Power battery state monitoring method and system based on real-time monitoring

Abstract

The application discloses a power battery state monitoring method and system based on real-time monitoring, and relates to the field of electric vehicle power devices.The method comprises the following steps: acquiring real-time battery information; performing normalized weighted analysis to obtain a real-time battery state index; obtaining a target real-time residual power when the state limit value is met; issuing a charging and discharging instruction when the power limit value is not met; introducing a temperature gradient feedback adjustment function to obtain a target temperature gradient; reading a preset temperature threshold value, performing compensation analysis, and obtaining a target compensation temperature gradient; and performing charging and discharging thermal management by a thermal management integrated module.The technical problem that, in the traditional battery state monitoring method, the monitoring precision is insufficient, the state information of the battery cannot be completely reflected, and errors exist in the detection results is solved, the monitoring precision and reliability are improved through real-time monitoring analysis and prediction of the battery, the technical effect of improving the energy utilization efficiency is achieved through accurate evaluation of the health state and residual life and provision of charging and discharging strategies.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of electric vehicle power devices, in particular to a power battery state monitoring method and system based on real-time monitoring. BACKGROUND

[0002] The emergence and popularity of new energy vehicles have made important contributions to environmental protection and sustainable development. As an important part of new energy vehicles, battery state monitoring technology plays a crucial role in ensuring the safe operation of vehicles, prolonging battery life, and improving energy utilization efficiency. New energy vehicle battery state monitoring methods mainly involve real-time and accurate monitoring and analysis of battery electrochemical parameters and state. This includes the detection of key parameters such as battery internal resistance, capacity, temperature, and the prediction and evaluation of battery health status, remaining life based on these parameters. Traditional battery state monitoring methods, such as battery discharge testing and voltage testing, can test the load capacity and total capacity of the battery, but cannot fully reflect the internal state and electrochemical parameter information of the battery, resulting in errors in the detection results. Some existing battery state monitoring technologies have certain deficiencies in accuracy, making it difficult to achieve accurate evaluation and prediction of battery performance. Some battery state monitoring systems have deficiencies in real-time performance and cannot reflect changes in battery state in a timely manner, leading to safety hazards.

[0003] In summary, the traditional battery state monitoring method has the technical problem of insufficient monitoring accuracy, which cannot fully reflect the state information of the battery, resulting in errors in the detection results. SUMMARY

[0004] Therefore, it is necessary to provide a power battery state monitoring method and system based on real-time monitoring to solve the technical problem of insufficient monitoring accuracy in the traditional battery state monitoring method, which cannot fully reflect the state information of the battery, resulting in errors in the detection results. Through real-time monitoring, analysis and prediction of the battery, the monitoring accuracy and reliability are improved, the health status and remaining life are accurately evaluated, the charging and discharging strategy is provided, and the technical effect of improving energy utilization efficiency is achieved.

[0005] In a first aspect, a real-time monitoring-based power battery state monitoring method is provided, which comprises: obtaining real-time battery information, which is dynamically monitored by a sensor integrated module arranged in a wire harness of a target battery; performing normalized weighted analysis on real-time battery characteristic data extracted from the real-time battery information based on predetermined battery characteristics to obtain a real-time battery state index; when the real-time battery state index meets a state limit value, adjusting a real-time residual capacity in the real-time battery information by using a battery performance index obtained by analyzing historical battery use records to obtain a target real-time residual capacity; when the target real-time residual capacity does not meet a capacity limit value, issuing a charging and discharging instruction, and performing multi-dimensional charging and discharging condition detection of the target battery based on the charging and discharging instruction to obtain a target charging and discharging condition index; introducing a temperature gradient feedback adjustment function, and adjusting a preset temperature gradient in combination with the battery performance index and the target charging and discharging condition index to obtain a target temperature gradient; reading a preset temperature threshold, and performing compensation analysis on the target temperature gradient according to the preset temperature threshold to obtain a target compensation temperature gradient; and a thermal management integrated module performs charging and discharging thermal management of the target battery of a new energy vehicle based on the target compensation temperature gradient.

[0006] In a second aspect, a real-time monitoring-based power battery state monitoring system is provided, which comprises: a real-time battery information acquisition module, configured to acquire real-time battery information, which is dynamically monitored by a sensor integrated module arranged in a wire harness of a target battery; a normalized weighted analysis module, configured to perform normalized weighted analysis on real-time battery characteristic data extracted from the real-time battery information based on a predetermined battery characteristic, to obtain a real-time battery state index; a real-time residual capacity obtaining module, configured to, when the real-time battery state index meets a state limit value, adjust a real-time residual capacity in the real-time battery information based on a battery performance index obtained by analyzing historical battery use records, to obtain a target real-time residual capacity; a charging and discharging instruction issuing module, configured to, when the target real-time residual capacity does not meet a capacity limit value, issue a charging and discharging instruction, and perform multi-dimensional charging and discharging condition detection on the target battery based on the charging and discharging instruction to obtain a target charging and discharging condition index; a preset temperature gradient adjusting module, configured to introduce a temperature gradient feedback adjusting function, and adjust a preset temperature gradient in combination with the battery performance index and the target charging and discharging condition index, to obtain a target temperature gradient; a compensation analysis module, configured to read a preset temperature threshold, and perform compensation analysis on the target temperature gradient according to the preset temperature threshold, to obtain a target compensation temperature gradient; and a charging and discharging thermal management performing module, configured to perform charging and discharging thermal management on the target battery of a new energy vehicle by a thermal management integrated module based on the target compensation temperature gradient.

