An electric vehicle battery pre-heating method and system

Abstract

The application provides an electric vehicle battery preheating method and system, the method comprising: obtaining battery state data of a target vehicle, and performing anomaly detection on the battery state data based on battery characteristics to obtain a state detection result; when the state detection result is normal, obtaining battery loss data of the target vehicle, performing secondary regression analysis on the battery loss data, determining an optimal preheating temperature, and preheating the battery of the target vehicle until the temperature of the battery reaches the optimal preheating temperature. The application can improve the effectiveness and safety of battery preheating and improve the operating performance of the electric vehicle.

Description

Technical Field

[0001] This invention belongs to the field of vehicle battery preheating technology, specifically relating to a method and system for preheating electric vehicle batteries. Background Technology

[0002] With the increasing popularity of electric vehicles (EVs), the battery, as a core component, significantly impacts the vehicle's range, charging efficiency, and lifespan. Battery performance is typically poor in low-temperature environments, especially for lithium-ion batteries. Decreased low-temperature performance leads to reduced discharge capacity, slower charging speeds, and even potential battery damage. Therefore, battery preheating technology has become a key technology for improving the low-temperature performance of electric vehicles and extending battery lifespan.

[0003] Currently, common methods for preheating electric vehicle batteries mainly include two types: one is to directly heat the battery pack using external heating elements, such as electric heaters or hot air; the other is to use the heat generated by the current or resistance inside the battery for preheating. The core objective of these methods is to raise the battery temperature before it enters a low-temperature environment through preheating, thereby optimizing its operating state and charge / discharge performance.

[0004] However, existing battery preheating methods have many shortcomings, particularly in failing to adequately consider the specific operating state of the battery. In current technologies, most preheating schemes rely on a fixed preset temperature or simple empirical judgment to initiate the heating process. For example, many electric vehicle battery preheating processes determine whether to heat and to what temperature solely based on the ambient temperature or the battery's initial temperature. These methods typically do not comprehensively consider the battery's current state (such as SOC, temperature uniformity, and battery health), which can easily lead to the following problems:

[0005] 1. Low efficiency: Due to the lack of real-time monitoring and evaluation of battery status in current technology, the preheating process is often too simple and fails to be adjusted according to the actual needs of the battery, resulting in low heating efficiency. For example, in low-temperature environments, the battery's charging and discharging efficiency decreases, and overheating or heating too quickly may lead to uneven temperature distribution inside the battery, or even aggravate battery damage.

[0006] 2. Inappropriate preheating temperature: In existing methods, the preheating temperature is often set based on experience or the external ambient temperature, without taking into account the specific operating state of the battery. For example, factors such as the battery's SOC, internal impedance, and service life may affect its temperature requirements. Blindly setting the temperature can easily lead to overheating or underheating, thereby affecting the battery's performance.

[0007] 3. Reliance on subjective judgment: Most current battery preheating strategies rely on human experience and preset rules to determine whether to activate preheating or set the preheating temperature. This experience-based judgment method cannot accurately assess the actual condition of the battery, so the results may have significant uncertainty, further affecting the vehicle's operating performance in low-temperature environments.

[0008] 4. Safety hazards: Blindly preheating the battery when it is in an abnormal state will lead to safety hazards and threaten personal safety. Summary of the Invention

[0009] The technical problem solved by this invention is to provide a method and system for preheating electric vehicle batteries, so as to improve the effectiveness and safety of battery preheating and enhance the operating performance of electric vehicles.

[0010] In a first aspect, the present invention provides a method for preheating an electric vehicle battery, the method comprising the following steps:

[0011] The system acquires battery status data of the target vehicle and performs anomaly detection on the battery status data based on battery characteristics to obtain status detection results. The battery status data includes battery voltage and battery temperature, and the status detection results are normal or abnormal.

[0012] When the status detection result is normal, the battery loss data of the target vehicle is acquired, a secondary regression analysis is performed on the battery loss data to determine the optimal preheating temperature, and the battery of the target vehicle is preheated until the battery temperature reaches the optimal preheating temperature; the battery loss data includes the power consumption of the target vehicle at multiple temperatures.

