Overcharge control method and system for electric vehicle

Abstract

The invention discloses an overcharge control method and system for an electric vehicle, and relates to the technical field of overcharge control, and the method can accurately predict and evaluate the temperature change of a battery by collecting battery data in real time in the overcharge process and combining a normalized data set and a charging feature vector set. Particularly, by constructing a heat and temperature algorithm model, the system can predict the change trend of the battery temperature in the overcharge process. And when the predicted temperature value Tb exceeds the temperature threshold T, the system can immediately trigger an adaptive charging power and cooling system cooperation mechanism, and automatically adjust the overcharge power Pya and the cooling power Pcool. Through the mechanism, when the battery temperature is close to the safety upper limit, the overheating risk can be effectively avoided, and the safety of the charging process is ensured. According to the measure, the accuracy of battery temperature management is remarkably improved, the risk of battery damage caused by abnormal temperature is reduced, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of supercharging control, and in particular to a supercharging control method and system for an electric vehicle. Background Art

[0002] As an important development direction in the global transportation field, electric vehicles (EVs) represent the trend of green travel in the future. With the rapid development of new energy vehicles, the charging technology and management system of electric vehicles are also constantly innovating and upgrading, especially in supercharging technology. Supercharging technology refers to a charging method that charges a large amount of electricity into the battery of an electric vehicle in a relatively short period of time. It is widely used in long-distance travel and emergency charging needs of electric vehicles. In order to improve the efficiency of supercharging and the service life of the battery, the battery management system BMS and the thermal management system play a vital role in the charging process of electric vehicles, ensuring that the temperature control and power regulation during the battery charging process are in the optimal state. Therefore, the innovation of supercharging control methods is particularly important, especially in the optimization of battery thermal management.

[0003] At present, in the existing supercharging technology, although the battery charging state is monitored by the intelligent battery management system, there are still a series of problems, especially in the temperature management and charging efficiency of the battery during the charging process. During the supercharging process, the battery generates a lot of heat due to the charging effect of the large current, which will cause the battery temperature to rise rapidly. Most of the existing thermal management systems rely on static temperature monitoring and cooling strategies, and fail to dynamically adapt to the needs under different charging conditions. Even if equipped with a cooling system, the real-time coordinated adjustment of charging power and temperature is often extensive and lacks accurate prediction and adjustment. Current technology cannot effectively avoid battery overheating or uneven temperature distribution, resulting in low charging efficiency, overcharging, and even loss of battery life. These problems have hindered the further development of supercharging technology, especially in the context of the popularization of fast charging piles and the increasing demand for ultra-long-range electric vehicles. How to ensure safe and efficient charging has become a key issue that needs to be solved urgently. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a supercharging control method and system for an electric vehicle, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps:

[0006] S1. After supercharging starts, the built-in sensor group is automatically turned on through the battery management system BMS of the electric vehicle to collect battery data in real time during the supercharging process, transmit it to the battery control system, and pre-process the battery data to obtain a normalized data set;

[0007] S2, extracting features from the normalized data set to obtain a charging feature vector set, connecting to a cloud server, building a cloud database in the cloud server, and transmitting the charging feature vector set to the cloud database for storage;

[0008] S3, construct a heat and temperature algorithm model, extract the charging feature vector set and input it into the heat and temperature algorithm model, calculate the output heat generation value Qgen and the predicted temperature value Tb, and set the temperature threshold T, perform temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb, and analyze the temperature change of the battery of the electric vehicle during supercharging;

[0009] S4. When the temperature prediction and evaluation show that the battery temperature is abnormal, the adaptive charging power and cooling system coordination mechanism is executed to calculate the output supercharging power adjustment value Pcha and the cooling power adjustment value Pcool respectively;

[0010] S5. After the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff is calculated and output, and the efficiency threshold E is set. Then, the efficiency threshold E and the battery supercharging efficiency Eeff are evaluated to determine the efficiency status and temperature adjustment status during the supercharging process.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S11, directly connect to the electric vehicle through the supercharging cable, and access the battery management system BMS of the electric vehicle, automatically turn on the built-in sensor group of the electric vehicle, and collect the battery data of the electric vehicle in real time during the supercharging process;

[0013] The built-in sensor group includes a voltage sensor, a current sensor, a temperature sensor, and a thermistor;

[0014] The battery data includes voltage Vbat, charging current Icha, battery temperature T, battery heat transfer Q, ambient temperature Tenv and coolant temperature Tcoo;

[0015] S12, transmitting the battery data to a battery control system of the electric vehicle, and preprocessing the battery data in the battery control system to obtain a normalized data set, wherein the preprocessing includes denoising, outlier detection, timestamp marking, missing value filling, and normalization;

[0016] The denoising is performed by using a sliding average method to denoise the battery data;

[0017] The outlier detection is performed by using a Z-score standard deviation method to identify outliers in the battery data and remove the outliers;

[0018] The timestamp marking is performed by marking the battery data with a timestamp based on the acquisition time of the built-in sensor group;

[0019] The missing value filling is performed by using data at adjacent moments;

[0020] The normalization method maps the value of each parameter in the battery data to a fixed interval through the Min-Max normalization method, thereby eliminating the dimension differences of each parameter in the battery data.

[0021] The normalized data set includes the voltage Vbat(t) at time t, the charging current Icha(t) at time t, the battery temperature T(t) at time t, the battery transfer heat Q(t) at time t, the ambient temperature Tenv(t) at time t, and the coolant temperature Tcoo(t) at time t.