[0007] The real-time monitoring-based power battery state monitoring method and system solve the technical problem of insufficient monitoring accuracy in the conventional battery state monitoring method, which cannot completely reflect the state information of the battery, resulting in errors in the detection result, improve the monitoring accuracy and reliability through real-time monitoring analysis and prediction of the battery, provide a charging and discharging strategy through accurate evaluation of the health state and residual life, and improve the energy utilization efficiency.

[0008] The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented in accordance with the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 FIG. 1 is a flowchart of a real-time monitoring-based power battery state monitoring method according to an embodiment of the present application.

[0010] Figure 2 A flowchart for obtaining a target charge-discharge condition index for a power battery state monitoring method based on real-time monitoring in an embodiment.

[0011] Figure 3 A structural block diagram of a power battery state monitoring system based on real-time monitoring in an embodiment.

[0012] Legend: real-time battery information acquisition module 11, normalized weighted analysis module 12, real-time remaining capacity obtaining module 13, charge-discharge instruction issuing module 14, preset temperature gradient adjusting module 15, compensation analysis module 16, charge-discharge thermal management performing module 17. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0014] Embodiment one, as shown in the present application provides a power battery state monitoring method based on real-time monitoring, which comprises: Figure 1

[0015] Obtaining real-time battery information, which is dynamically monitored by a sensor integrated module arranged in a wire harness of a target battery.

[0016] The power battery is a battery system for new energy vehicles, usually a lithium battery or other high-performance battery, which provides a power source and is a storage battery that provides energy for electric vehicles. It is a landmark component that distinguishes new energy vehicles from traditional fuel vehicles and is directly related to the safety of the driving range and driving safety. Battery state monitoring refers to real-time and accurate observation and evaluation of the working state of the new energy vehicle battery to ensure that the battery works within a safe and reliable range. Through comprehensive monitoring and analysis of the quality and performance of the new energy vehicle battery, the accuracy and reliability of the detection are improved, the detection efficiency is improved and the cost is reduced, real-time dynamic monitoring is achieved, and the data analysis and mining capabilities are improved.

[0017] ​Battery state monitoring refers to obtaining various performance indicators and state information of the battery in the running process in real time through specific technical means and methods to ensure the safe and efficient operation of the battery. The sensor integrated module is a device that integrates multiple sensors and can sense various physical and chemical parameters of the battery in real time. In battery state monitoring, the sensor integrated module is usually arranged in the wire harness of the battery to directly obtain various data of the battery in operation. The sensor integrated module includes temperature sensors, voltage sensors, current sensors, and quantum sensors. Each sensor in the sensor integrated module collects parameters such as voltage, current, and temperature of the battery in real time. Dynamic monitoring refers to real-time and continuous monitoring of the monitored object through modern technologies such as multi-system, multi-sensor, automatic measurement, real-time transmission, rapid processing and computer intelligent analysis and evaluation, and timely giving correct prediction and early warning through analysis and evaluation. In this application, dynamic monitoring specifically refers to collecting key parameters such as voltage, current, and temperature of the battery in real time and continuously through sensors or other monitoring devices arranged on the battery, and rapidly processing and analyzing the data to evaluate the health status, performance degradation, and remaining life of the battery, so as to timely discover abnormal conditions or potential risks of the battery and provide important support for the safe operation and effective management of the battery. Through dynamic monitoring by the sensor integrated module arranged in the wire harness of the target battery, various performance indicators and state information of the battery can be obtained in real time, providing strong guarantee for the safe and efficient operation of the battery.

[0018] The sensor integrated module includes temperature sensors, voltage sensors, current sensors, and quantum sensors; the real-time battery temperature of the target battery is monitored by the temperature sensors, the real-time battery voltage of the target battery is monitored by the voltage sensors, the real-time battery current of the target battery is monitored by the current sensors, and the real-time remaining power of the target battery is monitored by the quantum sensors; the real-time battery temperature, the real-time battery voltage, the real-time battery current, and the real-time remaining power together constitute the real-time battery information.

