Charging and discharging protection method, device and storage medium for lithium battery circuit protection board

Abstract

The present invention relates to a charge and discharge protection method, device and storage medium for a lithium battery circuit protection board, the method comprising calculating the actual battery pack capacity value of a lithium battery pack; obtaining the power change data of the lithium battery, and generating a power prediction value according to the power change data; obtaining the real-time power value of the lithium battery, and comparing the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value; obtaining the historical speed data of the electric vehicle, and associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor; obtaining the real-time road conditions of the road, and generating predicted speed data according to the real-time road conditions, and calculating the predicted power deviation value based on the predicted speed data and the speed factor; calculating the power prediction value based on the predicted power deviation value to obtain the corrected power prediction value, and setting the charge and discharge protection strategy based on the corrected power prediction value. The present application has the effect of extending the service life of the lithium battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management control, and in particular to a charge and discharge protection method, device and storage medium for a lithium battery circuit protection board. Background Art

[0002] Lithium battery packs are widely used in electric vehicles and other fields. In order to protect the safety of lithium battery packs and extend their lifespan, circuit protection boards and battery management systems (BMS) are usually used to monitor and control lithium battery packs.

[0003] The existing BMS mainly monitors the voltage, temperature, current and other parameters of the entire battery pack to prevent overcharging or over-discharging, thereby protecting the safety of the battery pack. The circuit protection board used in electric vehicles monitors the charging and discharging current of the battery pack through a current sensor. By detecting the direction and magnitude of the current, the circuit protection board determines whether the battery pack is currently in a charging or discharging state. When the power (predicted value) exceeds the threshold set by the BMS, the BMS will take measures, such as reducing the charging speed or stopping charging, to prevent excessive current from damaging the battery pack.

[0004] Specifically, the circuit protection board of the electric vehicle predicts the power consumption through the current power consumption efficiency, and generates a charge and discharge protection strategy to remind the user to charge and stop discharging (park) in time.

[0005] Regarding the above-mentioned related technologies, there are the following defects: there are many power consumption factors in the driving process of electric vehicles, and the existing power prediction based on the current power consumption efficiency is not accurate. There is a situation where the power consumption is higher than the predicted value, that is, the set threshold is reached in advance, and the battery is over-discharged, causing damage to the lithium battery and reducing the overall life of the battery pack. Therefore, improvement is needed. Summary of the invention

[0006] In order to extend the service life of lithium batteries, the present application provides a charging and discharging protection method, device and storage medium for a lithium battery circuit protection board.

[0007] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0008] A charging and discharging protection method for a lithium battery circuit protection board, the method comprising the steps of:

[0009] Obtain the current actual battery capacity of all lithium batteries in the lithium battery pack, and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0010] Acquire power change data of the lithium battery pack, and generate a power prediction value of the lithium battery pack according to the power change data;

[0011] Acquire the real-time power value of the lithium battery pack, and compare the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value;

[0012] Acquire historical speed data of the electric vehicle, associate the historical speed data with the actual power deviation value according to a timestamp, and calculate a speed factor according to the actual power deviation value and the speed data;

[0013] Acquiring real-time road conditions, generating predicted vehicle speed data according to the real-time road conditions, and calculating a predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0014] Based on the predicted power deviation value, the power prediction value is corrected and calculated to obtain a corrected power prediction value, and a charge and discharge protection strategy is set based on the corrected power prediction value. The charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

[0015] By adopting the above technical solution, the historical speed data of the electric vehicle is obtained, and the historical speed data is associated with the actual power deviation value according to the timestamp, and the speed factor is calculated. This step takes into account the impact of the speed on the power consumption, making the power prediction more targeted and accurate; the real-time road conditions are obtained, and the predicted speed data is generated according to the real-time road conditions. The predicted power deviation value is calculated based on the predicted speed data and the speed factor, and the impact of the real-time road conditions on the speed is further considered, so as to more comprehensively predict the power change; the power prediction value is corrected based on the predicted power deviation value to obtain the corrected power prediction value. Compared with the existing power prediction based only on the power consumption rate, and using this as the prediction result, a warning is issued when approaching overcharging or over-discharging. This solution comprehensively considers the impact of speed prediction and real-time road conditions on power, and combines speed prediction with real-time road conditions. Therefore, it can more accurately predict the battery usage of the electric vehicle, provide a more accurate basis for the setting of the charging and discharging protection strategy, and help avoid over-discharge caused by excessive use, thereby extending the battery life and improving safety.

[0016] In a preferred example, the present application can be further configured as follows: after obtaining the real-time power value of the lithium battery pack and comparing the real-time power value with the change prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value, the following steps are included:

[0017] Acquire environmental data, the environmental data including temperature data, humidity data and wind data, and construct an environmental usage profile based on the environmental data;

[0018] Associating the environment usage profile with the actual power deviation value based on the timestamp, and analyzing the correlation between the environment usage profile and the actual power deviation value to generate an environment correction factor;

[0019] Acquire predicted environmental data, and calculate an environmental factor deviation value based on the predicted environmental data and an environmental correction factor;

[0020] The power prediction value is corrected based on the environmental factor deviation value to generate an environmental correction power prediction value.

[0021] By adopting the above technical solution, environmental data is obtained and an environmental usage portrait is constructed, an environmental correction factor is generated, the environmental factor deviation value and the correction power prediction value are calculated. By obtaining environmental data, generating environmental correction factors and calculating environmental factor deviation values, the actual power change of the lithium battery can be predicted more accurately compared with the existing technology. Such power prediction results can provide a more accurate basis for the setting of charging and discharging protection strategies, avoiding over-discharge caused by excessive use. At the same time, through the correction calculation of environmental data, the user can be informed of the power status in time, thereby improving the convenience and safety of electric vehicles.

[0022] In a preferred example, the present application can be further configured as follows: in the step of obtaining historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor according to the actual power deviation value and the speed data, the speed factor also includes a uniform speed sub-factor, including the following steps:

[0023] Filtering the uniform speed data in the historical vehicle speed data, and associating the uniform speed data with the actual power deviation value based on a timestamp to construct a uniform speed data-actual power deviation value data set, and storing the uniform speed data-actual power deviation value data set in a uniform speed database;

[0024] The factor calculation model calculates the uniform speed sub-factor based on the uniform speed efficiency-actual power deviation value data set in the uniform speed database.