[0022] Preferably, S2 includes S21 and S22;

[0023] S21. Perform feature extraction based on the normalized data set to obtain a charging feature vector set;

[0024] The charging feature vector set includes the battery internal resistance Rbat(t) at time t, the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t, the cooling efficiency Ncoo(t) at time t and the charging current Icha(t) at time t;

[0025] The battery internal resistance Rbat(t) at time t is extracted by combining the voltage Vbat(t) at time t and the charging current Icha(t) at time t. The specific feature extraction algorithm formula is: ; Wherein, Vopen represents the open circuit voltage of the battery;

[0026] The battery temperature gradient △Tbat(t) at time t is extracted by calculating the temperature difference of the battery temperature T at different positions of the battery. The specific feature extraction algorithm formula is: Among them, Tbat max Indicates the upper limit of the battery temperature, Tbat min Indicates the lower limit of the temperature inside the battery;

[0027] The battery heat capacity Cbat(t) at time t is calculated and extracted by using the heat balance equation, combining the battery temperature T(t) at time t and the battery transfer heat Q(t) at time t. The specific feature extraction algorithm formula is: ; Where d represents a small variable, dQ(t) represents a small change in the amount of heat transferred by the battery at time t, and dT(t) represents a small change in the battery temperature at time t;

[0028] The cooling efficiency Ncoo(t) at time t is calculated and extracted by monitoring the temperature change of the cooling system. The specific feature extraction algorithm formula is: , where △Tcoo(t) represents the change in coolant temperature at time t, Tx max Indicates the upper limit of the design temperature difference of the cooling system;

[0029] S22, connecting the cloud server to the battery control system via a communication network, building a cloud database in the cloud server, uploading the charging feature vector set to the cloud database via the communication network, and performing cloud storage on the charging feature vector set.

[0030] Preferably, said S3 includes S31, S32 and S33;

[0031] S31, constructing the heat and temperature algorithm model in the cloud server, the heat and temperature algorithm model includes a heat algorithm model and a temperature algorithm model, extracting a charging feature vector set and inputting it into the heat algorithm model, calculating and outputting a heat generation value Qgen, and predicting the heat generated by the battery of the electric vehicle during the supercharging process;

[0032] The heat generation value Qgen is calculated and output by the following heat algorithm model; ;

[0033] Where Qgen(t) represents the heat generation value at time t.

[0034] Preferably, S32, based on the heat generation value Qgen, combined with the charging feature vector set, input into the temperature algorithm model, calculate and output the predicted temperature value Tb, and perform prediction and analysis on the temperature of the battery of the electric vehicle during the supercharging process of the electric vehicle;

[0035] The predicted temperature value Tb is calculated and output by the following temperature algorithm model; ;

[0036] Where Tb(t) represents the predicted temperature value at time t;

[0037] S33, initially setting a temperature threshold T based on the battery temperature standard of the electric vehicle, and then performing temperature prediction evaluation on the predicted temperature value Tb(t) at the time t and the temperature threshold T, analyzing the abnormal change of the battery temperature during the supercharging process, and generating trigger conditions for the adaptive charging power and cooling system coordination mechanism based on the evaluation results. The specific evaluation contents are as follows;

[0038] When the predicted temperature value Tb(t) at time t is greater than the temperature threshold T, it indicates that the battery temperature changes abnormally during the supercharging process, and the adaptive charging power and cooling system coordination mechanism is triggered.

[0039] When the predicted temperature value Tb(t) at time t ≤ the temperature threshold T, it indicates that the predicted temperature change of the battery during the overcharging process is normal and no intervention and continuous monitoring are required.

[0040] Preferably, said S4 includes S41 and S42;

[0041] S41. When the temperature change is abnormal, the temperature prediction and evaluation are performed, and the adaptive charging power and cooling system coordination mechanism is executed. The adaptive charging power and cooling system coordination mechanism includes an adaptive charging power adjustment mechanism and a cooling system adjustment mechanism.

[0042] The adaptive charging power adjustment mechanism calculates and outputs the supercharging power adjustment value Pcha according to the predicted temperature value Tb(t) of the battery at time t during the supercharging process. When the battery temperature is close to the upper limit, the supercharging power adjustment value Pcha is sent to the battery control system through the cloud server, and the supercharging power is controlled by the battery control system.

[0043] The supercharge power adjustment value Pcha is calculated and output by the following algorithm formula: ;

[0044] Where Pcha(t) represents the supercharging power adjustment value at time t, Pmax represents the supercharging power upper limit, and Tmax represents the safe temperature upper limit of the battery.

[0045] Preferably, S42, the cooling system adjustment mechanism dynamically adjusts the battery cooling system power by calculating the output cooling power adjustment value Pcool according to the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t and the cooling efficiency Ncoo(t) at time t, and the predicted temperature value Tb(t) at time t.

[0046] The cooling power adjustment value Pcool is calculated and output by the following algorithm formula; ;

[0047] Where Pcool(t) represents the cooling power adjustment value at time t.

[0048] Preferably, said S5 includes S51 and S52;

[0049] S51, after the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff of the electric vehicle is comprehensively calculated to quantify the thermal management and supercharging power of the electric vehicle during the supercharging process;

[0050] The battery supercharge efficiency Eeff is calculated and output by the following algorithm formula: .

[0051] Preferably, S52, based on the standard of the electric vehicle battery charging temperature, an efficiency threshold E is set, and then the battery supercharging efficiency Eeff and the efficiency threshold E are compared to perform efficiency evaluation, and the thermal management and charging power during the supercharging process are analyzed. The specific evaluation content is as follows;

[0052] When the battery supercharge efficiency Eeff ≥ efficiency threshold E, the battery temperature is in a normal state under the currently adjusted supercharge efficiency, and no intervention is required to continue monitoring;

[0053] When the battery supercharging efficiency Eeff is less than the efficiency threshold E, the battery temperature is in an abnormal state under the currently adjusted supercharging efficiency. At this time, the cooling system efficiency is automatically increased to 100%, and S5 is re-executed to re-analyze the thermal management and charging power during the supercharging process. If the secondary analysis shows an abnormal state, the supercharging is automatically stopped. If the secondary analysis shows a normal state, monitoring continues.

