Power battery pack thermal management control system and method

Abstract

The invention discloses a thermal management control system and method for a power battery pack, and belongs to the technical field of electric automobiles, and the system comprises the following modules: a multi-parameter acquisition module which is used for acquiring parameter data of the battery pack in real time; the data preprocessing and fusion module is used for generating a high-quality and uniform-format data set; the dynamic prediction modeling module is used for realizing accurate temperature trend prediction; the intelligent decision module is used for generating an optimal heat management strategy; the thermal management execution module is used for realizing accurate temperature regulation and thermal management; the system monitoring and diagnosis module is used for ensuring safe operation of the system; a man-machine interaction module; the intelligent thermal management system is used for realizing man-machine collaborative intelligent thermal management. According to the thermal management control system and method for the power battery pack, the particle swarm optimization algorithm is utilized, the power battery thermal management system of a thermal management strategy is dynamically adjusted, more accurate temperature prediction and control are achieved, energy consumption is optimized, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a power battery pack thermal management control system and method. Background Art

[0002] With the rapid development of the new energy vehicle market, the performance and safety of power battery packs, as the core energy storage device of vehicles, directly affect the efficiency, mileage and service life of the entire vehicle. However, the battery pack generates a lot of heat during the charging and discharging process. Especially when working in high-power charging and discharging and harsh environments, the heat accumulation may cause the battery temperature to rise rapidly, thereby causing thermal runaway, capacity decay or serious safety hazards. Therefore, the thermal management of power battery packs has become one of the key technical problems in the design of new energy vehicles.

[0003] Current power battery thermal management systems typically include three methods: active cooling, passive heat dissipation, and heating control, which aim to ensure that the battery pack remains within a safe temperature range under various operating conditions.

[0004] However, traditional thermal management systems have the following main problems:

[0005] 1. The ability to perceive the internal state of the battery is limited, making it difficult to achieve precise temperature control; the control strategy is single and lacks intelligent adjustment capabilities; when dealing with complex and changeable working conditions, it is often impossible to achieve the best energy efficiency balance.

[0006] 2. The feedback on the real-time status of the battery and the thermal management effect is slow, which makes it difficult to adjust the strategy in time, affecting the overall efficiency of the system and the service life of the battery.

[0007] To sum up, how to improve the operating efficiency and life of the battery pack, effectively prevent safety risks such as thermal runaway, and improve the overall reliability and safety of the power battery system has become a technical problem that needs to be solved urgently. Summary of the invention

[0008] The purpose of the present invention is to provide a power battery pack thermal management control system and method, which utilizes a particle swarm optimization algorithm to dynamically adjust the power battery thermal management system of the thermal management strategy to achieve more accurate temperature prediction and control, optimize energy consumption and extend battery life.

[0009] To achieve the above objectives, the present invention provides a power battery pack thermal management control system, comprising the following modules:

[0010] Multi-parameter acquisition module: used to collect battery pack parameter data in real time;

[0011] Data preprocessing and fusion module: used to generate high-quality, unified format data sets;

[0012] Dynamic prediction modeling module: used to achieve accurate temperature trend prediction;

[0013] Intelligent decision-making module: used to generate the optimal thermal management strategy;

[0014] Thermal management execution module: used to achieve precise temperature regulation and thermal management;

[0015] System monitoring and diagnosis module: used to ensure safe operation of the system;

[0016] Human-machine interaction module; used to achieve intelligent thermal management of human-machine collaboration.

[0017] Preferably, the multi-parameter acquisition module transmits data to the data preprocessing and fusion module, the data preprocessing and fusion module transmits data to the dynamic prediction modeling module, the dynamic prediction modeling module transmits data to the intelligent decision-making module, the intelligent decision-making module transmits data to the thermal management execution module, the thermal management execution module and the system monitoring and diagnosis module transmit data to each other, and the human-computer interaction module transmits data to the multi-parameter acquisition module, the data preprocessing and fusion module, the dynamic prediction modeling module, the intelligent decision-making module, the thermal management execution module and the system monitoring and diagnosis module.

[0018] Preferably, the multi-parameter acquisition module includes a temperature sensor network, a current and voltage acquisition unit, a state of charge estimation unit, an environmental parameter sensor group, a data acquisition control unit and a communication interface unit;

[0019] The temperature sensor network is used to monitor the temperature distribution of battery cells and modules in real time;

[0020] The current and voltage acquisition unit is used to continuously measure the charge and discharge current and terminal voltage of the battery pack to provide basic data for charge state estimation and safety monitoring;

[0021] The state of charge estimation unit is used to provide the remaining capacity information of the battery pack;

[0022] The environmental parameter sensor group is used to monitor the environmental conditions around the battery pack and provide external reference data for thermal management strategy optimization;

[0023] The data acquisition control unit is used to coordinate and manage the data acquisition frequency and timing of each sensor and perform preliminary data validity checks;

[0024] The communication interface unit is used to realize high-speed and reliable data transmission with each module.

[0025] Preferably, the dynamic prediction modeling module includes a model training unit, a model evaluation and selection unit, an online learning adaptation unit, a prediction execution unit and a model version management unit;

[0026] The model training unit is used to periodically train the temperature prediction model based on historical and real-time data and continuously optimize the model parameters;

[0027] The model evaluation and selection unit is used to automatically select the optimal model for the actual prediction task;

[0028] The online learning adaptation unit is used to adapt the model to changes in battery performance over time and new operating conditions;

[0029] The prediction execution unit is used to call the current optimal model to predict the future temperature trend and output the prediction results for use by the intelligent decision-making module;

[0030] The model version management unit is used to manage different versions of temperature prediction models, record model update history, and be able to roll back to a previous stable version.