[0025] By adopting the above technical solution, on the basis of obtaining historical vehicle speed data, the uniform speed data is further screened out, and the uniform speed data is associated with the actual power deviation value, a uniform speed data-actual power deviation value data set is constructed, and it is stored in the uniform speed database. Then, based on the uniform speed efficiency-actual power deviation value data set in the uniform speed database, the uniform speed sub-factor is calculated, and the uniform speed data in the historical vehicle speed data is screened and associated with the actual power deviation value. A uniform speed data-actual power deviation value data set is constructed. In this way, the power change under uniform speed driving can be analyzed and modeled, providing a more accurate data base. Based on the uniform speed efficiency-actual power deviation value data set in the uniform speed database, this scheme calculates the uniform speed sub-factor, which can be used to measure the power consumption efficiency under uniform speed driving conditions, and provide a more accurate indicator for the calculation of the vehicle speed factor. Through these steps, this scheme can more accurately calculate the vehicle speed factor, including the uniform speed sub-factor. Such calculation results can more accurately reflect the impact of vehicle speed on power consumption, and provide a more reliable basis for power prediction and the formulation of charging and discharging protection strategies. At the same time, by storing uniform speed data and building a uniform speed database, it can provide a useful foundation for subsequent data analysis and model optimization.

[0026] In a preferred example, the present application can be further configured as follows: in the step of obtaining historical vehicle speed data of the electric vehicle, associating the historical vehicle speed data with the actual power deviation value according to the timestamp, and calculating the vehicle speed factor according to the actual power deviation value and the vehicle speed data, the vehicle speed factor includes a speed change sub-factor, including the following steps:

[0027] screening the speed change data in the historical vehicle speed data, and calculating the speed change efficiency according to the speed change data;

[0028] Associating the speed change efficiency with the actual power deviation value based on the timestamp to construct a speed change efficiency-actual power deviation value data set, and storing the speed change efficiency-actual power deviation value data set in a speed change database;

[0029] The preset factor calculation model calculates the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database.

[0030] By adopting the above technical scheme, on the basis of obtaining historical vehicle speed data, the speed change data is further screened out, and the speed change efficiency is calculated based on the speed change data. Then, the speed change efficiency is associated with the actual power deviation value according to the timestamp, and a speed change efficiency-actual power deviation value data set is constructed and stored in the speed change database. Finally, the preset factor calculation model is based on the speed change efficiency-actual power deviation value data set in the speed change database to calculate the speed change sub-factor. Compared with the existing technology, this scheme filters the speed change data and calculates the speed change efficiency: this scheme filters the speed change data in the historical vehicle speed data, and calculates the speed change efficiency based on this data, so that the power change under the speed change driving condition can be analyzed and modeled, providing a more accurate data basis; stored in the speed change database: this scheme will change The speed efficiency-actual power deviation value data set is stored in the speed change database. By establishing such a database, the data under speed change driving conditions can be easily stored and managed, and it is convenient for subsequent factor calculations; calculate the speed change sub-factor: based on the speed change efficiency-actual power deviation value data set in the speed change database, this solution uses a preset factor calculation model to calculate the speed change sub-factor, which can be used to measure the power consumption efficiency under speed change driving conditions, and provide a more accurate indicator for the calculation of the vehicle speed factor. Such calculation results can more accurately reflect the impact of vehicle speed on power consumption, and provide a more reliable basis for power prediction and the formulation of charging and discharging protection strategies. At the same time, by storing speed change data and building a speed change database, it can provide a useful foundation for subsequent data analysis and model optimization.

[0031] In a preferred example, the present application may be further configured as follows: after the step of calculating the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database by the preset factor calculation model, the following steps are included:

[0032] Acquire braking data of the electric vehicle, and construct a braking-power data set by associating the timestamp with the corresponding actual battery pack capacity value;

[0033] The preset kinetic energy recovery calculation model analyzes the kinetic energy recovery amount corresponding to the braking data based on the braking-electricity data set, and the kinetic energy recovery amount is used to compensate the lithium battery for electricity;

[0034] A kinetic energy recovery factor is generated based on the braking data and the corresponding kinetic energy recovery amount, and the kinetic energy recovery factor is used to calculate the electric energy recovery value during deceleration.

[0035] By adopting the above technical solution, the braking data is associated with the corresponding actual battery capacity, and a braking-electricity data set is constructed, so that the change of electricity during the braking process can be analyzed, and an accurate data basis can be provided for the subsequent kinetic energy recovery calculation; this solution uses a preset kinetic energy recovery calculation model to analyze the kinetic energy recovery amount corresponding to the braking data, so as to accurately calculate the kinetic energy recovered during the braking process, thereby compensating the lithium battery for electricity and improving energy utilization efficiency; the kinetic energy recovery factor is used to quantify the efficiency of electricity recovery during deceleration, providing a more accurate basis for electricity management and prediction. Compared with the existing electricity prediction, this solution can more accurately calculate the electricity recovery value during deceleration, and compensate the lithium battery for electricity according to the kinetic energy recovery amount, which can improve the energy utilization efficiency of the battery, extend the cruising range of the electric vehicle, and optimize the service life of the battery. At the same time, by constructing a braking-electricity data set and using a kinetic energy recovery calculation model, a useful basis can be provided for subsequent data analysis and model optimization.

[0036] In a preferred example, the present application can be further configured as follows: in the step of obtaining the real-time road condition of the road, generating predicted vehicle speed data according to the real-time road condition, and calculating the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor, the following steps are included:

[0037] Acquire real-time traffic conditions of roads, including road congestion and the number of traffic lights;

[0038] Calculating the speed state of the electric vehicle on each road section based on the road congestion condition, and calculating the expected uniform speed power deviation value of the electric vehicle in a uniform speed state based on the speed factor;

[0039] Generate the number of braking times of the electric vehicle based on the number of traffic lights, and predict the expected braking data corresponding to braking at each traffic light according to the vehicle speed state;

[0040] An expected electric power recovery value is calculated based on the expected braking data and the kinetic energy recovery factor, and the expected electric power recovery value is used to calculate electric power surplus compensation.

