Power battery charging pre-heat management strategy based on charging station navigation identification

Abstract

The invention relates to the technical field of battery charging pre-heat management, and discloses a power battery charging pre-heat management strategy based on charging station navigation identification, which comprises the following steps of: analyzing charging station destination information set by a user in real time through a vehicle-mounted navigation system; the pre-charging thermal management operation is accurately triggered by combining a multi-parameter dynamic judgment mechanism such as the remaining capacity of the battery, the pre-estimated remaining mileage and the temperature of the battery; the method specifically comprises the following steps of navigation destination intelligent identification and instruction issuing, multi-threshold dynamic collaborative judgment (a residual electric quantity threshold and a residual mileage threshold of safety coefficient adjustment), user interactive delay triggering, dynamic power adjustment of a thermal management system and endurance safety guarantee. According to the method, multi-dimensional fusion of the navigation data and the battery state is innovatively introduced, and the problems of single trigger logic, conflict between energy consumption and endurance, low user participation degree and the like in a traditional scheme are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging pre-thermal management, and in particular to a power battery charging pre-thermal management strategy based on charging station navigation identification. Background Art

[0002] With the popularity of electric vehicles, the charging efficiency and thermal management of power batteries have become core pain points for users. In existing technologies, thermal management strategies for power batteries mainly focus on battery temperature control and charging optimization, but their triggering logic and execution mechanisms still have significant flaws, specifically in the following aspects: 1. Limitations of existing thermal management strategies Current mainstream power battery thermal management solutions mostly use a triggering mechanism based on remaining range and time. However, such solutions do not incorporate real-time destination information from the navigation system, resulting in the system's inability to dynamically adapt to changes in the driving route. In actual applications, if the user temporarily changes the destination or the charging station location update is delayed, the system may initiate thermal management too early (causing power waste) or trigger it too late, ultimately extending charging time. Experimental data shows that under traditional strategies, the battery temperature before charging is often between 40°C and 45°C, and fast charging takes 45 to 50 minutes, significantly increasing user waiting time.

[0003] 2. Static parameters rely on the lack of user interaction Existing solutions generally adopt a fully automatic triggering mode, and users cannot actively intervene and customize policy execution. For example, in a low-temperature environment, users may want to preheat the battery in advance to increase charging speed, but the system refuses to start thermal management due to the remaining power threshold limit, resulting in a poor user experience.

[0004] 3. The contradiction between energy consumption and battery life The core contradiction of existing technologies lies in the conflict between thermal management energy consumption and vehicle range. Starting thermal management too early will consume additional battery energy and may prevent the vehicle from reaching the charging station; starting it too late will cause the battery temperature to exceed the suitable range for fast charging, forcing charging to slow down. According to statistics, under traditional strategies, the range loss caused by improper thermal management timing can reach 8% to 12%, and in extreme temperature environments, the charging efficiency drops by as much as 30%.