[0013] Optionally, anomaly detection can be performed on the battery state data based on battery characteristics to obtain state detection results, including:

[0014] When the highest voltage value of the battery is less than the first preset voltage threshold or greater than the second preset voltage threshold, the state detection result is determined to be abnormal.

[0015] When the lowest value of the battery voltage is less than the third preset voltage threshold or greater than the fourth preset voltage threshold, the state detection result is determined to be abnormal.

[0016] When the highest temperature of the battery is less than the first preset temperature threshold or greater than the second preset temperature threshold, the state detection result is determined to be abnormal.

[0017] When the lowest temperature of the battery is less than the third preset temperature threshold or greater than the fourth preset temperature threshold, the status detection result is determined to be abnormal.

[0018] Otherwise, the status detection result is determined to be normal; the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, and the fourth preset voltage threshold are determined by the electrochemical stability window of the battery in the target vehicle, and the first preset temperature threshold, the second preset temperature threshold, the third preset temperature threshold, and the fourth preset temperature threshold are determined by the SEI film thickness of the battery in the target vehicle.

[0019] Optionally, a secondary regression analysis can be performed on the battery loss data to determine the optimal preheating temperature, including:

[0020] Through calculation formula

[0021]

[0022] E = β0 + β1T + β2T 2 +∈

[0023]

[0024] Obtain the optimal preheating temperature T opt Where β0 represents the intercept, indicating the expected power consumption when the preheating temperature T is 0 degrees Celsius; β1 is the coefficient of the first term, representing the influence of the preheating temperature T on the power consumption; β2 is the coefficient of the second term, representing the influence of the square of the preheating temperature T on the power consumption; ∈ represents the error term; X represents the design matrix, including the first and second terms of temperature; and Y represents the vector representation of the power consumption. This represents the estimated value of the regression coefficient, and E represents the power consumption.

[0025] Optionally, before acquiring the battery state data of the target vehicle, performing anomaly detection on the battery state data based on battery characteristics, and obtaining the state detection result, the electric vehicle battery preheating method further includes:

[0026] Obtain multiple historical mileage information for the target vehicle; the historical mileage information includes a timestamp and the mileage corresponding to that timestamp;

[0027] If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

[0028] Optionally, before acquiring the battery state data of the target vehicle, performing anomaly detection on the battery state data based on battery characteristics, and obtaining the state detection result, the electric vehicle battery preheating method further includes:

[0029] Obtain the data transmission status of the target vehicle; the data transmission status is normal, missing, or interrupted.

[0030] If the data transmission status is missing or interrupted, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

[0031] Optionally, battery status data may also include battery capacity;

[0032] The system acquires battery state data of the target vehicle, performs anomaly detection on the battery state data based on battery characteristics, and obtains state detection results, including:

[0033] When the battery capacity is less than the preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal; the preset capacity threshold is 90%.

[0034] Secondly, the present invention also provides an electric vehicle battery preheating system, comprising:

[0035] The battery status detection module is used to acquire battery status data of the target vehicle and perform anomaly detection on the battery status data based on battery characteristics to obtain status detection results; the battery status data includes battery voltage and battery temperature, and the status detection result is normal or abnormal.

[0036] The preheating module is used to acquire battery loss data of the target vehicle when the status detection result is normal, perform secondary regression analysis on the battery loss data to determine the optimal preheating temperature, and preheat the battery of the target vehicle until the battery temperature reaches the optimal preheating temperature; the battery loss data includes the power consumption of the target vehicle at multiple temperatures.

[0037] Optionally, the electric vehicle battery preheating system also includes:

[0038] The data transmission status monitoring module is used to obtain the data transmission status of the target vehicle. When the data transmission status is missing or interrupted, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The data transmission status is normal, missing or interrupted.