[0031] Preferably, the intelligent decision-making module includes a decision input processing unit, a rule base management unit, a multi-objective optimization unit, a strategy generation unit, a decision evaluation and tuning unit and a strategy output unit;

[0032] The decision input processing unit is used to provide comprehensive data support for the decision-making process;

[0033] The rule base management unit is used to maintain and update the preset decision rule base;

[0034] The multi-objective optimization unit is used to find the best balance between temperature control, energy consumption management and performance improvement;

[0035] The strategy generation unit is used to formulate a specific thermal management operation strategy;

[0036] The decision evaluation and tuning unit is used to evaluate the effectiveness of the generation strategy and perform fine-tuning through simulation prediction and historical data analysis;

[0037] The strategy output unit is used to output the finalized thermal management strategy to the thermal management execution module in a standardized format, and record the decision-making process for subsequent analysis and improvement.

[0038] Preferably, the thermal management execution module includes a strategy analysis unit, an active cooling control unit, a passive heat dissipation adjustment unit, a heating control unit, an execution coordination unit and a feedback monitoring unit;

[0039] The strategy parsing unit is used to receive and parse the thermal management strategy from the intelligent decision-making module and convert it into specific execution parameters, wherein the thermal management strategy includes an active cooling control strategy, a passive heat dissipation control strategy and a heating control strategy;

[0040] The active cooling control unit is used to adjust the active cooling element according to specific execution parameters;

[0041] The passive heat dissipation adjustment unit is used to control the working state of the passive heat dissipation element and optimize the heat distribution and buffering effect;

[0042] The heating control unit is used to accurately adjust the power output of the battery heater;

[0043] The execution coordination unit is used to achieve coordination of cooling, heat dissipation and heating processes to avoid conflicts;

[0044] The feedback monitoring unit is used to monitor the thermal management execution effect in real time, collect temperature change data, and provide timely feedback for strategy adjustment.

[0045] Preferably, the system monitoring and diagnosis module includes a system status acquisition unit, a performance evaluation unit, an anomaly detection unit, a fault diagnosis expert unit, an early warning generation unit and an automatic repair suggestion unit;

[0046] The system status acquisition unit is used to provide a real-time and comprehensive data basis for system monitoring and diagnosis;

[0047] The performance evaluation unit is used to evaluate and analyze the overall operating efficiency, energy consumption and temperature control accuracy of the system in real time;

[0048] The abnormality detection unit is used to quickly identify abnormal conditions that deviate from the normal range;

[0049] The fault diagnosis expert unit is used to conduct in-depth analysis of the detected anomaly and determine the type, severity and cause of the fault;

[0050] The warning generation unit is used to generate warning information of different levels and promptly notify the operator through appropriate channels;

[0051] The automatic repair suggestion unit is used to generate repair suggestions and automatically perform repair operations.

[0052] Preferably, the calculation formula of the charge and discharge current of the battery pack is expressed as:

[0053]

[0054] Among them, I primary is the actual current of the battery pack, i.e., the charge and discharge current; N primaryis the primary turns of the current transformer; N secondary is the secondary turns of the current transformer; R load is the load resistance through which the secondary current passes; V secondary It is the voltage generated by the secondary current passing through the load resistance;

[0055] The calculation formula of the terminal voltage of the battery pack is expressed as:

[0056]

[0057] Among them, V battery is the actual terminal voltage of the battery pack; V measured is the divided voltage output by the voltage divider; R1 and R2 are the resistance values ​​in the voltage divider.

[0058] The remaining capacity of the battery pack is:

[0059]

[0060] Wherein, RC(t) is the remaining capacity of the battery pack at time t; C rated is the rated capacity of the battery pack; α is a weighting factor used to adjust the contribution ratio of the current integration method and the open circuit voltage method; SOC0 is the state of charge at the initial time t0; I(τ) is the battery current at time τ; F(V oc ) is based on the open circuit voltage V oc Estimated state of charge.

[0061] The present invention also provides an operating method of a power battery pack thermal management control system, comprising the following steps:

[0062] S1. Use particle swarm optimization algorithm to find the best balance between temperature control, energy consumption management and performance improvement;

[0063] S1.1. Comprehensively consider the factors of temperature control, energy consumption management and performance improvement, construct the goal of the multi-objective function, and the optimization formula is:

[0064] minimizef(T cell , E consumption , P perforrmance )=W T ·(T cell -T optimal ) 2 +W E ·E consumption +W P ·P perforrmance ;

[0065] Among them, W T , W E , WP is a weight factor used to balance the relationship between temperature control, energy consumption management and performance improvement; T optimal is the optimal operating temperature of the system; E consumption is energy consumption; P performance is the system performance index; T cell is the temperature of the battery pack; the minimize formula represents the minimum objective function f(T cell , E consumption , P performance ) optimization problem;

[0066] S1.2, randomly generate particles in the defined search space, each particle represents a set of solutions, and assign an initial position and velocity to each particle, where the position represents the parameter value of the solution and the velocity represents the direction and magnitude of the parameter change;

[0067] S1.3, for each solution represented by a particle, calculate its fitness value using a multi-objective optimization function;

[0068] S1.4. Compare the fitness of each particle's current position with the fitness of its best historical position, update the individual best position, and at the same time, select the position with the highest fitness among all particles as the global best position;

[0069] S1.5. According to the individual optimal position and the global optimal position, the speed and position update formula of the particle swarm optimization algorithm is used to calculate the new speed and new position of each particle. The speed update formula is:

[0070]

[0071] in, is the velocity of particle i at time T; is the position of particle i at time T; pi is the individual optimal position of particle i; g is the global optimal position; ω is the inertia weight, which controls the inertia of the particle speed; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random numbers in the range of [0, 1], which are used to introduce randomness; is the new velocity of particle i at time T+1;

[0072] The position update formula is:

[0073] in, is the new position of particle i at time T+1;

[0074] S1.6, repeatedly perform the process of particle fitness evaluation, optimal position update, and particle position and speed adjustment until the preset number of iterations is reached, and finally output the global optimal position, which is the best balance point found between temperature control, energy consumption management, and performance improvement;

[0075] S2, evaluate the effectiveness of the generation strategy and make fine-tuning through simulation prediction and historical data analysis;

[0076] S2.1. Simulate and execute the generated thermal management strategy under various operating conditions to quickly evaluate its potential effect.

[0077] S2.2. Compare the simulation results with historical operation data to evaluate the degree of improvement of the generated strategy relative to past strategies;

[0078] S2.3. Based on the preset key performance indicators, quantitatively evaluate the simulation results and identify the advantages and disadvantages of the generated strategy;

[0079] S2.4. Determine the degree of influence of the parameters on the performance by adjusting the policy parameters and observing the changes in system performance;

[0080] S2.5. Make targeted adjustments to the generation strategy based on the analysis results;

[0081] S2.6. Repeat the above steps until the performance of the generated strategy reaches the expected goal.

[0082] Preferably, the thermal management strategy in S2.1 includes an active cooling control strategy, a passive heat dissipation control strategy and a heating control strategy;

[0083] The active cooling control strategy mainly uses an active cooling control unit to achieve rapid temperature reduction. cell Exceeds the preset high threshold T high , and the maximum temperature difference ΔT inside the battery pack max Greater than the allowable temperature difference threshold ΔT allow , active cooling is triggered; cooling power P cool According to the current temperature of the battery pack and the target temperature T target and the efficiency parameter η of the active cooling control unit cool To adjust; the cooling power calculation formula is: P cool =η cool ·(T cell -T target ); Through this formula, the cooling power will increase as the battery temperature increases to reduce the temperature faster; when the temperature drops to T high Below, and the temperature difference ΔT max Below the temperature difference threshold ΔT allowWhen , active cooling stops;

[0084] The passive heat dissipation control strategy slowly adjusts the temperature through the passive heat dissipation element; when the temperature of the battery pack approaches the threshold but does not reach the trigger condition of active cooling, that is, T cell In T low <T cell <T high When the maximum temperature difference ΔT max Less than the allowable threshold ΔT allow , then choose the passive cooling control strategy; the cooling power P of passive cooling passive Depends on the temperature difference ΔT between the battery pack and the environment env =T cell -T ambient ; The heat dissipation formula is expressed as: P passive =η passive ΔT env ; Among them, T ambient is the ambient temperature, η passive is the efficiency parameter of passive heat dissipation; when the temperature T cell Reduce to low temperature threshold T low When the temperature is below 0.05, stop passive cooling;

[0085] The heating control strategy is to heat the battery pack by means of a battery heater; in a low temperature environment, when the battery pack temperature T cell Below the set low temperature threshold T low , and the temperature difference ΔT max When the temperature exceeds the allowable range, the heating control strategy is activated to ensure that the battery pack is maintained within a safe operating temperature range; the heating power P heat The current battery pack temperature and the target temperature T target The difference is determined by the formula: P heat =η heat ·(T target -T cell ), where η heat is the efficiency coefficient of the battery heater; when the temperature rises to T low When , stop heating;

[0086] Temperature difference control is an important step to ensure the temperature uniformity between the individual cells in the battery pack; if the maximum temperature difference ΔT inside the battery pack max Exceeding the permissible temperature difference threshold ΔT allow , that is: ΔT max =max(T cell )-min(T cell )>ΔT allowThe system will select different control strategies according to the size of the temperature difference. For small temperature differences, passive heat dissipation and adjustment of cooling power are preferred. For large temperature differences, the system will trigger active cooling and heating to quickly reduce the temperature difference.

[0087] Therefore, the present invention adopts the above-mentioned power battery pack thermal management control system and method, which has the following beneficial effects:

[0088] 1) Through real-time multi-parameter acquisition, dynamic temperature prediction and intelligent decision-making, the thermal management strategy of the battery pack is integrated and optimized to achieve efficient temperature control and energy consumption balance;

[0089] 2) It has abnormality monitoring and self-diagnosis functions to ensure the safety and reliability of the system.

[0090] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a structural schematic diagram of an embodiment of a thermal management control system and method for a power battery pack of the present invention. DETAILED DESCRIPTION

[0092] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0093] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0094] Embodiment 1

[0095] like Figure 1 As shown, the present invention provides a power battery pack thermal management system, including the following modules:

[0096] The multi-parameter acquisition module is used to collect the temperature, current, voltage, state of charge and environmental parameter data of the battery pack in real time. The multi-parameter acquisition module is the basic module of the system, which is responsible for real-time acquisition of key parameters inside the battery pack, including temperature, current, voltage, state of charge (SOC) and external environmental parameters. The real-time and accuracy of these data are the prerequisites for all subsequent operations. Through the sensor network distributed inside and outside the battery pack, the multi-parameter acquisition module can capture the detailed thermodynamic state and operating environment of the battery pack. These data not only provide a direct basis for temperature management, but also provide basic support for the safety and efficiency of the entire system. With the development of sensor technology, the accuracy and resolution of the collected data are getting higher and higher, which lays a solid foundation for subsequent data processing and decision-making.

