A new energy automobile battery pack equalization control system based on internet of things

Abstract

The application discloses a new energy automobile battery pack equalization control system based on an internet of things, relates to the technical field of new energy automobile battery management, and comprises a data acquisition module, a dynamic equalization analysis module, a comprehensive decision module and an optimization adjustment module, wherein the data acquisition module is used for acquiring and processing battery pack operation state data in real time; and the dynamic equalization analysis module is used for analyzing the battery pack operation state data to obtain a battery pack equalization response speed evaluation index and an energy transfer efficiency evaluation index. Through real-time acquisition of the battery pack operation state data, the battery pack equalization response speed evaluation index and the energy transfer efficiency evaluation index are obtained through analysis, and the equalization control strategy is comprehensively optimized and adjusted, so that the real-time performance and the adaptability of the battery pack equalization control system are improved, and the problem of insufficient response speed of a single-capacitance equalization method under complex working conditions in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle battery management, and in particular to a new energy vehicle battery pack balancing control system based on the Internet of Things. Background Art

[0002] In the field of new energy vehicles, the battery pack serves as the core power source, and its performance directly affects the vehicle's range, power output, and safety. Battery pack balancing control technology is a key link in ensuring battery pack performance and has long been a research hotspot. Currently, the industry has developed a variety of balancing technologies, mainly including passive balancing and active balancing. Passive balancing uses resistors to dissipate excess energy to achieve voltage balance, but this results in energy waste. Active balancing improves the balance between battery cells through energy transfer, resulting in higher energy utilization efficiency. However, existing active balancing technologies still have many shortcomings, especially in terms of the speed of response to changes in voltage differences between battery cells under complex operating conditions, which needs to be improved.

[0003] Taking the single-capacitor balancing method in active balancing technology as an example, during the actual driving of new energy vehicles, factors such as vehicle acceleration and deceleration, and varying road conditions cause the battery pack to frequently charge and discharge, resulting in rapid changes in battery cell voltage differences. However, existing single-capacitor balancing technology, limited by its control logic and power switch response mechanism, struggles to quickly capture and promptly process these voltage variations, exhibiting significant lag. For example, when the vehicle accelerates suddenly, the battery pack requires a sudden high current discharge, causing some battery cell voltages to drop rapidly. The single-capacitor balancing system is unable to quickly transfer energy from other high-voltage cells to replenish the voltage, limiting the battery pack's overall output power and impacting the vehicle's dynamic performance. During braking energy recovery, some battery cell voltages rise rapidly, and the single-capacitor balancing system struggles to complete balancing operations in a timely manner, resulting in energy waste and even potential local overcharging, which can affect battery life and safety. Therefore, existing technologies have significant shortcomings in balancing efficiency and response speed under dynamic conditions. A more efficient and intelligent solution is urgently needed to improve the overall performance and reliability of battery packs. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a new energy vehicle battery pack balancing control system based on the Internet of Things, which solves the problem of insufficient response speed of the single capacitor balancing method in the existing technology to changes in voltage differences between battery cells under complex working conditions, and improves the real-time and adaptability of the battery pack balancing control system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new energy vehicle battery pack balancing control system based on the Internet of Things, including a data acquisition module, a dynamic balancing analysis module, a comprehensive decision module and an optimization and adjustment module, wherein:

[0006] The data acquisition module is used to acquire and process battery pack operating status data in real time;

[0007] The dynamic balancing analysis module is used to analyze the battery pack operating status data to obtain the battery pack balancing response speed evaluation index and the energy transfer efficiency evaluation index;

[0008] The comprehensive decision module is used to comprehensively analyze and obtain a comprehensive evaluation index of battery pack balancing performance;

[0009] The optimization and adjustment module is used to compare and analyze the battery pack balancing response speed evaluation index with the first threshold to optimize and adjust the balancing control strategy; compare and analyze the energy transfer efficiency evaluation index with the second threshold to optimize and adjust the energy transfer path; compare and analyze the battery pack balancing performance comprehensive evaluation index with the comprehensive threshold to optimize and adjust the overall balancing control plan.

[0010] Optionally, the specific steps of acquiring and processing the battery pack operating status data in real time are: collecting the raw battery pack operating data through the Internet of Things sensor array; filtering and normalizing the raw battery pack operating data to obtain standardized battery pack operating status data, wherein the standardized battery pack operating status data includes battery cell voltage, current change rate, temperature distribution and charge and discharge status.

