtain the acceleration variation coefficient. For example, the first-order differences of the acceleration time series can be calculated, and the average of these differences can be used to obtain the acceleration variation coefficient. A larger acceleration variation coefficient indicates a more dramatic acceleration change.

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

[0056] Further, if the real-time speed is not within the predetermined speed limit, 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 the subsequent acceleration data extraction and analysis process is not carried out. Through the judgment of two dimensions of real-time speed and acceleration change trend, it is intelligently identified whether energy recovery is suitable based on the current operating state of the vehicle, ensuring the rationality and efficiency of the energy recovery process.

[0058] Further, step S3 includes:

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

[0060] Step S32: Randomly extract the first control parameters in the first conversion control space and obtain the first control record under the first control parameters.

[0061] Step S33: Traverse in the first control record based on the first predetermined conversion feature and analyze to obtain the first conversion fitness.

[0062] Step S34: Optimize with the goal of maximizing the first conversion fitness to obtain the first optimal control parameter.

[0063] Specifically, according to the specifications and real-time operating state of the DC-DC converter, with the goal of ensuring stable output electrical energy, the value range of each control parameter is set based on conditions such as current load demand, battery SOC, voltage upper and lower limits, etc., including duty cycle, voltage control level, current limit level, etc. A multi-dimensional parameter space is constructed according to the value range of each control parameter, with each dimension corresponding to a control parameter, to obtain the first conversion control space. Among the control parameters, the duty cycle 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 incorporated into the control parameter space design as the target constraint dimensions for 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 allowable output current (such as 10A to 50A), which is used to constrain the output load range and ensure the safe execution of the control strategy.

[0064] Within the first conversion control space, a set of parameters is randomly selected as the first control parameter using a random number generation algorithm. Then, using the first control parameter as an index, a search is conducted in the historical conversion control data to obtain the control result records of the DC-DC converter under the first control parameter, 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 balance degree, etc. According to the requirements of the first predetermined conversion feature, each item of data in the first control record is traversed and analyzed to calculate the corresponding feature index. These feature indexes are weighted and calculated to obtain the first conversion fitness to measure the quality of the first control parameter. With the goal of maximizing the fitness, algorithms such as genetic algorithms and particle swarm optimization are used to conduct multiple iterative searches in the first conversion control space. In each iteration, a set of first control parameters is selected, and then the first conversion fitness under this control parameter is calculated according to the aforementioned method and compared with the previous first conversion fitness. The larger value is retained, and multiple rounds of iteration are performed until the preset number of iterations is satisfied. Then, the first control parameter corresponding to the maximum first conversion fitness is selected as the optimal solution and output as the first optimal control parameter.

[0066] Exemplarily, 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]. Based on this, a three-dimensional parameter space C1 is constructed 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 parameter, 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] Moment 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 balance degree, and γ is the conversion efficiency weight.

[0070] For the average conversion efficiency, first calculate the conversion efficiency at each moment, that is, the ratio of the output power to the input power; among them, the output power is the product of the output current and the output voltage, and the input power is the product of the input current and the input voltage. The conversion efficiency at each moment is calculated as = (0.80, 0.81, 0.82). Then, the average value of the conversion efficiency at each moment is obtained to get η = 0.81.

[0071] For the conversion load balance degree L, combining the standard deviation of the output current and the extreme value of the output current, the following index is constructed: Where, σ I is the standard deviation of the output current; 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 it is preset that α = 0.6 and β = 0.4. This load balance degree index combines the standard deviation and the range ratio, taking into account the fluctuation average and the influence of extreme values, and can more comprehensively and quantitatively reflect the stable response ability of control parameters to load changes. According to the above index calculation formula, the conversion load balance degree L = 0.8928 is obtained.

[0072] Set γ = 0.7, then the first fitness = 0.7*η + 0.3*≈0.83. The particle swarm optimization algorithm is used to iteratively search for different combinations in C1, record the fitness values corresponding to each set of control parameters, and retain the control parameters corresponding to the current optimal fitness until the preset number of iterations is reached. Finally, a set of control parameters with the maximum fitness is output as the first optimal control parameter.

[0073] By constructing a conversion control space and extracting and evaluating features based on the control records of historical data, the adaptive optimization selection of the control parameters of the DC-DC converter is realized. On the premise of maintaining safety constraints, the optimal conversion strategy can be dynamically selected to improve the energy transfer efficiency and the battery operation stability.

[0074] Further, step S32 includes:

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

[0076] Step S322: Traverse the first control parameter in the conversion control database to obtain the first historical control data set.