[0054] A supercharging control system for an electric vehicle, comprising a supercharging data acquisition module, a feature extraction module, a heat and temperature analysis module, an adaptive adjustment module and a supercharging efficiency and temperature analysis module;

[0055] The supercharging data acquisition module automatically turns on the built-in sensor group through the battery management system BMS of the electric vehicle after supercharging starts, collects the battery data in real time during the supercharging process, transmits it to the battery control system, and pre-processes the battery data to obtain a normalized data set;

[0056] The feature extraction module extracts features from the normalized data set to obtain a charging feature vector set, and simultaneously connects to a cloud server, builds a cloud database in the cloud server, and transmits the charging feature vector set to the cloud database for storage;

[0057] The heat and temperature analysis module constructs a heat and temperature algorithm model, extracts the charging feature vector set and inputs it into the heat and temperature algorithm model, calculates and outputs a heat generation value Qgen and a predicted temperature value Tb, and sets a temperature threshold T, performs temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb, and analyzes the temperature change of the battery of the electric vehicle during supercharging;

[0058] When the temperature prediction and evaluation of the battery temperature is abnormal, the adaptive adjustment module executes the adaptive charging power and cooling system coordination mechanism to calculate the output supercharging power adjustment value Pcha and the cooling power adjustment value Pcool respectively;

[0059] After the adaptive charging power and cooling system collaborative mechanism is executed, the supercharging efficiency and temperature analysis module calculates and outputs the battery supercharging efficiency Eeff, sets the efficiency threshold E, and then performs efficiency evaluation on the efficiency threshold E and the battery supercharging efficiency Eeff to determine the efficiency status and temperature adjustment status during the supercharging process.

[0060] The present invention provides a supercharging control method and system for an electric vehicle, which has the following beneficial effects:

[0061] (1) This method can accurately predict and evaluate the temperature changes of the battery by collecting battery data in real time during the supercharging process, combining the normalized data set with the charging feature vector set. In particular, by constructing a heat and temperature algorithm model, the system can predict the changing trend of the battery temperature during the supercharging process. When the predicted temperature value Tb exceeds the temperature threshold T, the system can immediately trigger the adaptive charging power and cooling system coordination mechanism to automatically adjust the supercharging power Pcha and cooling power Pcool. Through this mechanism, when the battery temperature approaches the safety upper limit, the risk of overheating can be effectively avoided and the safety of the charging process can be ensured. This measure significantly improves the accuracy of battery temperature management, reduces the risk of battery damage due to abnormal temperature, and helps to extend the battery life.

[0062] (2) This method comprehensively calculates the battery's supercharging efficiency Eeff and sets an efficiency threshold E for evaluation, so that the system can effectively optimize the thermal management and power adjustment during the charging process. In the present invention, when the battery's charging efficiency during the supercharging process is lower than the predetermined efficiency threshold E, the system will automatically start the cooling system adjustment mechanism, increase the cooling system efficiency to 100%, and re-evaluate the battery's thermal management and charging power. This process ensures that even if efficiency anomalies occur during the supercharging process, the system can restore to normal through intelligent adaptive adjustment. In addition, the system can dynamically adjust the charging power and cooling power according to the real-time temperature and thermal management of the battery, thereby maintaining the battery's optimal charging efficiency throughout the charging process. This technology not only improves the efficiency of the battery charging process, but also effectively reduces energy loss and excessive heat accumulation during the charging process.

[0063] (3) This method adopts the storage and calculation method of charging feature vector sets supported by cloud servers. By uploading the real-time data of the battery to the cloud database, the system can uniformly store and analyze large-scale electric vehicle charging data and optimize the algorithm. The construction of the cloud database and the cloud learning function enable the battery control system to self-optimize in terms of charging characteristics, temperature control and power regulation based on the continuously accumulated battery data. This intelligent system based on data analysis can further improve the charging efficiency and safety of the battery based on the calculation results of the cloud server. At the same time, the support of cloud data also enables the experience between vehicles to be shared, thereby providing a more accurate and efficient supercharging control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of a supercharging control method for an electric vehicle according to the present invention;

[0065] Figure 2 The present invention is a schematic diagram of the steps of a supercharging control system for an electric vehicle. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0067] Example 1

[0068] See also Figure 1 The present invention provides a supercharging control method for an electric vehicle. To achieve the above object, the present invention is implemented by the following technical scheme: comprising the following steps:

[0069] S1. After supercharging starts, the built-in sensor group is automatically turned on through the battery management system BMS of the electric vehicle to collect battery data in real time during the supercharging process, transmit it to the battery control system, and pre-process the battery data to obtain a normalized data set;

[0070] S2, extracting features from the normalized data set to obtain a charging feature vector set, connecting to a cloud server, building a cloud database in the cloud server, and transmitting the charging feature vector set to the cloud database for storage;

[0071] S3, construct a heat and temperature algorithm model, extract the charging feature vector set and input it into the heat and temperature algorithm model, calculate the output heat generation value Qgen and the predicted temperature value Tb, and set the temperature threshold T, perform temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb, and analyze the temperature change of the battery of the electric vehicle during supercharging;

[0072] S4. When the temperature prediction and evaluation show that the battery temperature is abnormal, the adaptive charging power and cooling system coordination mechanism is executed to calculate the output supercharging power adjustment value Pcha and the cooling power adjustment value Pcool respectively;

[0073] S5. After the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff is calculated and output, and the efficiency threshold E is set. The efficiency threshold E and the battery supercharging efficiency Eeff are then evaluated to determine the efficiency status and temperature adjustment status during the supercharging process.