[0039] Optionally, the electric vehicle battery preheating system also includes:

[0040] The mileage status detection module is used to obtain multiple historical mileage information of the target vehicle. If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

[0041] Optionally, the electric vehicle battery preheating system also includes:

[0042] The battery capacity detection module is used to obtain the battery capacity of the target vehicle. When the battery capacity is less than the preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The preset capacity threshold is 90%.

[0043] The beneficial effects of this invention are:

[0044] The electric vehicle battery preheating method provided by this invention acquires battery state data of the target vehicle and performs anomaly detection on the battery state data based on battery characteristics to obtain state detection results. When the state detection result is normal, the battery loss data of the target vehicle is acquired, and a secondary regression analysis is performed on the battery loss data to determine the optimal preheating temperature. The battery of the target vehicle is then preheated until the battery temperature reaches the optimal preheating temperature. Anomaly detection based on battery characteristics allows for preheating of the battery while ensuring its normal operation, greatly improving the safety of battery preheating. Furthermore, the optimal preheating temperature is determined based on the battery's own loss data, without relying on preset temperatures or simple empirical judgments, which helps improve the effectiveness of battery preheating and thus enhances the operating performance of the electric vehicle. Attached Figure Description

[0045] Figure 1 This is a flowchart of a method for preheating an electric vehicle battery in one embodiment of this application;

[0046] Figure 2 This is a structural diagram of an electric vehicle battery preheating system according to one embodiment of this application. Detailed Implementation

[0047] To address the technical problems of low effectiveness, poor safety, and unsatisfactory operating performance of electric vehicles caused by traditional methods, this invention provides a method and system for preheating electric vehicle batteries. The method involves acquiring battery state data of the target vehicle and performing anomaly detection on this data based on battery characteristics to obtain a state detection result. When the state detection result is normal, battery wear data is acquired, and secondary regression analysis is performed to determine the optimal preheating temperature. The battery is then preheated until the optimal preheating temperature is reached. The anomaly detection based on battery characteristics ensures that the battery is preheated while maintaining normal operation, significantly improving the safety of battery preheating. Furthermore, the optimal preheating temperature is determined based on the battery's own wear data, without relying on preset temperatures or simple empirical judgments, which improves the effectiveness of battery preheating and thus enhances the operating performance of the electric vehicle.

[0048] The method for preheating electric vehicle batteries provided by the present invention will be described below.

[0049] like Figure 1As shown, in this embodiment of the invention, the electric vehicle battery preheating method includes steps 101 to 102.

[0050] Step 101: Obtain the battery status data of the target vehicle, and perform anomaly detection on the battery status data based on battery characteristics to obtain the status detection result.

[0051] Specifically, the battery status data mentioned above includes battery voltage and battery temperature.

[0052] In some embodiments of the present invention, battery voltage and battery temperature can be directly obtained through the vehicle’s own battery management system (BMS). The BMS is a key system in electric vehicles, responsible for monitoring and managing the health, charging status and performance of the battery.

[0053] For example, the BMS measures the total voltage of the battery pack in real time using a battery voltage monitoring module. In electric vehicles, battery pack voltages typically range from several hundred volts (e.g., 400V or 800V). This voltage data is converted from analog to digital signals by an A / D converter (analog-to-digital converter) for processing by the BMS system. In addition to the total voltage of the battery pack, the BMS also monitors the voltage of each individual battery cell. This is typically achieved through voltage sampling circuitry. Voltage sensors acquire the voltage of each individual battery cell in real time and transmit it to the BMS via a data bus (such as a CAN bus).

[0054] Regarding battery temperature, the BMS (Battery Management System) can acquire the battery temperature of the target vehicle through temperature sensors. Typically, multiple temperature sensors are placed within the battery pack to monitor the battery's operating temperature in real time. Common temperature sensors include thermocouples and resistance temperature detectors (RTDs). These sensors can be placed in different locations within the battery pack, especially in high-temperature areas and critical components, to ensure comprehensive monitoring of the battery temperature.

[0055] The following describes the process of detecting anomalies in battery state data based on battery characteristics and obtaining state detection results.