[0019] The sensor integration module includes a temperature sensor, a voltage sensor, a current sensor, and a quantum sensor. The temperature sensor is used to monitor the real-time temperature of the target battery. Battery temperature is an important indicator for evaluating battery performance and safety. Both excessively high or low temperatures affect the performance and lifespan of the battery, and even cause safety accidents. Therefore, real-time monitoring of battery temperature is crucial for ensuring the safe operation of the battery. The voltage sensor is used to monitor the real-time voltage of the target battery. Battery voltage is an important indicator of battery performance and state of charge. By monitoring the battery voltage in real time, the state of charge, discharge efficiency, and whether there is overcharging or overdischarging can be understood. The current sensor is used to monitor the real-time current of the target battery. Battery current reflects the energy flow during the charging and discharging process of the battery. By monitoring the battery current in real time, the charging and discharging performance of the battery can be evaluated, abnormal conditions such as short circuits or overloads can be detected, and charging and discharging strategies can be optimized accordingly. The quantum sensor is used to monitor the real-time remaining capacity of the target battery. Remaining capacity is one of the most important indicators for electric vehicle users, directly affecting the range and user experience of electric vehicles. By using a high-precision quantum sensor to monitor the remaining capacity of the battery in real time, more accurate range estimates and charging prompts can be provided to users. Integrating the temperature sensor, voltage sensor, current sensor, and quantum sensor into one module obtains the sensor integration module, which can collect key parameters of the target battery in real time and continuously, and transmit these parameters to the battery management system for processing and analysis. Real-time battery information, including real-time battery temperature, real-time battery voltage, real-time battery current, and real-time remaining capacity, is important for evaluating the health status of the battery, predicting battery performance degradation, optimizing charging and discharging strategies, and ensuring the safe operation of the battery.

[0020] The real-time battery feature data extracted from the real-time battery information based on the predetermined battery features is normalized and weighted to obtain a real-time battery state index.

[0021] The predetermined battery features include temperature, voltage, and current.

[0022] The predetermined battery characteristics are set by the staff, such as temperature, voltage, current, remaining power, etc. In this application, the predetermined battery characteristics are determined as temperature, voltage and current, and the corresponding feature data is extracted from the real-time battery information, which reflects the specific state of the battery at a certain time, denoted as real-time battery feature data. Due to the different dimensions and value ranges of different feature data, such as temperature in Celsius and voltage in volts, direct comparison or weighted analysis will lead to distorted results. Therefore, it is necessary to normalize the real-time battery feature data to convert it into dimensionless values for comparison and analysis on the same scale. After normalization, each feature data is weighted, and the weight reflects the importance or influence of the feature on the battery state. The setting of the weight can be determined according to experience, expert opinion or machine learning algorithm. The process of weighted analysis is to multiply the normalized feature data with the corresponding weight, and add the weighted results of all feature data to get a comprehensive score or index. This score or index is the real-time battery state index, which reflects the overall state of the battery at a certain time. The real-time battery state index obtained by normalization and weighted analysis is a quantitative indicator that comprehensively reflects the real-time state of the battery, which can provide strong support for the health state evaluation, charging and discharging strategy optimization, and early warning and fault diagnosis of the battery.

[0023] When the real-time battery state index meets the state limit value, the real-time remaining power in the real-time battery information is adjusted by calling the battery performance index obtained by analyzing the historical battery usage records to obtain the target real-time remaining power.

[0024] The real-time battery state index is a quantitative indicator that comprehensively reflects the real-time state of the battery. It is calculated based on real-time battery information such as temperature, voltage, current, etc., and predetermined battery characteristics. When the real-time battery state index reaches or exceeds a certain preset state limit value, the system considers that the state of the battery has changed and is no longer suitable for direct use of the original real-time remaining capacity data. The historical battery usage record contains various data of the battery in the past usage process, such as the number of charging times, the depth of discharging, the temperature range, the performance degradation, etc. Through the analysis of these data, the battery performance index can be obtained, which reflects the performance change and health condition of the battery in the long-term use process. When the real-time battery state index reaches or exceeds the state limit value, the system will adjust the real-time remaining capacity by using the battery performance index obtained by analyzing the historical battery usage record. The real-time remaining capacity is adjusted because there is virtual electricity, and the higher the performance, the higher the virtual electricity ratio. The purpose is to more accurately reflect the actual available capacity of the battery in the current state. If the performance of the battery has decreased, the actual available capacity will also decrease accordingly. After the above adjustment, the system will obtain a new, corrected real-time remaining capacity, i.e. the target real-time remaining capacity. This capacity value more accurately reflects the actual available capacity of the battery in the current state, and can provide more reliable suggestions for the user's endurance and charging.

[0025] extracting a first historical usage record in the historical battery usage record, the first historical usage record including a first historical charging power, a first historical overcharge rate and a first historical overdischarge rate; taking the first historical charging power, the first historical overcharge rate and the first historical overdischarge rate as input information of a performance influence prediction model to obtain a first predicted influence degree; reading an initial performance index of the target battery, and adjusting the initial performance index based on a comprehensive performance influence degree obtained by adding the first predicted influence degree to obtain the battery performance index; wherein the performance influence prediction model is an intelligent model obtained by supervised learning of a training data set based on neural network principles, each data set in the training data set including charging power, overcharge rate, overdischarge rate and performance influence degree, and the performance influence degree is the ratio of the post-charging performance to the pre-charging performance of the battery.