[0074] In this embodiment, the method automatically starts the built-in sensor group through the battery management system BMS of the electric vehicle to collect battery data in real time during the supercharging process. The collected battery data is preprocessed to obtain a normalized data set to ensure the dimensional consistency between different data types. Then, the normalized data is feature extracted to generate a charging feature vector set, which is uploaded to the database in the cloud server for storage, providing a basis for subsequent data analysis and model optimization. On the cloud server, based on the extracted charging feature vector set, a heat and temperature algorithm model is constructed, and the output heat generation value Qgen and the predicted temperature value Tb are calculated and the temperature threshold T is set to evaluate the temperature change of the battery in real time. If the prediction result shows that the battery temperature is abnormal, the system will execute the adaptive charging power and cooling system coordination mechanism to adjust the supercharging power Pcha and the cooling power Pcool to effectively manage the battery temperature, prevent overheating and optimize the charging efficiency. Finally, based on the temperature management and power adjustment during the supercharging process, the supercharging efficiency Eeff of the battery is calculated, and compared and evaluated with the efficiency threshold E to further judge the thermal management effect and the optimization level of the charging power during the battery charging process. Through this method, the present invention can accurately manage the battery temperature during the supercharging process, avoid risks such as overheating, and ensure the safety and health of the battery; at the same time, by dynamically adjusting the charging power and cooling power, it not only optimizes the charging efficiency of the battery, but also reduces energy waste and excessive heat accumulation, and improves the efficiency of the overall charging process. In addition, based on cloud data analysis and feature extraction, the system can achieve intelligent adaptive adjustment, improve the automation and intelligence level of the electric vehicle charging system, and ensure the efficient and safe operation of the supercharging process.

[0075] Example 2

[0076] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0077] S11, directly connect to the electric vehicle through the supercharging cable, and access the battery management system BMS of the electric vehicle, automatically turn on the built-in sensor group of the electric vehicle, and collect the battery data of the electric vehicle in real time during the supercharging process;

[0078] The built-in sensor group includes voltage sensor, current sensor, temperature sensor, and thermistor;

[0079] The battery data includes voltage Vbat, charging current Icha, battery temperature T, battery heat transfer Q, ambient temperature Tenv and coolant temperature Tcoo;

[0080] The battery temperature T, ambient temperature Tenv and coolant temperature Tcoo are collected and obtained through temperature sensors installed at different positions of the battery;

[0081] The voltage Vbat, charging current Icha and battery heat Q are collected and acquired through voltage sensor, current sensor and thermal sensor respectively;

[0082] S12, then transmitting the battery data to the battery control system of the electric vehicle, and preprocessing the battery data in the battery control system to obtain a normalized data set, the preprocessing including denoising, outlier detection, timestamp marking, missing value filling and normalization;

[0083] Denoising The battery data was denoised by using a sliding average method;

[0084] Outlier detection uses the Z-score standard deviation method to identify outliers in battery data and removes them;

[0085] Timestamp tagging: Battery data is timestamped based on the acquisition time of the built-in sensor group;

[0086] Missing values ​​are filled by using data from adjacent moments;

[0087] Normalization: The Min-Max normalization method is used to map the value of each parameter in the battery data to a fixed interval, eliminating the dimensional differences of each parameter in the battery data.

[0088] The normalized data set includes the voltage Vbat(t) at time t, the charging current Icha(t) at time t, the battery temperature T(t) at time t, the battery heat transferred Q(t) at time t, the ambient temperature Tenv(t) at time t, and the coolant temperature Tcoo(t) at time t.

[0089] In this embodiment, the method automatically starts the built-in sensor group and collects battery data in real time by directly connecting the electric vehicle to the battery management system BMS through the charging cable at the beginning of supercharging. Subsequently, the collected battery data is transmitted to the battery control system, and a series of preprocessing steps are performed in the control system to ensure the quality and accuracy of the data. These preprocessing steps include denoising, eliminating invalid data through outlier detection, using timestamp markers for data time alignment, filling missing values ​​with adjacent time data, and converting data to a unified standard interval through the Min-Max normalization method to eliminate dimensional differences between different parameters. After preprocessing, a normalized data set is formed. This series of processing steps first ensures the accuracy and consistency of the data, thereby improving the reliability of subsequent analysis.

[0090] Example 3

[0091] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0092] S21. Extracting features based on the normalized data set to obtain a charging feature vector set;

[0093] The charging feature vector set includes the battery internal resistance Rbat(t) at time t, the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t, the cooling efficiency Ncoo(t) at time t and the charging current Icha(t) at time t;

[0094] The battery internal resistance Rbat(t) at time t is extracted by combining the voltage Vbat(t) at time t and the charging current Icha(t) at time t. The specific feature extraction algorithm formula is: ; Wherein, Vopen represents the open circuit voltage of the battery, that is, the voltage when there is no load;

[0095] The battery temperature gradient △Tbat (t) at time t is extracted by calculating the temperature difference of the battery temperature T at different positions of the battery. The specific feature extraction algorithm formula is: Among them, Tbat max Indicates the upper limit of the battery temperature, Tbat min Indicates the lower limit of the temperature inside the battery;

[0096] The battery heat capacity Cbat(t) at time t is calculated and extracted by using the heat balance equation, combining the battery temperature T(t) at time t and the battery transfer heat Q(t) at time t. The specific feature extraction algorithm formula is: ; Where d represents a small variable, dQ(t) represents a small change in the amount of heat transferred by the battery at time t, and dT(t) represents a small change in the battery temperature at time t;

[0097] The cooling efficiency Ncoo (t) at time t is calculated and extracted by monitoring the temperature change of the cooling system. The specific feature extraction algorithm formula is: , where △Tcoo(t) represents the change in coolant temperature at time t, Tx max Indicates the upper limit of the design temperature difference of the cooling system;

[0098] S22, connecting the cloud server and the battery control system through a communication network, building a cloud database in the cloud server, uploading the charging feature vector set to the cloud database through the communication network, and performing cloud storage on the charging feature vector set.