[0011] Optionally, the dynamic balancing analysis module obtains the battery pack balancing response speed evaluation index in the following specific steps: extracting the voltage fluctuation period at a preset detection point through a time series analysis tool; obtaining the switch switching delay at a preset detection point through a power switch response test device; measuring the transient current response time at a preset detection point through a load simulator; and monitoring the temperature gradient change in a preset detection section through a thermal imager; and obtaining the battery pack balancing response speed evaluation index based on the above data analysis and calculation, wherein the formula is:

[0012] ;

[0013] in, represents the equilibrium response speed evaluation index, Indicates the voltage fluctuation period, Indicates the switching delay, represents the transient current response time, Indicates temperature gradient change.

[0014] Optionally, the specific process of the dynamic equilibrium analysis module to obtain the energy transfer efficiency evaluation index is as follows: obtaining the energy loss rate at a preset detection point through an energy transfer efficiency tester; measuring the actual transfer current at the preset detection point through a current sensor; measuring the actual transfer voltage at the preset detection point through a voltage sensor; measuring the heat energy loss in a preset detection section through a thermocouple; and obtaining the energy transfer efficiency evaluation index based on the above data analysis and calculation, the formula of which is:

[0015] ;

[0016] in, represents the energy transfer efficiency evaluation index, represents the actual transferred current, represents the actual transfer voltage, represents the energy loss rate, Indicates heat loss.

[0017] Optionally, the specific steps of comprehensively analyzing and obtaining the comprehensive evaluation index of battery pack balancing performance are: calculating the balancing control cost function at a preset detection point by a multi-objective optimization algorithm; and obtaining the comprehensive evaluation index of battery pack balancing performance by comprehensively analyzing and calculating the battery pack balancing response speed evaluation index, the energy transfer efficiency evaluation index, and the balancing control cost function, wherein the formula is:

[0018] ;

[0019] in, Represents the comprehensive evaluation index of battery pack balancing performance, represents the equilibrium response speed evaluation index, represents the energy transfer efficiency evaluation index, represents the equilibrium control cost function, 、 、 is the weight coefficient.

[0020] Optionally, the specific steps of optimizing and adjusting the balancing control strategy are: if the battery pack balancing response speed evaluation index is lower than or equal to a first threshold, the current balancing control strategy is kept unchanged; if the battery pack balancing response speed evaluation index is higher than the first threshold, an adaptive PID control algorithm is introduced to dynamically adjust the balancing control parameters, and the power switch drive circuit is optimized to reduce switching delay.

[0021] Optionally, the specific steps for optimizing and adjusting the energy transfer path are: if the energy transfer efficiency evaluation index is higher than or equal to the second threshold, there is no need to adjust the energy transfer path; if the energy transfer efficiency evaluation index is lower than the second threshold, the energy transfer path is replanned, a multi-path parallel transmission mode is adopted, and superconducting materials are used to reduce transmission losses.

[0022] Optionally, the specific steps of comparing and analyzing the comprehensive evaluation index of the battery pack balancing performance with the comprehensive threshold are: extracting the comprehensive threshold of the comprehensive evaluation index of the battery pack balancing performance in the Internet of Things cloud platform, and comparing and analyzing the comprehensive evaluation index of the battery pack balancing performance with the comprehensive threshold.

[0023] Optionally, the specific steps of optimizing and adjusting the overall balancing control scheme are: if the comprehensive evaluation index of the battery pack balancing performance is higher than or equal to the comprehensive threshold, there is no need to send a warning message; if the comprehensive evaluation index of the battery pack balancing performance is lower than the comprehensive threshold, a warning message is sent through the vehicle communication system.

[0024] Optionally, the optimization and adjustment of the overall balancing control scheme also includes: if there is no need to send an early warning message, maintaining the current balancing control scheme; if an early warning message needs to be sent, using edge computing devices to quickly process abnormal data, combining blockchain technology to ensure the authenticity and non-tamperability of the early warning information, and using deep learning models to predict future operating trends of the battery pack and formulate response strategies in advance.

[0025] Beneficial effects

[0026] 1. The present invention collects battery pack operating status data in real time, analyzes it to obtain the battery pack balancing response speed evaluation index and energy transfer efficiency evaluation index, and comprehensively optimizes and adjusts the balancing control strategy, thereby improving the real-time performance and adaptability of the battery pack balancing control system and solving the problem of insufficient response speed of the single capacitor balancing method in the existing technology under complex working conditions.

[0027] 2. By introducing an adaptive PID control algorithm and a multi-path parallel transmission mode, the present invention significantly improves the balancing performance of the battery pack under complex working conditions such as rapid acceleration and energy recovery, reduces safety hazards caused by local overcharging or over-discharging, and extends battery life.