[0056] For example, in an embodiment of the present invention, when the highest voltage value of the battery is less than a first preset voltage threshold or greater than a second preset voltage threshold, the state detection result is determined to be abnormal; when the lowest voltage value of the battery is less than a third preset voltage threshold or greater than a fourth preset voltage threshold, the state detection result is determined to be abnormal.

[0057] The aforementioned first, second, third, and fourth preset voltage thresholds are determined by the electrochemical stability window of the battery in the target vehicle. At higher voltages, the electrolyte inside the battery may decompose, generating gases and harmful substances, further increasing internal pressure and causing battery failure. Conversely, when the battery voltage drops too low, the negative electrode material may undergo structural changes, leading to irreversible loss of battery capacity. Furthermore, the electrolyte in the battery may not effectively support the battery's chemical reactions at low voltages, thus affecting battery performance.

[0058] For example, in one embodiment of the present invention, the electric vehicle is a Tesla Model S, the first preset voltage threshold is 650V, the second preset voltage threshold is 800V, the third preset voltage threshold is 400V, and the fourth preset voltage threshold is 640V.

[0059] The aforementioned electrochemical stability window can be determined using cyclic voltammetry. For example, the electrolyte and electrodes are placed in an electrochemical cell; a linearly varying voltage (scanning potential) is applied to the electrodes, typically from negative to positive, or from positive to negative; the current versus voltage curve (voltammetric curve) is recorded. If the oxidation peak of the electrolyte appears at a positive potential and the reduction peak appears at a negative potential, then the electrochemical stability window is the range between these two potentials.

[0060] When the battery voltage meets the above discrimination conditions (the highest value of the battery voltage is less than the first preset voltage threshold or greater than the second preset voltage threshold; the lowest value of the battery voltage is less than the third preset voltage threshold or greater than the fourth preset voltage threshold), the state detection result is determined to be abnormal, and a voltage alarm can be sent to the driver, the vehicle terminal, or the cloud platform to facilitate fault diagnosis and maintenance of the electric vehicle and ensure vehicle safety.

[0061] When the highest battery temperature is less than the first preset temperature threshold or greater than the second preset temperature threshold, the status detection result is determined to be abnormal; when the lowest battery temperature is less than the third preset temperature threshold or greater than the fourth preset temperature threshold, the status detection result is determined to be abnormal.

[0062] In an embodiment of the present invention, the first preset temperature threshold, the second preset temperature threshold, the third preset temperature threshold, and the fourth preset temperature threshold are determined by the SEI film thickness of the battery in the target vehicle.

[0063] The SEI film is a thin film formed on the surface of the negative electrode in a lithium-ion battery. It serves a protective function, preventing direct contact between the negative electrode and the electrolyte and avoiding unnecessary side reactions. Within a suitable temperature range (typically 20°C to 60°C), the SEI film is stable and effectively protects the negative electrode. However, at high temperatures (above 100°C), the SEI film may become unstable, leading to rupture or failure. This exposes the negative electrode to the electrolyte, triggering side reactions and potentially causing short circuits or thermal runaway. At low temperatures, the molecular motion of the electrolyte slows down, reducing the reaction rate between the electrolyte and the electrode surface. Therefore, the formation process of the SEI film may not be as efficient as at room temperature, resulting in insufficient or uneven film thickness.

[0064] For example, in one embodiment of the present invention, the first preset temperature threshold is 60°C, the second preset temperature threshold is 100°C, the third preset temperature threshold is -30°C, and the fourth preset temperature threshold is 0°C.

[0065] When the battery temperature meets the above discrimination conditions (the highest battery temperature is less than the first preset temperature threshold or greater than the second preset temperature threshold; the lowest battery temperature is less than the third preset temperature threshold or greater than the fourth preset temperature threshold), the state detection result is determined to be abnormal, and a temperature alarm can be sent to the driver, the vehicle terminal, or the cloud platform to facilitate fault diagnosis and maintenance of the electric vehicle and ensure vehicle safety.