[0099] In this embodiment, the method performs feature extraction on battery data based on a normalized data set to form a charging feature vector set. These parameters are calculated and extracted through different algorithm formulas, reflecting the dynamic changes of the battery during the supercharging process. Subsequently, by uploading the charging feature vector set to a cloud database for cloud storage, combined with the computing power and remote storage advantages of the cloud server, efficient management and processing of data are ensured. This approach not only makes data storage safer and more flexible, but also supports more complex analysis and optimization calculations. Through the above-mentioned implementation mode, the present invention can obtain various operating states and change data of the battery in real time and accurately, and through the extraction of feature vectors, it helps the battery management system to promptly identify abnormal conditions of the battery, thereby optimizing the power control and thermal management strategies during the charging process.

[0100] Example 4

[0101] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31, S32 and S33;

[0102] S31, constructing a heat and temperature algorithm model in the cloud server, the heat and temperature algorithm model includes a heat algorithm model and a temperature algorithm model, extracting a charging feature vector set and inputting it into the heat algorithm model, calculating and outputting a heat generation value Qgen, and predicting the heat generated by the battery of the electric vehicle during the supercharging process;

[0103] The heat generation value Qgen is calculated and output by the following heat algorithm model; ;

[0104] Where Qgen(t) represents the heat generation value at time t.

[0105] S32, based on the heat generation value Qgen, combined with the charging feature vector set, input into the temperature algorithm model, calculate and output the predicted temperature value Tb, and perform prediction and analysis on the temperature of the battery of the electric vehicle during the supercharging process;

[0106] The predicted temperature value Tb is calculated and output by the following temperature algorithm model; ;

[0107] Where Tb(t) represents the predicted temperature value at time t;

[0108] S33, initially setting the temperature threshold T based on the battery temperature standard of electric vehicles, and then performing temperature prediction evaluation on the predicted temperature value Tb(t) at time t and the temperature threshold T, analyzing the abnormal change of battery temperature during the supercharging process, and generating the triggering conditions of the adaptive charging power and cooling system coordination mechanism based on the evaluation results. The specific evaluation contents are as follows;

[0109] When the predicted temperature value Tb(t) at time t is greater than the temperature threshold T, it indicates that the battery temperature changes abnormally during the supercharging process, and the adaptive charging power and cooling system coordination mechanism is triggered.

[0110] When the predicted temperature value Tb(t) at time t ≤ the temperature threshold T, it indicates that the predicted temperature change of the battery during the overcharging process is normal and no intervention and continuous monitoring are required.

[0111] In this embodiment, the method calculates the heat generation value Qgen generated by the battery by constructing a heat and temperature algorithm model in the cloud server and combining the charging feature vector set. This value reflects the heat generated by the battery during the supercharging process, which is crucial for monitoring the temperature change of the battery. Through the following heat algorithm model, the heat generation value Qgen (t) at time t can be accurately calculated to help predict the change of heat in the battery during the supercharging process, thereby providing data support for the subsequent temperature control strategy. Next, based on the calculated heat generation value Qgen (t), combined with the charging feature vector, the data is further input into the temperature algorithm model to predict and output the temperature value Tb of the battery. The temperature prediction value reflects the temperature that the battery may reach during the supercharging process, and can predict the occurrence of temperature anomalies in advance, thereby providing a basis for the intervention of the temperature control system. The calculation accuracy and real-time performance of the temperature algorithm model ensure the accurate prediction of the battery temperature and lay the foundation for the optimization of battery temperature control. Finally, combined with the temperature standard of the electric vehicle battery, the initial temperature threshold T is set. The predicted battery temperature value Tb is compared and evaluated with this threshold. When the predicted temperature value Tb at time t is greater than the temperature threshold T, the system recognizes that the battery temperature is abnormal during the supercharging process and triggers the adaptive charging power and cooling system coordination mechanism to adjust the charging power and cooling system working state to ensure that the battery temperature is within a safe range. When the predicted temperature value Tb is less than or equal to the temperature threshold T, the battery temperature changes normally and the system continues to monitor without intervention.

[0112] Example 5

[0113] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S4 includes S41 and S42;

[0114] S41. When the temperature change is abnormal, the adaptive charging power and cooling system coordination mechanism is executed, wherein the adaptive charging power and cooling system coordination mechanism includes an adaptive charging power adjustment mechanism and a cooling system adjustment mechanism.

[0115] The adaptive charging power adjustment mechanism calculates and outputs the supercharging power adjustment value Pcha according to the predicted temperature value Tb(t) of the battery at time t during the supercharging process. When the battery temperature is close to the upper limit, the supercharging power adjustment value Pcha is sent to the battery control system through the cloud server, and the supercharging power is controlled by the battery control system.

[0116] The supercharge power adjustment value Pcha is calculated and output by the following algorithm formula; ;

[0117] Where Pcha(t) represents the supercharging power adjustment value at time t, Pmax represents the supercharging power upper limit, and Tmax represents the safe temperature upper limit of the battery.