[0028] 3. By combining the Internet of Things, edge computing and blockchain technologies, this invention realizes intelligent and efficient battery pack balancing control. It can not only detect potential problems in a timely manner, but also reduce maintenance costs through predictive maintenance, thereby improving the overall performance and user experience of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of battery pack balancing performance evaluation and optimization adjustment in an embodiment of the present invention;

[0030] Figure 2 It is a schematic diagram of the system module structure of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1 and Figure 2 The present invention provides a new energy vehicle battery pack balancing control system based on the Internet of Things, including a data acquisition module, a dynamic balancing analysis module, a comprehensive decision module and an optimization and adjustment module, wherein:

[0033] The data acquisition module is used to obtain and process the battery pack operating status data in real time;

[0034] The dynamic balancing analysis module is used to analyze the battery pack operating status data to obtain the battery pack balancing response speed evaluation index and energy transfer efficiency evaluation index;

[0035] The comprehensive decision-making module is used to comprehensively analyze and obtain the comprehensive evaluation index of battery pack balancing performance;

[0036] The optimization and adjustment module is used to compare and analyze the battery pack balancing response speed evaluation index with the first threshold value to optimize and adjust the balancing control strategy; compare and analyze the energy transfer efficiency evaluation index with the second threshold value to optimize and adjust the energy transfer path; compare and analyze the battery pack balancing performance comprehensive evaluation index with the comprehensive threshold value to optimize and adjust the overall balancing control plan.

[0037] Furthermore, the specific steps for real-time acquisition and processing of battery pack operating status data are as follows: collecting raw battery pack operating data through an IoT sensor array; filtering and normalizing the raw battery pack operating data to obtain standardized battery pack operating status data, which includes battery cell voltage, current change rate, temperature distribution, and charge and discharge status.

[0038] Furthermore, the specific steps for obtaining the battery pack balancing response speed evaluation index are as follows: extracting the voltage fluctuation period at a preset detection point through a time series analysis tool; obtaining the switch switching delay at a preset detection point through a power switch response test device; measuring the transient current response time at a preset detection point through a load simulator; and monitoring the temperature gradient change in a preset detection section through a thermal imager. The battery pack balancing response speed evaluation index is calculated based on the above data analysis, and its formula is:

[0039] ;

[0040] in, represents the equilibrium response speed evaluation index, Indicates the voltage fluctuation period, Indicates the switching delay, represents the transient current response time, Indicates temperature gradient change.

[0041] Furthermore, the specific process for obtaining the energy transfer efficiency evaluation index is as follows: obtain the energy loss rate at the preset detection point through the energy transfer efficiency tester; measure the actual transfer current at the preset detection point through the current sensor; measure the actual transfer voltage at the preset detection point through the voltage sensor; measure the heat energy loss in the preset detection section through the thermocouple; and calculate the energy transfer efficiency evaluation index based on the above data analysis. The formula is:

[0042] ;

[0043] in, represents the energy transfer efficiency evaluation index, represents the actual transferred current, represents the actual transfer voltage, represents the energy loss rate, Indicates heat loss.

[0044] Furthermore, the specific steps of comprehensively analyzing and obtaining the comprehensive evaluation index of battery pack balancing performance are as follows: calculating the balancing control cost function under the preset detection point through a multi-objective optimization algorithm; and obtaining the comprehensive evaluation index of battery pack balancing performance through comprehensive analysis and calculation of the battery pack balancing response speed evaluation index, energy transfer efficiency evaluation index and balancing control cost function. The formula is:

[0045] ;

[0046] in, Represents the comprehensive evaluation index of battery pack balancing performance, represents the equilibrium response speed evaluation index, represents the energy transfer efficiency evaluation index, represents the equilibrium control cost function, 、 、 is the weight coefficient.

[0047] Furthermore, the specific steps for optimizing and adjusting the balancing control strategy are as follows: if the battery pack balancing response speed evaluation index is lower than or equal to the first threshold, the current balancing control strategy remains unchanged; if the battery pack balancing response speed evaluation index is higher than the first threshold, an adaptive PID control algorithm is introduced to dynamically adjust the balancing control parameters, and the power switch drive circuit is optimized to reduce switching delay.

[0048] Furthermore, the specific steps for optimizing and adjusting the energy transfer path are as follows: if the energy transfer efficiency evaluation index is higher than or equal to the second threshold, there is no need to adjust the energy transfer path; if the energy transfer efficiency evaluation index is lower than the second threshold, the energy transfer path is replanned, a multi-path parallel transmission mode is adopted, and superconducting materials are used to reduce transmission losses.

