A thermal management system and method for electric commercial vehicles

Abstract

This invention discloses a thermal management system and method for electric commercial vehicles, comprising: a data acquisition module, a data processing module, and a thermal management control module. It relates to the field of vehicle thermal management technology and solves the technical problem of low thermal management efficiency in existing electric commercial vehicles. This invention achieves precise thermal management of the battery pack by collecting the initial temperature of the battery pack and weather forecast information from the target charging station, combining driving data and charging distance to calculate the estimated charging time, and determining the charging ambient temperature. This system effectively improves the charging efficiency and safety of the battery under different environments, reduces energy loss and battery aging caused by unsuitable temperatures, and extends battery life. Simultaneously, based on a real-time adjusted thermal management mode, it ensures that the battery is always within the optimal operating temperature range, improving charging efficiency and vehicle operational stability.

Description

Technical Field

[0001] This invention belongs to the field of vehicle thermal management technology, specifically a thermal management system and method for electric commercial vehicles. Background Technology

[0002] To improve the economic efficiency of electric commercial vehicles, large battery capacity and fast charging speed are required, so fast-charging batteries are being used more and more widely in the commercial vehicle sector.

[0003] Existing technologies achieve some degree of thermal management for commercial vehicles' batteries by acquiring the real-time temperature of the vehicle's battery pack and comparing it with preset temperature thresholds, then adjusting the battery pack temperature based on the comparison results. However, in practice, existing technologies struggle to predict the time required for battery pack temperature adjustment. Adjusting the temperature too early results in additional energy consumption, while adjusting it too late means the battery pack temperature may not reach the optimal charging temperature range when the commercial vehicle arrives at the charging station, leading to low thermal management efficiency for electric commercial vehicles.

[0004] This invention proposes a thermal management system and method for electric commercial vehicles to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a thermal management system and method for electric commercial vehicles, which solves the technical problem that the prior art is unable to accurately calculate the time required for the battery pack to regulate temperature, thus resulting in low thermal management efficiency of electric commercial vehicles.

[0006] To achieve the above objectives, a first aspect of the present invention provides a thermal management system for electric commercial vehicles, comprising: a data processing module, and a data acquisition module and a thermal management control module connected thereto;

[0007] The data acquisition module is used to collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; obtain the driving information and charging distance of the target vehicle; wherein, the driving information includes the average vehicle speed and the remaining battery power; the charging distance is the navigation distance between the target vehicle and the target charging station.

[0008] The data processing module is used to calculate the travel time to the target charging station based on the target vehicle's driving information and charging distance; and to determine the charging environment temperature of the target vehicle based on the estimated charging time and weather forecast information; wherein the estimated charging time is obtained by adding the travel time to the current time.

[0009] The thermal management control module is used to perform thermal management on the target vehicle based on the initial temperature of the battery pack, driving time, and charging ambient temperature.

[0010] Preferably, the calculation of the travel time to the target charging station based on the target vehicle's driving information and charging distance includes:

[0011] Extract the average speed, remaining battery power, and charging distance from the target vehicle's driving information; determine the target vehicle's range based on the remaining battery power; determine if the range is less than the charging distance; if yes, navigate to the nearest charging station within the range; otherwise, calculate the ratio of charging distance to average speed to obtain the travel time.

[0012] Preferably, determining the target vehicle's driving range based on remaining battery power includes:

[0013] Obtain the remaining battery power and average power consumption of the target vehicle, calculate the ratio of remaining battery power to average power consumption, and obtain the driving range of the target vehicle.

[0014] Preferably, determining the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information includes:

[0015] Extract the estimated charging time of the target vehicle and the weather forecast information of the target charging station; the weather forecast information is temperature data that changes over time; obtain the temperature data at the time corresponding to the estimated charging time, and mark the temperature data at the corresponding time as the charging ambient temperature.