[0005] 4. Urgent need for technological upgrades With the promotion of high-power fast charging technology, the temperature rise rate and heat dissipation requirements of batteries during charging have increased significantly. If the thermal management strategy cannot accurately match the navigation information and real-time operating conditions of the charging station, it will accelerate battery aging and even cause safety hazards. In addition, users' demand for personalized driving experience is growing, and a thermal management solution that supports dynamic parameter adjustment, user-friendly interaction, and compatibility with multiple vehicle models and platforms is urgently needed. Existing technologies have problems such as single trigger logic, lack of dynamic adaptability, and low user participation, making it difficult to meet the needs of intelligent and efficient charging of electric vehicles. Therefore, there is an urgent need for a new thermal management strategy based on navigation destination recognition and multi-parameter collaborative decision-making to balance energy consumption, battery life and charging efficiency, and improve user experience and system reliability. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a power battery charging pre-thermal management strategy based on charging station navigation identification, which solves the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a power battery charging pre-heat management strategy based on charging station navigation identification, the method comprising the following steps: S1. Navigation destination identification and command triggering: The navigation application on the vehicle's central control screen interprets the destination name entered by the user in real time. If the destination is identified as a charging station, a charging preparation instruction is sent to the battery management system via the body control module. S2. Multi-parameter dynamic judgment and start condition triggering: The battery management system determines whether the thermal management activation conditions are met based on the following parameters: First threshold: The remaining power is higher than the preset value, which is 30% by default and can be configured in the range of 20% to 40%; Second threshold: The estimated remaining mileage should not be less than the mileage to the navigation destination multiplied by a safety factor. The safety factor defaults to 1.2 and can be configured in the range of 1.1 to 1.5. Whether the battery temperature deviates from the preset fast charging temperature range, which is 10°C to 35°C; S3. User interaction and delayed triggering: If the start conditions are met, a prompt message will pop up on the central control screen, and the user can choose to "Start immediately" or "Cancel" thermal management. If the user does not operate, the system can be configured with a delay time. The default delay time is 30 seconds, ranging from 10 to 60 seconds. Thermal management will be automatically executed after the delay time reaches the range. S4. Thermal management execution and dynamic regulation: The thermal management system dynamically adjusts cooling and heating power based on the difference between the current battery temperature and the target temperature, while also monitoring the remaining battery charge and driving energy consumption in real time. If the remaining battery charge falls below a first threshold and the remaining range is insufficient, thermal management is automatically terminated. S5. Endurance safety assurance and collaborative optimization: During the thermal management process, navigation information and battery status parameters are continuously updated, and the thermal management power and threshold parameters are dynamically adjusted to ensure that the vehicle can still safely arrive at the charging station during the thermal management operation.

[0008] Preferably, the S1 specifically includes the following steps: S11. Charging station name feature extraction: The navigation application uses a semantic analysis module to identify keywords in the destination name and match them with a pre-set database of charging station names; S12. Instruction issuance and communication link establishment: The body control module sends the charging preparation instruction to the battery management system through the CAN bus, and simultaneously identifies the real-time data of the battery temperature sensor and energy consumption monitoring module.

[0009] Preferably, the determination logic of the first threshold and the second threshold in S2 includes: S21. Dynamic correction of remaining power: Dynamically correct the trigger boundary of the first threshold using a linear interpolation algorithm based on the current driving environment and historical energy consumption data; S22, safety factor adaptive adjustment: Based on real-time road and weather conditions, a fuzzy control algorithm is used to adjust the safety factor of the second threshold to ensure that the remaining mileage redundancy meets actual driving needs.

[0010] Preferably, the user interaction interface design in S3 includes: S31. Multimodal interaction function: Users can choose to enable or disable thermal management strategies through voice commands, touch operations, and physical buttons; S32, policy priority configuration: Users customize the thermal management trigger delay time, power threshold and safety factor in the central control screen setting interface, and the system generates a personalized strategy profile based on user preferences.

[0011] Preferably, the thermal management power adjustment method in S4 includes: S41, temperature difference graded control: The difference between the battery temperature and the target temperature is divided into four intervals, each corresponding to a different cooling / heating power level. To precisely control temperature and energy consumption, the temperature difference intervals may not be limited to four.

[0012] S42, Energy Consumption Feedback Compensation Mechanism: The maximum available power limit of the thermal management system is calculated based on real-time energy consumption data, and the power output is dynamically adjusted through a proportional-integral controller to avoid excessive battery energy consumption.

[0013] Preferably, the endurance safety assurance method in S5 includes: S51, Dynamic Remaining Mileage Prediction: Based on the real-time traffic information of the navigation route and the vehicle energy consumption model, the minimum remaining power required to reach the charging station is calculated and a dynamic warning threshold is generated; S52, thermal management forced termination conditions: If the remaining battery power is lower than the dynamic warning threshold and the remaining mileage is lower than 1.1 times the mileage of the navigation destination, the system immediately terminates thermal management and switches to low power mode.

[0014] Preferably, the method further includes a compatibility extension function: S71, multi-car adaptation interface: Integrate and migrate thermal management strategies to battery management systems of different vehicle models through standardized communication protocols, and adjust temperature control parameters based on battery type; S72. Navigation system data fusion: Supports API interfaces of third-party navigation applications to achieve real-time synchronization of charging station location data and vehicle control systems.

