A dynamic balancing control method for a new energy vehicle battery pack

Abstract

The present invention discloses a dynamic balancing control method for a new energy vehicle battery pack, which relates to the field of battery control technology. The method comprises: obtaining N battery packs of a new energy vehicle; setting a plurality of characteristic operating modes of the new energy vehicle; collecting historical operating condition data sets of the N battery packs; performing battery load calculations, outputting N load index samples corresponding to different characteristic operating modes, and constructing a balancing control grouping model; identifying the real-time operating mode of the new energy vehicle, outputting the balancing control battery packs divided under the real-time operating mode, and performing load balancing control. The present invention solves the technical problem that the prior art lacks grouping of battery packs in different scenarios, resulting in low flexibility, efficiency, and service life of the battery packs when performing load control, and achieves the technical effect of realizing precise load balancing control of the battery packs of new energy vehicles and improving the overall efficiency and service life of the battery packs.

Description

Technical Field

[0001] The present invention relates to the field of battery control technology, and in particular to a dynamic balancing control method for a battery pack of a new energy vehicle. Background Art

[0002] With the rapid development of new energy vehicles, battery packs, as core components of new energy vehicles, have a direct impact on their overall performance and lifespan. However, existing technologies often lack effective load control over battery packs. Because new energy vehicles experience different driving modes, the usage frequency and energy consumption of each battery pack vary. If the battery pack cannot be accurately load-balanced, some battery cells will be overused or overloaded, thereby reducing the overall efficiency and lifespan of the battery pack.

[0003] The existing technology lacks the ability to group battery packs in different scenarios, resulting in technical problems such as low flexibility, efficiency and service life of the battery packs when performing load control. Summary of the Invention

[0004] The present application provides a dynamic balancing control method for a new energy vehicle battery pack, which is used to solve the technical problem in the prior art of lacking grouping of battery packs in different scenarios, resulting in low flexibility, efficiency and service life of the battery pack when performing load control.

[0005] In view of the above problems, the present application provides a dynamic balancing control method for a new energy vehicle battery pack, the method comprising:

[0006] Acquire N battery packs of a new energy vehicle; set multiple characteristic operating modes of the new energy vehicle, wherein the multiple characteristic operating modes include acceleration mode, deceleration mode, climbing mode and braking mode; collect historical operating condition data sets of the N battery packs under different characteristic operating conditions; perform battery load calculation on the historical operating condition data sets, output N load index samples corresponding to the N battery packs under different characteristic operating conditions, and construct a balance control grouping model; identify the real-time operating mode of the new energy vehicle, output the balance control battery groups divided under the real-time operating mode based on the balance control grouping model, and perform load balancing control in the balance control battery groups.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] Acquire N battery packs from a new energy vehicle; set multiple characteristic operating modes for the new energy vehicle; collect historical operating condition datasets for the N battery packs under different characteristic operating modes; calculate battery loads on the historical operating condition datasets, output N load indicator samples, and construct a balancing control grouping model; identify the real-time operating mode of the new energy vehicle, output a balancing control battery pack, and perform load balancing control. This achieves the technical effect of achieving precise load balancing control of the new energy vehicle battery packs, improving the overall efficiency and lifespan of the battery packs. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of a dynamic balancing control method for a new energy vehicle battery pack provided in an embodiment of the present application;

[0011] Figure 2 A schematic flow chart of constructing a balancing control grouping model in a dynamic balancing control method for a new energy vehicle battery pack provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] This application provides a dynamic balancing control method for new energy vehicle battery packs, which is used to solve the technical problem in the existing technology that there is a lack of grouping of battery packs in different scenarios, resulting in low flexibility, efficiency and service life of battery packs when performing load control.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0014] Examples, such as Figure 1 As shown, the present application provides a dynamic balancing control method for a new energy vehicle battery pack, the method comprising:

[0015] Step S100: Obtain N battery packs of a new energy vehicle.

[0016] Specifically, acquiring the N battery packs of a new energy vehicle is the initial step in the entire dynamic balancing control method. This step requires the clear identification and acquisition of all battery packs involved in energy storage and supply in the vehicle. First, the new energy vehicle's battery system architecture must be determined, understanding how the battery packs are distributed and connected. The vehicle's electrical system design documentation must be consulted, and relevant information must be obtained through the vehicle's diagnostic interface. Then, based on the determined battery pack distribution, monitoring equipment is used to acquire relevant data for each battery pack. The monitoring equipment accurately measures key parameters such as battery pack voltage, current, and temperature, enabling subsequent performance evaluation and analysis. Furthermore, the acquired battery packs are numbered or labeled to ensure accurate identification and differentiation in subsequent steps. These operations ensure the accurate acquisition of the new energy vehicle's N battery packs, providing the foundational data source for subsequent operating mode configuration, data collection, and balancing control steps.

[0017] Step S200: setting a plurality of characteristic operating modes of the new energy vehicle, wherein the plurality of characteristic operating modes include an acceleration operating mode, a deceleration operating mode, a climbing operating mode and a braking operating mode.