[0118] S42, the cooling system adjustment mechanism dynamically adjusts the battery cooling system power by calculating the output cooling power adjustment value Pcool according to the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t and the cooling efficiency Ncoo(t) at time t, and the predicted temperature value Tb(t) at time t;

[0119] The cooling power adjustment value Pcool is calculated and output by the following algorithm formula; ;

[0120] Where Pcool(t) represents the cooling power adjustment value at time t.

[0121] In this embodiment, the method automatically executes the adaptive charging power and cooling system coordination mechanism based on the temperature prediction evaluation results when the predicted battery temperature is abnormal, that is, the battery temperature is close to or exceeds the upper limit of the safe temperature. The mechanism includes an adaptive charging power adjustment mechanism and a cooling system adjustment mechanism, wherein the adaptive charging power adjustment mechanism dynamically adjusts the predicted temperature value Tb of the battery at time t by calculating the supercharge power adjustment value Pcha. If the predicted temperature value Tb is close to the upper limit of the safe temperature of the battery Tmax, the system will automatically adjust the charging power, reduce the supercharge power, and prevent the battery from overheating. Next, the cooling system adjustment mechanism calculates the cooling power adjustment value Pcool. When the predicted temperature value Tb is too high, the cooling system will automatically increase the cooling power, reduce the battery temperature by adjusting the working state of the cooling system, and ensure that the battery operates within a safe temperature range. The cooling power adjustment value Pcool is calculated and output by the following algorithm formula, dynamically adjusts the cooling system power, and effectively controls the battery temperature change.

[0122] Example 6

[0123] This embodiment is explained in Example 5. Please refer to Figure 1 , specifically: S5 includes S51 and S52;

[0124] S51, after the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff of the electric vehicle is comprehensively calculated to quantify the thermal management and supercharging power of the electric vehicle during the supercharging process;

[0125] The battery supercharge efficiency Eeff is calculated and output by the following algorithm formula; ;

[0126] S52. Based on the standard of electric vehicle battery charging temperature, that is, the design temperature range of the battery usually stipulates the temperature at which the battery has the best charging efficiency, the efficiency threshold E is set, and then the battery supercharging efficiency Eeff is compared with the efficiency threshold E to perform efficiency evaluation, analyze the thermal management and charging power during the supercharging process, and the specific evaluation content is as follows;

[0127] When the battery supercharge efficiency Eeff ≥ efficiency threshold E, the battery temperature is in a normal state under the currently adjusted supercharge efficiency, and no intervention is required to continue monitoring;

[0128] When the battery supercharging efficiency Eeff is less than the efficiency threshold E, the battery temperature is in an abnormal state under the currently adjusted supercharging efficiency. At this time, the cooling system efficiency is automatically increased to 100%, and S5 is re-executed to re-analyze the thermal management and charging power during the supercharging process. If the secondary analysis shows an abnormal state, the supercharging is automatically stopped. If the secondary analysis shows a normal state, monitoring continues.

[0129] In this embodiment, the method quantifies the thermal management and charging power of the electric vehicle during the supercharging process by comprehensively calculating the battery supercharging efficiency Eeff after the adaptive charging power and cooling system coordination mechanism is completed. The calculation of the battery supercharging efficiency Eeff combines multiple factors such as charging power, temperature control, cooling efficiency, etc. to ensure the efficient operation of the battery throughout the charging process. The output of the efficiency calculation formula is Eeff, which reflects the overall performance of the battery during the supercharging process. Then, the efficiency threshold E is set according to the battery design temperature range and the charging efficiency standard, and the battery supercharging efficiency Eeff is compared and evaluated with the efficiency threshold E. Based on the evaluation results, the system can determine whether the battery temperature and supercharging efficiency are in normal state. By calculating and evaluating the battery supercharging efficiency Eeff, the system can grasp the thermal management and power usage of the battery during the supercharging process in real time to ensure the high efficiency of the charging process. When the battery supercharging efficiency is lower than the set threshold, the system automatically adjusts the cooling system to improve efficiency, avoiding overheating or damage of the battery due to excessive temperature or excessive power, thereby enhancing the safety of the charging process. By dynamically adjusting the working state of the cooling system and re-executing the efficiency analysis, the optimal regulation of temperature and power during the charging process is ensured, avoiding low charging efficiency or battery damage due to temperature control failure or improper charging power. By setting efficiency thresholds and implementing automated intervention mechanisms, the intelligence level of the system is significantly improved, the need for human intervention is reduced, and electric vehicles can be adaptively adjusted in different working environments, thereby improving the user experience.

[0130] Example 7

[0131] See also Figure 1 and Figure 2 , a supercharging control system for an electric vehicle, comprising a supercharging data acquisition module, a feature extraction module, a heat and temperature analysis module, an adaptive adjustment module and a supercharging efficiency and temperature analysis module;

[0132] The supercharging data acquisition module automatically turns on the built-in sensor group through the electric vehicle's battery management system BMS after supercharging begins, collects battery data in real time during the supercharging process, transmits it to the battery control system, and pre-processes the battery data to obtain a normalized data set;

[0133] The feature extraction module extracts features from the normalized data set to obtain a charging feature vector set, and simultaneously connects to a cloud server, builds a cloud database in the cloud server, and transmits the charging feature vector set to the cloud database for storage;

[0134] The heat and temperature analysis module constructs a heat and temperature algorithm model, extracts the charging feature vector set and inputs it into the heat and temperature algorithm model, calculates the output heat generation value Qgen and the predicted temperature value Tb, sets the temperature threshold T, and performs temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb to analyze the temperature change of the battery of the electric vehicle during supercharging;

[0135] When the temperature prediction and evaluation of the battery temperature is abnormal, the adaptive adjustment module executes the adaptive charging power and cooling system coordination mechanism to calculate the output supercharging power adjustment value Pcha and cooling power adjustment value Pcool respectively;

[0136] After the adaptive charging power and cooling system coordination mechanism is executed, the supercharging efficiency and temperature analysis module calculates and outputs the battery supercharging efficiency Eeff, sets the efficiency threshold E, and then evaluates the efficiency threshold E and the battery supercharging efficiency Eeff to determine the efficiency status and temperature adjustment status during the supercharging process.