[0049] Furthermore, the specific steps of comparing and analyzing the comprehensive evaluation index of the battery pack balancing performance with the comprehensive threshold are as follows: extracting the comprehensive threshold of the comprehensive evaluation index of the battery pack balancing performance in the Internet of Things cloud platform, and comparing and analyzing the comprehensive evaluation index of the battery pack balancing performance with the comprehensive threshold.

[0050] Furthermore, the specific steps for optimizing and adjusting the overall balancing control scheme are as follows: if the comprehensive evaluation index of the battery pack balancing performance is higher than or equal to the comprehensive threshold, there is no need to send a warning message; if the comprehensive evaluation index of the battery pack balancing performance is lower than the comprehensive threshold, a warning message is sent through the vehicle communication system.

[0051] Furthermore, optimizing and adjusting the overall balancing control plan also includes: if there is no need to send an early warning message, the current balancing control plan is maintained; if an early warning message is required, edge computing devices are used to quickly process abnormal data, combined with blockchain technology to ensure the authenticity and non-tamperability of the early warning information, and deep learning models are used to predict the future operating trends of the battery pack and formulate response strategies in advance.

[0052] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0053] In practical applications, the system first acquires battery pack operating status data through a data acquisition module. This data acquisition module consists of an IoT sensor array, including voltage sensors, current sensors, temperature sensors, and a charge-discharge status monitor. In actual operation, the sensor array collects raw battery pack operating data in real time, such as the voltage, current change rate, temperature distribution, and charge-discharge status of each battery cell. To ensure data accuracy and consistency, the collected raw data undergoes filtering and normalization to eliminate noise interference and standardize the data format. Specifically, filtering uses a low-pass digital filtering algorithm to remove high-frequency noise; normalization uses a linear transformation to map the data to a uniform interval, ultimately obtaining standardized battery pack operating status data, laying the foundation for subsequent analysis.

[0054] Specifically, the implementation of the data acquisition module:

[0055] The data acquisition module uses a distributed IoT sensor array, and the specific configuration is as follows:

[0056] Voltage sensor: A high-precision Hall voltage sensor with an accuracy of ±0.5mV is selected. The sampling frequency is set to 1kHz, and the voltage data of each battery cell is collected every 10ms to ensure that subtle changes in voltage can be captured.

[0057] Current sensor: A closed-loop Hall effect current sensor with a bandwidth of 50kHz is used, with a measurement range of -500A to +500A, which can accurately monitor current fluctuations during charging and discharging.

[0058] Temperature sensor: An NTC thermistor (negative temperature coefficient thermistor) is placed every 5 cm inside the battery pack, with a temperature measurement range of -40°C to 125°C and an accuracy of ±0.3°C, to monitor temperature distribution in real time.

[0059] Charge and discharge status monitor: Communicates with the vehicle's BMS (battery management system) through the CAN bus to obtain SOC (State of Charge) and SOH (State of Health) data, with an update frequency of 1Hz.

[0060] The data processing flow adopts a two-level processing mechanism:

[0061] 1. Primary filtering: A Butterworth low-pass filter with a cutoff frequency of 10Hz is applied to the raw data to effectively remove high-frequency noise generated by the motor. For example, during rapid acceleration, the high-frequency interference generated by the motor can cause glitches in the voltage data. After filtering, the data curve becomes smoother, better reflecting the actual battery voltage status.

[0062] 2. Normalization: Data of different dimensions, such as voltage (0-5V), current (-500A-+500A), and temperature (-40°C-125°C), are mapped to the [0,1] interval through linear transformation. The calculation formula is:

[0063] ;

[0064] Among them, x is the original data, and The standardized data is uploaded to the IoT cloud platform in real time via the 5G module, with transmission delays kept within 50ms.

[0065] The dynamic balancing analysis module is the core part of the system, responsible for in-depth analysis of standardized data and calculating the battery pack balancing response speed evaluation index and energy transfer efficiency evaluation index. The calculation of the balancing response speed evaluation index requires the support of multiple key parameters. First, the voltage fluctuation period under the preset detection point is extracted through the time series analysis tool. This process uses the sliding window technology to analyze the time characteristics of the voltage signal to determine the periodic law of voltage fluctuation. Secondly, the power switch response test equipment is used to measure the switch switching delay, which is accomplished by simulating the switching action under different load conditions. Next, the transient current response time is measured by the load simulator. This step aims to evaluate the dynamic performance of the battery pack under rapidly changing load conditions. Finally, the thermal imager is used to monitor the temperature gradient changes under the preset detection section to ensure that the battery pack will not affect the balancing performance due to local overheating under complex working conditions. After the above parameters are integrated, the formula Calculate the equilibrium response speed evaluation index, where 、 and Represent the voltage fluctuation period, switch switching delay and transient current response time respectively. The physical significance of this formula is that it quantifies the impact of various factors on the response speed through weighted summation, thereby comprehensively reflecting the balancing performance of the battery pack under dynamic conditions.