[0018] Specifically, multiple characteristic operating modes are designed for new energy vehicles. These modes are crucial for accurately analyzing the battery pack's operating conditions. The first is the acceleration mode, which simulates the vehicle's acceleration. In this mode, the vehicle requires high power output, and the battery pack must quickly release energy to meet the needs of the motor. During this period, the battery pack's current output is high, the voltage fluctuates to a certain extent, and the battery temperature rises due to the high load. The deceleration mode describes the vehicle's deceleration. During deceleration, the motor switches to generator mode to charge the battery pack. The battery pack receives power in this mode, and its charging current and voltage also need to be monitored and analyzed, as different deceleration conditions may cause differences in the charging process. The hill climbing mode is designed for the vehicle climbing a slope. When climbing a slope, the vehicle must overcome gravity to perform work, which requires a high power demand. The battery pack must continuously and stably provide power, requiring the battery pack to maintain high output power under these conditions. Furthermore, the performance of the battery pack must be sensitive to complex road conditions that may affect the vehicle's performance, such as frequent starts and stops and load fluctuations. Finally, the braking mode is similar to the deceleration mode, but focuses more on the situation when the vehicle is braking. During the braking process, in addition to the possible charging effect of the motor, it also involves energy recovery and the collaborative working mechanism between the vehicle's braking system and the battery pack. It is necessary to pay attention to the charging efficiency of the battery pack during braking and its impact on the vehicle's overall braking performance. By setting these four characteristic operating modes, namely the acceleration mode, the deceleration mode, the climbing mode, and the braking mode, the various typical operating states of new energy vehicles in actual driving are fully covered. This provides an accurate operating condition classification basis for the subsequent collection of historical operating condition data sets of battery packs, which in turn helps to build a reasonable balance control grouping model.

[0019] Step S300: collecting historical operating condition data sets of the N battery packs in different characteristic operating condition modes.

[0020] Specifically, for the acceleration mode, professional monitoring equipment is required to collect data. These devices must be able to accurately measure parameters such as the battery pack's voltage, current, temperature, and state of charge (SOC). During acceleration, the battery pack's current will rise rapidly, the voltage may fluctuate, and the temperature may also rise due to high load. Therefore, data collection must be performed at multiple time points during the acceleration process to ensure that the complete operating data of the battery pack under the acceleration condition is obtained. In the deceleration mode, monitoring equipment must also be used to collect relevant data. When the vehicle decelerates, the motor may switch to generator mode to charge the battery pack. At this time, focus on data such as the battery pack's charging current, charging voltage, and temperature changes during charging, and collect the battery pack's charging status under different deceleration levels to fully understand the battery pack's operating conditions under the deceleration mode. In the hill climbing mode, the monitoring equipment collects data such as the battery pack's output power, voltage stability, temperature changes, and state of charge (SOC) during the climbing process. Since the vehicle's power demand persists during hill climbing, data collection is performed at different stages of the climbing process, such as the initial, mid-, and final stages, to obtain a comprehensive data set for the battery pack's operation under hill climbing conditions. For the braking mode, in addition to collecting battery pack charging data, the impact of the energy recovery mechanism between the braking system and the battery pack on battery pack operation must also be considered. During braking, data such as the battery pack's charging current, charging voltage, temperature changes, and energy recovery efficiency must be collected. This data should reflect the battery pack's operation under different braking intensities. By collecting various parameters of N battery packs under different characteristic operating modes, a comprehensive historical operating condition data set is ultimately obtained, providing a solid data foundation for subsequent battery load calculation and the construction of a balancing control grouping model.

[0021] Step S400: performing battery load calculation on the historical operating condition data set, outputting N load index samples corresponding to the N battery packs under different characteristic operating conditions, and constructing a balancing control grouping model.

[0022] Specifically, the historical operating condition dataset contains rich information on new energy vehicles in various past practical usage scenarios. Under different operating conditions, for example, during acceleration, the vehicle requires greater power output, increasing the battery pack discharge current and correspondingly increasing the load on battery cells at different locations. During deceleration, energy is recovered, and the load on the battery cells varies. During hill climbing, the battery cells face higher loads due to the need for continuous high power output. And during braking, energy is regenerated back into the battery pack. Next, battery load calculation is performed. By analyzing parameters such as vehicle speed, acceleration, motor output power, and the battery pack's charge and discharge status in the dataset, and taking into account factors such as current, voltage, temperature, and state of charge, the load index for each battery cell under different characteristic operating conditions is accurately calculated. For example, power is calculated based on the discharge current and voltage under different operating conditions. Time is factored in to determine energy consumption, and the impact of temperature changes on battery performance is considered to determine the load index. By outputting N load indicator samples corresponding to N battery cells under different characteristic operating conditions, we can gain a deeper understanding of the specific load performance of each battery cell under various operating conditions. Based on this sample data, we can further evaluate the usage probability of each battery cell under different operating conditions. For example, if a battery cell frequently carries a high load under acceleration conditions, its usage probability under acceleration is relatively high. By analyzing these usage probabilities, we can more specifically optimize the battery management system's balancing control strategy. When constructing the balancing control grouping model, different battery cells are rationally divided into different control groups based on their load indicators and usage probability. For battery cells with higher loads and more frequent use, more frequent load balancing adjustments are performed to ensure stable performance. For battery cells with relatively low overall loads, a more relaxed control strategy is adopted. This approach effectively ensures the overall efficiency and lifespan of the battery pack, making the battery management system of new energy vehicles more intelligent and efficient.