[0041] By adopting the above technical scheme, the real-time road conditions, including road congestion and the number of traffic lights, are comprehensively considered, and the energy management of electric vehicles is carried out based on this information. Specifically, this scheme obtains the real-time road conditions, calculates the speed status of electric vehicles in each section, and uses the speed factor to calculate the expected power deviation value under the uniform speed state. At the same time, based on the number of traffic lights, the number of braking times of the electric vehicle is predicted, and the expected braking data corresponding to the braking at each traffic light is predicted according to the speed status. Finally, based on the expected braking data and the kinetic energy recovery factor, the expected power recovery value is calculated for power surplus compensation. Such comprehensive analysis and prediction can more accurately evaluate the energy consumption and recovery of electric vehicles under different sections and traffic lights, thereby optimizing energy utilization, extending the cruising range, and increasing the service life of the battery. By considering the impact of real-time road conditions on the speed and the number of braking times, this scheme provides a more refined and scientific method for the energy management of electric vehicles, which has significant benefits.

[0042] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0043] A charging and discharging protection device for a lithium battery circuit protection board, the device comprising:

[0044] The actual battery capacity calculation module is used to obtain the actual battery capacity of all lithium batteries in the lithium battery pack and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0045] A power prediction value generation module is used to obtain power change data of the lithium battery and generate a power prediction value according to the power change data;

[0046] The actual power deviation value calculation module is used to obtain the real-time power value of the lithium battery pack and compare the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value;

[0047] A vehicle speed factor calculation module, used for obtaining historical vehicle speed data of the electric vehicle, associating the historical vehicle speed data with the actual power deviation value according to a timestamp, and calculating a vehicle speed factor according to the actual power deviation value and the vehicle speed data;

[0048] A predicted power deviation value calculation module, used to obtain real-time road conditions, generate predicted vehicle speed data according to the real-time road conditions, and calculate the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0049] A charge and discharge protection strategy generation module is used to perform a correction calculation on the power prediction value based on the predicted power deviation value to obtain a correction power prediction value, and set a charge and discharge protection strategy based on the correction power prediction value, wherein the charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

[0050] By adopting the above technical solution, the historical speed data of the electric vehicle is obtained, and the historical speed data is associated with the actual power deviation value according to the timestamp, and the speed factor is calculated. This step takes into account the impact of the speed on the power consumption, making the power prediction more targeted and accurate; the real-time road conditions are obtained, and the predicted speed data is generated according to the real-time road conditions. The predicted power deviation value is calculated based on the predicted speed data and the speed factor, and the impact of the real-time road conditions on the speed is further considered, so as to more comprehensively predict the power change; the power prediction value is corrected based on the predicted power deviation value to obtain the corrected power prediction value. Compared with the existing power prediction based only on the power consumption rate, and using this as the prediction result, a warning is issued when approaching overcharging or over-discharging. This solution comprehensively considers the impact of speed prediction and real-time road conditions on power, and combines speed prediction with real-time road conditions. Therefore, it can more accurately predict the battery usage of the electric vehicle, provide a more accurate basis for the setting of the charging and discharging protection strategy, and help avoid over-discharge caused by excessive use, thereby extending the battery life and improving safety.

[0051] Preferably, it also includes an environment usage portrait building module, which is used to obtain environmental data, the environmental data including temperature data, humidity data and wind data, and build an environment usage portrait based on the environmental data;

[0052] An environmental correction factor generation module, used to associate the environmental usage portrait with the actual power deviation value based on a timestamp, and analyze the correlation between the environmental usage portrait and the actual power deviation value to generate an environmental correction factor;

[0053] An environmental factor deviation value calculation module, used to obtain predicted environmental data, and calculate the environmental factor deviation value based on the predicted environmental data and the environmental correction factor;

[0054] The environment correction power prediction value generation module is used to perform correction calculation on the power prediction value based on the environmental factor deviation value to generate the environment correction power prediction value.

[0055] By adopting the above technical solution, environmental data is obtained and an environmental usage portrait is constructed, an environmental correction factor is generated, the environmental factor deviation value and the correction power prediction value are calculated. By obtaining environmental data, generating environmental correction factors and calculating environmental factor deviation values, the actual power change of the lithium battery can be predicted more accurately compared with the existing technology. Such power prediction results can provide a more accurate basis for the setting of charging and discharging protection strategies, avoiding over-discharge caused by excessive use. At the same time, through the correction calculation of environmental data, the user can be informed of the power status in time, thereby improving the convenience and safety of electric vehicles.

[0056] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0057] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned charging and discharging protection method for a lithium battery circuit protection board are implemented.

[0058] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0059] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the charging and discharging protection method for a lithium battery circuit protection board are implemented.

[0060] In summary, the present application includes at least one of the following beneficial technical effects:

[0061] 1. Obtain the historical speed data of the electric vehicle, and associate the historical speed data with the actual power deviation value according to the timestamp, and calculate the speed factor. This step considers the impact of speed on power consumption, making the power prediction more targeted and accurate; obtain the real-time road conditions, and generate predicted speed data according to the real-time road conditions, calculate the predicted power deviation value based on the predicted speed data and the speed factor, and further consider the impact of the real-time road conditions on the speed, so as to more comprehensively predict the power change; perform correction calculation on the power prediction value based on the predicted power deviation value to obtain the corrected power prediction value. Compared with the existing power prediction based only on the power consumption rate, and use this as the prediction result, when approaching overcharge or over-discharge, a warning is issued. This solution comprehensively considers the impact of speed prediction and real-time road conditions on power, and combines speed prediction with real-time road conditions. Therefore, it can more accurately predict the battery usage of electric vehicles, provide a more accurate basis for the setting of charging and discharging protection strategies, and help avoid over-discharge caused by excessive use, thereby extending the battery life and improving safety;

[0062] 2. Obtain environmental data and build an environmental use portrait, generate environmental correction factors, calculate environmental factor deviation values, and correction power prediction values. By obtaining environmental data, generating environmental correction factors, and calculating environmental factor deviation values, the actual power changes of lithium batteries can be predicted more accurately than the existing technology. Such power prediction results can provide a more accurate basis for the setting of charge and discharge protection strategies, avoiding over-discharge caused by excessive use. At the same time, through the correction calculation of environmental data, users can be informed of the power status in a timely manner, thereby improving the convenience and safety of electric vehicles.