[0066] Specifically, the calculation implementation of the dynamic equilibrium analysis module:

[0067] (1) Calculation of the equilibrium response speed evaluation index (R)

[0068] 1. Parameter acquisition

[0069] Voltage fluctuation period ( A sliding window algorithm (with a window size of 100 sampling points) is used to perform a Fourier transform on the voltage data to extract the period value corresponding to the main frequency. When the vehicle is climbing a hill, the battery pack discharge current increases, and the voltage fluctuation period is typically shortened to 0.5-2 seconds.

[0070] Switching delay ( ): The time difference between the gate drive signal and the drain-source voltage change of the power MOSFET (metal-oxide semiconductor field-effect transistor) is measured using an oscilloscope. The measured value is approximately 50~200ns.

[0071] Transient current response time ( ): Use a load simulator to simulate a current change rate of 100A / s and record the time required for the output current of the balancing circuit to reach 90% of the stable value from 0. The typical value is 1~5ms.

[0072] Temperature gradient change ( ): Use an infrared thermal imager (resolution 640×512) to capture the surface temperature field of the battery pack and calculate the maximum temperature difference between adjacent battery cells. Under normal operating conditions, it should be less than 5°C.

[0073] 2. Index calculation example

[0074] when , , hour:

[0075] .

[0076] (II) Calculation of energy transfer efficiency evaluation index (E)

[0077] 1. Parameter acquisition

[0078] Energy loss rate ( ): The difference between the input power and output power of the balancing circuit is measured by a power analyzer, in W.

[0079] Actual transfer current ( ) and voltage ( ): A high-precision multimeter (accuracy 0.1%) is used to monitor the current and voltage values ​​of the energy transfer circuit in real time.

[0080] Heat loss ( ): The temperature change of the balancing resistor is measured by a thermocouple array, and the heat loss is calculated based on the specific heat capacity in J / s.

[0081] 2. Index calculation example

[0082] when , , , hour:

[0083] .

[0084] At the same time, the dynamic equilibrium analysis module is also responsible for calculating the energy transfer efficiency evaluation index. This process involves the collaborative work of multiple test instruments, including energy transfer efficiency testers, current sensors, voltage sensors, and thermocouples. The energy transfer efficiency tester is used to obtain the energy loss rate under the preset detection point, which is a key indicator for evaluating energy loss during the energy transfer process. The current sensor and voltage sensor measure the actual transfer current and the actual transfer voltage respectively. These data directly reflect the actual effect of the energy transfer. In addition, thermocouples are used to measure the thermal energy loss under the preset detection section in order to comprehensively evaluate the thermal effect during the energy transfer process. Based on the above parameters, the formula Calculate the energy transfer efficiency evaluation index, where and represent the actual transfer current and actual transfer voltage respectively, and The ratio of the formula reflects the efficiency of the energy transfer process and provides an important basis for subsequent optimization.

[0085] The task of the comprehensive decision module is to conduct a comprehensive analysis of the evaluation index output by the dynamic balancing analysis module and calculate the comprehensive evaluation index of the battery pack balancing performance. This process uses a multi-objective optimization algorithm, combined with the balancing response speed evaluation index, energy transfer efficiency evaluation index and balancing control cost function, through the formula Calculate the comprehensive evaluation index. 、 and are weight coefficients, which respectively represent the importance of response speed, energy efficiency and control cost. Balanced control cost function It is a comprehensive indicator to measure resource consumption during the balancing control process, including hardware energy consumption, computing resource usage, and battery cycle life loss. The formula is: ,in: To balance the real-time energy consumption of the control circuit (unit: Wh), it is calculated by multiplying and integrating the current sensor and the voltage sensor; The computing resource utilization rate of the control algorithm (unit: %) is obtained by monitoring the CPU utilization of the edge computing device; is the battery cycle life loss coefficient (unit: ), derived based on the fitting model of charge and discharge depth (DOD) and cycle number; 、 and is the weight coefficient (the sum is 1), which is taken as =0.4, =0.4, =0.2. The weight coefficient setting needs to be adjusted according to the specific application scenario. For example, it can be appropriately increased in high performance demand scenarios. The value of , and should be lowered in cost-sensitive scenarios Through linear combination, the formula realizes multi-objective optimization and can fully reflect the overall balancing performance of the battery pack.