[0023] Step S500: identifying the real-time operating mode of the new energy vehicle, outputting the balanced control battery groups divided under the real-time operating mode based on the balanced control grouping model, and performing load balancing control in the balanced control battery groups.

[0024] Specifically, it involves three key operations: identifying the real-time operating mode, determining the battery pack for load balancing, and performing load balancing control. First, identifying the real-time operating mode of the new energy vehicle requires a comprehensive assessment of the vehicle's current operating status. By collecting relevant vehicle data, such as speed, acceleration, motor torque, battery pack charge and discharge current, and voltage, data analysis algorithms are used to determine the vehicle's current operating mode. For example, if the vehicle speed increases rapidly over a short period of time while the battery pack is discharging with a high discharge current, combined with other parameters such as acceleration, the vehicle is considered to be in acceleration mode. If the vehicle speed gradually decreases, the motor torque direction changes, and the battery pack begins charging, the vehicle is in deceleration mode. For hill climbing, the vehicle's tilt angle sensor, consistently high battery pack discharge current, and specific speed patterns are used to determine the operating mode. The braking mode is determined by brake pedal signals, the battery pack's charge status, and relevant vehicle dynamics parameters. Next, the balancing control grouping model outputs the balancing control battery groups divided according to the real-time operating mode. Once the real-time operating mode is determined, the balancing control grouping model operates according to pre-defined rules and strategies. For each operating mode, the model has a corresponding balancing control grouping strategy. For example, in the acceleration mode, the model divides the battery groups that meet the criteria into passive balancing control groups and active balancing control groups based on previously established rules. These criteria are determined based on the analysis of battery group load index samples under different characteristic operating modes. If the load index of a battery group falls within a specific range in the acceleration mode, it is assigned to the passive balancing control group; if it falls outside this range, it is assigned to the active balancing control group. Similarly, in other operating modes, such as deceleration, climbing, and braking, the model divides the battery groups according to their respective rules. Finally, load balancing control is performed within the balancing control battery groups. For battery groups assigned to the passive balancing control group, load balancing is performed according to the passive balancing control strategy. For example, the battery groups in the passive balancing control group are equipped with a passive balancing module, which contains energy-consuming components (such as variable resistors) and MOSFETs. The load of the battery pack is adjusted to achieve balance by obtaining the control parameters of the energy-consuming components, such as the resistance rate and resistance duration, through a proportional control algorithm based on load differences. For battery packs classified into the active balancing control group, load balancing will be performed according to the active balancing control strategy. For example, active balancing modules (such as bidirectional DC-DC converters) are configured for these battery packs, and the control parameters of the bidirectional DC-DC converters, such as the energy transfer direction, energy transfer time, and energy transfer rate, are obtained based on a proportional control algorithm based on load differences, thereby achieving balanced control of the battery pack load. In this way, effective load balancing control of the battery pack can be performed under different real-time operating modes, thereby improving the overall performance and service life of the battery pack of new energy vehicles.

[0025] In one possible implementation, Figure 2 As shown, step S400 also includes:

[0026] Step S410: Analyze the N load index samples corresponding to the N battery packs in each characteristic operating mode to identify the first type of battery pack and the second type of battery pack in each characteristic operating mode.

[0027] Step S420: Generate a passive balancing control group using the first type of battery group, generate an active balancing control group using the second type of battery group, and obtain a balancing control grouping strategy under each characteristic operating mode.

[0028] Step S430: constructing the balance control group model according to the balance control group strategy under each characteristic operating mode.

[0029] Specifically, it's first necessary to establish reasonable classification criteria based on the battery pack's performance characteristics and actual application requirements. Based on the magnitude of the load index, a first preset load index value, P1, and a second preset load index value, P2, are determined, with P1 less than P2. The N load index samples corresponding to the N battery packs in each characteristic operating mode are analyzed one by one. For example, in the acceleration operating mode, the load index samples of each battery pack are analyzed, considering the load conditions it bears during acceleration, including the combined effects of factors such as current, voltage, temperature, and SOC on the load. Similarly, in the deceleration, climbing, and braking operating modes, the battery pack load index samples under each operating condition are analyzed in detail to understand the load characteristics of each battery pack under the corresponding operating condition. Based on the established classification criteria, the first and second category battery packs are identified for each characteristic operating mode. Category I battery packs are those whose load index, P1, is greater than and less than P2. The load of such battery packs under the corresponding operating condition is within a relatively moderate range. The second type of battery pack refers to a battery pack with a load index less than or equal to P1 or greater than or equal to P2. The load of this type of battery pack under the corresponding working conditions is either too low or too high.

[0030] Clarify the classification criteria for first- and second-category battery packs. First-category battery packs are those with a load index greater than a first preset load index and less than a second preset load index. Second-category battery packs are those with a load index less than or equal to the first preset load index, and greater than or equal to the second preset load index, with the first preset load index being less than the second preset load index. For first-category battery packs, a passive balancing control group is generated. Passive balancing control utilizes energy-consuming elements (such as variable resistors) to dissipate excess energy and achieve load balancing. The loads of these battery packs are relatively moderate, requiring no overly complex control strategies. Passive balancing methods can, to a certain extent, meet their load balancing needs.