[0026] A historical usage record is randomly selected from the historical battery usage records, denoted as a first historical usage record, which includes a first historical charging power, a first historical overcharge rate, and a first historical overdischarge rate. The first historical battery receives an electrical power during the charging process, the first historical overcharge rate refers to the extent to which the battery exceeds its designed capacity or safe charging threshold during the charging process, and the first historical overdischarge rate refers to the extent to which the battery is lower than its designed minimum discharge voltage or capacity during the discharging process. A model for predicting changes in battery performance is constructed based on historical data and machine learning principles. The extracted first historical charging power, first historical overcharge rate, and first historical overdischarge rate are used as input information for a performance impact prediction model, which is an intelligent model obtained from a training data set through supervised learning based on neural network principles. The training data set contains multiple groups of data, each group of data including charging power, overcharge rate, overdischarge rate, and performance impact degree. The performance impact degree is a ratio calculated by comparing the post-charging performance and the pre-charging performance of the battery, which reflects the changes in battery performance during charging and discharging. The performance impact prediction model predicts the first predicted impact degree of the battery performance based on the input charging power, overcharge rate, and overdischarge rate, which is a quantitative indicator used to represent the potential impact of the current usage conditions on the battery performance. An initial performance index of the target battery is obtained, which typically represents the performance level of the battery in a brand-new state, such as the performance index when it is just out of the factory. The first predicted impact degree is combined with the initial performance index through addition or other appropriate mathematical operations to obtain a comprehensive performance impact degree. This comprehensive performance impact degree reflects the changes in battery performance relative to the initial state after considering the current usage conditions. Based on the comprehensive performance impact degree, the initial performance index is adjusted to obtain the final battery performance index. This index not only considers the initial performance of the battery, but also considers the performance degradation and health status of the battery during use. After obtaining the battery performance index, the remaining life, performance degradation of the battery can be evaluated based on this index, or it can be used as a basis for battery replacement and maintenance.

[0027] When the target real-time remaining power does not meet the power limit value, a charging and discharging instruction is issued, and a multi-dimensional charging and discharging condition index of the target battery is obtained based on the charging and discharging instruction.

[0028] When the real-time remaining capacity of the target battery is lower or higher than the set capacity limit, it indicates that although the battery is normal at this time, the capacity is abnormal, and subsequent abnormalities will occur due to the abnormal capacity. Therefore, charging or discharging treatment is needed, and the system will issue corresponding charging and discharging instructions. If the capacity is too low, the system will issue a charging instruction; if the capacity is too high, the system will issue a discharging instruction to ensure that the battery operates within a safe range. After issuing the charging and discharging instructions, the system will perform multi-dimensional charging and discharging condition detection on the target battery, including battery temperature, charging / discharging current, battery voltage, and battery health status, etc. Based on the above multi-dimensional charging and discharging condition detection, the system will comprehensively evaluate and calculate a target charging and discharging condition index. This index reflects the appropriateness or safety of battery charging and discharging under the current conditions and can be a comprehensive score or a collection of a series of parameters, used to guide subsequent charging and discharging operations. After obtaining the target charging and discharging condition index, the system can develop specific charging and discharging strategies based on this index. If the index shows that the current conditions are not suitable for charging and discharging, the system can suspend operation and wait for the conditions to improve; if the index shows that the conditions are suitable, the system will continue to perform charging and discharging operations and dynamically adjust the charging and discharging strategy according to the changes in the index. By issuing charging and discharging instructions and performing multi-dimensional charging and discharging condition detection, the system can ensure that the target battery operates under safe and appropriate conditions for charging and discharging, thereby prolonging the service life of the battery and improving the reliability of the system.

[0029] As shown in Figure 2 predetermined charging and discharging condition characteristics are read, and multi-dimensional charging and discharging condition monitoring is performed on the target battery based on the predetermined charging and discharging condition characteristics to obtain target charging and discharging condition information; the normalized target charging and discharging condition information is analyzed using the coefficient of variation principle to obtain the target charging and discharging condition index; wherein the predetermined charging and discharging condition characteristics include charging and discharging current, charging and discharging rate, charging and discharging time, and charging and discharging environmental temperature.

[0030] The predetermined charging and discharging condition characteristics are read, which are set by the staff, such as charging and discharging current, charging and discharging rate, charging and discharging time length, and charging and discharging environment temperature, etc. Based on the above-mentioned predetermined charging and discharging condition characteristics, the system carries out multi-dimensional charging and discharging condition monitoring on the target battery, that is, real-time detection of the charging and discharging current, charging and discharging rate, charging and discharging time length, and charging and discharging environment temperature of the battery, and comparison with the predetermined condition characteristics. Through multi-dimensional charging and discharging condition monitoring, the system collects real-time charging and discharging condition information of the target battery, reflecting the actual state of the current battery charging and discharging. Since the units and dimensions of each parameter in the charging and discharging condition information are different, in order to facilitate subsequent analysis, it is necessary to carry out normalization processing on these information. Normalization can convert data to a unified scale, so that different parameters can be compared and calculated. After normalization processing, the system uses the principle of coefficient of variation to carry out weighted analysis on the target charging and discharging condition information. The coefficient of variation is an index to measure the degree of dispersion of data, which can reflect the importance or influence of different parameters in the whole. By calculating the coefficient of variation of each parameter, and determining its weight in the target charging and discharging condition index according to the size of the coefficient of variation. Then, multiply the normalized parameter value by the corresponding weight, and sum to get the target charging and discharging condition index. The target charging and discharging condition index is a comprehensive index, which reflects the overall state or health degree of the target battery under the current charging and discharging condition. This index can be used to guide the subsequent charging and discharging operation, such as determining whether it is necessary to adjust the charging and discharging parameters, suspend the charging and discharging operation, or take other measures to protect the battery. By reading the predetermined charging and discharging condition characteristics, carrying out multi-dimensional charging and discharging condition monitoring, normalization processing, and using the principle of coefficient of variation for weighted analysis, the system can get the target charging and discharging condition index, which provides strong support for the safe and efficient charging and discharging of the battery.