[0016] Preferably, the thermal management of the target vehicle based on the initial temperature of the battery pack, driving time, and charging ambient temperature includes:

[0017] X1: Extract the initial temperature of the battery pack, driving time, and charging ambient temperature; obtain the average speed and remaining battery power of the target vehicle; calculate the sum of the current time and driving time and mark it as the estimated charging time;

[0018] X2: Input the initial temperature of the battery pack, the expected charging time, the average speed of the target vehicle, and the remaining charge into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack during the expected charging time; wherein, the battery pack temperature prediction model is built based on an artificial intelligence model;

[0019] X3: Obtain the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes; among which, the temperature control modes include heating mode and cooling mode;

[0020] X4: Calculates temperature control adjustment time based on battery predicted temperature, charging ambient temperature, and temperature adjustment rate;

[0021] X5: Determine if the temperature control adjustment time is greater than the driving time; if yes, immediately activate the thermal management equipment to adjust the battery pack temperature of the target vehicle; if no, calculate the difference between the estimated charging time and the temperature control adjustment time to obtain the temperature control adjustment start time.

[0022] X6: Determine if the current time is equal to the start time of temperature control adjustment; if yes, automatically activate the thermal management equipment to adjust the battery pack temperature of the target vehicle; if no, temporarily do not adjust the battery pack temperature of the target vehicle.

[0023] Preferably, the battery pack temperature prediction model is built based on an artificial intelligence model, including:

[0024] Extract the initial temperature, remaining charge, average vehicle speed, and actual battery pack temperature after a certain driving time from the target vehicle's battery pack. Integrate the initial temperature, remaining charge, average speed, and driving time of the target vehicle's battery pack into a standard input dataset, and integrate the actual battery pack temperature after a certain driving time into a standard output dataset. Train the artificial intelligence model based on the standard input and standard output datasets, and label the trained artificial intelligence model as the battery pack temperature prediction model. The artificial intelligence model includes either a BP neural network model or an RBF neural network model.

[0025] Preferably, the step of obtaining the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes includes:

[0026] The temperature change data of the target vehicle in heating mode and cooling mode are acquired separately; the temperature change data in heating mode is linearly fitted to obtain the heating adjustment rate; the temperature change data in cooling mode is linearly fitted to obtain the cooling adjustment rate; the heating adjustment rate and cooling adjustment rate are integrated into the temperature adjustment rate.

[0027] Preferably, the calculation of the temperature control adjustment time based on the battery predicted temperature, the charging ambient temperature, and the temperature adjustment rate includes:

[0028] T1: Extracts predicted battery temperature, charging ambient temperature, and temperature adjustment rate;

[0029] T2: Input the charging ambient temperature into the charging temperature database for matching to obtain the corresponding optimal charging temperature range;

[0030] T3: Determine if the predicted battery temperature is greater than the upper limit of the optimal charging temperature range; if yes, calculate the difference between the predicted battery temperature and the upper limit and mark it as the temperature difference; otherwise, jump to T4.

[0031] T4: Determine if the predicted battery temperature is lower than the lower limit of the optimal charging temperature range; if yes, calculate the difference between the predicted battery temperature and the lower limit and mark it as the temperature difference; if no, set the temperature control adjustment time to 0.

[0032] T5: Through formula Calculate the temperature control adjustment time WTS; where WDC is the temperature difference, SWS is the heating adjustment rate, JWS is the cooling adjustment rate; k1 and k2 are both proportional coefficients greater than 0; ln() is a logarithmic function with the natural constant as the base.

[0033] Preferably, the charging temperature database is constructed in the following manner:

[0034] The system acquires the battery temperature data of the target vehicle under different charging ambient temperatures. The maximum battery temperature at which the charging speed equals the preset rated charging speed is marked as the upper temperature limit, and the minimum battery temperature at which the charging speed equals the preset rated charging speed is marked as the lower temperature limit. The lower temperature limit and the upper temperature limit are integrated into the optimal charging temperature range of the target vehicle under the corresponding ambient temperature, and the mapping relationship between several optimal charging temperature ranges and the corresponding charging ambient temperatures is saved to the charging temperature database.