[0086] Specifically, the algorithm implementation of the comprehensive decision-making module:

[0087] The comprehensive decision module adopts the improved NSGA-II (non-dominated sorting genetic algorithm II, whose input is the equilibrium control cost function F, response speed evaluation index R, energy transfer efficiency evaluation index E, and the output is the optimized weight coefficient 、 、 , used to calculate the comprehensive evaluation index C) as a multi-objective optimization algorithm, the specific process is as follows:

[0088] Initialize the population: Set the population size to 100 and the chromosome length to 3 (corresponding to 、 、 Three weight coefficients), all in the range of [0,1] and satisfy .

[0089] 2. Fitness calculation: Calculate the fitness value of each individual with the optimization goal of balancing response speed, energy efficiency and control cost.

[0090] 3. Selection and crossover: The parent individuals are selected using the tournament selection method, the crossover probability is set to 0.8, and the offspring are generated by simulating binary crossover.

[0091] 4. Mutation operation: The mutation probability is set to 0.01, and the chromosome genes are randomly perturbed.

[0092] 5. Iteration termination: When the number of iterations reaches 500 or the population converges, the optimal weight coefficient is output.

[0093] In the urban commuting scenario, the typical weight obtained after optimization is , , At this time, if R=0.05, E=5.33, F=2 (control cost function value), then the comprehensive evaluation index is:

[0094] C=0.4×0.05+0.4×5.33-0.2×2=0.02+2.132-0.4=1.752.

[0095] The optimization and adjustment module is a crucial component of the system, responsible for optimizing the balancing control strategy, energy transfer path, and overall balancing control scheme based on comprehensive evaluation results. Regarding balancing control strategy optimization, if the balancing response speed evaluation index is lower than or equal to the first threshold, the current balancing control strategy remains unchanged. If it is higher than the first threshold, an adaptive PID control algorithm is introduced to dynamically adjust the balancing control parameters and optimize the power switch drive circuit to reduce switching delay. By online adjusting the proportional, integral, and differential parameters, the adaptive PID control algorithm can significantly improve the system's dynamic response capability, making it particularly suitable for complex operating conditions such as rapid acceleration and energy recovery. Regarding energy transfer path optimization, if the energy transfer efficiency evaluation index is higher than or equal to the second threshold, no adjustment of the energy transfer path is required. If it is lower than the second threshold, the energy transfer path is replanned, employing a multi-path parallel transmission mode while utilizing superconducting materials to reduce transmission losses. The multi-path parallel transmission mode effectively reduces energy losses along a single path by dispersing the energy flow path, while the use of superconducting materials further improves energy transfer efficiency.

[0096] Specifically, the execution mechanism of the optimization and adjustment module:

[0097] (1) Balance control strategy optimization

[0098] The first threshold is set to 0.03 (obtained through 1000 actual vehicle tests). When R=0.05>0.03, adaptive PID control is started:

[0099] The initial value of the proportional coefficient (Kp) is set to 0.5 and is dynamically adjusted according to the voltage deviation. The larger the deviation, the larger the Kp.

[0100] The integral coefficient (Ki) is set to 0.1 to eliminate steady-state errors.

[0101] The differential coefficient (Kd) is set to 0.2 to suppress overshoot.

[0102] Power switch drive circuit optimization: Using a totem-pole drive structure, the gate resistance is reduced from 10Ω to 5Ω, shortening the switch switching delay from 100ns to 60ns.

[0103] (2) Energy transfer path optimization

[0104] The second threshold is set to 4.0. When E=3.5<4.0, path optimization is performed:

[0105] Multi-path parallel transmission: The original single path is divided into three parallel branches, each branch is equipped with an independent MOSFET switch, and energy diversion is achieved through time division multiplexing.

[0106] Application of superconducting materials: YBCO (yttrium barium copper oxide) high-temperature superconducting tape is used to make transmission wires. In a 77K liquid nitrogen environment, the resistance approaches zero, which can reduce transmission loss by more than 30%.

[0107] (III) Optimization of overall balance control scheme

[0108] The comprehensive threshold is dynamically updated through big data analysis on the cloud platform. The current default value is 1.5. When C=1.752>1.5, the current solution is maintained; when C<1.5:

[0109] Edge computing processing: Using NVIDIA Jetson Xavier NX edge computing devices to perform real-time analysis of abnormal data, with a response time of less than 100ms.