[0063] 3. The braking data is associated with the corresponding actual battery capacity, and a braking-power data set is constructed, so that the power changes during the braking process can be analyzed and an accurate data basis can be provided for the subsequent kinetic energy recovery calculation. This solution uses a preset kinetic energy recovery calculation model to analyze the kinetic energy recovery amount corresponding to the braking data, so as to accurately calculate the kinetic energy recovered during the braking process, thereby compensating the lithium battery for power and improving energy utilization efficiency. The kinetic energy recovery factor is used to quantify the power recovery efficiency during the deceleration process, providing a more accurate basis for power management and prediction. Compared with the existing power prediction, this solution can more accurately calculate the power recovery value during deceleration, and compensate the lithium battery for power according to the kinetic energy recovery amount, which can improve the energy utilization efficiency of the battery, extend the cruising range of the electric vehicle, and optimize the battery life. At the same time, by constructing a braking-power data set and using a kinetic energy recovery calculation model, a useful basis can be provided for subsequent data analysis and model optimization.

[0064] 4. The real-time road conditions, including road congestion and the number of traffic lights, are comprehensively considered, and the energy management of electric vehicles is carried out based on this information. Specifically, this solution obtains the real-time road conditions, calculates the speed status of electric vehicles in each section, and uses the speed factor to calculate the expected power deviation value under uniform speed. At the same time, based on the number of traffic lights, the number of braking times of electric vehicles is predicted, and the expected braking data corresponding to braking at each traffic light is predicted based on the speed status. Finally, based on the expected braking data and the kinetic energy recovery factor, the expected power recovery value is calculated for power surplus compensation. Such comprehensive analysis and prediction can more accurately evaluate the energy consumption and recovery of electric vehicles under different sections and traffic lights, thereby optimizing energy utilization, extending the cruising range, and increasing the service life of the battery. By considering the impact of real-time road conditions on speed and braking times, this solution provides a more refined and scientific method for the energy management of electric vehicles, which has significant benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the steps of a charging and discharging protection method for a lithium battery circuit protection board in an embodiment of the present application;

[0066] Figure 2 It is a flow chart for implementing the method for charging and discharging protection of a lithium battery circuit protection board after step S30 in an embodiment of the present application;

[0067] Figure 3 It is a flowchart for implementing step S40 in a charging and discharging protection method for a lithium battery circuit protection board in an embodiment of the present application;

[0068] Figure 4 It is a flowchart of the implementation after step S45 in a charge and discharge protection method for a lithium battery circuit protection board in an embodiment of the present application;

[0069] Figure 5 It is a flowchart for implementing step S50 in a charging and discharging protection method for a lithium battery circuit protection board in an embodiment of the present application;

[0070] Figure 6 It is a principle block diagram of a charge and discharge protection device for a lithium battery circuit protection board in an embodiment of the present application;

[0071] Figure 7 is a schematic diagram of an electronic device in an embodiment of the present application.

[0072] Figure Number:

[0073] 1. Actual battery capacity calculation module; 2. Power prediction value generation module; 3. Actual power deviation value calculation module; 4. Vehicle speed factor calculation module; 5. Prediction power deviation value calculation module; 6. Charge and discharge protection strategy generation module; 7. Environmental usage portrait construction module; 8. Environmental correction factor generation module; 9. Environmental factor deviation value calculation module; 10. Environmental correction power prediction value generation module. DETAILED DESCRIPTION

[0074] The present application is further described in detail below in conjunction with the accompanying drawings.

[0075] In one embodiment, if Figure 1-5 As shown, the present application discloses a charging and discharging protection method for a lithium battery circuit protection board, which specifically includes the following steps:

[0076] S10: acquiring the actual battery capacity of all lithium batteries in the lithium battery pack in real time, and calculating the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0077] Specifically, the calculation formula for the actual battery pack capacity value is: Where i is the serial number of the lithium battery, E iis the actual capacity value of each battery, n represents the number of lithium batteries in the lithium battery pack, and in the embodiment of the present application, the battery pack of the electric vehicle includes ten lithium battery cells. According to the data obtained by the power monitoring, the capacity of each lithium battery cell is 5Ah, and the actual battery pack capacity value E is calculated. 实际 It is 50Ah.

[0078] S20: Acquire power change data of the lithium battery pack, and generate a power prediction value according to the power change data;

[0079] Specifically, the power level of each lithium battery is monitored in real time, and the overall real-time power level of the lithium battery pack is calculated based on the real-time capacity of each lithium battery. The power change efficiency within the preset time interval is calculated. In an embodiment of the present application, analysis and prediction are performed based on a preset regression analysis model. Further, a linear regression model is established based on the power change efficiency for prediction to generate the next time interval and subsequent power prediction values. In an embodiment of the present application, the power prediction value is E1, that is, the power prediction value of the battery pack at a certain point in time.

[0080] S30: Acquire a real-time power value of the lithium battery pack, and compare the real-time power value with the power prediction value to calculate an actual power deviation value between the real-time power value and the power prediction value;

[0081] Specifically, within the set timestamp, the power prediction value corresponding to a certain time point is E1, and the actual power value monitored at this time point is E2, E0=E2-E1, wherein E0 is the actual power deviation value, and E0 is a vector value, if E0 is greater than 0, the actual power value is greater than the predicted power prediction value E1 (the actual power consumption is less than the predicted amount), if E0 is less than 0, the actual power value is less than the predicted power prediction value E1 (the actual power consumption is greater than the predicted amount), E0 is used in the regression root model for power prediction as a compensation value for subsequent analysis, and compensation and correction are performed, thereby improving the prediction accuracy of the regression analysis model used for power prediction.