[0015] Preferably, the method further includes a fault diagnosis and fault tolerance mechanism: S81, sensor abnormality detection: When the data from the battery temperature sensor and energy consumption monitoring module are abnormal, the system continues to perform thermal management based on the prediction model of historical data and the battery's own thermal characteristic model; S82, policy rollback function: If the safety threshold is triggered during the thermal management process, the system automatically terminates the current strategy and returns to the default driving thermal management mode.

[0016] Preferably, the system includes the following modules: Navigation recognition and command generation module: integrated into the vehicle's central control system; Multi-parameter dynamic decision module: deployed in the battery management system, performs threshold judgment, user interaction logic and thermal management trigger control; Thermal management execution and feedback module: This includes cooling / heating devices, power controllers, and sensor networks to dynamically adjust battery temperature and provide real-time data feedback. User interaction and configuration module: provides a graphical interface for users to customize policy parameters and supports multi-modal interactive operations.

[0017] Preferably, the system further includes a cloud collaboration module: Data synchronization and remote configuration: The on-board T-Box uploads thermal management strategy execution data to the cloud server to update threshold parameters and fault diagnosis; Charging station dynamic database: The cloud server updates the charging station location, idle status and fast charging power information in real time, and pushes it to the vehicle navigation system through the Internet of Vehicles to optimize the timing of thermal management triggering.

[0018] Compared with the existing technology, the present invention provides a power battery charging pre-heat management strategy based on charging station navigation recognition, which has the following beneficial effects: 1. Accurately trigger thermal management to improve charging efficiency By intelligently identifying navigation destinations and dynamically integrating multiple parameters, the system accurately determines the optimal time to activate thermal management. Compared to traditional single-threshold triggering methods, this strategy avoids energy waste and charging inefficiencies caused by activating thermal management too early or too late. Experimental data shows that implementing this strategy can reduce battery temperature by 10°C before charging and shorten fast-charging time by 25% to 30%, improving the user charging experience.

[0019] 2. Dynamically balance energy consumption and endurance to ensure driving safety A dual-threshold coordinated control mechanism is introduced, combining dynamic parameters such as real-time road conditions and ambient temperature to adaptively adjust thermal management power and trigger conditions. For example, in congested or low-temperature environments, the system automatically increases safety margins to ensure the vehicle has sufficient power to reach the charging station during thermal management operation. This mechanism increases the range guarantee rate to over 98%, effectively resolving the conflict between thermal management energy consumption and range requirements in traditional solutions.

[0020] 3. Enhance user interaction flexibility and improve driving experience Through the multimodal interface on the central control screen, users can choose whether to enable thermal management strategies and customize parameters such as trigger delay time and battery threshold. Compared to fully automatic triggering solutions, user engagement increases by approximately 40%, and the system supports storage of personalized profiles to meet the needs of different driving habits.

[0021] 4. Compatibility and scalability advantages The system utilizes standardized communication protocols and an open API interface to adapt to different vehicle battery types and third-party navigation applications. In compatibility testing, the system achieved an adaptation success rate of over 95% for 10 mainstream vehicle models, and the dynamic update latency of the charging station database was less than 1 second, ensuring the real-time and universal applicability of the strategy.

[0022] 5. Improved fault tolerance and safety mechanisms If a sensor anomaly or a sudden temperature change occurs during thermal management, the system can switch to a redundant sensor or a predictive model based on historical data and the battery's own thermal characteristics to continue operation. It also automatically triggers a policy rollback function to restore the default charging mode. This mechanism reduces the thermal management failure rate to below 0.5% and controls battery temperature fluctuations within ±2°C during abnormal conditions, improving system reliability.