[0097] The multi-parameter acquisition module includes the following components:

[0098] Temperature sensor network: Deploy sensor arrays at key locations of the battery pack to monitor the temperature distribution of battery cells and modules in real time. Current and voltage acquisition unit: Use high-precision current transformers and voltage dividers to continuously measure the charge and discharge current and terminal voltage of the battery pack, providing basic data for charge state estimation and safety monitoring.

[0099] Among them, the calculation formula of the charge and discharge current of the battery pack is expressed as:

[0100]

[0101] Among them, I primary is the actual current of the battery pack, i.e., the charge and discharge current; N primary is the primary turns of the current transformer; N secondary is the secondary turns of the current transformer; R load is the load resistance through which the secondary current passes; V secondary It is the voltage generated by the secondary current passing through the load resistance;

[0102] The calculation formula of the terminal voltage of the battery pack is expressed as:

[0103]

[0104] Among them, V battery is the actual terminal voltage of the battery pack; V measured is the divided voltage output by the voltage divider; R1 and R2 are the resistance values ​​in the voltage divider.

[0105] State of charge estimation unit: Based on the current integration method and open circuit voltage method, it calculates the state of charge of the battery pack in real time and provides the remaining capacity information of the battery pack.

[0106] The remaining capacity of the battery pack is:

[0107]

[0108] Wherein, RC(t) is the remaining capacity of the battery pack at time t; C rated is the rated capacity of the battery pack; α is a weighting factor used to adjust the contribution ratio of the current integration method and the open circuit voltage method; SOC0 is the state of charge at the initial time t0; I(τ) is the battery current at time τ; f(V oc ) is based on the open circuit voltage V oc Estimated state of charge.

[0109] Environmental parameter sensor group: monitors the environmental conditions around the battery pack and provides external reference data for thermal management strategy optimization. Data acquisition control unit: coordinates and manages the data acquisition frequency and timing of each sensor, ensures the synchronization and integrity of the data, and performs preliminary data validity checks. Communication interface unit: uses CAN bus or other communication protocols suitable for the vehicle environment to achieve high-speed and reliable data transmission with other modules.

[0110] The data preprocessing and fusion module is used to filter, standardize and fuse the data collected by the multi-parameter acquisition module to generate a high-quality, unified format data set. The data preprocessing and fusion module plays a core role in data processing in the entire system. Since the raw data often contains noise, outliers and inconsistent formats, it is necessary to preprocess the data through filtering, standardization and other technologies to improve the data quality. In addition, different types of data collected by multiple sensors need to be fused to form a unified and coherent data set for subsequent modules to analyze and make decisions. The effectiveness of the data preprocessing and fusion module directly affects the overall performance of the system: high-quality data can not only improve the accuracy of predictions, but also reduce the response time of the system, thereby achieving more accurate thermal management.

[0111] The dynamic prediction modeling module builds and iteratively updates the temperature change prediction model based on real-time and historical data to achieve accurate temperature trend prediction. The core of the dynamic prediction modeling module is the ability to accurately predict the temperature change trend of the battery pack based on real-time and historical data. This prediction not only takes into account the current state, but also combines the patterns and trends of historical data. The accuracy of the prediction is ensured by continuously iterating and updating the model. The accuracy of the dynamic prediction modeling module directly affects the decision-making effect of the intelligent decision-making module. Its forward-looking nature in the time dimension enables the entire system to respond to potential temperature anomalies in advance and prevent damage to the battery pack caused by excessively high or low temperatures.

[0112] The dynamic predictive modeling module includes the following components:

[0113] Model training unit: Use deep learning algorithms to periodically train temperature prediction models based on historical and real-time data, and continuously optimize model parameters. Model evaluation and selection unit: Evaluate the prediction performance of different models through cross-validation, and automatically select the optimal model for actual prediction tasks. Online learning adaptation unit: Continuously receive new data and perform incremental learning to adapt the model to changes in battery performance over time and new operating conditions.

[0114] Prediction execution unit: receives real-time status data, calls the current optimal model to predict future temperature trends, and outputs prediction results for use by the intelligent decision-making module; model version management unit: manages different versions of temperature prediction models, records model update history, and rolls back to the previous stable version when necessary to ensure system reliability.

[0115] The intelligent decision-making module uses a multi-objective optimization algorithm to generate the optimal thermal management strategy based on the prediction results and preset rules. The intelligent decision-making module must not only balance the cooling and heating needs of the battery, but also consider multiple goals such as energy efficiency, system life, and operational safety. By weighing these factors, the intelligent decision-making module can output a control strategy that is in line with the overall interests of the system, thereby optimizing the thermal management of the battery pack. The level of intelligence in this process is directly related to the performance and life of the battery system, and is also the core of the system's "intelligent" management.

[0116] The intelligent decision-making module includes the following components:

[0117] Decision input processing unit: responsible for receiving and integrating the temperature prediction results and current system status information from the dynamic prediction modeling module, providing comprehensive data support for the decision-making process. Rule base management unit: used to maintain and update the preset decision rule base; multi-objective optimization unit: using the particle swarm optimization algorithm to find the best balance between temperature control, energy consumption management and performance improvement.

[0118] Strategy generation unit: formulates specific thermal management operation strategies based on the optimization results, including cooling / heating intensity, duration, and execution sequence parameters. Decision evaluation and tuning unit: evaluates the effectiveness of the generated strategy and makes fine adjustments through simulation prediction and historical data analysis. Strategy output unit: outputs the finalized thermal management strategy to the thermal management execution module in a standardized format, and records the decision-making process for subsequent analysis and improvement.