[0035] A second aspect of the present invention provides a thermal management method for electric commercial vehicles, comprising:

[0036] S1: Collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station;

[0037] S2: Obtain the target vehicle's driving information and charging distance;

[0038] S3: Calculate the travel time to the target charging station based on the target vehicle's driving information and charging distance;

[0039] S4: Determine the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information;

[0040] S5: Performs thermal management on the target vehicle based on the initial temperature of the battery pack, driving time, and charging ambient temperature.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. This invention achieves precise thermal management of the battery pack by collecting the initial temperature of the battery pack and weather forecast information of the target charging station, combining driving data and charging distance to calculate the estimated charging time, and determining the charging ambient temperature. This system effectively improves the charging efficiency and safety of the battery in different environments, reduces energy loss and battery aging caused by unsuitable temperatures, and extends battery life. Simultaneously, based on a real-time adjusted thermal management mode, it ensures that the battery is always within the optimal operating temperature range, improving charging efficiency and vehicle operational stability.

[0043] 2. This invention extracts key parameters such as the initial battery pack temperature, driving time, and charging ambient temperature, and calculates the estimated charging time. Utilizing an advanced battery pack temperature prediction model, it accurately predicts the battery temperature at a future point in time. Based on the temperature adjustment rate under different temperature control modes, and combining the predicted temperature with the charging ambient temperature, it precisely calculates the temperature control adjustment time and intelligently decides whether to activate the thermal management system immediately or delay it until a specific time point. This method ensures that the battery pack reaches its optimal operating temperature before entering the charging state, effectively avoiding low charging efficiency and potential safety hazards caused by unsuitable temperatures. Simultaneously, this strategy prevents unnecessary power consumption caused by premature temperature adjustment of the battery pack, thereby significantly improving the efficiency and economy of thermal management for electric commercial vehicles. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an overall flowchart of the thermal management method for electric commercial vehicles according to the present invention;

[0046] Figure 2 This is a schematic diagram of the thermal management system for electric commercial vehicles according to the present invention;

[0047] Figure 3 This is a flowchart illustrating the thermal management of a target vehicle based on initial temperature, expected charging time, and charging ambient temperature according to the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figures 1-3 The first aspect of the present invention provides a thermal management system for electric commercial vehicles, including: a data processing module, and a data acquisition module and a thermal management control module connected thereto;

[0050] Data acquisition module: used to collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; to obtain the driving information and charging distance of the target vehicle; among which, the driving information includes the average vehicle speed and the remaining battery power; the charging distance is the navigation distance between the target vehicle and the target charging station;

[0051] Data processing module: used to calculate the travel time to the target charging station based on the target vehicle's driving information and charging distance; and to determine the charging environment temperature of the target vehicle based on the estimated charging time and weather forecast information; wherein, the estimated charging time is obtained by adding the travel time to the current time;

[0052] Thermal management control module: Used to perform thermal management of the target vehicle based on the initial temperature of the battery pack, driving time and charging ambient temperature.

[0053] In this embodiment, the calculation of the travel time to the target charging station based on the target vehicle's driving information and charging distance includes:

[0054] Extract the average speed, remaining battery power, and charging distance from the target vehicle's driving information; determine the target vehicle's range based on the remaining battery power; determine if the range is less than the charging distance; if yes, navigate to the nearest charging station within the range; otherwise, calculate the ratio of charging distance to average speed to obtain the travel time.

[0055] For example, the average speed of the target vehicle is set to 80 km / h, the remaining battery power is 25 kWh, and the charging distance is . The driving range of the target vehicle is determined based on the remaining battery power. Since the driving range is greater than the charging distance of 150 km, the ratio of the charging distance to the average speed is calculated, and the driving time is 1.875 h, or 1 hour, 52 minutes and 30 seconds.

[0056] In this embodiment, determining the target vehicle's driving range based on the remaining battery power includes:

[0057] With the target vehicle's remaining battery power set at 25 kWh and average power consumption at 12 kWh / 100 km, the ratio of remaining battery power to average power consumption is calculated, resulting in a target vehicle range of 208.33 km.