[0023] 6. Cloud-based collaborative optimization of global energy efficiency By synchronizing data between the vehicle-to-everything (V2X) network and the cloud server, the system can obtain dynamic information about the charging station in real time and intelligently adjust the timing of thermal management startup based on the vehicle status. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] The method of the power battery charging pre-heat management strategy based on charging station navigation identification includes the following steps: S1. Navigation destination identification and command triggering: The navigation application on the vehicle's central control screen interprets the destination name entered by the user in real time. If the destination is identified as a charging station, a charging preparation instruction is sent to the battery management system via the body control module. S2. Multi-parameter dynamic judgment and start condition triggering: The battery management system determines whether the thermal management activation conditions are met based on the following parameters: First threshold: The remaining power is higher than the preset value, which is 30% by default and can be configured in the range of 20% to 40%; Second threshold: The estimated remaining mileage should not be less than the mileage to the navigation destination multiplied by a safety factor. The safety factor defaults to 1.2 and can be configured in the range of 1.1 to 1.5. Whether the battery temperature deviates from the preset fast charging temperature range, which is 10°C to 35°C; S3. User interaction and delayed triggering: If the start conditions are met, a prompt message will pop up on the central control screen, and the user can choose to "Start immediately" or "Cancel" thermal management. If the user does not operate, the system can be configured with a delay time. The default delay time is 30 seconds, ranging from 10 to 60 seconds. Thermal management will be automatically executed after the delay time reaches the range. S4. Thermal management execution and dynamic regulation: The thermal management system dynamically adjusts cooling and heating power based on the difference between the current battery temperature and the target temperature, while also monitoring the remaining battery charge and driving energy consumption in real time. If the remaining battery charge falls below a first threshold and the remaining range is insufficient, thermal management is automatically terminated. S5. Endurance safety assurance and collaborative optimization: During the thermal management process, navigation information and battery status parameters are continuously updated, and thermal management power and threshold parameters are dynamically adjusted to ensure that the vehicle can still safely reach the charging station during thermal management operation; S1 specifically includes the following steps: S11. Charging station name feature extraction: The navigation application uses a semantic analysis module to identify keywords in the destination name and match them with a pre-set database of charging station names; S12. Instruction issuance and communication link establishment: The body control module sends the charge preparation instruction to the battery management system via the CAN bus, and simultaneously activates the real-time data collection function of the battery temperature sensor and energy consumption monitoring module; The decision logic for the first threshold and the second threshold in S2 includes: S21. Dynamic correction of remaining power: Dynamically correct the trigger boundary of the first threshold using a linear interpolation algorithm based on the current driving environment and historical energy consumption data; S22, safety factor adaptive adjustment: Based on real-time road and weather conditions, a fuzzy control algorithm is used to adjust the safety factor of the second threshold to ensure that the remaining mileage margin meets actual driving needs; The user interface design in S3 includes: S31. Multimodal interaction function: Users can choose to enable or disable thermal management strategies through voice commands, touch operations, and physical buttons; S32, policy priority configuration: Users can customize the thermal management trigger delay time, power threshold and safety factor in the central control screen setting interface, and the system will generate a personalized policy profile based on user preferences; Thermal management power adjustment methods in S4 include: S41, temperature difference graded control: The difference between the battery temperature and the target temperature is divided into four intervals, each of which corresponds to a different cooling / heating power level. S42, Energy Consumption Feedback Compensation Mechanism: The maximum available power limit of the thermal management system is calculated based on real-time energy consumption data, and the power output is dynamically adjusted through the proportional-integral controller to avoid excessive battery energy consumption. The battery life safety guarantee methods in S5 include: S51, Dynamic Remaining Mileage Prediction: Based on the real-time traffic information of the navigation route and the vehicle energy consumption model, the minimum remaining power required to reach the charging station is calculated and a dynamic warning threshold is generated; S52, thermal management forced termination conditions: If the remaining battery power falls below the dynamic warning threshold and the remaining mileage falls below 1.1 times the mileage of the navigation destination, the system immediately terminates thermal management and switches to low-power mode. The method also includes compatibility extension functions: S71, multi-car adaptation interface: Integrate and migrate thermal management strategies to battery management systems of different vehicle models through standardized communication protocols, and adjust temperature control parameters based on battery type; S72. Navigation system data fusion: Supports API interfaces for third-party navigation applications to achieve real-time synchronization of charging station location data with vehicle control systems; The method further includes fault diagnosis and fault tolerance mechanisms: S81, sensor abnormality detection: When the data from the battery temperature sensor and energy consumption monitoring module are abnormal, the system continues to perform thermal management based on the prediction model of historical data and the battery's own thermal characteristic model; S82, policy rollback function: If the safety threshold is triggered during thermal management, the system automatically terminates the current strategy and returns to the default driving thermal management mode; The system includes the following modules: Navigation recognition and command generation module: integrated into the vehicle's central control system; Multi-parameter dynamic decision module: deployed in the battery management system, performs threshold judgment, user interaction logic and thermal management trigger control; Thermal management execution and feedback module: This includes cooling / heating devices, power controllers, and sensor networks to dynamically adjust battery temperature and provide real-time data feedback. User interaction and configuration module: provides a graphical interface for users to customize policy parameters and supports multi-modal interactive operations; The system further includes a cloud collaboration module: Data synchronization and remote configuration: The on-board T-Box uploads thermal management strategy execution data to the cloud server to update threshold parameters and fault diagnosis; Charging station dynamic database: The cloud server updates the charging station location, idle status and fast charging power information in real time, and pushes it to the vehicle navigation system through the Internet of Vehicles to optimize the timing of thermal management triggering.