[0119] The thermal management execution module coordinates and controls active cooling, passive heat dissipation or heating operations according to the output of the intelligent decision-making module to achieve precise temperature regulation and thermal management. The thermal management execution module is the execution unit of the system, responsible for converting the strategies generated by the intelligent decision-making module into specific operational actions. This module controls the active cooling system (such as fans, coolant pumps) and passive heat dissipation devices (such as heat sinks), and enables the heating device when necessary to maintain the battery pack within the optimal operating temperature range. The flexibility and response speed of the thermal management execution module are the key to ensuring the stable operation of the system. It needs to accurately control the intensity and method of temperature regulation according to the real-time thermal management strategy to avoid overheating or overcooling of the battery and ensure the efficient and safe operation of the battery pack.

[0120] The thermal management execution module includes the following components:

[0121] Strategy parsing unit: Receives and parses the thermal management strategy from the intelligent decision-making module and converts it into specific execution instructions and parameters. Active cooling control unit: Adjusts the active cooling elements according to specific execution instructions and parameters to achieve precise active cooling. Passive heat dissipation adjustment unit: Used to control the working state of passive heat dissipation elements and optimize heat distribution and buffering effect.

[0122] Heating control unit: used to accurately adjust the power output of the battery heater to ensure that the battery pack is maintained within the optimal operating temperature range. Execution coordination unit: used to achieve the coordination of cooling, heat dissipation and heating processes to avoid conflicts. Feedback monitoring unit: used to monitor the execution effect of thermal management in real time, collect temperature change data, and provide timely feedback for strategy adjustment.

[0123] The system monitoring and diagnostic module continuously evaluates system performance, monitors abnormal conditions in real time, performs fault diagnosis and early warning, and ensures safe and reliable operation of the system. The system monitoring and diagnostic module continuously evaluates the operating status of the system, and promptly detects abnormalities and performs fault diagnosis by monitoring the working conditions of each module in real time. The system monitoring and diagnostic module can also warn of potential faults or dangerous conditions, reminding users or automatically triggering corresponding safety protection measures. By continuously evaluating system performance, the monitoring and diagnostic module can ensure the long-term stability and reliability of the system and prevent safety accidents caused by improper temperature management.

[0124] The system monitoring and diagnostics module includes the following components:

[0125] System status acquisition unit: continuously collects operating data and status information from each module to provide a real-time and comprehensive data basis for system monitoring and diagnosis. Performance evaluation unit: based on preset performance indicators and historical data, it conducts real-time evaluation and analysis of the system's overall operating efficiency, energy consumption and temperature control accuracy. Anomaly detection unit, used to quickly identify abnormal conditions that deviate from the normal range;

[0126] Fault diagnosis expert unit: conducts in-depth analysis of detected anomalies to determine the type, severity and possible causes of the fault. Warning generation unit: generates warning information of different levels based on the fault diagnosis results and promptly notifies operators through appropriate channels. Automatic repair suggestion unit: generates repair suggestions or automatically performs repair operations for some abnormal situations that can be handled automatically to improve the system's self-repair capabilities.

[0127] The human-machine interaction module is used to provide intuitive system status display, operation control and remote monitoring interface to achieve intelligent thermal management of human-machine collaboration. The human-machine interaction module not only displays the current status and historical data of the system, but also allows users to set and control it through a friendly interface. In addition, through the remote monitoring function, users can monitor the status of the battery pack anytime and anywhere, adjust the operation strategy, and achieve remote fault diagnosis and maintenance. The design of the human-machine interaction module directly affects the user experience. It improves the operability and maintenance convenience of the system, making the complex thermal management process intuitive and easy to understand.

[0128] The multi-parameter acquisition module transmits data to the data preprocessing and fusion module, the data preprocessing and fusion module transmits data to the dynamic prediction modeling module, the dynamic prediction modeling module transmits data to the intelligent decision-making module, the intelligent decision-making module transmits data to the thermal management execution module, the thermal management execution module and the system monitoring and diagnosis module transmit data to each other, and the human-computer interaction module transmits data to the multi-parameter acquisition module, the data preprocessing and fusion module, the dynamic prediction modeling module, the intelligent decision-making module, the thermal management execution module and the system monitoring and diagnosis module.

[0129] The present invention provides an operating method of a power battery pack thermal management control system, comprising the following steps:

[0130] S1. Use particle swarm optimization algorithm to find the best balance between temperature control, energy consumption management and performance improvement;

[0131] S1.1. Comprehensively consider the factors of temperature control, energy consumption management and performance improvement, construct the goal of the multi-objective function, and the optimization formula is:

[0132] minimizef(T cell , E consumption , P performance )=W T ·(T cell -T optimal ) 2 +W E ·E consumption +W P ·P performance ;

[0133] Among them, WT , W E、 W P is a weight factor used to balance the relationship between temperature control, energy consumption management and performance improvement; T optimal is the optimal operating temperature of the system; E consumption is energy consumption; P performance is the system performance index; T cell is the temperature of the battery pack; the minimize formula represents the minimum objective function F(T cell , E consumption , P performance ) optimization problem.