[0082] S40: acquiring historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to a timestamp, and calculating a speed factor according to the actual power deviation value and the speed data;

[0083] Specifically, the embodiment of the present application is exemplified as follows: the speed data obtained from the historical speed data of the electric vehicle are 50km / h, 60km / h and 70km / h respectively, and the difference p between the actual power deviation value E0 and the power prediction value E1 corresponds to -5%, -6% and -7% respectively. In the embodiment of the present application, the speed factor is defined as the difference of p for every 10km / h interval of speed (the difference is 1% in the present application), then the speed factor t is 0.01 in the embodiment of the present application, and the speed factor is used to calculate the actual power deviation value when calculating the actual power deviation value. E0 = (V*t / 10)*100%*E1, where V is the actual speed. In the embodiment of the present application, the speed factor is used to indicate the degree of influence of the speed on the actual power deviation value, and in the present application, the speed factor is positively correlated with the degree of influence of the actual power deviation value (the larger the speed factor, the larger the actual power deviation value).

[0084] S50: acquiring real-time road conditions, generating predicted vehicle speed data according to the real-time road conditions, and calculating a predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0085] Specifically, the speed of the electric vehicle is predicted by the real-time road conditions. If the predicted speed data is 60 km / h, the predicted power deviation value E01 is calculated based on the predicted speed data (60 km / h) and the speed factor t (0.01) = (V 预测 *t / 10)*100%*E1, where E01 is a vector value.

[0086] S60: performing a deviation correction calculation on the power prediction value based on the predicted power deviation value to obtain a deviation correction power prediction value E3, and setting a charge and discharge protection strategy based on the deviation correction power prediction value E3, wherein the charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time;

[0087] Specifically, the correction power prediction value E3=E1+E01. In the embodiment of the present application, the charge and discharge protection strategy includes a charge and discharge protection threshold. When E3 reaches the charge and discharge protection threshold, an over-discharge warning notification is issued, and the remaining power usage time (i.e., the remaining charge and discharge time) is calculated based on E3. On the premise of improving the accuracy of power prediction, more reminder information for vehicle use is given to vehicle users, which is convenient for vehicle users to change the vehicle usage status and charge in time, thereby improving the vehicle battery life and the user experience.

[0088] After the step of obtaining the real-time power value of the lithium battery pack and comparing the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value, refer to Figure 2 , the method further comprises the following steps:

[0089] S301: Acquire environmental data, the environmental data including temperature data, humidity data and wind data, and construct an environmental use portrait based on the environmental data;

[0090] Specifically, real-time temperature data, humidity data, and wind data are obtained through sensors or weather stations and other equipment. Based on these environmental data, an environmental usage profile is constructed to record temperature changes, humidity levels, and wind speeds in different time periods.

[0091] S302: Associating the environment usage portrait with the actual power deviation value E0 based on the timestamp, analyzing the correlation between the environment usage portrait and the actual power deviation value E0, and generating an environment correction factor;

[0092] Specifically, the environmental usage profile and the actual power deviation value are associated by timestamp, for example, the relationship between the temperature, humidity and wind speed in a certain period of time and the actual power deviation value is analyzed. Through statistical analysis or regression analysis and other methods, an environmental correction factor can be generated to correct the environmental impact in power forecasting.

[0093] In practical applications, environmental correction factors and environmental factor deviation values ​​can be generated by analyzing a variety of data through methods such as statistical analysis or regression analysis, or they can be customized or calculated through other models.

[0094] S303: Acquire predicted environmental data, and calculate an environmental factor deviation value based on the predicted environmental data and an environmental correction factor;

[0095] Specifically, the predicted environmental data for a period of time in the future, such as the predicted values ​​of temperature, humidity and wind speed, are obtained based on the prediction model or weather forecast information. Combined with the previously generated environmental correction factor, the environmental factor deviation value can be calculated, that is, the difference between the predicted environmental data and the normal environmental data. By calculating the environmental factor deviation value, the impact of future environmental changes on power forecasting is considered, providing accurate environmental factor data for correction calculation.

[0096] S304: performing deviation correction calculation on the power prediction value based on the environmental factor deviation value, and generating an environmental deviation correction power prediction value to be used as a new power prediction value;

[0097] Specifically, according to the environmental factor deviation value and the power prediction value, a correction calculation is performed to correct the predicted power change value. For example, according to the positive or negative direction and size of the environmental factor deviation value, the power prediction value is corrected accordingly.

[0098] In some feasible solutions, in step S40: obtaining historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor according to the actual power deviation value and the speed data, the speed factor also includes a uniform speed sub-factor, see Figure 3 , S40 further includes the following steps:

[0099] S41: Filtering the uniform speed data in the historical vehicle speed data, and associating the uniform speed data with the actual power deviation value based on a timestamp to construct a uniform speed data-actual power deviation value data set, and storing the uniform speed data-actual power deviation value data set in a uniform speed database;

[0100] Specifically, suppose we have a set of historical vehicle speed data and corresponding actual power deviation values. We select the time periods with small speed changes as the time periods of uniform speed driving and obtain uniform speed data. We associate these uniform speed data with the corresponding actual power deviation values ​​to form a data pair of uniform speed data-actual power deviation value.

[0101] For example, in an embodiment of the present application, we choose to filter out the time period between 55km / h and 65km / h from the historical vehicle speed data as uniform speed data, and then associate these uniform speed data with the corresponding actual power deviation values ​​to obtain a uniform speed data-actual power deviation value data set. This data set will include the vehicle speed during uniform driving and the corresponding actual power deviation value. It should be noted that in other embodiments, the definition of uniform speed can be a constant speed or a fluctuating speed range.

[0102] By constructing a uniform speed data-actual power deviation value dataset, we can more accurately understand the actual power deviation when driving at a uniform speed, and provide accurate data support for subsequent uniform speed sub-factor calculations.

[0103] S42: The preset factor calculation model calculates the uniform speed sub-factor based on the uniform speed efficiency-actual power deviation value data set in the uniform speed database;

[0104] Specifically, in the embodiment of the present application, the uniform speed data-actual power deviation value data set is self-learned and analyzed through the regression analysis model to generate the uniform speed sub-factor a, and when the vehicle speed is at a uniform speed, t=a, the predicted power deviation value E01=(V 预测*a / 10)*100%*E1. By calculating the uniform speed sub-factor, we can understand the influence of vehicle speed on the actual power deviation during uniform driving, so as to more accurately predict the power consumption during uniform driving and provide an important reference for the energy management of electric vehicles. It should be noted that the uniform speed sub-factor is calculated based on the data of specific vehicles and battery systems, and the uniform speed sub-factors of different vehicles and different battery systems may be different.