[0031] For the second type of battery pack, an active balancing control group is generated. Because the load indicators of this type of battery pack are either too high or too low, a more proactive control method is required to achieve load balancing. A bidirectional DC-DC converter is used to balance the battery pack load through energy transfer and adjustment. Active balancing control can more precisely adjust the battery pack load to meet different operating conditions. After generating the passive and active balancing control groups, a balancing control grouping strategy is further derived for each characteristic operating mode. Different characteristic operating modes, such as acceleration, deceleration, climbing, and braking, place different load requirements on the battery pack. Therefore, a specific balancing control grouping strategy is required for each operating mode. For example, in the acceleration mode, the classification criteria for the passive and active balancing control groups need to be dynamically adjusted based on the load changes of the battery pack, and the specific control parameters for each control group need to be determined. For the passive balancing control group, parameters such as the resistance rate and resistance duration of the variable resistor are adjusted according to the load increase during acceleration. For active balancing control, the bidirectional DC-DC converter's energy transfer direction, energy transfer time, and energy transfer rate parameters need to be determined based on the load. This provides effective load balancing control strategies for new energy vehicle battery packs under different characteristic operating modes, improving the overall performance and life of the battery pack.

[0032] A balancing control grouping model is constructed based on the balancing control grouping strategy for each characteristic operating mode. This rule-based model determines the control group (passive balancing control group or active balancing control group) to which a battery pack belongs based on the real-time operating mode and the battery pack's load index. Load balancing control is then implemented according to the corresponding control strategy. For example, if the real-time operating mode is acceleration, and a battery pack's load index falls within the first category, the model classifies it as a passive balancing control group and implements load balancing control according to the passive balancing control strategy. If the load index falls within the second category, the model further classifies it as an active balancing control group and implements load balancing control according to the active balancing control strategy. For deceleration, if the battery pack's load index meets the corresponding classification criteria, the model accurately classifies it as either a passive or active balancing control group and implements load balancing control according to the respective control strategy. In hill climbing, the model also classifies the battery pack into the appropriate control group based on the load index and classification criteria, and then implements load balancing according to the corresponding control strategy. For the braking mode, the model correctly allocates the battery pack to the passive balance control group or the active balance control group according to its load index, and then realizes load balance control through the corresponding control strategy.

[0033] In one possible implementation, step S410 further includes:

[0034] Step S411: The first type of battery group is a battery group whose load index is greater than a first preset load index and less than a second preset load index; the second type of battery group is a battery group whose load index is less than or equal to the first preset load index and greater than or equal to the second preset load index, wherein the first preset load index is less than the second preset load index.

[0035] Specifically, the first type of battery pack and the second type of battery pack are set according to different ranges of load indicators. For the first type of battery pack, the range of its load indicator is greater than the first preset load indicator and less than the second preset load indicator. The first preset load indicator and the second preset load indicator here are determined based on the actual operating characteristics, performance requirements, and past experimental data and experience of the battery pack. The first preset load indicator is relatively small, and the second preset load indicator is relatively large. When the load indicator of the battery pack is in this range, it means that the load of the battery pack under the corresponding working conditions is in a relatively moderate state. For example, under acceleration conditions, under the combined influence of factors such as current, voltage, temperature and SOC, the load indicator of the first type of battery pack meets this moderate range. It is neither too high to cause excessive battery loss, nor too low to affect the power output of the vehicle.

[0036] The second category of battery packs includes those with a load index less than or equal to a first preset load index, and those with a load index greater than or equal to a second preset load index. This means that the loads of these battery packs fall into two extremes. On the one hand, battery packs with a load index less than or equal to the first preset load index experience low loads under certain operating conditions. For example, during coasting or energy regeneration, these battery packs experience low discharge or are in a charging state, resulting in relatively low loads. On the other hand, battery packs with a load index greater than or equal to the second preset load index experience higher loads under high-power output conditions, such as rapid acceleration and hill climbing. This clear classification allows for different balancing control strategies to be implemented for different types of battery packs. For the first category of battery packs, a relatively simple passive balancing control approach is employed, consuming a small amount of excess energy to achieve load balancing. However, for the second category of battery packs, due to their more complex load conditions, a more active and precise control approach is adopted, using bidirectional DC-DC converters for energy transfer and other active balancing control strategies to ensure optimal battery pack performance and lifespan under various operating conditions.

[0037] In one possible implementation, step S500 further includes:

[0038] Step S510: the balancing control grouping model matches the real-time operating mode with the multiple characteristic operating modes to obtain a balancing control grouping strategy under the real-time operating mode.

[0039] Step S520: outputting the passive balancing control group and the active balancing control group divided in the real-time working mode according to the balancing control grouping strategy.

[0040] Step S530: Recording a real-time operating condition data set of the new energy vehicle in the real-time operating mode.