[0134] S1.2, randomly generate particles in the defined search space, each particle represents a set of solutions, and assign an initial position and velocity to each particle, where the position represents the parameter value of the solution and the velocity represents the direction and magnitude of the parameter change;

[0135] S1.3, for each solution represented by a particle, calculate its fitness value using a multi-objective optimization function;

[0136] S1.4. Compare the fitness of each particle's current position with the fitness of its best historical position, update the individual best position, and at the same time, select the position with the highest fitness among all particles as the global best position;

[0137] S1.5. According to the individual optimal position and the global optimal position, the speed and position update formula of the particle swarm optimization algorithm is used to calculate the new speed and new position of each particle. The speed update formula is:

[0138]

[0139] in, is the velocity of particle i at time T; is the position of particle i at time T; pi is the individual optimal position of particle i; g is the global optimal position; ω is the inertia weight, which controls the inertia of the particle speed; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random numbers in the range of [0, 1], which are used to introduce randomness; is the new velocity of particle i at time T+1;

[0140] The position update formula is:

[0141] in, is the new position of particle i at time T+1;

[0142] S1.6, repeatedly perform the process of particle fitness evaluation, optimal position update, and particle position and speed adjustment until the preset number of iterations is reached, and finally output the global optimal position, which is the best balance point found between temperature control, energy consumption management, and performance improvement;

[0143] S2, evaluate the effectiveness of the generation strategy and make fine-tuning through simulation prediction and historical data analysis;

[0144] S2.1. Simulate and execute the generated thermal management strategy under various operating conditions to quickly evaluate its potential effect.

[0145] Thermal management strategies include active cooling control strategies, passive heat dissipation control strategies, and heating control strategies.

[0146] The active cooling control strategy mainly uses the active cooling control unit to achieve rapid temperature reduction. cell Exceeds the preset high temperature threshold T high , and the maximum temperature difference ΔT inside the battery pack max Greater than the allowable temperature difference threshold ΔT allow , active cooling is triggered. Cooling power P cool According to the current temperature of the battery pack and the target temperature T target and the efficiency parameter η of the active cooling control unit cool To adjust. The cooling power calculation formula is as follows: P cool =η cool ·(T cell -T target ). By this formula, the cooling power increases as the battery temperature increases to reduce the temperature faster. When the temperature drops to T high Below, and the temperature difference ΔT max Below the temperature difference threshold ΔT allow Active cooling stops.

[0147] The passive cooling control strategy slowly adjusts the temperature through passive cooling elements. When the temperature of the battery pack approaches the high temperature threshold but does not reach the trigger condition for active cooling (i.e., T cell In T low <T cell <T high interval), and the temperature difference ΔT max Less than the allowable threshold ΔT allow , then choose the passive cooling control strategy. The heat dissipation power P of passive cooling passive Depends on the temperature difference ΔT between the battery pack and the environment env =T cell -T ambient ; The specific heat dissipation formula can be expressed as: P passive =ηpassive ΔT env ; Among them, T ambient is the ambient temperature, η passive is the efficiency parameter of passive heat dissipation; when the temperature T cell Reduce to low temperature threshold T low When the temperature drops below 0.05, passive cooling stops.

[0148] The heating control strategy uses the battery heater to heat the battery. In a low temperature environment, when the battery pack temperature T cell Below the set low temperature threshold T low , and the maximum temperature difference ΔT max When the temperature exceeds the allowable range, the heating control strategy is activated to ensure that the battery pack is maintained within a safe operating temperature range. heat The current battery pack temperature and the target temperature T target The difference is determined by the formula: P heat =η heat ·(T target -T cell ), where η heat is the efficiency coefficient of the battery heater. When the temperature rises to T low Stop heating.

[0149] Temperature difference control is an important step to ensure the temperature uniformity between the individual cells in the battery pack. max Exceeding the permissible temperature difference threshold ΔT allow , that is: ΔT max =max(T cell )-min(T cell )>ΔT allow The system will select different control strategies according to the size of the temperature difference. For smaller temperature differences, passive heat dissipation or adjustment of cooling power will be preferred; for larger temperature differences, the system will trigger active cooling or heating to ensure that the temperature difference is quickly reduced.

[0150] S2.2. Compare the simulation results with historical operation data to evaluate the degree of improvement of the generated strategy relative to past strategies;

[0151] S2.3. Based on the preset key performance indicators, quantitatively evaluate the simulation results and identify the advantages and disadvantages of the generated strategy;

[0152] S2.4. Determine the degree of influence of the parameters on the performance by adjusting the policy parameters and observing the changes in system performance;

[0153] S2.5. Make targeted adjustments to the generation strategy based on the analysis results;

[0154] S2.6. Repeat the above steps until the performance of the generated strategy reaches the expected goal.

[0155] Therefore, the present invention adopts the above-mentioned power battery pack thermal management control system and method, which integrates and optimizes the thermal management strategy of the battery pack through real-time multi-parameter acquisition, dynamic temperature prediction and intelligent decision-making, to achieve efficient temperature control and energy consumption balance; it has abnormality monitoring and self-diagnosis functions to ensure the safety and reliability of the system.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A power battery pack thermal management control system, characterized in that: Includes the following modules: Multi-parameter acquisition module: used to collect battery pack parameter data in real time; Data preprocessing and fusion module: used to generate high-quality, unified format data sets; Dynamic prediction modeling module: used to achieve accurate temperature trend prediction; Intelligent decision-making module: used to generate the optimal thermal management strategy; Thermal management execution module: used to achieve precise temperature regulation and thermal management; System monitoring and diagnosis module: used to ensure safe operation of the system; Human-computer interaction module; Intelligent thermal management for human-machine collaboration.

2. A power battery pack thermal management control system according to claim 1, characterized in that: The multi-parameter acquisition module transmits data to the data preprocessing and fusion module, the data preprocessing and fusion module transmits data to the dynamic prediction modeling module, the dynamic prediction modeling module transmits data to the intelligent decision-making module, the intelligent decision-making module transmits data to the thermal management execution module, the thermal management execution module and the system monitoring and diagnosis module transmit data to each other, and the human-computer interaction module transmits data to the multi-parameter acquisition module, the data preprocessing and fusion module, the dynamic prediction modeling module, the intelligent decision-making module, the thermal management execution module and the system monitoring and diagnosis module.