[0105] In some feasible solutions, in step S40: acquiring historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor according to the actual power deviation value and the speed data, the speed factor includes a speed change sub-factor, and step S40 further includes the following steps:

[0106] S43: screening the speed change data in the historical vehicle speed data, and calculating the speed change efficiency according to the speed change data;

[0107] Specifically, in the embodiment of the present application, the time period in which the vehicle speed changes by more than 10 km / h is selected from the historical vehicle speed data as the speed change data (the time interval is set to 10 km / h when calculating the uniform speed sub-factor in this embodiment). Then, we calculate the ratio of the vehicle speed change amount to time in these speed change data, that is, the speed change efficiency. The speed change efficiency can represent the change amount of the vehicle speed per unit time, reflecting the speed and efficiency of the speed change. By calculating the speed change efficiency, the energy consumption of the vehicle during the speed change process can be understood, providing accurate data support for the subsequent speed change sub-factor calculation.

[0108] S44: associating the speed change efficiency with the actual power deviation value based on the timestamp to construct a speed change efficiency-actual power deviation value data set, and storing the speed change efficiency-actual power deviation value data set in a speed change database;

[0109] Specifically, the data of the speed change efficiency of 0.1 (indicating that the vehicle speed changes by 10 km / h per second) is associated with the corresponding actual power deviation value to obtain a speed change efficiency-actual power deviation value data set. This data set will contain the speed change efficiency and the corresponding actual power deviation value.

[0110] S45: The preset factor calculation model calculates the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database;

[0111] Specifically, a regression analysis model is used to perform regression analysis on the speed change efficiency-actual power deviation value data set to obtain the best speed change sub-factor estimate, and the estimated value of the speed change sub-factor is obtained according to the result of the regression analysis. In the embodiment of the present application, the speed change sub-factor is 0.03. It means that for every unit increase in speed change efficiency, the actual power deviation value will increase by 0.03 units.

[0112] In some feasible solutions, after the step of S45: using the preset factor calculation model to calculate the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database, refer to Figure 4 , the method further comprises the following steps:

[0113] S451: Acquire braking data of the electric vehicle, and construct a braking-power data set by associating the timestamp with the corresponding actual battery capacity value;

[0114] Specifically, it is necessary to obtain the braking data of the electric vehicle, and associate the braking data with the corresponding actual battery capacity according to the timestamp to construct a braking-power data set.

[0115] For example, suppose we have a set of electric vehicle braking data, which contains information about vehicle speed changes and braking force during braking. These braking data are associated with the corresponding actual battery capacity according to the timestamp to form a braking-power data set, which will contain braking data (vehicle speed changes and braking force information) and the corresponding actual battery capacity.

[0116] S452: The preset kinetic energy recovery calculation model analyzes the kinetic energy recovery amount corresponding to the braking data based on the braking-electricity data set, and the kinetic energy recovery amount is used to compensate the lithium battery for electricity;

[0117] Specifically, a predefined formula or algorithm is used to calculate the kinetic energy recovery amount based on the braking data in the brake-electricity data set. This kinetic energy recovery amount can represent the amount of electricity that can be recovered during braking. By analyzing the kinetic energy recovery amount (setting a conversion factor to convert the kinetic energy recovery amount into recovered electricity), the energy recovery situation during braking can be understood, providing an important reference for the energy management of electric vehicles and used to compensate the lithium battery for electricity.

[0118] S453: generating a kinetic energy recovery factor based on the braking data and the corresponding kinetic energy recovery amount, wherein the kinetic energy recovery factor is used to calculate the electric energy recovery value during deceleration;

[0119] Specifically, in the embodiments of the present application, the formula is used: kinetic energy recovery amount = braking data * kinetic energy recovery factor, and the corresponding expected recovered electricity is calculated through the kinetic energy recovery factor and the expected electricity value is calculated; further, the actual electricity value is obtained to calculate the actual electricity recovery value, and the kinetic energy recovery factor is optimized through a preset optimization model (such as a genetic algorithm model and a particle swarm algorithm model) by comparing the expected recovered electricity and the actual recovered electricity, so as to improve the prediction of the kinetic energy recovery amount during braking, thereby more accurately predicting the electricity consumption and recovery during the deceleration process. In other embodiments, the kinetic energy recovery factor can be a pre-set parameter used to adjust the kinetic energy recovery amount corresponding to the braking data.

[0120] In some feasible solutions, in step S50: obtaining the real-time road condition, generating predicted vehicle speed data according to the real-time road condition, and calculating the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor, please refer to Figure 5 , further comprising the following steps:

[0121] S51: Acquire real-time traffic conditions of the road, where the real-time traffic conditions include road congestion and the number of traffic lights;

[0122] Specifically, real-time traffic information of a city's main roads can be obtained through the data interface of the traffic management department or the city navigation software, including road congestion (such as smooth, crowded, blocked, etc.) and the number of traffic lights. By obtaining the real-time traffic conditions of the roads, we can understand the current traffic conditions on the roads, including the degree of congestion and the number of traffic lights, providing an important reference for subsequent vehicle speed and energy consumption predictions.

[0123] S52: calculating the speed state of the electric vehicle on each road section based on the road congestion condition, and calculating the expected uniform speed power deviation value of the electric vehicle in a uniform speed state based on the speed factor;

[0124] Specifically, according to the road congestion situation, we can predict the speed status of electric vehicles in various sections of the road. For example, the speed is slower on crowded roads and faster on unobstructed roads. In the embodiment of the present application, the speed limit requirements of the comprehensive road are used to predict the speed, and the expected power deviation value under a uniform speed state is calculated in combination with the speed factor. By calculating the expected power deviation value under a uniform speed state, we can predict the energy consumption of electric vehicles in different sections of the road according to the road congestion situation, and provide a basis for power management and cruising range prediction.