[0027] Example 1: Application of thermal management strategy in long-distance driving scenarios 1. Application Scenario Description A user of an electric vehicle equipped with this thermal management strategy plans to drive from City A to City B, a distance of approximately 300 kilometers. At departure, the battery's remaining charge is 80%, and the ambient temperature is 25°C. The user uses the in-car navigation system (which integrates the AutoNavi Maps API) to set the destination to a "Fast Charging Station" in City B. The system parses the destination name in real time, recognizes it as a charging station, and activates the thermal management strategy.

[0028] 2. System composition and parameter configuration Navigation recognition module BCM: parses the destination name in real time and matches charging station keywords (such as "charging station" and "fast charging pile").

[0029] Battery Management System (BMS): Configuration parameters include the first threshold (remaining power ≥ 30%), the second threshold (safety factor 1.2), and the suitable fast charging temperature range (10°C~35°C).

[0030] User interaction interface: The central control screen supports touch and voice commands, and the trigger delay time is set to a default of 30 seconds.

[0031] Thermal management execution module: includes a liquid cooling system and a PTC heater, with a dynamic power adjustment range of 0.5kW to 5kW.

[0032] 3. Operational procedures Step 1: Navigation destination identification and command triggering After the user enters "Swift Charging Station", the navigation system confirms that the destination is a charging station through semantic analysis, and the BCM sends a charging preparation instruction to the BMS.

[0033] The BMS, including a temperature sensor and an energy consumption monitoring module, collects battery temperature (initial value 25°C) and remaining power (80%) in real time.

[0034] Step 2: Multi-parameter dynamic judgment First threshold judgment: remaining power 80%>30%, meeting the condition.

[0035] Second threshold calculation: The navigation shows a remaining range of 280 kilometers (300 kilometers total minus 20 kilometers traveled). The safety range requirement is 280 × 1.2 = 336 kilometers. The current remaining range (based on the energy consumption model) is 350 kilometers, which meets the requirement.

[0036] Temperature determination: A battery temperature of 25°C is within the optimal fast charging range (10°C to 35°C) and does not require active heating or cooling.

[0037] Step 3: User Interaction and Delayed Triggering A prompt pops up on the central control screen: "Charging station destination detected, it is recommended to start pre-charging thermal management. Start / cancel now."

[0038] If the user selects "Start immediately", the system skips the delay and directly performs thermal management; if the user does not take any action, it will automatically start after 30 seconds.

[0039] Step 4: Thermal management implementation and dynamic regulation Because the battery temperature is already within the appropriate range, the system only starts a low-power cycle (0.5kW) to maintain the temperature and monitors the remaining power and energy consumption in real time.

[0040] If congestion occurs during driving (the remaining mileage requirement increases to 300×1.5=450 kilometers), the BMS dynamically adjusts the safety factor to 1.5 and limits the thermal management power to 1kW to save energy.

[0041] Step 5: Battery life safety and charging optimization When arriving at the charging station, the battery temperature was stable at 30°C and the remaining power was 15%.

[0042] The fast-charging system charges at maximum power (150kW), and the battery capacity is charged from 15% to 80% in 25 minutes, which is 44% shorter than the traditional solution (45 minutes) without the strategy enabled.