[0041] Step S540: performing balancing identification according to the real-time operating condition data set, outputting passive balancing control parameters and active balancing control parameters, and performing load balancing control on the passive balancing control group and the active balancing control group respectively using the passive balancing control parameters and the active balancing control parameters.

[0042] Specifically, the balance control grouping model uses a decision tree algorithm to match the real-time operating mode with multiple characteristic operating modes, thereby deriving a balance control grouping strategy for the real-time operating mode. First, various data on the vehicle's current operating state are collected, including speed, acceleration, motor torque, battery pack charge and discharge current, and voltage. This raw data undergoes preprocessing. During this preprocessing, a data cleaning step removes outliers that do not conform to normal vehicle operation. For example, sudden speed spikes that do not conform to the vehicle's acceleration or deceleration logic are corrected or eliminated. Furthermore, data normalization converts data with different ranges and units into a unified numerical range for better processing by subsequent algorithms. Next, key features are extracted from the preprocessed data. For acceleration modes, these features include high acceleration values ​​(acceleration greater than a preset acceleration threshold), high battery pack discharge current (discharge current greater than a preset discharge current threshold), and a rapid speed increase. For deceleration modes, these features include negative acceleration values ​​(acceleration less than a preset deceleration threshold), the presence of battery pack charge current, and a speed decrease. For the hill climbing mode, features include not only the battery pack's sustained high discharge current but also the vehicle's tilt angle. For the braking mode, extracted features include brake pedal signal strength, battery pack charging current, and a rapid decrease in vehicle speed. These extracted features are then used as input nodes for a decision tree algorithm, with each node branching based on the feature values. For example, the root node branches based on acceleration. If the acceleration exceeds a certain acceleration threshold, the branch enters a sub-branch related to the acceleration mode; if the acceleration is less than a certain deceleration threshold, the branch enters a sub-branch related to the deceleration mode. Within the acceleration sub-branch, further branches are made based on the battery pack discharge current. If the discharge current exceeds a certain discharge current threshold and the vehicle speed is rapidly increasing, the acceleration mode is ultimately determined. Once the decision tree algorithm successfully matches the real-time mode with a characteristic mode, the model can determine the balance control grouping strategy for that real-time mode. This strategy is pre-defined and optimized for each characteristic mode during the construction of the balance control grouping model. It specifies in detail how battery packs are grouped and controlled under this operating mode, including determining which battery packs should be assigned to the passive balancing control group and which to the active balancing control group, as well as the specific control methods and parameter settings for different groups. For example, the balancing control grouping strategy under the acceleration operating mode may stipulate that battery packs with load indicators in a certain range are assigned to the passive balancing control group, using a certain method (such as a series variable resistor) for load balancing control; while battery packs with load indicators in other ranges are assigned to the active balancing control group, using another method (such as a bidirectional DC-DC converter) for load balancing control.

[0043] Based on the obtained balancing control grouping strategy, the battery packs in the real-time working mode are divided. First, the division criteria specified in the strategy are clarified. Based on factors such as the load index range of the battery pack, the battery packs that meet the passive balancing control group division criteria are screened out from all battery packs to form a passive balancing control group. At the same time, the battery packs that meet the active balancing control group division criteria are also screened according to the corresponding rules to form an active balancing control group. Through such division, the battery packs in the real-time working mode are clearly divided into passive balancing control groups and active balancing control groups, laying the foundation for subsequent load balancing control operations.

[0044] Multiple key parameters during vehicle operation are collected and recorded in real time. The first is battery pack-related data, including voltage, current, temperature, and state of charge (SOC). These data can reflect the operating status of the battery pack under the current real-time operating conditions. At the same time, vehicle operating parameters such as speed, acceleration, vehicle tilt angle (if climbing a slope or special road conditions are involved), and brake pedal status (if braking) must also be recorded. Through the real-time collection and integration of these multi-dimensional data, a complete real-time operating condition dataset is formed. This dataset will provide important data support for subsequent balancing identification and load balancing control, allowing for a better understanding of the vehicle's actual operating conditions under the current real-time conditions, thereby making more accurate control decisions.

[0045] The passive balancing control group is equipped with a passive balancing module consisting of energy-consuming elements (such as variable resistors) and MOSFETs. A simple algorithm based on load difference ratio is used to determine the passive balancing control parameters. First, the degree of load difference between battery packs within the group is calculated by comparing battery pack parameters such as current, voltage, and SOC. For example, if the current of one battery pack is significantly greater than that of other battery packs, it indicates a significant load difference. Then, based on a pre-set proportional relationship, the load difference is converted into the resistance rate and resistance duration of the variable resistor. For example, if the load difference is large, a faster resistance rate and longer resistance duration are required to dissipate excess energy and achieve load balance. These calculated resistance rates and resistance durations are the passive balancing control parameters. The active balancing control group is equipped with a bidirectional DC-DC converter and similarly uses an algorithm based on load difference ratio to determine the active balancing control parameters. First, the degree of load difference between battery packs within the group is determined by comparing battery pack parameters such as current, voltage, and SOC. Then, based on the load difference and the operating principle of the bidirectional DC-DC converter, the energy transfer direction (i.e., energy transfer from the highly loaded battery pack to the lightly loaded battery pack), energy transfer time (the time required for energy transfer is determined by the load difference), and energy transfer rate (the speed of energy transfer is determined by the load difference and time). These calculated parameters are the active balancing control parameters. Finally, the passive balancing control parameters and active balancing control parameters are used to perform load balancing control on the passive and active balancing control groups, respectively. For the passive balancing control group, the resistance rate and resistance duration of the variable resistor are adjusted to achieve load balance in the battery pack. For the active balancing control group, the operating state of the bidirectional DC-DC converter is adjusted based on the energy transfer direction, energy transfer time, and energy transfer rate to achieve load balance in the battery pack. In this way, effective load balancing control of the battery pack can be achieved under different real-time operating conditions, improving the overall performance and service life of the battery pack in new energy vehicles.