[0031] A temperature gradient feedback adjustment function is introduced, and the preset temperature gradient is adjusted in combination with the battery performance index and the target charging and discharging condition index to obtain a target temperature gradient.

[0032] The expression of the temperature gradient feedback adjustment function is as follows:

[0033] ; wherein, characterizes the adjustment result of the preset temperature gradient , that is, the target temperature gradient, and characterize the battery performance index and the target charging and discharging condition index, respectively.

[0034] A temperature gradient feedback adjustment function is defined, which can output an adjustment value based on the battery performance index, the target charge-discharge condition index, and the current temperature gradient. The preset temperature gradient is a desired temperature change range or rate set before starting the charge-discharge operation. Using the temperature gradient feedback adjustment function, the adjustment value is calculated by combining the battery performance index, the target charge-discharge condition index, and the current temperature gradient to obtain the target temperature gradient. The expression of the temperature gradient feedback adjustment function is as follows: ; wherein, represents the adjustment result of the preset temperature gradient, represents the target temperature gradient, and represent the battery performance index and the target charge-discharge condition index, respectively.

[0035] A preset temperature threshold is read, and a compensation analysis is performed on the target temperature gradient based on the preset temperature threshold to obtain a target compensation temperature gradient.

[0036] The temperature range that the battery can withstand is obtained, including a maximum temperature threshold, such as the maximum safe temperature of the battery, and a minimum temperature threshold, such as the minimum temperature at which the battery or device can operate normally. It is determined by the physical characteristics of the device and the working environment. The target temperature gradient describes the rate of temperature change within a certain time interval, which can be a positive value indicating temperature rise or a negative value indicating temperature drop. This gradient is determined by the current demand or operating conditions. Based on the current temperature and the target temperature gradient, the temperature of the device at a future time point can be predicted. This is usually done by multiplying the current temperature by the target temperature gradient and adding the time interval. Compare with the preset temperature threshold. If the predicted temperature exceeds the maximum temperature threshold, it means that if the current target temperature gradient continues, the temperature of the device will be too high, causing damage or safety risks. Therefore, the target temperature gradient needs to be reduced to slow down the temperature rise. If the predicted temperature is lower than the minimum temperature threshold, it means that the temperature of the device will be too low to affect its normal operation. In this case, the target temperature gradient needs to be increased to speed up the temperature rise. According to the results of the compensation analysis, a new target temperature gradient, i.e. the target compensation temperature gradient, is calculated. This gradient takes into account the preset temperature threshold to ensure that the temperature of the device will not exceed these thresholds at a future time point. Apply the calculated target compensation temperature gradient to the system or device to control the change in its temperature. This can be achieved by adjusting the power of the heating or cooling device, changing the operating mode of the system, or taking other appropriate measures. Through this process, the temperature of the system or device can be ensured to be within a safe range at all times, while meeting its operating requirements and performance requirements.

[0037] The thermal management integrated module performs charging and discharging thermal management on the target battery of the new energy vehicle based on the target compensation temperature gradient.

[0038] The thermal management integrated module is a unit integrating thermal management functions, mainly used for effectively managing heat dispersion and heat dissipation of equipment, thereby improving overall performance and reliability of the equipment. When performing charging and discharging thermal management on the target battery in the new energy vehicle, the thermal management integrated module ensures that the battery works in a safe and suitable temperature range based on the target compensation temperature gradient. According to the current battery temperature, the target temperature gradient, and the preset temperature threshold, the thermal management integrated module performs compensation analysis. If the predicted battery temperature exceeds the maximum temperature threshold, the module reduces the target temperature gradient to slow down the temperature rising speed. If the predicted battery temperature is lower than the minimum temperature threshold, the module increases the target temperature gradient to speed up the temperature rising speed. Implementing charging and discharging thermal management means that, during the charging and discharging process, the thermal management integrated module dynamically adjusts the thermal management strategy according to the target compensation temperature gradient. When the battery is charging, if the module detects that the battery temperature is close to or exceeds the maximum temperature threshold, it reduces the battery temperature by adjusting the power of the cooling system, such as liquid cooling or fan control. During discharging, if the battery temperature is too low, the module starts the heating system, such as an electric heater, to increase the battery temperature.

[0039] Extracting a first temperature in the target temperature gradient, the first temperature corresponding to a first charging and discharging time; determining whether the first temperature is within the preset temperature threshold; when the first temperature is less than the preset temperature threshold, activating a heating device in the thermal management integrated module to perform charging and discharging heating management on the target battery at the first charging and discharging time.