[0058] In this embodiment, determining the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information includes:

[0059] Extract the estimated charging time of the target vehicle and the weather forecast information of the target charging station; the weather forecast information is temperature data that changes over time; obtain the temperature data at the time corresponding to the estimated charging time, and mark the temperature data at the corresponding time as the charging ambient temperature.

[0060] In this embodiment, thermal management of the target vehicle is performed based on the initial temperature of the battery pack, driving time, and charging ambient temperature, including:

[0061] X1: Extract the initial temperature of the battery pack, driving time, and charging ambient temperature; obtain the average speed and remaining battery power of the target vehicle; calculate the sum of the current time and driving time and mark it as the estimated charging time;

[0062] X2: Input the initial temperature of the battery pack, the expected charging time, the average speed of the target vehicle, and the remaining charge into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack during the expected charging time; wherein, the battery pack temperature prediction model is built based on an artificial intelligence model;

[0063] X3: Obtain the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes; among which, the temperature control modes include heating mode and cooling mode;

[0064] X4: Calculates temperature control adjustment time based on battery predicted temperature, charging ambient temperature, and temperature adjustment rate;

[0065] X5: Determine if the temperature control adjustment time is greater than the driving time; if yes, immediately activate the thermal management equipment to adjust the battery pack temperature of the target vehicle; if no, calculate the difference between the estimated charging time and the temperature control adjustment time to obtain the temperature control adjustment start time.

[0066] X6: Determine if the current time is equal to the start time of temperature control adjustment; if yes, automatically activate the thermal management equipment to adjust the battery pack temperature of the target vehicle; if no, temporarily do not adjust the battery pack temperature of the target vehicle.

[0067] For example, the initial temperature of the battery pack is set to 30°C, the driving time is 1 hour, 52 minutes, and 30 seconds, and the ambient temperature is set to charging. The current time of 9:00 AM is added to the driving time of 1.875 hours to obtain the estimated charging time of 10:00 AM, 52 minutes, and 30 seconds. The average speed of the target vehicle is set to 80 km / h, and the remaining battery capacity is set to 25 kWh. The initial temperature of the battery pack, the estimated charging time, the average speed of the target vehicle, and the remaining battery capacity are input into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack at the estimated charging time as 45°C.

[0068] The temperature control adjustment time for the target vehicle is set to 49.05 minutes. Since the temperature control adjustment time is less than the driving time, the difference between the estimated charging time and the temperature control adjustment time is calculated, resulting in a temperature control adjustment start time of 10:03:27. When the temperature control adjustment start time equals the current time, the thermal management equipment is automatically activated to adjust the battery pack temperature of the target vehicle.

[0069] This invention extracts relevant parameters and calculates the expected charging time, then uses a battery pack temperature prediction model to obtain the predicted battery temperature at a future moment. Subsequently, based on the temperature adjustment rate under different temperature control modes, and combining the predicted temperature with the charging ambient temperature, the temperature control adjustment duration is calculated, and a decision is made on whether to immediately activate or delay the activation of the thermal management system until a specific time. This method ensures that the battery pack reaches its optimal operating temperature before entering the charging state, avoiding low charging efficiency or safety hazards caused by unsuitable temperatures, and preventing additional power consumption caused by premature temperature adjustment of the battery pack in commercial vehicles, thereby improving the efficiency of thermal management of batteries in electric commercial vehicles.

[0070] In this embodiment, the battery pack temperature prediction model is built based on an artificial intelligence model, including:

[0071] Extract the initial temperature, remaining charge, average vehicle speed, and actual battery pack temperature after a certain driving time from the target vehicle's battery pack. Integrate the initial temperature, remaining charge, average speed, and driving time of the target vehicle's battery pack into a standard input dataset, and integrate the actual battery pack temperature after a certain driving time into a standard output dataset. Train the artificial intelligence model based on the standard input and standard output datasets, and label the trained artificial intelligence model as the battery pack temperature prediction model. The artificial intelligence model includes either a BP neural network model or an RBF neural network model.