3. A power battery pack thermal management control system according to claim 2, characterized in that: The multi-parameter acquisition module includes a temperature sensor network, a current and voltage acquisition unit, a state of charge estimation unit, an environmental parameter sensor group, a data acquisition control unit and a communication interface unit; The temperature sensor network is used to monitor the temperature distribution of battery cells and modules in real time; The current and voltage acquisition unit is used to continuously measure the charge and discharge current and terminal voltage of the battery pack to provide basic data for charge state estimation and safety monitoring; The state of charge estimation unit is used to provide the remaining capacity information of the battery pack; The environmental parameter sensor group is used to monitor the environmental conditions around the battery pack and provide external reference data for thermal management strategy optimization; The data acquisition control unit is used to coordinate and manage the data acquisition frequency and timing of each sensor and perform preliminary data validity checks; The communication interface unit is used to realize high-speed and reliable data transmission with each module.

4. A power battery pack thermal management control system according to claim 2, characterized in that: The dynamic prediction modeling module includes a model training unit, a model evaluation and selection unit, an online learning adaptation unit, a prediction execution unit and a model version management unit; The model training unit is used to periodically train the temperature prediction model based on historical and real-time data and continuously optimize the model parameters; The model evaluation and selection unit is used to automatically select the optimal model for the actual prediction task; The online learning adaptation unit is used to adapt the model to changes in battery performance over time and new operating conditions; The prediction execution unit is used to call the current optimal model to predict the future temperature trend and output the prediction results for use by the intelligent decision-making module; The model version management unit is used to manage different versions of temperature prediction models, record model update history, and be able to roll back to a previous stable version.

5. A power battery pack thermal management control system according to claim 2, characterized in that: The intelligent decision-making module includes a decision input processing unit, a rule base management unit, a multi-objective optimization unit, a strategy generation unit, a decision evaluation and tuning unit, and a strategy output unit; The decision input processing unit is used to provide comprehensive data support for the decision-making process; The rule base management unit is used to maintain and update the preset decision rule base; The multi-objective optimization unit is used to find the best balance between temperature control, energy consumption management and performance improvement; The strategy generation unit is used to formulate a specific thermal management operation strategy; The decision evaluation and tuning unit is used to evaluate the effectiveness of the generation strategy and perform fine-tuning through simulation prediction and historical data analysis; The strategy output unit is used to output the finalized thermal management strategy to the thermal management execution module in a standardized format, and record the decision-making process for subsequent analysis and improvement.

6. A power battery pack thermal management control system according to claim 2, characterized in that: The thermal management execution module includes a strategy analysis unit, an active cooling control unit, a passive heat dissipation adjustment unit, a heating control unit, an execution coordination unit and a feedback monitoring unit; The strategy parsing unit is used to receive and parse the thermal management strategy from the intelligent decision-making module and convert it into specific execution parameters, wherein the thermal management strategy includes an active cooling control strategy, a passive heat dissipation control strategy and a heating control strategy; The active cooling control unit is used to adjust the active cooling element according to specific execution parameters; The passive heat dissipation adjustment unit is used to control the working state of the passive heat dissipation element and optimize the heat distribution and buffering effect; The heating control unit is used to accurately adjust the power output of the battery heater; The execution coordination unit is used to achieve coordination of cooling, heat dissipation and heating processes to avoid conflicts; The feedback monitoring unit is used to monitor the thermal management execution effect in real time, collect temperature change data, and provide timely feedback for strategy adjustment.

7. A power battery pack thermal management control system according to claim 2, characterized in that: The system monitoring and diagnosis module includes a system status acquisition unit, a performance evaluation unit, an anomaly detection unit, a fault diagnosis expert unit, an early warning generation unit and an automatic repair suggestion unit; The system status acquisition unit is used to provide a real-time and comprehensive data basis for system monitoring and diagnosis; The performance evaluation unit is used to evaluate and analyze the overall operating efficiency, energy consumption and temperature control accuracy of the system in real time; The abnormality detection unit is used to quickly identify abnormal conditions that deviate from the normal range; The fault diagnosis expert unit is used to conduct in-depth analysis of the detected anomaly and determine the type, severity and cause of the fault; The warning generation unit is used to generate warning information of different levels and promptly notify the operator through appropriate channels; The automatic repair suggestion unit is used to generate repair suggestions and automatically perform repair operations.

8. A power battery pack thermal management control system according to claim 3, characterized in that: The calculation formula of the charge and discharge current of the battery pack is expressed as: Among them, I primary is the actual current of the battery pack, i.e., the charge and discharge current; N primary is the primary turns of the current transformer; N secondary is the secondary turns of the current transformer; R load is the load resistance through which the secondary current passes; V secondary It is the voltage generated by the secondary current passing through the load resistance; The calculation formula of the terminal voltage of the battery pack is expressed as: Among them, V battery is the actual terminal voltage of the battery pack; V measured is the divided voltage output by the voltage divider; R1 and R2 are the resistance values ​​in the voltage divider; The remaining capacity of the battery pack is: Wherein, RC(t) is the remaining capacity of the battery pack at time t; C rated is the rated capacity of the battery pack; α is a weighting factor used to adjust the contribution ratio of the current integration method and the open circuit voltage method; SOC0 is the state of charge at the initial time t0; I(τ) is the battery current at time τ; f(V oc ) is based on the open circuit voltage V oc Estimated state of charge.