[0110] Blockchain evidence storage: Based on the Hyperledger Fabric consortium chain, the warning information is written into the block, and the block generation interval is 10s to ensure that the data cannot be tampered with.

[0111] Deep learning prediction: Using the LSTM (Long Short-Term Memory) model, the system takes the past 24 hours of battery operating data (voltage, temperature, and current) as input and outputs a prediction of voltage and temperature trends over the next hour, with an accuracy rate of 92%.

[0112] To optimize the overall balancing control scheme, the system extracts the comprehensive threshold of the battery pack's balancing performance evaluation index through the IoT cloud platform and compares it with the actual calculated value. If the comprehensive evaluation index is above or equal to the comprehensive threshold, no warning message is issued; if it is below the comprehensive threshold, a warning message is sent via the vehicle communication system. This warning information transmission mechanism combines edge computing devices and blockchain technology. The blockchain technology takes the warning information as input and triggers it when C falls below the comprehensive threshold. The output is an immutable distributed evidence record, which is synchronized with the vehicle communication system in real time to ensure the authenticity and immutability of the information. The edge computing device can quickly process abnormal data and promptly identify potential problems. The blockchain technology records warning information in a distributed ledger to prevent data tampering or falsification. In addition, the system uses a deep learning model to predict future battery pack operating trends and formulate response strategies in advance. The deep learning model is trained based on historical operating data and can accurately predict battery pack performance changes under different operating conditions, providing users with reliable maintenance recommendations.

[0113] Specifically, actual application scenarios verify:

[0114] In a real-car test of a pure electric SUV, the system performed as follows:

[0115] Rapid acceleration conditions (0-100km / h acceleration time 8s): The balancing response time is shortened from 50ms in the traditional single-capacitor solution to 15ms, and the battery pack output power is increased by 12%.

[0116] Braking energy recovery working conditions: The energy recovery rate is increased from 65% to 82%, and the single-charge range is increased by 15km.

[0117] Continuous driving for 2 hours in a high temperature environment (40°C): The maximum temperature difference of the battery pack is controlled within 3°C, and there is no local overheating.

[0118] In practical application scenarios, this system can be widely used in battery management systems for new energy vehicles. For example, during urban rush hour, frequent vehicle starts and stops can cause significant battery pack voltage fluctuations. In these situations, the system can effectively mitigate voltage differences between battery cells through a fast-response balancing control strategy, preventing localized overcharging or over-discharging. During high-speed driving, the system optimizes energy transfer paths to maximize battery pack energy utilization, thereby extending driving range. Furthermore, in extreme weather conditions, the system monitors battery pack temperature distribution in real time and dynamically adjusts balancing control parameters to prevent performance degradation caused by excessively high or low temperatures.

[0119] In summary, the IoT-based new energy vehicle battery pack balancing control system provided by the embodiment of the present invention collects battery pack operating status data in real time, analyzes and obtains the balancing response speed evaluation index and energy transfer efficiency evaluation index, and comprehensively optimizes and adjusts the balancing control strategy, thereby significantly improving the real-time performance and adaptability of the system. At the same time, by introducing an adaptive PID control algorithm, a multi-path parallel transmission mode, and edge computing and blockchain technology, it not only solves the problem of insufficient response speed of the single capacitor balancing method in the prior art under complex working conditions, but also realizes the intelligent and efficient battery pack balancing control, providing a strong guarantee for the safety and economy of new energy vehicles.

[0120] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0121] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A new energy vehicle battery pack balancing control system based on the Internet of Things, characterized in that: It includes data acquisition module, dynamic equilibrium analysis module, comprehensive decision-making module and optimization adjustment module, among which: The data acquisition module is used to acquire and process battery pack operating status data in real time; The dynamic balancing analysis module is used to analyze the battery pack operating status data to obtain the battery pack balancing response speed evaluation index and the energy transfer efficiency evaluation index; The comprehensive decision module is used to comprehensively analyze and obtain a comprehensive evaluation index of battery pack balancing performance; The optimization and adjustment module is used to compare and analyze the battery pack balancing response speed evaluation index with the first threshold value to optimize and adjust the balancing control strategy; compare and analyze the energy transfer efficiency evaluation index with the second threshold value to optimize and adjust the energy transfer path; compare and analyze the battery pack balancing performance comprehensive evaluation index with the comprehensive threshold value to optimize and adjust the overall balancing control solution; The specific steps of the dynamic balancing analysis module to obtain the battery pack balancing response speed evaluation index are as follows: extracting the voltage fluctuation period at a preset detection point through a time series analysis tool; obtaining the switch switching delay at a preset detection point through a power switch response test device; measuring the transient current response time at a preset detection point through a load simulator; and monitoring the temperature gradient change in a preset detection section through a thermal imager. The battery pack balancing response speed evaluation index is calculated based on the above data analysis, and its formula is: ； in, represents the equilibrium response speed evaluation index, Indicates the voltage fluctuation period, Indicates the switching delay, represents the transient current response time, Indicates temperature gradient change.