[0072] This invention trains an artificial intelligence model using the initial temperature, remaining charge, average vehicle speed, and actual battery pack temperature after a certain driving time of the target vehicle's battery pack. The trained artificial intelligence model is then labeled as a battery pack temperature prediction model. This allows the battery pack temperature prediction model to obtain the ambient temperature of the target vehicle when it arrives at the target charging station in advance, facilitating the determination of the optimal charging temperature range based on the ambient temperature of the target charging station, thereby improving the charging speed of the target vehicle.

[0073] In this embodiment, the temperature adjustment rate is obtained based on the temperature change data of the target vehicle under different temperature control modes, including:

[0074] The temperature change data of the target vehicle in heating mode and cooling mode are acquired separately; the temperature change data in heating mode is linearly fitted to obtain the heating adjustment rate; the temperature change data in cooling mode is linearly fitted to obtain the cooling adjustment rate; the heating adjustment rate and cooling adjustment rate are integrated into the temperature adjustment rate.

[0075] In this embodiment, the temperature control adjustment time is calculated based on the predicted battery temperature, the charging ambient temperature, and the temperature adjustment rate, including:

[0076] T1: Extracts predicted battery temperature, charging ambient temperature, and temperature adjustment rate;

[0077] T2: Input the charging ambient temperature into the charging temperature database for matching to obtain the corresponding optimal charging temperature range;

[0078] T3: Determine if the predicted battery temperature is greater than the upper limit of the optimal charging temperature range; if yes, calculate the difference between the predicted battery temperature and the upper limit and mark it as the temperature difference; otherwise, jump to T4.

[0079] T4: Determine if the predicted battery temperature is lower than the lower limit of the optimal charging temperature range; if yes, calculate the difference between the predicted battery temperature and the lower limit and mark it as the temperature difference; if no, set the temperature control adjustment time to 0.

[0080] T5: Through formula Calculate the temperature control adjustment time WTS; where WDC is the temperature difference, SWS is the heating adjustment rate, JWS is the cooling adjustment rate; k1 and k2 are both proportional coefficients greater than 0; ln() is a logarithmic function with the natural constant as the base.

[0081] For example, set the proportional coefficients k1 = 2 and k2 = 1.8; the predicted battery temperature is 45℃ and the charging ambient temperature is 30℃; the temperature adjustment rate is set as follows: the heating adjustment rate SWS = 1℃ / min and the cooling adjustment rate JWS = 0.8℃ / min; the charging ambient temperature is input into the charging temperature database for matching, and the corresponding optimal charging temperature range is obtained as 18℃-28℃; since the predicted battery temperature is greater than the upper limit of the optimal charging temperature range, the difference between the predicted battery temperature and the upper limit is calculated and marked as the temperature difference WDC = 17℃; the temperature control adjustment time WTS≈49.05min is calculated by the formula.

[0082] This invention extracts the predicted battery temperature, ambient charging temperature, and temperature adjustment rate, and matches the ambient charging temperature with a charging temperature database to obtain the optimal charging temperature range. Based on the relationship between the predicted battery temperature and the optimal charging temperature range, it calculates whether temperature control adjustment is needed and the specific adjustment duration. If the predicted battery temperature exceeds the optimal range, it calculates the temperature difference and determines the precise duration required for heating or cooling, ensuring the battery is charged at the optimal temperature and improving charging efficiency and safety. This, in turn, improves the efficiency of thermal management of batteries in commercial vehicles.

[0083] In this embodiment, the charging temperature database is constructed in the following manner:

[0084] The system acquires the battery temperature data of the target vehicle under different charging ambient temperatures. The maximum battery temperature at which the charging speed equals the preset rated charging speed is marked as the upper temperature limit, and the minimum battery temperature at which the charging speed equals the preset rated charging speed is marked as the lower temperature limit. The lower temperature limit and the upper temperature limit are integrated into the optimal charging temperature range of the target vehicle under the corresponding ambient temperature, and the mapping relationship between several optimal charging temperature ranges and the corresponding charging ambient temperatures is saved to the charging temperature database.