[0040] A first temperature is extracted from the target temperature gradient. This temperature usually corresponds to a certain specific charging and discharging time point, namely the first charging and discharging time, representing the temperature that the target battery is expected to reach at that time point. The extracted first temperature is compared with a preset temperature threshold. The preset temperature threshold is usually set based on the performance, safety and optimal working conditions of the battery, including a minimum working temperature and a maximum working temperature. If the judgment result shows that the first temperature is less than the preset minimum working temperature threshold, the heating device in the thermal management integrated module is activated at the first charging and discharging time. The activation of the heating device is to raise the temperature of the battery to reach or approach the optimal working temperature range, so as to ensure that the battery can work efficiently and safely during the charging and discharging process. Once the heating device is activated, it will start to heat the target battery. This heating process needs to be precisely controlled to ensure that the battery temperature is not too high or too low, while considering the impact of heating speed on battery performance. The controller in the thermal management integrated module will monitor the battery temperature in real time and adjust the power of the heating device as needed to achieve the best heating effect. During the charging and discharging heating management process, the thermal management integrated module will continuously monitor the temperature change of the battery and adjust the working state of the heating device as needed. If the battery temperature rises above the preset minimum working temperature threshold, the heating device will be turned off or the power will be reduced. If the battery temperature continues to drop, the heating device will increase the power to keep the battery temperature stable. This process ensures the performance and safety of the battery of new energy vehicles in low temperature environment. By precisely controlling the working state of the heating device, the thermal management integrated module can effectively manage the temperature of the battery to keep it within the optimal working temperature range during the charging and discharging process.

[0041] When the first temperature is greater than the preset temperature threshold, the cooling device in the thermal management integrated module is activated to perform charging and discharging cooling management on the target battery at the first charging and discharging time.

[0042] When the first temperature is greater than the preset temperature threshold, the cooling device in the thermal management integrated module is activated at the first charging and discharging time. The activation of the cooling device is to reduce the temperature of the battery to prevent it from overheating, so as to ensure that the battery can work efficiently and safely during charging and discharging. Once the cooling device is activated, it will start to cool the target battery. This process needs to be precisely controlled to ensure that the battery temperature is not too low, while considering the impact of cooling speed on battery performance. The controller in the thermal management integrated module will monitor the battery temperature in real time and adjust the power of the cooling device as needed to achieve the best cooling effect. During the charging and discharging cooling management process, the thermal management integrated module will continuously monitor the temperature change of the battery and adjust the working state of the cooling device as needed. If the battery temperature drops below the preset maximum working temperature threshold, the cooling device will be turned off or the power will be reduced. If the battery temperature continues to rise, the cooling device will increase the power to keep the battery temperature stable. This ensures the performance and safety of the battery of the new energy vehicle in high temperature environment. By precisely controlling the working state of the cooling device, the thermal management integrated module can effectively manage the temperature of the battery to prevent it from overheating, thereby prolonging the service life of the battery and improving the performance of the vehicle. When the first temperature is greater than the preset temperature threshold, the cooling device in the thermal management integrated module is activated to perform charging and discharging cooling management on the target battery, ensuring that the battery can maintain within the optimal working temperature range during charging and discharging.

[0043] In summary, the beneficial effects of the present application include:

[0044] 1. Real-time and accurate monitoring and analysis of battery electrochemical parameters and state, improving monitoring accuracy and reliability.

[0045] 2. Through real-time monitoring and prediction of battery state, battery performance decline or potential safety hazards can be found in time, so that corresponding measures can be taken for maintenance and repair, prolonging the service life of the battery.

[0046] 3. By accurately assessing the health status and remaining life of the battery, more scientific charging and discharging strategies can be provided for new energy vehicles, avoiding waste and overuse of energy, and improving energy utilization efficiency.

[0047] As shown in Figure 3 the present application embodiment includes a power battery state monitoring system based on real-time monitoring, which comprises:

[0048] The system includes: a real-time battery information acquisition module 11, which acquires real-time battery information dynamically through a sensor integration module deployed in the wiring harness of the target battery; a normalized weighted analysis module 12, which performs normalized weighted analysis on the real-time battery feature data extracted from the real-time battery information based on predetermined battery features to obtain a real-time battery state index; a real-time remaining power acquisition module 13, which adjusts the real-time remaining power in the real-time battery information by calling the battery performance index obtained from analyzing historical battery usage records when the real-time battery state index meets the state limit, to obtain the target real-time remaining power; and a charge / discharge command issuance module 14, which issues a charge / discharge command when the target battery state index meets the limit. When the remaining charge level does not meet the charge limit, a charge / discharge command is issued, and a target charge / discharge condition index is obtained by multi-dimensional charge / discharge condition detection of the target battery based on the charge / discharge command; a preset temperature gradient adjustment module 15 is used to introduce a temperature gradient feedback adjustment function and adjust the preset temperature gradient in combination with the battery performance index and the target charge / discharge condition index to obtain the target temperature gradient; a compensation analysis module 16 is used to read a preset temperature threshold and perform compensation analysis on the target temperature gradient according to the preset temperature threshold to obtain the target compensation temperature gradient; a charge / discharge thermal management module 17 is used by the thermal management integration module to perform charge / discharge thermal management on the target battery of the new energy vehicle based on the target compensation temperature gradient.