[0066] In another embodiment of the present invention, before acquiring the battery state data of the target vehicle, performing anomaly detection on the battery state data based on battery characteristics, and obtaining the state detection result, the electric vehicle battery preheating method further includes:

[0067] Obtain multiple historical mileage records for the target vehicle.

[0068] Historical mileage information includes a timestamp and the mileage corresponding to that timestamp.

[0069] If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal. Otherwise, the status detection result is determined to be normal.

[0070] It should be noted that in the embodiments of the present invention, the timestamp is fixed at 10s. Since the normal operating speed range of the vehicle is from 0km / h to 360km / h, if the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, it indicates that the vehicle's operating speed exceeds 360km / h. At this time, there may be two situations: abnormal vehicle speed or abnormal data transmission. Either situation indicates that the vehicle's status is abnormal.

[0071] In another embodiment of the present invention, before acquiring the battery state data of the target vehicle, performing anomaly detection on the battery state data based on battery characteristics, and obtaining the state detection result, the electric vehicle battery preheating method further includes:

[0072] Obtain the data transmission status of the target vehicle.

[0073] The data transmission status is categorized as normal, missing, or interrupted. For example, this data transmission status can be obtained through the vehicle-mounted OBO-II interface.

[0074] When the data transmission status is missing or interrupted, the status detection result is determined to be abnormal.

[0075] Otherwise, the status detection result is determined to be normal.

[0076] It should be noted that when data transmission is missing or interrupted, vehicle status data (battery status data) may not be accurately obtained, posing a safety hazard and making it impossible to accurately determine the optimal preheating temperature.

[0077] In another embodiment of the present invention, the battery state data further includes battery capacity. Accordingly, acquiring the battery state data of the target vehicle and performing anomaly detection on the battery state data based on battery characteristics to obtain a state detection result further includes:

[0078] When the battery capacity is less than the preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

[0079] The preset capacity threshold is 90%. When the SOH value is 90%-100%, it indicates that the battery is in a brand new, unaged state, with the best performance, and finding the optimal suitable temperature strategy is effective. When the SOH value gradually decreases and SOH < 90%, it indicates that the battery performance is gradually deteriorating and the degree of aging is increasing, and finding the optimal suitable temperature strategy is ineffective.

[0080] Step 102: When the status detection result is normal, acquire the battery loss data of the target vehicle, perform secondary regression analysis on the battery loss data, determine the optimal preheating temperature, and preheat the battery of the target vehicle until the battery temperature reaches the optimal preheating temperature.

[0081] The battery degradation data mentioned above includes the power consumption of the target vehicle at multiple temperatures.

[0082] Specifically, in the embodiments of the present invention, after obtaining battery loss data, a calculation formula is used...

[0083]

[0084] Obtain the average power consumption corresponding to temperature i. and standard deviation S i m represents the sample size, E ij Let represent the power consumption corresponding to temperature i, and j represent the j-th sample. Each sample corresponds to the power consumption of the target vehicle at a given temperature.

[0085] The ANOVA (analysis of variance) test showed that there were significant differences in the mean power consumption at different temperature points. Therefore, this invention determines the optimal preheating temperature by performing a secondary regression analysis on the battery loss data.

[0086] The following explains the process of performing a secondary regression analysis on the battery loss data in step 102 to determine the optimal preheating temperature. Specifically, this is done through the calculation formula...

[0087]

[0088] E = β0 + β1T + β2T 2 +∈

[0089]

[0090] Obtain the optimal preheating temperature T opt Where β0 represents the intercept, indicating the expected power consumption when the preheating temperature T is 0 degrees Celsius; β1 is the coefficient of the first term, representing the influence of the preheating temperature T on the power consumption; β2 is the coefficient of the second term, representing the influence of the square of the preheating temperature T on the power consumption; ∈ represents the error term; X represents the design matrix, including the first and second terms of temperature; and Y represents the vector representation of the power consumption. This represents the estimated value of the regression coefficient, and E represents the power consumption.

[0091] Once the optimal preheating temperature is determined, the battery of the target vehicle can be preheated according to the optimal preheating temperature.