2. The Internet of Things-based new energy vehicle battery pack balancing control system according to claim 1, characterized in that: The specific steps of real-time acquisition and processing of battery pack operating status data are as follows: collecting raw battery pack operating data through an Internet of Things sensor array; filtering and normalizing the raw battery pack operating data to obtain standardized battery pack operating status data, wherein the standardized battery pack operating status data includes battery cell voltage, current change rate, temperature distribution, and charge and discharge status.

3. The new energy vehicle battery pack balancing control system based on the Internet of Things as claimed in claim 1, characterized in that: The specific process of obtaining the energy transfer efficiency evaluation index by the dynamic equilibrium analysis module is as follows: obtaining the energy loss rate at a preset detection point through an energy transfer efficiency tester; measuring the actual transfer current at the preset detection point through a current sensor; measuring the actual transfer voltage at the preset detection point through a voltage sensor; and measuring the heat energy loss in a preset detection section through a thermocouple; and obtaining the energy transfer efficiency evaluation index based on the above data analysis and calculation, and its formula is: ； in, represents the energy transfer efficiency evaluation index, represents the actual transferred current, represents the actual transfer voltage, represents the energy loss rate, Indicates heat loss.

4. The new energy vehicle battery pack balancing control system based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of comprehensively analyzing and obtaining the comprehensive evaluation index of battery pack balancing performance are as follows: calculating the balancing control cost function at a preset detection point through a multi-objective optimization algorithm; and obtaining the comprehensive evaluation index of battery pack balancing performance through comprehensive analysis and calculation of the battery pack balancing response speed evaluation index, the energy transfer efficiency evaluation index, and the balancing control cost function. The formula is: ； in, Represents the comprehensive evaluation index of battery pack balancing performance, represents the equilibrium response speed evaluation index, represents the energy transfer efficiency evaluation index, represents the equilibrium control cost function, 、 、 is the weight coefficient.

5. The new energy vehicle battery pack balancing control system based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of optimizing and adjusting the balancing control strategy are as follows: if the battery pack balancing response speed evaluation index is lower than or equal to a first threshold, the current balancing control strategy is maintained unchanged; if the battery pack balancing response speed evaluation index is higher than the first threshold, an adaptive PID control algorithm is introduced to dynamically adjust the balancing control parameters, and the power switch drive circuit is optimized to reduce switching delay.

6. The new energy vehicle battery pack balancing control system based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of optimizing and adjusting the energy transfer path are as follows: if the energy transfer efficiency evaluation index is higher than or equal to the second threshold, there is no need to adjust the energy transfer path; if the energy transfer efficiency evaluation index is lower than the second threshold, the energy transfer path is replanned, a multi-path parallel transmission mode is adopted, and superconducting materials are used to reduce transmission losses.

7. The Internet of Things-based new energy vehicle battery pack balancing control system according to claim 1, characterized in that: The specific steps of comparing and analyzing the battery pack balancing performance comprehensive evaluation index with the comprehensive threshold are: extracting the comprehensive threshold of the battery pack balancing performance comprehensive evaluation index in the Internet of Things cloud platform, and comparing and analyzing the battery pack balancing performance comprehensive evaluation index with the comprehensive threshold.

8. The Internet of Things-based new energy vehicle battery pack balancing control system according to claim 1, characterized in that: The specific steps of optimizing and adjusting the overall balancing control scheme are as follows: if the comprehensive evaluation index of the battery pack balancing performance is higher than or equal to the comprehensive threshold, no warning information needs to be sent; if the comprehensive evaluation index of the battery pack balancing performance is lower than the comprehensive threshold, a warning information is sent through the vehicle communication system.

9. The Internet of Things-based new energy vehicle battery pack balancing control system according to claim 1, characterized in that: The optimization and adjustment of the overall balancing control plan also includes: if there is no need to send an early warning message, the current balancing control plan is maintained; if an early warning message is required, the abnormal data is quickly processed using edge computing devices, and blockchain technology is combined to ensure the authenticity and non-tamperability of the early warning information. At the same time, a deep learning model is used to predict the future operating trends of the battery pack and formulate a response strategy in advance.

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