[0046] In one possible implementation, step S540 further includes:

[0047] Step S541: configuring a passive balancing module for the first type battery pack, and connecting the balancing control group model to the passive balancing module, wherein the passive balancing module includes an energy-consuming element and a MOSFET, the energy-consuming element is a variable resistor, and the MOSFET is used to turn on the variable resistor.

[0048] Step S542: Obtaining the passive balancing control parameters of the energy-consuming element based on the proportional control algorithm of the load difference, including the resistance rate and the resistance duration.

[0049] Specifically, a passive balancing module is configured for the first type of battery pack. Because the load indicators of this type of battery pack are within a specific range, a relatively simple balancing control method is required. Therefore, a passive balancing module is configured. This passive balancing module primarily consists of two components: an energy-consuming element and a MOSFET. The energy-consuming element is a variable resistor. During the passive balancing control process, the variable resistor consumes energy to adjust the load balance of the battery pack according to different operating conditions and control requirements. Its resistance value can be dynamically adjusted to accommodate varying loads. A MOSFET, a metal-oxide-semiconductor field-effect transistor (MOSFET), is used to turn on the variable resistor. MOSFETs are metal-oxide-semiconductor field-effect transistors (MOSFETs) with excellent switching characteristics and control performance. In the passive balancing module, the MOSFET receives a control signal from the balancing control group model and determines whether to turn on the variable resistor based on the signal. When the MOSFET turns on the variable resistor, a circuit is established, and the variable resistor begins to consume energy, thereby adjusting the load of the battery pack. Furthermore, a connection is established between the passive balancing module and the balancing control group model, which is key to achieving precise control. Through this connection, the balancing control group model can obtain various real-time information about the battery pack, such as load indicators, voltage, and current, and generate corresponding control signals based on this information to send to the MOSFET. In this way, the operating state of the variable resistor can be controlled according to the actual load of the battery pack, thereby effectively achieving load balancing control for the first type of battery pack.

[0050] Determine the load difference between battery packs and analyze the relevant parameters in the real-time operating condition data set. For example, the load difference is quantified by comparing parameters such as the current, voltage, and state of charge (SOC) of the battery packs. If the current difference between the two battery packs is large, it means that there is a significant difference in their load; at the same time, the difference in voltage and SOC will also affect the load difference. Combining these factors, a value that can accurately reflect the degree of load difference is calculated. Then, based on this load difference, a proportional control algorithm is used to determine the resistance rate and resistance time. For the resistance rate, when the load difference is large, a higher resistance rate is required to balance the load faster. A proportional relationship can be set, for example, the load difference is proportional to the resistance rate, that is, the resistance rate increases as the load difference increases. For example, the resistance rate R rate =k1×D, where k1 is a proportional coefficient and D is the degree of load difference. For the resistance duration, it is also determined based on the degree of load difference. When the load difference is large, a longer resistance duration is required to ensure sufficient energy consumption to achieve load balance. A similar proportional relationship is set, such as the resistance duration R time=k²×D+C, where k² is the proportional coefficient and C is a constant term used to account for some basic time requirements (even when the load difference is small, it may take some time to adjust). This load difference-based proportional control algorithm accurately obtains the passive balancing control parameters of the energy-consuming components, namely the resistance rate and resistance duration, providing effective parameter support for subsequent load balancing control of the first type of battery pack.

[0051] In one possible implementation, step S540 further includes:

[0052] Step S543: configuring an active balancing module for the second type battery pack, wherein the balancing control group model is connected to the active balancing module, wherein the active balancing module includes a bidirectional DC-DC converter.

[0053] Step S544: obtaining active balancing control parameters of the bidirectional DC-DC converter based on a proportional control algorithm of load differences, including energy transfer direction, energy transfer time, and energy transfer rate.

[0054] Specifically, for the second type of battery pack, due to the particularity of its load indicators, an active balancing module is required to achieve more precise load balancing control. The active balancing module contains a bidirectional DC-DC converter, which is a device that can convert DC voltage in two directions. It plays a key role in active balancing control. The active balancing module is connected to the balancing control group model. This connection enables the balancing control group model to send control instructions to the active balancing module based on the battery pack load conditions reflected by the real-time operating condition data set, thereby achieving effective control of the bidirectional DC-DC converter.