[0092] For example, in one embodiment of the present invention, the process is as follows:

[0093] The cloud platform generates preheating instructions based on the optimal preheating temperature;

[0094] The preheating command is sent to the vehicle's TBOX via the vehicle network gateway;

[0095] After receiving the preheating command, the vehicle-mounted TBOX directly forwards it to the battery management system;

[0096] The battery management system executes the preheating command and reports the power battery temperature to the vehicle's TBOX in real time;

[0097] The vehicle-mounted TBOX reports the power battery temperature to the cloud platform in real time. When the feedback temperature is lower than the optimal preheating temperature, the platform does not issue a temperature stop command and continues heating; when the feedback temperature is greater than or equal to the optimal preheating temperature, the platform issues a temperature stop command and stops heating.

[0098] As can be seen from the above, the electric vehicle battery preheating method provided by this invention acquires battery state data of the target vehicle and performs anomaly detection on the battery state data based on battery characteristics to obtain state detection results. When the state detection result is normal, the battery loss data of the target vehicle is acquired, and a secondary regression analysis is performed on the battery loss data to determine the optimal preheating temperature. The battery of the target vehicle is then preheated until the battery temperature reaches the optimal preheating temperature. Specifically, anomaly detection of battery state data based on battery characteristics ensures that the battery is preheated while maintaining normal operation, greatly improving the safety of battery preheating. Furthermore, the optimal preheating temperature is determined based on the battery's own loss data, without relying on preset temperatures or simple empirical judgments, which helps improve the effectiveness of battery preheating and thus improves the operating performance of the electric vehicle.

[0099] The electric vehicle battery preheating system provided by the present invention will be described below.

[0100] like Figure 2 As shown, the electric vehicle battery preheating system 200 includes:

[0101] The battery status detection module 201 is used to acquire battery status data of the target vehicle and perform anomaly detection on the battery status data based on battery characteristics to obtain status detection results; the battery status data includes battery voltage and battery temperature, and the status detection results are normal or abnormal.

[0102] The data transmission status monitoring module 202 is used to acquire the data transmission status of the target vehicle. When the data transmission status is missing or interrupted, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The data transmission status is normal, missing or interrupted.

[0103] The mileage status detection module 203 is used to acquire multiple historical mileage information of the target vehicle. If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

[0104] The battery capacity detection module 204 is used to obtain the battery capacity of the target vehicle. When the battery capacity is less than a preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The preset capacity threshold is 90%.

[0105] The preheating module 205 is used to acquire the battery loss data of the target vehicle when the status detection result is normal, perform secondary regression analysis on the battery loss data to determine the optimal preheating temperature, and preheat the battery of the target vehicle until the battery temperature reaches the optimal preheating temperature; the battery loss data includes the power consumption of the target vehicle at multiple temperatures.

[0106] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here. Those skilled in the art will understand that, for the sake of convenience and brevity, the division of the above-mentioned functional units and modules is only used as an example. In practical applications, the above functions can be assigned to 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. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0108] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method for preheating an electric vehicle battery, characterized in that, include: Obtain the battery status data of the target vehicle, and perform anomaly detection on the battery status data based on battery characteristics to obtain the status detection result; The battery status data includes battery voltage and battery temperature, and the status detection result is normal or abnormal. When the status detection result is normal, the battery loss data of the target vehicle is acquired, a secondary regression analysis is performed on the battery loss data to determine the optimal preheating temperature, and the battery of the target vehicle is preheated until the battery temperature reaches the optimal preheating temperature; the battery loss data includes the power consumption of the target vehicle at multiple temperatures.