[0049] Furthermore, embodiments of this application also include:

[0050] The sensor integration module includes modules for: a temperature sensor, a voltage sensor, a current sensor, and a quantum sensor; a real-time information monitoring module for monitoring the real-time battery temperature of the target battery via the temperature sensor, the real-time battery voltage of the target battery via the voltage sensor, the real-time battery current of the target battery via the current sensor, and the real-time remaining charge of the target battery via the quantum sensor; and a real-time battery information composition module for composing real-time battery information from the real-time battery temperature, the real-time battery voltage, the real-time battery current, and the real-time remaining charge.

[0051] Furthermore, embodiments of this application also include:

[0052] predetermined battery characteristic module, the predetermined battery characteristic module is used for the predetermined battery characteristic to include temperature, voltage and current.

[0053] Further, the embodiments of the present application further include:

[0054] a historical use record extraction module, the historical use record extraction module is used for extracting a first historical use record in the historical battery use record, the first historical use record includes a first historical charging power, a first historical overcharge rate and a first historical overdischarge rate; a predicted influence degree obtaining module, the predicted influence degree obtaining module is used for taking the first historical charging power, the first historical overcharge rate and the first historical overdischarge rate as input information of a performance influence prediction model, and obtaining a first predicted influence degree; a battery performance index obtaining module, the battery performance index obtaining module is used for reading an initial performance index of the target battery, and adjusting the initial performance index based on a comprehensive performance influence degree obtained by adding the first predicted influence degree, to obtain the battery performance index; a performance influence prediction model supervised training module, the performance influence prediction model supervised training module is used for wherein, the performance influence prediction model is an intelligent model obtained by supervised learning of a training data set based on a neural network principle, each data set in the training data set includes charging power, overcharge rate, overdischarge rate and performance influence degree, and the performance influence degree is the ratio of the post-charging performance to the pre-charging performance of the battery.

[0055] Further, the embodiments of the present application further include:

[0056] a multi-dimensional charging and discharging condition monitoring module, the multi-dimensional charging and discharging condition monitoring module is used for reading predetermined charging and discharging condition characteristics, and performing multi-dimensional charging and discharging condition monitoring on the target battery based on the predetermined charging and discharging condition characteristics, to obtain target charging and discharging condition information; a target charging and discharging condition index obtaining module, the target charging and discharging condition index obtaining module is used for performing weighted analysis on the normalized target charging and discharging condition information by using a coefficient of variation principle, to obtain the target charging and discharging condition index; a predetermined charging and discharging condition characteristic including module, the predetermined charging and discharging condition characteristic including module is used for wherein, the predetermined charging and discharging condition characteristics include charging and discharging current, charging and discharging rate, charging and discharging time length and charging and discharging environment temperature.

[0057] Further, the embodiments of the present application further include:

[0058] a temperature gradient feedback adjustment function module, the temperature gradient feedback adjustment function module is used for the expression of the temperature gradient feedback adjustment function is as follows: ; wherein, characterizes the adjustment result of the preset temperature gradient , that is, characterizes the target temperature gradient, and characterize the battery performance index and the target charge-discharge condition index, respectively.

[0059] Further, the embodiments of the present application further include:

[0060] a temperature information extraction module configured to extract a first temperature in the target temperature gradient, the first temperature corresponding to a first charge-discharge time; a temperature judgment module configured to judge whether the first temperature is within the preset temperature threshold; and a temperature less than module configured to activate a heating device in the thermal management integrated module to perform charge-discharge heating management on the target battery at the first charge-discharge time when the first temperature is less than the preset temperature threshold.

[0061] Further, the embodiments of the present application further include:

[0062] a temperature greater than module configured to activate a cooling device in the thermal management integrated module to perform charge-discharge cooling management on the target battery at the first charge-discharge time when the first temperature is greater than the preset temperature threshold.

[0063] For specific embodiments of the power battery state monitoring system based on real-time monitoring, reference can be made to the embodiments of the power battery state monitoring method based on real-time monitoring described above, which will not be repeated here. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0064] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0065] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that, for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application.