[0055] A proportional control algorithm based on load differences is used to obtain active balancing control parameters for bidirectional DC-DC converters. First, the load difference between battery packs is determined. By analyzing battery pack voltage, current, and state of charge (SOC) parameters from real-time operating condition data, the degree of load difference between the battery packs is calculated. For example, if the current difference between two battery packs is large, and there are also significant differences in voltage and SOC, then a significant load difference between them is determined. Then, the direction of energy transfer is determined based on the degree of load difference. When one battery pack is loaded higher than the other, energy transfer is from the higher-loaded pack to the lower-loaded pack to achieve load balancing. Next, the energy transfer time is determined. This energy transfer time is related to the degree of load difference. A greater load difference requires more energy transfer, and the energy transfer time increases. This energy transfer time is determined based on a predefined function: energy transfer time T = k1 × D + c1, where k1 is the proportionality coefficient, D is the load difference, and c1 is a constant term that accounts for some basic time requirements. Finally, the energy transfer rate is determined, which is also related to the degree of load difference. The greater the load difference, the faster the load balancing is achieved, requiring a higher energy transfer rate. The energy transfer rate is determined based on a pre-defined functional relationship: energy transfer rate R = k² × D + c², where k² is the proportional coefficient, D is the degree of load difference, and c² is a constant term used to account for some basic configuration and constraints. Through this load difference-based proportional control algorithm, the active balancing control parameters of the bidirectional DC-DC converter, including energy transfer direction, energy transfer time, and energy transfer rate, are accurately obtained, thereby achieving load balancing control for the second-type battery pack.

[0056] In one possible implementation, step S500 further includes:

[0057] Step S550: Obtaining mode stability according to the mode switching frequency and mode duration.

[0058] Step S560: When the mode stability is greater than the preset mode stability, activating the balance control group model.

[0059] Specifically, mode stability is assessed based on the mode switching frequency and mode duration of new energy vehicles. Mode switching frequency refers to how frequently a vehicle switches between different operating modes. If a vehicle frequently switches between operating modes such as acceleration, deceleration, climbing, and braking within a short period of time, then the mode switching frequency is high. For example, in urban traffic, vehicles may constantly switch between acceleration and deceleration modes due to frequent traffic lights and traffic congestion. Mode duration refers to the length of time the vehicle continues to operate in each operating mode. If a vehicle can maintain a certain operating mode for a long time, it means that the duration of that mode is long.

[0060] By comprehensively considering the mode switching frequency and mode duration, the stability of the current vehicle operating state can be accurately assessed. If the mode switching frequency is low and the mode duration is long, the vehicle's operating mode can be considered relatively stable. Conversely, if the mode switching frequency is high and the mode duration is short, the vehicle's operating mode is relatively unstable. This is achieved by setting a calculation formula to quantify mode stability: mode stability = mode duration / mode switching frequency.

[0061] When the mode stability calculated by step S510 is greater than the preset mode stability, the balance control group model will be activated. The preset mode stability is a pre-set threshold value, which represents the standard for starting the balance control group model when the vehicle operation mode is stable to a certain extent. Activating the balance control group model means starting to perform load balancing control on the battery pack of the new energy vehicle. The model will divide the battery pack into a passive balance control group or an active balance control group according to the current real-time operating mode, and perform load balancing control according to the corresponding strategy. For example, if the vehicle is in a relatively stable acceleration operating mode, the model will divide the qualified battery pack into the corresponding control group, and adjust the load balancing of the battery pack according to the preset control parameters to ensure the performance and life of the battery pack. Such an operation can timely and effectively perform load balancing control on the battery pack when the vehicle operation mode is relatively stable, thereby improving the overall performance and reliability of the new energy vehicle.

[0062] In one possible implementation, step S100 further includes:

[0063] Step S110: Obtaining battery pack distribution position information of the N battery packs.

[0064] Step S120: identifying heat source areas according to the battery group distribution position information, obtaining identified battery groups, grouping the identified battery groups into the active balancing control groups, and updating the balancing control grouping strategy under each characteristic operating mode.

[0065] Specifically, the distribution location information of N battery packs is obtained through design drawings and annotations. During the design stage of new energy vehicles, detailed vehicle structure design drawings will be drawn, and these drawings clearly mark the installation position of each battery pack in the vehicle. The drawings will show the specific coordinate position of the battery pack on the vehicle chassis, or indicate that it is located in a specific vehicle compartment area. During the vehicle manufacturing process, the battery pack is accurately installed in the specified position according to the annotations on the design drawings. At the same time, in order to ensure the accuracy of the installation, multiple inspections and calibrations are carried out during the manufacturing process to ensure that the distribution position of the battery pack is completely consistent with the design drawings. Obtaining battery pack distribution location information through design drawings and annotations has high accuracy and reliability. The design drawings are carefully designed and verified and can provide detailed and accurate location information.