[0085] For example, the data in the charging temperature database is shown in the table below:

[0086]

[0087] A second aspect of the present invention provides a thermal management method for electric commercial vehicles, comprising:

[0088] S1: Collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station;

[0089] S2: Obtain the target vehicle's driving information and charging distance;

[0090] S3: Calculate the travel time to the target charging station based on the target vehicle's driving information and charging distance;

[0091] S4: Determine the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information;

[0092] S5: Performs thermal management on the target vehicle based on the initial temperature of the battery pack, driving time, and charging ambient temperature.

[0093] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0094] Working principle of the invention:

[0095] This invention collects the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; obtains the driving information and charging distance of the target vehicle; calculates the driving time to reach the target charging station based on the driving information and charging distance of the target vehicle; determines the charging environment temperature of the target vehicle based on the estimated charging time and weather forecast information; and performs thermal management on the target vehicle based on the initial temperature of the battery pack, driving time, and charging environment temperature.

[0096] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A thermal management system for an electric commercial vehicle, comprising: The data processing module, the data acquisition module connected with the data processing module, and the thermal management control module connected with the data processing module; characterized in that The data acquisition module is configured to acquire an initial temperature of a battery pack in a target vehicle and weather forecast information of a target charging station, and obtain driving information and a charging distance of the target vehicle; the driving information includes an average vehicle speed and a remaining power level; and the charging distance is a navigation distance between the target vehicle and the target charging station. The data processing module is configured to calculate a driving duration to the target charging station based on the driving information and the charging distance of the target vehicle, and determine a charging ambient temperature of the target vehicle based on a predicted charging time and the weather forecast information; the predicted charging time is obtained by adding the driving duration to a current time. The thermal management control module is configured to perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature; and includes the following steps: X1: acquiring the initial temperature of the battery pack, the driving duration, and the charging ambient temperature, obtaining the average vehicle speed and the remaining power level of the target vehicle, and calculating a sum of the current time and the driving duration and marking the sum as the predicted charging time; X2: inputting the initial temperature of the battery pack, the predicted charging time, the average vehicle speed, and the remaining power level of the target vehicle into a battery pack temperature prediction model to obtain a battery prediction temperature of the battery pack at the predicted charging time; the battery pack temperature prediction model is constructed based on an artificial intelligence model; X3: obtaining a temperature adjustment rate corresponding to each temperature control mode based on temperature change data of the target vehicle in different temperature control modes; the temperature control modes include a temperature increasing mode and a temperature decreasing mode; X4: calculating a temperature control adjustment duration based on the battery prediction temperature, the charging ambient temperature, and the temperature adjustment rate; X5: determining whether the temperature control adjustment duration is greater than the driving duration; Yes, immediately starting a thermal management device to adjust the temperature of the battery pack of the target vehicle; No, calculating a difference between the predicted charging time and the temperature control adjustment duration to obtain a temperature control adjustment start time; X6: determining whether the current time is equal to the temperature control adjustment start time; yes, automatically starting the thermal management device to adjust the temperature of the battery pack of the target vehicle; and no, temporarily not adjusting the temperature of the battery pack of the target vehicle.

2. A thermal management system for an electric commercial vehicle according to claim 1, characterized in that, The calculation of the driving duration to the target charging station based on the driving information and the charging distance of the target vehicle includes the following steps: X1: acquiring the average vehicle speed, the remaining power level, and the charging distance from the driving information of the target vehicle, determining a driving range of the target vehicle based on the remaining power level, determining whether the driving range is less than the charging distance, and navigating to the nearest charging station within the driving range of the target vehicle if the driving range is less than the charging distance; otherwise, calculating a ratio of the charging distance to the average vehicle speed to obtain the driving duration.