Claims

1. A method for monitoring the state of a power battery based on real-time monitoring, characterized in that, include: Real-time battery information is acquired through dynamic monitoring by a sensor integration module deployed in the wiring harness of the target battery. This sensor integration module includes a temperature sensor, a voltage sensor, a current sensor, and a quantum sensor. The real-time battery temperature of the target battery is obtained by monitoring the temperature sensor, the real-time battery voltage of the target battery is obtained by monitoring the voltage sensor, the real-time battery current of the target battery is obtained by monitoring the current sensor, and the real-time remaining power of the target battery is obtained by monitoring the quantum sensor. The real-time battery temperature, the real-time battery voltage, the real-time battery current, and the real-time remaining power together constitute the real-time battery information; Normalized weighted analysis is performed on the real-time battery feature data extracted from the real-time battery information based on predetermined battery features to obtain the real-time battery state index. When the real-time battery status index meets the status limit, the battery performance index obtained by analyzing historical battery usage records is called to adjust the real-time remaining power in the real-time battery information to obtain the target real-time remaining power. When the target's real-time remaining power does not meet the power limit, a charge / discharge command is issued, and a target charge / discharge condition index is obtained by multi-dimensional charge / discharge condition detection of the target battery based on the charge / discharge command. A temperature gradient feedback adjustment function is introduced, and the preset temperature gradient is adjusted in conjunction with the battery performance index and the target charge / discharge condition index to obtain the target temperature gradient. The expression of the temperature gradient feedback adjustment function is as follows: ； in, Characterizing the preset temperature gradient The adjustment result, that is, characterizing the target temperature gradient, and These respectively characterize the battery performance index and the target charge / discharge condition index; Read the preset temperature threshold and perform compensation analysis on the target temperature gradient based on the preset temperature threshold to obtain the target compensated temperature gradient; The thermal management integrated module performs charging and discharging thermal management on the target battery of the new energy vehicle based on the target compensated temperature gradient.

2. The power battery state monitoring method based on real-time monitoring according to claim 1, characterized in that, The predetermined battery characteristics include temperature, voltage, and current.

3. The power battery state monitoring method based on real-time monitoring according to claim 1, characterized in that, The power battery state monitoring method based on real-time monitoring includes: Extract the first historical usage record from the historical battery usage records. The first historical usage record includes the first historical charging power, the first historical overcharge rate, and the first historical over-discharge rate. The first historical charging power, the first historical overcharge rate, and the first historical over-discharge rate are used as input information for the performance impact prediction model to obtain the first predicted impact degree. The initial performance index of the target battery is read, and the initial performance index is adjusted based on the comprehensive performance influence obtained by summing the first predicted influence to obtain the battery performance index. The performance impact prediction model is an intelligent model obtained by supervised learning of the training data set based on the principle of neural network. Each data set in the training data set includes charging power, overcharge rate, over-discharge rate, and performance impact degree. The performance impact degree is the ratio of the battery's post-charging performance to its pre-charging performance.

4. The power battery state monitoring method based on real-time monitoring according to claim 1, characterized in that, The power battery state monitoring method based on real-time monitoring includes: Read the predetermined charge and discharge condition characteristics, and perform multi-dimensional charge and discharge condition monitoring on the target battery based on the predetermined charge and discharge condition characteristics to obtain target charge and discharge condition information; The target charge-discharge condition information after normalization is weighted and analyzed using the principle of coefficient of variation to obtain the target charge-discharge condition index; The predetermined charge / discharge conditions include charge / discharge current, charge / discharge rate, charge / discharge duration, and charge / discharge ambient temperature.

5. The power battery state monitoring method based on real-time monitoring according to claim 1, characterized in that, The power battery state monitoring method based on real-time monitoring includes: Extract the first temperature from the target temperature gradient, where the first temperature corresponds to the first charge / discharge time; Determine whether the first temperature is within the preset temperature threshold; When the first temperature is less than the preset temperature threshold, the heating device in the thermal management integrated module is activated during the first charge / discharge time to perform charge / discharge heating management on the target battery.

6. The power battery state monitoring method based on real-time monitoring according to claim 5, characterized in that, When the first temperature is greater than the preset temperature threshold, the cooling device in the thermal management integrated module is activated during the first charge / discharge time to perform charge / discharge cooling management on the target battery.

7. A power battery state monitoring system based on real-time monitoring, characterized in that, The real-time monitoring-based power battery state monitoring system is used to implement the real-time monitoring-based power battery state monitoring method according to any one of claims 1-6, the system comprising: A real-time battery information acquisition module is used to acquire real-time battery information, which is obtained through dynamic monitoring by a sensor integrated module deployed in the wiring harness of the target battery. The normalized weighted analysis module is used to perform normalized weighted analysis on the real-time battery feature data extracted from the real-time battery information based on predetermined battery features, so as to obtain the real-time battery state index. A real-time remaining power acquisition module is used to adjust the real-time remaining power in the real-time battery information by calling the battery performance index obtained by analyzing historical battery usage records when the real-time battery state index meets the state limit, so as to obtain the target real-time remaining power. A charge / discharge command issuing module is used to issue a charge / discharge command when the target real-time remaining power does not meet the power limit, and to perform multi-dimensional charge / discharge condition detection of the target battery based on the charge / discharge command to obtain the target charge / discharge condition index. A preset temperature gradient adjustment module is used to introduce a temperature gradient feedback adjustment function and adjust the preset temperature gradient in combination with the battery performance index and the target charge / discharge condition index to obtain the target temperature gradient. The compensation analysis module is used to read a preset temperature threshold and perform compensation analysis on the target temperature gradient based on the preset temperature threshold to obtain the target compensated temperature gradient. A charge / discharge thermal management module is used by the thermal management integration module to perform charge / discharge thermal management on the target battery of the new energy vehicle based on the target compensated temperature gradient.

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