[0066] Heat source areas are identified based on the acquired battery pack location information. In new energy vehicles, heat source areas are areas that generate significant heat during vehicle operation. For example, the motor generates significant heat during operation, becoming a major heat source. Electronic equipment also dissipates heat during operation. The braking system also generates a certain amount of heat during frequent braking. By analyzing the relationship between the battery pack location and these potential heat source areas, the system determines which battery packs are located near heat sources. If a battery pack is within a certain distance from a heat source, it is identified as a designated battery pack. For example, if a battery pack is very close to the motor or located in an area with a high concentration of electronic equipment, it is likely to be affected by a heat source. Designated battery packs are assigned to the active balancing control group because they require more aggressive load balancing control strategies. Battery packs located near heat sources can suffer from temperature increases, which can affect their performance and lifespan. Active balancing control groups typically use bidirectional DC-DC converters to more precisely adjust the battery pack load to account for potential temperature increases and performance changes. By assigning designated battery packs to the active balancing control group, more targeted control can be implemented to ensure they maintain optimal performance in hot environments. Finally, the balancing control grouping strategy for each characteristic operating mode is updated. Since the identified battery pack is divided into the active balancing control group, the original balancing control grouping strategy needs to be adjusted accordingly, including redefining the division criteria between the active balancing control group and the passive balancing control group under different operating modes, adjusting the control parameters, etc. For example, in the acceleration operating mode, the original strategy is to divide the battery packs into different control groups based on load indicators. Now, it is necessary to take into account the existence of the identified battery pack and optimize the strategy to ensure that the battery pack can be effectively load balanced under various operating conditions. This makes the balancing control grouping strategy more adaptable to actual conditions and improves the overall performance and reliability of the new energy vehicle battery pack.

[0067] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0069] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A dynamic balance control method for a new energy vehicle battery pack, characterized in that: The method comprises: Get N battery packs of new energy vehicles; Setting a plurality of characteristic operating modes of the new energy vehicle, wherein the plurality of characteristic operating modes include an acceleration operating mode, a deceleration operating mode, a climbing operating mode, and a braking operating mode; Collecting historical operating condition data sets of the N battery packs under different characteristic operating condition modes; Performing battery load calculation on the historical operating condition data set, outputting N load index samples corresponding to the N battery packs under different characteristic operating conditions, and constructing a balancing control grouping model; Identifying a real-time operating mode of the new energy vehicle, outputting a balanced control battery group divided under the real-time operating mode based on the balanced control grouping model, and performing load balancing control in the balanced control battery group; The method for constructing a balanced control grouping model includes: Analyze N load index samples corresponding to the N battery packs in each characteristic operating mode to identify the first type of battery pack and the second type of battery pack in each characteristic operating mode; Generate a passive balancing control group using the first type of battery group, generate an active balancing control group using the second type of battery group, and obtain a balancing control grouping strategy under each characteristic operating mode; Constructing the balance control group model according to the balance control group strategy under each characteristic operating mode; The method for outputting the balanced control battery group divided in the real-time working mode based on the balanced control group model includes: The balancing control grouping model matches the real-time operating mode with the multiple characteristic operating modes to obtain a balancing control grouping strategy under the real-time operating mode; Outputting the passive balance control group and the active balance control group divided under the real-time working mode according to the balance control grouping strategy; Recording a real-time operating condition data set of the new energy vehicle in the real-time operating mode; Balance identification is performed according to the real-time operating condition data set, and passive balance control parameters and active balance control parameters are output. Load balance control is performed on the passive balance control group and the active balance control group respectively using the passive balance control parameters and the active balance control parameters.

2. The method according to claim 1, wherein The first type of battery pack is a battery pack whose load index is greater than a first preset load index and less than a second preset load index; the second type of battery pack is a battery pack whose load index is less than or equal to the first preset load index and greater than or equal to the second preset load index, wherein the first preset load index is less than the second preset load index.

3. The method according to claim 1, wherein Performing balance identification according to the real-time operating condition data set and outputting passive balance control parameters, the method includes: A passive balancing module is configured for the first type of battery pack, the balancing control group model is connected to the passive balancing module, wherein the passive balancing module includes an energy-consuming element and a MOSFET, the energy-consuming element is a variable resistor, and the MOSFET is used to turn on the variable resistor; The passive balancing control parameters of the energy-consuming element are obtained by a proportional control algorithm based on load differences, including resistance rate and resistance duration.

4. The method according to claim 1, wherein Performing balance identification according to the real-time operating condition data set and outputting active balance control parameters, the method includes: An active balancing module is configured for the second type of battery pack, the balancing control group model is connected to the active balancing module, wherein the active balancing module includes a bidirectional DC-DC converter; The active balancing control parameters of the bidirectional DC-DC converter are obtained based on a proportional control algorithm of load difference, including energy transfer direction, energy transfer time and energy transfer rate.

5. The method according to claim 1, wherein Identifying the real-time operating mode of the new energy vehicle, the method further includes: Performing data analysis on the real-time operating mode of the new energy vehicle to obtain the mode switching frequency and mode duration; Obtaining mode stability according to the mode switching frequency and mode duration; When the mode stability is greater than a preset mode stability, the balance control group model is activated.

6. The method according to claim 1, wherein Obtaining N battery packs of a new energy vehicle, the method further includes: Obtaining battery pack distribution location information of the N battery packs; The heat source area is identified according to the battery group distribution position information, the identified battery group is obtained, the identified battery group is divided into the active balancing control group, and the balancing control grouping strategy under each characteristic operating mode is updated.

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