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

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

[0079] In the conversion control database, a traversal algorithm is used to search with the first control parameter as the index to find all historical control records corresponding to the first control parameter or parameters similar to the first control parameter, and a first historical control data set is obtained.

[0080] From the first historical control data set, representative first historical control records are selected according to certain screening rules. The screening rules can be to select the latest historical record or the historical record with the best data integrity, etc. The first historical control record is output as the first control record for subsequent analysis and calculation based on the first conversion fitness, providing high-confidence performance feedback support for real-time control parameter optimization and effectively avoiding blind trial and error or inefficient parameter adjustment.

[0081] Furthermore, the first predetermined conversion feature at least includes conversion efficiency and conversion load balance.

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

[0083] Furthermore, after step S2, it further includes: if it meets the conditions, the conversion control plan is retrieved to perform control optimization on the real-time control parameter, and a second optimal control parameter is obtained. Among them, retrieving the conversion control plan to perform control optimization on the real-time control parameter and obtaining the second optimal control parameter includes:

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

[0085] Step B: Randomly extract a second control parameter from the second conversion control space and obtain a second control record under the second control parameter.

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

[0087] Step D: Optimize with the goal of maximizing the second conversion fitness to obtain the second optimal control parameter.

[0088] Specifically, when the vehicle is in a low speed or standby state and meets the predetermined conditions for energy recovery, the battery energy management no longer simply maintains the existing control strategy, but further optimizes the real-time control parameters by invoking the conversion control plan. At this time, a new optimization path will be formed. With the goal of improving the energy recovery efficiency, a second conversion control space is constructed based on the real-time control parameters, and the second optimal control parameters under the current working conditions are found through historical data 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 ability, recharge channel constraints, thermal constraints, etc., with the real-time parameters provided by the current BMS (such as the current battery SOC, output limit, voltage range, etc.) as the constraint conditions.

[0090] Within the second conversion control space, a set of parameters is randomly selected as the second control parameters using the random number generation algorithm. Then, using the second control parameters as the index, a search is performed in the historical conversion control data to obtain the control result record of the DC-DC converter under the second control parameters to form the second control record.

[0091] The second predetermined conversion feature is a specific feature type preset for evaluating the conversion control effect of the DC-DC converter in the energy recovery process, such as the recharge conversion efficiency, conversion load balance degree, etc. According to the requirements of the second predetermined conversion feature, the data in the second control record is traversed and analyzed to calculate the corresponding feature indicators. These feature indicators are weighted and calculated to obtain the second conversion fitness to measure the quality of the second control parameters.

[0092] With the goal of maximizing the fitness, algorithms such as genetic algorithms and particle swarm optimization are used to perform multiple iterative searches in the second conversion control space. Each iteration selects a set of second control parameters, calculates the second conversion fitness under this control parameter, and performs multiple rounds of iteration until the preset number of iterations is met. Then, the second control parameter corresponding to the maximum second conversion fitness is selected as the optimal solution and output as the second optimal control parameter. The optimization process here is similar to the optimization process of the first optimal control parameter, and the specific process can refer to the relevant descriptions above.

[0093] By optimizing to obtain the second optimal control parameter with the maximum second conversion fitness, the optimal adaptive control configuration selection in the energy recovery scenario is realized, and the recharge energy utilization rate is maximized to the greatest extent.

[0094] In summary, the battery management method integrating the DC-DC converter and the BMS provided by the embodiments of the present application has the following beneficial effects:

[0095] Through the real-time monitoring of the target battery pack by the BMS, accurate battery state information is obtained, and the 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, the judgment logic of the vehicle operating conditions is introduced to avoid energy recovery at inappropriate times, improving the rationality and efficiency of energy recovery. When the driving state does not meet the energy recovery constraint, the conversion control plan is retrieved to optimize the real-time control parameters of the DC-DC converter to obtain the first optimal control parameters. If it meets the energy recovery constraint, a second conversion control space is constructed and optimized to obtain the second 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 operating conditions and battery states to achieve the optimal conversion control in the power supply and energy recovery modes, ensuring the high efficiency and accuracy of battery energy management. The DC-DC converter is controlled for conversion according to the optimal control parameters, and the optimization result is implemented, driving the DC-DC converter to perform the optimized energy conversion operation, 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 embodiment of the present application constructs a closed-loop collaborative control mechanism between the BMS and the DC-DC converter. Relying on the real-time state perception and state index analysis of the battery pack by the BMS, combined with the dynamic discrimination of the vehicle driving state and energy recovery constraint, it triggers the online optimization and real-time adjustment of the control parameters of the DC-DC converter, realizing the efficient energy scheduling and adaptive control of the battery management system under complex operating conditions, achieving the technical effects of improving the energy recovery and utilization efficiency of the battery and the control conversion response speed, and providing strong support for the efficient battery energy recovery and utilization and performance improvement of electric vehicles.