[0043] Example 2: Optimization of thermal management strategy in low temperature environment 1. Application Scenario Description In winter, at an ambient temperature of -5°C, a user drives their vehicle (with a 40% remaining charge) to a charging station 50 kilometers away. The battery's initial temperature is -3°C and needs to be warmed to above 10°C to enable fast charging.

[0044] 2. Key parameter adjustment First threshold: Dynamically corrected to 25% (due to increased energy consumption due to low temperature).

[0045] Safety factor: adjusted to 1.3 (snow on the road leads to reduced driving efficiency).

[0046] 3. Operational procedures Step 1: After navigating and identifying the charging station, the BMS detects a battery temperature of -3°C, triggering a heating request.

[0047] Step 2: If the remaining battery power is 40% > 25%, the remaining range is calculated to be 120 kilometers (50×1.3=65 kilometers), which meets the requirements.

[0048] Step 3: The user confirms the start of heating, and the PTC heater runs at 6-3kW power, raising the battery temperature to 12°C within 20 minutes.

[0049] Step 4: The battery temperature is stabilized at 12°C during charging, and the fast charging time is 30 minutes (the traditional solution takes 50 minutes), with an efficiency increase of 40%.

[0050] Example 3: Multi-vehicle compatibility verification Test model: Model A (ternary lithium battery, power battery) Model B (lithium iron phosphate battery, energy type battery) Testing process: Model A: Directly integrated through a standardized interface, the thermal management triggering delay is 20 seconds, reducing fast charging time by 28%.

[0051] Vehicle Type B: By leveraging the battery's thermal characteristics, the system dynamically adjusts temperature control parameters (with the fast-charging range set between 5°C and 30°C), reducing fast-charging time by 25%.

[0052] Test results: Both types of vehicles successfully triggered the strategy, significantly improving charging efficiency and verifying system compatibility.

[0053] Example 4: Demonstration of fault tolerance mechanism 1. Simulate failure scenarios: The temperature sensor is abnormal (output value drifts ±10°C).

[0054] 2. System response: The BMS detects abnormal temperature data (the battery displays 50°C, but the actual temperature is 30°C) and switches to redundant sensor data.

[0055] If there is no redundant data, the historical temperature prediction model is enabled and the error is controlled within ±2°C.

[0056] Thermal management continues to operate, and charging efficiency only drops by 5%, which is far better than the complete failure of traditional solutions.

[0057] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0058] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A power battery charging pre-heat management strategy based on charging station navigation and identification, characterized by: The method comprises the following steps: S1. Navigation destination identification and command triggering: The navigation application on the vehicle's central control screen interprets the destination name entered by the user in real time. If the destination is identified as a charging station, a charging preparation instruction is sent to the battery management system via the body control module. S2. Multi-parameter dynamic judgment and start condition triggering: The battery management system determines whether the thermal management activation conditions are met based on the following parameters: First threshold: The remaining power is higher than the preset value, which is 30% by default and can be configured in the range of 20% to 40%; Second threshold: The estimated remaining mileage should not be less than the mileage to the navigation destination multiplied by a safety factor. The safety factor defaults to 1.2 and can be configured in the range of 1.1 to 1.

5. Whether the battery temperature deviates from the preset fast charging temperature range, which is 10°C to 35°C; S3. User interaction and delayed triggering: If the activation conditions are met, a prompt message will pop up on the central control screen, and the user can choose to "Start immediately" or "Cancel" thermal management. If the user does not take any action, the system can be configured with a delay time. The default delay time is 30 seconds, ranging from 10 to 60 seconds. Thermal management will be automatically executed after the delay time reaches the range. S4. Thermal management execution and dynamic regulation: The thermal management system dynamically adjusts cooling and heating power based on the difference between the current battery temperature and the target temperature, while also monitoring the remaining battery charge and driving energy consumption in real time. If the remaining battery charge falls below a first threshold and the remaining range is insufficient, thermal management is automatically terminated. S5. Endurance safety assurance and collaborative optimization: During the thermal management process, navigation information and battery status parameters are continuously updated, and the thermal management power and threshold parameters are dynamically adjusted to ensure that the vehicle can still safely arrive at the charging station during the thermal management operation.

2. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Charging station name feature extraction: The navigation application uses a semantic analysis module to identify keywords in the destination name and match them with a pre-set database of charging station names; S12. Instruction issuance and communication link establishment: The body control module sends the charging preparation instruction to the battery management system through the CAN bus, and simultaneously identifies the real-time data of the battery temperature sensor and energy consumption monitoring module.

3. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 2, characterized in that: The decision logic of the first threshold and the second threshold in S2 includes: S21. Dynamic correction of remaining power: Dynamically correct the trigger boundary of the first threshold using a linear interpolation algorithm based on the current driving environment and historical energy consumption data; S22, safety factor adaptive adjustment: Based on real-time road and weather conditions, a fuzzy control algorithm is used to adjust the safety factor of the second threshold to ensure that the remaining mileage redundancy meets actual driving needs.

4. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 3 is characterized by: The user interaction interface design in S3 includes: S31. Multimodal interaction function: Users can choose to enable or disable thermal management strategies through voice commands, touch operations, and physical buttons; S32, policy priority configuration: Users customize the thermal management trigger delay time, power threshold and safety factor in the central control screen setting interface, and the system generates a personalized strategy profile based on user preferences.

5. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 4 is characterized by: The thermal management power adjustment method in S4 includes: S41, temperature difference graded control: The difference between the battery temperature and the target temperature is divided into four intervals, each of which corresponds to a different cooling / heating power level. S42, Energy Consumption Feedback Compensation Mechanism: The maximum available power limit of the thermal management system is calculated based on real-time energy consumption data, and the power output is dynamically adjusted through a proportional-integral controller to avoid excessive battery energy consumption.

6. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 5, characterized in that: The endurance safety guarantee method in S5 includes: S51, Dynamic Remaining Mileage Prediction: Based on the real-time traffic information of the navigation route and the vehicle energy consumption model, the system calculates the minimum remaining power required to reach the charging station and generates a dynamic warning threshold. S52, thermal management forced termination conditions: If the remaining battery power is lower than the dynamic warning threshold and the remaining mileage is lower than 1.1 times the mileage of the navigation destination, the system immediately terminates the pre-thermal management and switches to low power consumption mode.

7. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 6, characterized in that: The method also includes compatibility extension functions: S71, multi-car adaptation interface: Integrate and migrate thermal management strategies to battery management systems of different vehicle models through standardized communication protocols, and adjust temperature control parameters based on battery type and corresponding thermal management strategies; S72. Navigation system data fusion: Supports API interfaces of third-party navigation applications to achieve real-time synchronization of charging station location data and vehicle control systems.

8. The power battery charging pre-heat management strategy based on charging station navigation and identification according to claim 7 is characterized by: The method further includes a fault diagnosis and fault tolerance mechanism: S81, sensor abnormality detection: When the data from the battery temperature sensor and energy consumption monitoring module are abnormal, the system continues to perform thermal management based on the prediction model of historical data and the battery's own thermal characteristic model; S82, policy rollback function: If the safety threshold is triggered during the thermal management process, the system automatically terminates the current strategy and returns to the default driving thermal management mode.

9. A power battery charging pre-heat management system based on charging station navigation and recognition, characterized by: The system includes the following modules: Navigation recognition and command generation module: integrated into the vehicle's central control system; Multi-parameter dynamic decision module: deployed in the battery management system, performs threshold judgment, user interaction logic and thermal management trigger control; Thermal management execution and feedback module: This includes cooling / heating devices, power controllers, and a sensor network to dynamically adjust battery temperature and provide real-time data feedback. User interaction and configuration module: provides a graphical interface for users to customize policy parameters and supports multi-modal interactive operations.

10. The power battery charging pre-heat management system based on charging station navigation and recognition according to claim 9, characterized in that: The system further includes a cloud collaboration module: Data synchronization and remote configuration: The on-board T-Box uploads thermal management strategy execution data to the cloud server to update threshold parameters and fault diagnosis; Charging station dynamic database: The cloud server updates the charging station location, idle status and fast charging power information in real time, and pushes it to the vehicle navigation system through the Internet of Vehicles to optimize the timing of thermal management triggering.

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