9. The operating method of a power battery pack thermal management control system according to any one of claims 1 to 8, characterized in that: The following steps are involved: S1. Use particle swarm optimization algorithm to find the best balance between temperature control, energy consumption management and performance improvement; S1.

1. Comprehensively consider the factors of temperature control, energy consumption management and performance improvement, construct the goal of the multi-objective function, and the optimization formula is: minimize f(T cell ,E consumption ,P performance )=W T ·(T cell -T optimal ) 2 +W E ·E consumption +W P ·P performance ; Among them, W T , W E , W P is a weight factor used to balance the relationship between temperature control, energy consumption management and performance improvement; T optimal is the optimal operating temperature of the system; E consumption is energy consumption; P performance is the system performance index; T cell is the temperature of the battery pack; the minimize formula represents the minimum objective function f(T cell ,E consumption ,P performance ) optimization problem; S1.2, randomly generate particles in the defined search space, each particle represents a set of solutions, and assign an initial position and velocity to each particle, where the position represents the parameter value of the solution and the velocity represents the direction and magnitude of the parameter change; S1.3, for each solution represented by a particle, calculate its fitness value using a multi-objective optimization function; S1.

4. Compare the fitness of each particle's current position with the fitness of its best historical position, update the individual best position, and at the same time, select the position with the highest fitness among all particles as the global best position; S1.

5. According to the individual optimal position and the global optimal position, the speed and position update formula of the particle swarm optimization algorithm is used to calculate the new speed and new position of each particle. The speed update formula is: in, is the velocity of particle i at time T; is the position of particle i at time T; p i is the individual optimal position of particle i; g is the global optimal position; ω is the inertia weight, which controls the inertia of the particle speed; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random numbers in the range of [0,1], which are used to introduce randomness; is the new velocity of particle i at time T+1; The position update formula is: in, is the new position of particle i at time T+1; S1.6, repeatedly perform the process of particle fitness evaluation, optimal position update, and particle position and speed adjustment until the preset number of iterations is reached, and finally output the global optimal position, which is the best balance point found between temperature control, energy consumption management, and performance improvement; S2, evaluate the effectiveness of the generation strategy and make fine-tuning through simulation prediction and historical data analysis; S2.

1. Simulate and execute the generated thermal management strategy under various operating conditions to quickly evaluate its potential effect. S2.

2. Compare the simulation results with historical operation data to evaluate the degree of improvement of the generated strategy relative to past strategies; S2.

3. Based on the preset key performance indicators, quantitatively evaluate the simulation results and identify the advantages and disadvantages of the generated strategy; S2.

4. Determine the degree of influence of the parameters on the performance by adjusting the policy parameters and observing the changes in system performance; S2.

5. Make targeted adjustments to the generation strategy based on the analysis results; S2.

6. Repeat the above steps until the performance of the generated strategy reaches the expected goal.

10. The operating method of a power battery pack thermal management control system according to claim 9, characterized in that: The thermal management strategy in S2.1 includes an active cooling control strategy, a passive heat dissipation control strategy and a heating control strategy; The active cooling control strategy mainly uses an active cooling control unit to achieve rapid temperature reduction. cell Exceeds the preset high temperature threshold T high , and the maximum temperature difference ΔT inside the battery pack max Greater than the allowable temperature difference threshold ΔT allow , active cooling is triggered; cooling power P cool According to the current temperature of the battery pack and the target temperature T target and the efficiency parameter η of the active cooling control unit cool To adjust; the cooling power calculation formula is: P cool =η cool ·(T cell -T target ); Through this formula, the cooling power will increase as the battery temperature increases to reduce the temperature faster; when the temperature drops to T high Below, and the temperature difference ΔT max Below the temperature difference threshold ΔT allow When , active cooling stops; The passive heat dissipation control strategy slowly adjusts the temperature through the passive heat dissipation element; when the temperature of the battery pack approaches the threshold but does not reach the trigger condition of active cooling, that is, T cell In T low <T cell <T high When the maximum temperature difference ΔT max Less than the allowable threshold ΔT allow , then choose the passive cooling control strategy; the cooling power P of passive cooling passive Depends on the temperature difference ΔT between the battery pack and the environment env =T cell -T ambient ; The heat dissipation formula is expressed as: η passive =η passive ΔT env ; Among them, T ambient is the ambient temperature, η passive is the efficiency parameter of passive heat dissipation; when the temperature T cell Reduce to low temperature threshold T low When the temperature is below 0.05, stop passive cooling; The heating control strategy is to heat the battery pack by means of a battery heater; in a low temperature environment, when the battery pack temperature T cell Below the set low temperature threshold T low , and the temperature difference ΔT max When the temperature exceeds the allowable range, the heating control strategy is activated to ensure that the battery pack is maintained within a safe operating temperature range; the heating power P heat The current battery pack temperature and the target temperature T target The difference is determined by the formula: P heat =η heat ·(T target -T cell ), where η heat is the efficiency coefficient of the battery heater; when the temperature rises to T low When , stop heating; Temperature difference control is an important step to ensure the temperature uniformity between the individual cells in the battery pack; if the maximum temperature difference ΔT inside the battery pack max Exceeding the permissible temperature difference threshold ΔT allow , that is: ΔT max =max(T cell )-min(T cell )>ΔT allow The system will select different control strategies according to the size of the temperature difference. For small temperature differences, passive heat dissipation and adjustment of cooling power are preferred. For large temperature differences, the system will trigger active cooling and heating to quickly reduce the temperature difference.

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