[0125] S53: generating the number of braking times of the electric vehicle based on the number of traffic lights, and predicting the expected braking data corresponding to braking at each traffic light according to the vehicle speed state;

[0126] Specifically, based on the number of traffic lights on the road, we predict the number of times the electric vehicle needs to brake, and combined with the vehicle speed status, we can predict the expected braking data corresponding to each traffic light braking, including the braking force and duration. By predicting the expected braking data when braking at each traffic light, we can more accurately evaluate the energy recovery of electric vehicles when driving on urban roads, and provide data support for subsequent energy management and power surplus compensation.

[0127] S54: calculating an expected power recovery value based on the expected braking data and the kinetic energy recovery factor, wherein the expected power recovery value is used to calculate power surplus compensation;

[0128] Specifically, the expected braking data includes the size and duration of the braking force, and the expected recovered electricity is calculated based on the kinetic energy recovery factor, that is, the amount of electricity recovered when passing through traffic lights while driving on urban roads. This expected electricity recovery value will be used to calculate electricity surplus compensation, increase the electricity prediction dimension of electric vehicles during use, and improve the accuracy of electricity prediction.

[0129] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] In one embodiment, a charge and discharge protection device for a lithium battery circuit protection board is provided, and the charge and discharge protection device for a lithium battery circuit protection board corresponds one-to-one with a charge and discharge protection method for a lithium battery circuit protection board in the above embodiment. Figure 6 As shown, the charging and discharging protection device for the lithium battery circuit protection board includes:

[0131] The actual battery capacity calculation module 1 is used to obtain the actual battery capacity of all lithium batteries in the lithium battery pack, and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0132] The power prediction value generating module 2 is used to obtain power change data of the lithium battery pack and generate a power prediction value according to the power change data;

[0133] The actual power deviation value calculation module 3 is used to obtain the real-time power value of the lithium battery pack and compare the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value;

[0134] The vehicle speed factor calculation module 4 is used to obtain the historical vehicle speed data of the electric vehicle, associate the historical vehicle speed data with the actual power deviation value according to the timestamp, and calculate the vehicle speed factor according to the actual power deviation value and the vehicle speed data;

[0135] The predicted power deviation value calculation module 5 is used to obtain the real-time road conditions, generate predicted vehicle speed data according to the real-time road conditions, and calculate the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0136] A charge and discharge protection strategy generating module 6 is used to perform a deviation correction calculation on the power prediction value based on the predicted power deviation value to obtain a deviation correction power prediction value, and to set a charge and discharge protection strategy based on the deviation correction power prediction value, wherein the charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time;

[0137] An environment usage portrait building module 7 is used to obtain environment data, the environment data including temperature data, humidity data and wind data, and build an environment usage portrait based on the environment data;

[0138] An environmental correction factor generation module 8 is used to associate the environmental usage portrait with the actual power deviation value based on a timestamp, and analyze the correlation between the environmental usage portrait and the actual power deviation value to generate an environmental correction factor;

[0139] An environmental factor deviation value calculation module 9 is used to obtain predicted environmental data and calculate the environmental factor deviation value based on the predicted environmental data and the environmental correction factor;

[0140] The environment correction power prediction value generation module 10 is used to perform correction calculation on the power prediction value based on the environmental factor deviation value to generate an environment correction power prediction value.

[0141] For the specific definition of the charge and discharge protection device for lithium battery circuit protection board, please refer to the definition of the charge and discharge protection method for lithium battery circuit protection board above, which will not be repeated here. Each module in the above-mentioned charge and discharge protection device for lithium battery circuit protection board can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0142] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure is shown in FIG. Figure 7As shown. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a charging and discharging protection method for a lithium battery circuit protection board is implemented.

[0143] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0144] Obtain the actual battery capacity of all lithium batteries in the lithium battery pack, and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0145] Acquire power change data of the lithium battery, and generate a power quantization prediction value according to the power change data;

[0146] Acquire the real-time power value of the lithium battery, and compare the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value;

[0147] Acquire historical speed data of the electric vehicle, associate the historical speed data with the actual power deviation value according to a timestamp, and calculate a speed factor according to the actual power deviation value and the speed data;

[0148] Acquiring real-time road conditions, generating predicted vehicle speed data according to the real-time road conditions, and calculating a predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0149] Based on the predicted power deviation value, the power prediction value is corrected and calculated to obtain a corrected power prediction value, and a charge and discharge protection strategy is set based on the corrected power prediction value. The charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

[0150] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0151] Obtain the actual battery capacity of all lithium batteries in the lithium battery pack, and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries;

[0152] Acquire power change data of the lithium battery, and generate a power prediction value based on the power change data;

[0153] Acquire the real-time power value of the lithium battery, and compare the real-time power value with the power prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value;

[0154] Acquire historical speed data of the electric vehicle, associate the historical speed data with the actual power deviation value according to a timestamp, and calculate a speed factor according to the actual power deviation value and the speed data;

[0155] Acquiring real-time road conditions, generating predicted vehicle speed data according to the real-time road conditions, and calculating a predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor;

[0156] Based on the predicted power deviation value, the power prediction value is corrected and calculated to obtain a corrected power prediction value, and a charge and discharge protection strategy is set based on the corrected power prediction value. The charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0158] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0159] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A charging and discharging protection method for a lithium battery circuit protection board, characterized in that: The method comprises the following steps: Obtain the current actual battery capacity of all lithium batteries in the lithium battery pack, and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries; Acquire power change data of the lithium battery pack, and generate a power prediction value of the lithium battery pack according to the power change data; Acquire a real-time power value of a lithium battery pack, and compare the real-time power value with a predicted power value of the lithium battery pack to calculate an actual power deviation value between the real-time power value and the predicted power value; Acquire historical speed data of the electric vehicle, associate the historical speed data with the actual power deviation value according to a timestamp, and calculate a speed factor according to the actual power deviation value and the speed data; Acquiring real-time road conditions, generating predicted vehicle speed data according to the real-time road conditions, and calculating a predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor; Based on the predicted power deviation value, the power prediction value is corrected and calculated to obtain a corrected power prediction value, and a charge and discharge protection strategy is set based on the corrected power prediction value. The charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

2. A charge and discharge protection method for a lithium battery circuit protection board according to claim 1, characterized in that: After the step of obtaining the real-time power value of the lithium battery pack and comparing the real-time power value with the power quantization prediction value to calculate the actual power deviation value between the real-time power value and the power prediction value, the method further includes the following steps: Acquire environmental data, the environmental data including temperature data, humidity data and wind data, and construct an environmental usage profile based on the environmental data; Associating the environment usage profile with the actual power deviation value based on the timestamp, and analyzing the correlation between the environment usage profile and the actual power deviation value to generate an environment correction factor; Acquire predicted environmental data, and calculate an environmental factor deviation value based on the predicted environmental data and an environmental correction factor; The power prediction value is corrected based on the environmental factor deviation value to generate an environmental correction power prediction value.