2. The method for preheating an electric vehicle battery according to claim 1, characterized in that, The anomaly detection of the battery state data based on battery characteristics, to obtain state detection results, includes: When the highest voltage value of the battery is less than a first preset voltage threshold or greater than a second preset voltage threshold, the state detection result is determined to be abnormal. When the lowest value of the battery voltage is less than the third preset voltage threshold or greater than the fourth preset voltage threshold, the state detection result is determined to be abnormal. When the highest temperature of the battery is less than the first preset temperature threshold or greater than the second preset temperature threshold, the state detection result is determined to be abnormal. When the lowest temperature of the battery is less than the third preset temperature threshold or greater than the fourth preset temperature threshold, the state detection result is determined to be abnormal. Otherwise, the state detection result is determined to be normal; the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, and the fourth preset voltage threshold are determined by the electrochemical stability window of the battery in the target vehicle, and the first preset temperature threshold, the second preset temperature threshold, the third preset temperature threshold, and the fourth preset temperature threshold are determined by the SEI film thickness of the battery in the target vehicle.

3. The method for preheating an electric vehicle battery according to claim 2, characterized in that, A quadratic regression analysis was performed on the battery loss data to determine the optimal preheating temperature, including: Through calculation formula E=β0+β1T+β2T 2 +∈ The optimal preheating temperature T is obtained. opt Where β0 represents the intercept, indicating the expected power consumption when the preheating temperature T is 0 degrees Celsius; β1 is the coefficient of the first term, representing the influence of the preheating temperature T on the power consumption; β2 is the coefficient of the second term, representing the influence of the square of the preheating temperature T on the power consumption; ∈ represents the error term; X represents the design matrix, including the first and second terms of temperature; and Y represents the vector representation of the power consumption. This represents the estimated value of the regression coefficient, and E represents the power consumption.

4. The method for preheating an electric vehicle battery according to claim 1, characterized in that, Before acquiring battery state data of the target vehicle and performing anomaly detection on the battery state data based on battery characteristics to obtain the state detection result, the electric vehicle battery preheating method further includes: Obtain multiple historical mileage information of the target vehicle; the historical mileage information includes a timestamp and the mileage corresponding to the timestamp; If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

5. The method for preheating an electric vehicle battery according to claim 1, characterized in that, Before acquiring battery state data of the target vehicle and performing anomaly detection on the battery state data based on battery characteristics to obtain the state detection result, the electric vehicle battery preheating method further includes: Obtain the data transmission status of the target vehicle; the data transmission status is normal, missing, or interrupted. When the data transmission status is missing or interrupted, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

6. The method for preheating an electric vehicle battery according to claim 1, characterized in that, The battery status data also includes battery capacity; The step of acquiring battery state data of the target vehicle and performing anomaly detection on the battery state data based on battery characteristics to obtain state detection results also includes: When the battery capacity is less than a preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal; the preset capacity threshold is 90%.

7. A preheating system for an electric vehicle battery, characterized in that, include: A battery status detection module is used to acquire battery status data of a target vehicle and perform anomaly detection on the battery status data based on battery characteristics to obtain a status detection result; the battery status data includes battery voltage and battery temperature, and the status detection result is normal or abnormal; The preheating module is used to acquire the battery loss data of the target vehicle when the status detection result is normal, perform secondary regression analysis on the battery loss data to determine the optimal preheating temperature, and preheat the battery of the target vehicle until the battery temperature reaches the optimal preheating temperature; the battery loss data includes the power consumption of the target vehicle at multiple temperatures.

8. The electric vehicle battery preheating system according to claim 7, characterized in that, The electric vehicle battery preheating system also includes: The data transmission status monitoring module is used to acquire the data transmission status of the target vehicle. When the data transmission status is missing or interrupted, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The data transmission status is normal, missing, or interrupted.

9. The electric vehicle battery preheating system according to claim 7, characterized in that, The electric vehicle battery preheating system also includes: The mileage status detection module is used to acquire multiple historical mileage information of the target vehicle. If the difference between the mileage corresponding to two adjacent timestamps is greater than or equal to 1 kilometer, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal.

10. The electric vehicle battery preheating system according to claim 7, characterized in that, The electric vehicle battery preheating system also includes: A battery capacity detection module is used to obtain the battery capacity of the target vehicle. When the battery capacity is less than a preset capacity threshold, the status detection result is determined to be abnormal; otherwise, the status detection result is determined to be normal. The preset capacity threshold is 90%.

Images