[0137] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A supercharging control method for an electric vehicle, characterized in that: The following steps are involved: S1. After supercharging starts, the built-in sensor group is automatically turned on through the battery management system BMS of the electric vehicle to collect battery data in real time during the supercharging process, transmit it to the battery control system, and pre-process the battery data to obtain a normalized data set; S2, extracting features from the normalized data set to obtain a charging feature vector set, connecting to a cloud server, building a cloud database in the cloud server, and transmitting the charging feature vector set to the cloud database for storage; S3, construct a heat and temperature algorithm model, extract the charging feature vector set and input it into the heat and temperature algorithm model, calculate the output heat generation value Qgen and the predicted temperature value Tb, and set the temperature threshold T, perform temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb, and analyze the temperature change of the battery of the electric vehicle during supercharging; S4. When the temperature prediction and evaluation show that the battery temperature is abnormal, the adaptive charging power and cooling system coordination mechanism is executed to calculate the output supercharging power adjustment value Pcha and the cooling power adjustment value Pcool respectively; S5. After the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff is calculated and output, and the efficiency threshold E is set. Then, the efficiency threshold E and the battery supercharging efficiency Eeff are evaluated to determine the efficiency status and temperature adjustment status during the supercharging process.

2. The supercharging control method of an electric vehicle according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, directly connect to the electric vehicle through the supercharging cable, and access the battery management system BMS of the electric vehicle, automatically turn on the built-in sensor group of the electric vehicle, and collect the battery data of the electric vehicle in real time during the supercharging process; The built-in sensor group includes a voltage sensor, a current sensor, a temperature sensor, and a thermistor; The battery data includes voltage Vbat, charging current Icha, battery temperature T, battery heat transfer Q, ambient temperature Tenv and coolant temperature Tcoo; S12, transmitting the battery data to a battery control system of the electric vehicle, and preprocessing the battery data in the battery control system to obtain a normalized data set, wherein the preprocessing includes denoising, outlier detection, timestamp marking, missing value filling, and normalization; The denoising is performed by using a sliding average method to denoise the battery data; The outlier detection is performed by using a Z-score standard deviation method to identify outliers in the battery data and remove the outliers; The timestamp marking is performed by marking the battery data with a timestamp based on the acquisition time of the built-in sensor group; The missing value filling is performed by using data at adjacent moments; The normalization method maps the value of each parameter in the battery data to a fixed interval through the Min-Max normalization method, thereby eliminating the dimension differences of each parameter in the battery data. The normalized data set includes the voltage Vbat(t) at time t, the charging current Icha(t) at time t, the battery temperature T(t) at time t, the battery transfer heat Q(t) at time t, the ambient temperature Tenv(t) at time t, and the coolant temperature Tcoo(t) at time t.

3. The supercharging control method of an electric vehicle according to claim 2, characterized in that: The S2 includes S21 and S22; S21. Perform feature extraction based on the normalized data set to obtain a charging feature vector set; The charging feature vector set includes the battery internal resistance Rbat(t) at time t, the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t, the cooling efficiency Ncoo(t) at time t and the charging current Icha(t) at time t; The battery internal resistance Rbat(t) at time t is extracted by combining the voltage Vbat(t) at time t and the charging current Icha(t) at time t. The specific feature extraction algorithm formula is: ; Wherein, Vopen represents the open circuit voltage of the battery; The battery temperature gradient △Tbat(t) at time t is extracted by calculating the temperature difference of the battery temperature T at different positions of the battery. The specific feature extraction algorithm formula is: Among them, Tbat max Indicates the upper limit of the battery temperature, Tbat min Indicates the lower limit of the temperature inside the battery; The battery heat capacity Cbat(t) at time t is calculated and extracted by using the heat balance equation, combining the battery temperature T(t) at time t and the battery transfer heat Q(t) at time t. The specific feature extraction algorithm formula is: ; Where d represents a small variable, dQ(t) represents a small change in the amount of heat transferred by the battery at time t, and dT(t) represents a small change in the battery temperature at time t; The cooling efficiency Ncoo(t) at time t is calculated and extracted by monitoring the temperature change of the cooling system. The specific feature extraction algorithm formula is: , where △Tcoo(t) represents the change in coolant temperature at time t, Tx max Indicates the upper limit of the design temperature difference of the cooling system; S22, connecting the cloud server to the battery control system via a communication network, building a cloud database in the cloud server, uploading the charging feature vector set to the cloud database via the communication network, and performing cloud storage on the charging feature vector set.

4. The supercharging control method of an electric vehicle according to claim 3, characterized in that: The S3 includes S31, S32 and S33; S31, constructing the heat and temperature algorithm model in the cloud server, the heat and temperature algorithm model includes a heat algorithm model and a temperature algorithm model, extracting a charging feature vector set and inputting it into the heat algorithm model, calculating and outputting a heat generation value Qgen, and predicting the heat generated by the battery of the electric vehicle during the supercharging process; The heat generation value Qgen is calculated and output by the following heat algorithm model; ； Where Qgen(t) represents the heat generation value at time t.