3. A charge and discharge protection method for a lithium battery circuit protection board according to claim 1, characterized in that: In the step of acquiring historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor according to the actual power deviation value and the speed data, the speed factor also includes a uniform speed sub-factor, including the following steps: Filtering the uniform speed data in the historical vehicle speed data, and associating the uniform speed data with the actual power deviation value based on a timestamp to construct a uniform speed data-actual power deviation value data set, and storing the uniform speed data-actual power deviation value data set in a uniform speed database; The uniform speed sub-factor is calculated by a preset factor calculation model based on the uniform speed data-actual power deviation value data set in the uniform speed database.

4. A charge and discharge protection method for a lithium battery circuit protection board according to claim 3, characterized in that: In the step of acquiring historical speed data of the electric vehicle, associating the historical speed data with the actual power deviation value according to the timestamp, and calculating the speed factor according to the actual power deviation value and the speed data, the speed factor includes a speed change sub-factor, including the following steps: screening the speed change data in the historical vehicle speed data, and calculating the speed change efficiency according to the speed change data; Associating the speed change efficiency with the actual power deviation value based on the timestamp to construct a speed change efficiency-actual power deviation value data set, and storing the speed change efficiency-actual power deviation value data set in a speed change database; The preset factor calculation model calculates the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database.

5. A charge and discharge protection method for a lithium battery circuit protection board according to claim 4, characterized in that: After the step of calculating the speed change sub-factor based on the speed change efficiency-actual power deviation value data set in the speed change database by using the preset factor calculation model, the following steps are included: Acquire braking data of the electric vehicle, and construct a braking-power data set by associating the timestamp with the corresponding actual battery pack capacity value; The preset kinetic energy recovery calculation model analyzes the kinetic energy recovery amount corresponding to the braking data based on the braking-electricity data set, and the kinetic energy recovery amount is used to compensate the lithium battery for electricity; A kinetic energy recovery factor is generated based on the braking data and the corresponding kinetic energy recovery amount, and the kinetic energy recovery factor is used to calculate the electric energy recovery value during deceleration.

6. A charge and discharge protection method for a lithium battery circuit protection board according to claim 5, characterized in that: The step of acquiring the real-time road condition, generating predicted vehicle speed data according to the real-time road condition, and calculating the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor includes the following steps: Acquire real-time traffic conditions of roads, including road congestion and the number of traffic lights; Calculating the speed state of the electric vehicle on each road section based on the road congestion condition, and calculating the expected uniform speed power deviation value of the electric vehicle in a uniform speed state based on the speed factor; Generate the number of braking times of the electric vehicle based on the number of traffic lights, and predict the expected braking data corresponding to braking at each traffic light according to the vehicle speed state; An expected electric power recovery value is calculated based on the expected braking data and the kinetic energy recovery factor, and the expected electric power recovery value is used to calculate electric power surplus compensation.

7. A charge and discharge protection device for a lithium battery circuit protection board, characterized in that: The device comprises: An actual battery capacity calculation module (1) is used to obtain the actual battery capacity of all lithium batteries in the lithium battery pack and calculate the actual battery pack capacity value of the lithium battery pack according to the actual battery capacity of the lithium batteries; A power change value generating module (2) is used to obtain power change data of a lithium battery pack and generate a power prediction value according to the power change data; An actual power deviation value calculation module (3) is used to obtain a real-time power value of the lithium battery pack and compare the real-time power value with the power prediction value to calculate an actual power deviation value between the real-time power value and the power prediction value; A vehicle speed factor calculation module (4) is used to obtain historical vehicle speed data of the electric vehicle, associate the historical vehicle speed data with the actual power deviation value according to a timestamp, and calculate a vehicle speed factor according to the actual power deviation value and the vehicle speed data; A predicted power deviation value calculation module (5) is used to obtain real-time road conditions, generate predicted vehicle speed data according to the real-time road conditions, and calculate the predicted power deviation value based on the predicted vehicle speed data and the vehicle speed factor; A charge and discharge protection strategy generation module (6) is used to perform a deviation correction calculation on the power prediction value based on the predicted power deviation value to obtain a deviation correction power prediction value, and to set a charge and discharge protection strategy based on the deviation correction power prediction value, wherein the charge and discharge protection strategy includes a charge and discharge protection threshold and a remaining charge and discharge time.

8. A charge and discharge protection device for a lithium battery circuit protection board according to claim 7, characterized in that: It also includes an environment usage portrait building module (7) for acquiring environment data, the environment data including temperature data, humidity data and wind data, and building an environment usage portrait based on the environment data; An environmental correction factor generation module (8), used for associating the environmental usage portrait with the actual power deviation value based on a timestamp, and analyzing the correlation between the environmental usage portrait and the actual power deviation value to generate an environmental correction factor; An environmental factor deviation value calculation module (9), used for obtaining predicted environmental data, and calculating the environmental factor deviation value based on the predicted environmental data and the environmental correction factor; The environment deviation correction power prediction value generation module (10) is used to perform deviation correction calculation on the power prediction value based on the environmental factor deviation value to generate the environment deviation correction power prediction value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the charge and discharge protection method for a lithium battery circuit protection board as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the charge and discharge protection method for a lithium battery circuit protection board as described in any one of claims 1 to 6 are implemented.

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