[0097] Embodiment 2, as Figure 3 shown, based on the same inventive concept as the foregoing Embodiment 1, the embodiment of the present application provides a battery management system integrating a DC-DC converter and a BMS, and the system includes:

[0098] A real-time state monitoring module 10, configured to dynamically monitor the real-time battery state of the target battery pack through the BMS and analyze the real-time state index, where the target battery pack refers to the battery pack loaded on the target vehicle.

[0099] An energy recovery judgment module 20, configured to judge 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 not meeting the requirements, retrieve the conversion control plan to perform control optimization on the real-time control parameters of the DC-DC converter, so as to obtain the first optimal control parameter, where 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] Furthermore, the real-time status monitoring module 10 of the embodiment of the present application is further configured to perform the following steps:

[0103] Form a battery cell set of the target battery pack, where the battery cell set includes a first cell; take the average value of the first real-time voltage of the first cell as the real-time battery voltage; take the average value of the first real-time current of the first cell as the real-time battery current; take the average value of the first real-time temperature of the first cell as the real-time battery temperature; obtain the first real-time load of the first cell, and screen to obtain the maximum load and the minimum load; compare the maximum load with the minimum load to obtain the real-time battery load deviation; based on the real-time battery voltage, the real-time battery current, the real-time battery temperature, and the real-time battery load deviation, form the real-time battery status.

[0104] Furthermore, the real-time status monitoring module 10 of the embodiment of the present application is further configured to perform the following steps:

[0105] Obtain a predetermined battery status, and compare the real-time battery status with the predetermined battery status to obtain a status deviation; perform normalized weighted analysis on the status deviation to obtain the real-time status index.

[0106] Furthermore, the energy recovery judgment module 20 of the embodiment of the present application is further configured to perform the following steps:

[0107] Extract the real-time speed in the real-time driving status; determine whether the real-time speed is within the predetermined speed limit in the predetermined energy recovery constraint; if it is within, extract the real-time acceleration in the real-time driving status; establish an acceleration time series according to the correspondence between the real-time acceleration and the real-time moment, and analyze to obtain an acceleration change coefficient; determine whether the acceleration change coefficient is within the predetermined acceleration change threshold in the predetermined energy recovery constraint; if it is not within, the real-time driving status does not meet the predetermined energy recovery constraint.

[0108] Furthermore, if the real-time speed is not within the predetermined speed limit, the real-time driving status does not meet the predetermined energy recovery constraint.

[0109] Furthermore, the control optimization module 30 of the embodiment of the present application is further configured to perform the following steps:

[0110] Construct a first conversion control space with the real-time control parameters as constraints; randomly extract a first control parameter from the first conversion control space, and obtain a first control record under the first control parameter; traverse in the first control record based on a first predetermined conversion feature, and analyze to obtain a first conversion fitness; optimize with the goal of maximizing the first conversion fitness to obtain the first optimal control parameter.

[0111] Further, the control optimization module 30 in the embodiment of the present application is further configured to execute the following steps:

[0112] Obtain the conversion control database of the DC-DC converter; traverse the first control parameter in the conversion control database to obtain a first historical control data set; extract the 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 balance degree.

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

[0115] If it meets the requirement, retrieve the conversion control plan to perform control optimization on the real-time control parameters to obtain a second optimal control parameter; wherein, retrieving the conversion control plan to perform control optimization on the real-time control parameters to obtain a second optimal control parameter includes: constructing a second conversion control space with the real-time control parameters as constraints; randomly extracting a second control parameter from 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; optimizing with the goal of maximizing the second conversion fitness to obtain the second optimal control parameter.

[0116] Through the foregoing detailed description of the battery management method integrating the DC-DC converter and the BMS in this specification, those skilled in the art can clearly know the battery management system integrating the DC-DC converter and the BMS in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0117] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to 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, Including: Dynamically monitor the real-time battery state of the target battery pack through the BMS, and analyze to obtain the real-time state index, where the target battery pack refers to the battery pack installed on the target vehicle; Judge whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint; If not, retrieve the conversion control plan to optimize the real-time control parameters of the DC-DC converter to obtain the first optimal control parameter, where the DC-DC converter is mounted on the target battery pack; Perform conversion control on the DC-DC converter according to the first optimal control parameter.