5. The method for controlling supercharging of an electric vehicle according to claim 4, characterized in that: S32, based on the heat generation value Qgen, combined with the charging feature vector set, input into the temperature algorithm model, calculate and output the predicted temperature value Tb, and perform prediction and analysis on the temperature of the battery of the electric vehicle during the supercharging process; The predicted temperature value Tb is calculated and output by the following temperature algorithm model; ； Where Tb(t) represents the predicted temperature value at time t; S33, initially setting a temperature threshold T based on the battery temperature standard of the electric vehicle, and then performing temperature prediction evaluation on the predicted temperature value Tb(t) at the time t and the temperature threshold T, analyzing the abnormal change of the battery temperature during the supercharging process, and generating trigger conditions for the adaptive charging power and cooling system coordination mechanism based on the evaluation results. The specific evaluation contents are as follows; When the predicted temperature value Tb(t) at time t is greater than the temperature threshold T, it indicates that the battery temperature is predicted to change abnormally during the supercharging process, and the adaptive charging power and cooling system coordination mechanism is triggered. When the predicted temperature value Tb(t) at time t ≤ the temperature threshold T, it indicates that the predicted temperature change of the battery during the overcharging process is normal and no intervention and continuous monitoring are required.

6. The supercharging control method of an electric vehicle according to claim 5, characterized in that: The S4 includes S41 and S42; S41. When the temperature change is abnormal, the temperature prediction and evaluation are performed, and the adaptive charging power and cooling system coordination mechanism is executed. The adaptive charging power and cooling system coordination mechanism includes an adaptive charging power adjustment mechanism and a cooling system adjustment mechanism. The adaptive charging power adjustment mechanism calculates and outputs the supercharging power adjustment value Pcha according to the predicted temperature value Tb(t) of the battery at time t during the supercharging process. When the battery temperature is close to the upper limit, the supercharging power adjustment value Pcha is sent to the battery control system through the cloud server, and the supercharging power is controlled by the battery control system. The supercharge power adjustment value Pcha is calculated and output by the following algorithm formula: ； Where Pcha(t) represents the supercharging power adjustment value at time t, Pmax represents the supercharging power upper limit, and Tmax represents the safe temperature upper limit of the battery.

7. The supercharging control method of an electric vehicle according to claim 6, characterized in that: S42, the cooling system adjustment mechanism dynamically adjusts the battery cooling system power by calculating the output cooling power adjustment value Pcool according to the battery temperature gradient △Tbat(t) at time t, the battery heat capacity Cbat(t) at time t, the ambient temperature Tenv(t) at time t and the cooling efficiency Ncoo(t) at time t, and the predicted temperature value Tb(t) at time t; The cooling power adjustment value Pcool is calculated and output by the following algorithm formula; ； Where Pcool(t) represents the cooling power adjustment value at time t.

8. The method for controlling supercharging of an electric vehicle according to claim 6, characterized in that: The S5 includes S51 and S52; S51, after the adaptive charging power and cooling system coordination mechanism is executed, the battery supercharging efficiency Eeff of the electric vehicle is comprehensively calculated to quantify the thermal management and supercharging power of the electric vehicle during the supercharging process; The battery supercharge efficiency Eeff is calculated and output by the following algorithm formula: 。 9. The method for controlling supercharging of an electric vehicle according to claim 8, characterized in that: S52, based on the standard of the electric vehicle battery charging temperature, set the efficiency threshold E, then compare the battery supercharging efficiency Eeff with the efficiency threshold E, perform efficiency evaluation, analyze the thermal management and charging power during the supercharging process, and the specific evaluation content is as follows; When the battery supercharge efficiency Eeff ≥ efficiency threshold E, the battery temperature is in a normal state under the currently adjusted supercharge efficiency, and no intervention is required to continue monitoring; When the battery supercharging efficiency Eeff is less than the efficiency threshold E, the battery temperature is in an abnormal state under the currently adjusted supercharging efficiency. At this time, the cooling system efficiency is automatically increased to 100%, and S5 is re-executed to re-analyze the thermal management and charging power during the supercharging process. If the secondary analysis shows an abnormal state, the supercharging is automatically stopped. If the secondary analysis shows a normal state, monitoring continues.

10. A supercharging control system for an electric vehicle, applied to a supercharging control method for an electric vehicle according to any one of claims 1 to 9, characterized in that: It includes supercharging data acquisition module, feature extraction module, heat and temperature analysis module, adaptive adjustment module and supercharging efficiency and temperature analysis module; The supercharging data acquisition module automatically turns on the built-in sensor group through the battery management system BMS of the electric vehicle after supercharging starts, collects the battery data in real time during the supercharging process, transmits it to the battery control system, and pre-processes the battery data to obtain a normalized data set; The feature extraction module extracts features from the normalized data set to obtain a charging feature vector set, and simultaneously connects to a cloud server, builds a cloud database in the cloud server, and transmits the charging feature vector set to the cloud database for storage; The heat and temperature analysis module constructs a heat and temperature algorithm model, extracts the charging feature vector set and inputs it into the heat and temperature algorithm model, calculates and outputs a heat generation value Qgen and a predicted temperature value Tb, and sets a temperature threshold T, performs temperature prediction evaluation on the temperature threshold T and the predicted temperature value Tb, and analyzes the temperature change of the battery of the electric vehicle during supercharging; When the temperature prediction and evaluation of the battery temperature is abnormal, the adaptive adjustment module executes the adaptive charging power and cooling system coordination mechanism to calculate the output supercharging power adjustment value Pcha and the cooling power adjustment value Pcool respectively; After the adaptive charging power and cooling system collaborative mechanism is executed, the supercharging efficiency and temperature analysis module calculates and outputs the battery supercharging efficiency Eeff, sets the efficiency threshold E, and then performs efficiency evaluation on the efficiency threshold E and the battery supercharging efficiency Eeff to determine the efficiency status and temperature adjustment status during the supercharging process.

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