2. The battery management method integrating a DC-DC converter and a BMS according to claim 1, characterized in that, Dynamically monitor the real-time battery state of the target battery pack through the BMS, including: Form a battery cell set of the target battery pack, where the battery cell set includes a first cell; Take the average value of the first real-time voltage of the first cell as the real-time battery voltage; Take the average value of the first real-time current of the first cell as the real-time battery current; Take the average value of the first real-time temperature of the first cell as the real-time battery temperature; Obtain the first real-time load of the first cell, and screen to obtain the maximum load and the minimum load; Compare the maximum load with the minimum load to obtain the real-time battery load deviation; Based on the real-time battery voltage, the real-time battery current, the real-time battery temperature, and the real-time battery load deviation, form the real-time battery state.

3. The battery management method integrating a DC-DC converter and a BMS according to claim 2, wherein, Dynamically monitor the real-time battery state of the target battery pack through the BMS, and analyze to obtain the real-time state index, including: Obtain the predetermined battery state, and compare the real-time battery state with the predetermined battery state to obtain the state deviation; Perform normalized weighted analysis on the state deviation to obtain the real-time state index.

4. The battery management method integrating a DC-DC converter and a BMS according to claim 1, wherein Judge whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint, including: Extract the real-time speed in the real-time driving state; Judge whether the real-time speed is within the predetermined speed limit in the predetermined energy recovery constraint; If it is within, extract the real-time acceleration in the real-time driving state; Establish an acceleration time series according to the corresponding relationship between the real-time acceleration and the real-time moment, and analyze to obtain the acceleration change coefficient; Judge whether the acceleration change coefficient is within 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.

5. The battery management method integrating a DC-DC converter and a BMS according to claim 4, wherein Including: If the real-time speed is not within the predetermined speed limit, the real-time driving state does not meet the predetermined energy recovery constraint.

6. The battery management method integrating a DC-DC converter and a BMS according to claim 1, wherein, If not, retrieve the conversion control plan to optimize the real-time control parameters of the DC-DC converter to obtain the first optimal control parameter, including: Construct a first conversion control space with the real-time control parameter as a constraint; Randomly extract the first control parameter in the first conversion control space, and obtain the first control record under the first control parameter; Traverse in the first control record based on the first predetermined conversion feature, and analyze to obtain the first conversion fitness; Optimize with the goal of maximizing the first conversion fitness to obtain the first optimal control parameter.

7. The battery management method integrating a DC-DC converter and a BMS according to claim 6, wherein Randomly extract the first control parameter in the first conversion control space and obtain the first control record under the first control parameter, including: Obtain the conversion control database of the DC-DC converter; Traverse the first control parameter in the conversion control database to obtain the first historical control data set; Extract the first historical control record in the first historical control data set as the first control record.

8. The battery management method integrating a DC-DC converter and a BMS according to claim 6, wherein The first predetermined conversion feature at least includes conversion efficiency and conversion load balance.

9. The battery management method integrating a DC-DC converter and a BMS according to claim 7, wherein After determining whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint, it further includes: If it meets, retrieve the conversion control plan to perform control optimization on the real-time control parameter to obtain the second optimal control parameter; Among them, retrieving the conversion control plan to perform control optimization on the real-time control parameter to obtain the second optimal control parameter includes: Construct a second conversion control space with the real-time control parameter as a constraint; Randomly extract the second control parameter in the second conversion control space and obtain the second control record under the second control parameter; Traverse in the second control record based on the second predetermined conversion feature and analyze to obtain the second conversion fitness; Optimize with the goal of maximizing the second conversion fitness to obtain the second optimal control parameter.

10. A battery management system integrating a DC-DC converter and a BMS, characterized in that, The system is used to execute the battery management method integrating the DC-DC converter and the BMS according to any one of claims 1-9, including: A real-time status monitoring module for dynamically monitoring the real-time battery status of the target battery pack through the BMS and analyzing to obtain the real-time status index, where the target battery pack refers to the battery pack installed on the target vehicle; An energy recovery judgment module for judging whether the real-time driving state of the target vehicle meets the predetermined energy recovery constraint; A control optimization module for, if not meeting, retrieving the conversion control plan to perform control optimization on the real-time control parameter of the DC-DC converter to obtain the first optimal control parameter, where 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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