3. A thermal management system for an electric commercial vehicle according to claim 2, characterized in that, The determination of the driving range of the target vehicle based on the remaining power level includes the following steps: X1: acquiring the remaining power level and an average power consumption of the target vehicle, calculating a ratio of the remaining power level to the average power consumption to obtain the driving range of the target vehicle.

4. A thermal management system for an electric commercial vehicle according to claim 1, characterized in that, The determination of the charging ambient temperature of the target vehicle based on the predicted charging time and the weather forecast information includes the following steps: X1: acquiring the predicted charging time and the weather forecast information, and determining the charging ambient temperature of the target vehicle based on the predicted charging time and the weather forecast information. extracting a predicted charging time of the target vehicle and weather forecast information of the target charging station; wherein the weather forecast information is temperature data changing over time; obtaining temperature data at a time corresponding to the predicted charging time, and marking the temperature data at the corresponding time as a charging environment temperature.

5. A thermal management system for an electric commercial vehicle according to claim 1, characterized in that, The battery pack temperature prediction model is constructed based on an artificial intelligence model, and includes: extracting an initial temperature of the battery pack in the target vehicle, a remaining power, an average speed, and an actual temperature of the battery pack after a driving duration of the target vehicle; integrating the initial temperature of the battery pack in the target vehicle, the remaining power, the average speed, and the driving duration as a standard input data set, and integrating the actual temperature of the battery pack after the driving duration of the target vehicle as a standard output data set; training the artificial intelligence model according to the standard input data set and the standard output data set, and marking the trained artificial intelligence model as the battery pack temperature prediction model; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

6. A thermal management system for an electric commercial vehicle of claim 1, characterized in that, The temperature adjustment rate corresponding to the temperature change data of the target vehicle under different temperature control modes is obtained based on the temperature change data, including: respectively obtaining temperature change data of the target vehicle in a temperature increasing mode and a temperature decreasing mode; obtaining a temperature increasing adjustment rate by linear fitting of the temperature change data in the temperature increasing mode; obtaining a temperature decreasing adjustment rate by linear fitting of the temperature change data in the temperature decreasing mode; and integrating the temperature increasing adjustment rate and the temperature decreasing adjustment rate as the temperature adjustment rate.

7. A thermal management system for an electric commercial vehicle of claim 1, characterized in that, The temperature control adjustment duration is calculated based on the battery predicted temperature, the charging environment temperature, and the temperature adjustment rate, including: T1: extracting the battery predicted temperature, the charging environment temperature, and the temperature adjustment rate; T2: inputting the charging environment temperature into a charging temperature database for matching to obtain a corresponding best charging temperature range; T3: determining whether the battery predicted temperature is greater than an upper limit of the temperature of the best charging temperature range; if yes, calculating a difference between the battery predicted temperature and the upper limit of the temperature and marking the difference as a temperature difference; if no, jumping to T4; T4: determining whether the battery predicted temperature is less than a lower limit of the temperature of the best charging temperature range; if yes, calculating a difference between the battery predicted temperature and the lower limit of the temperature and marking the difference as the temperature difference; if no, setting the temperature control adjustment duration as 0; T5: by formula Calculate the temperature control adjustment duration ; wherein, is the temperature difference, is the temperature adjustment rate, is the temperature adjustment rate; k1, k2 are both proportional coefficients greater than 0; ln() is the logarithmic function with natural constant as base.

8. A thermal management system for an electric commercial vehicle according to claim 7, characterized in that, The charging temperature database is constructed by the following method: obtaining charging speed of battery temperature data of the target vehicle under different charging environment temperatures, marking a maximum battery temperature at which the charging speed is equal to a preset rated charging speed as the upper limit of the temperature, and marking a minimum battery temperature at which the charging speed is equal to the preset rated charging speed as the lower limit of the temperature; integrating the lower limit of the temperature and the upper limit of the temperature as the best charging temperature range of the target vehicle under the corresponding environment temperature, and saving a mapping relationship between the several best charging temperature ranges and the corresponding charging environment temperatures to the charging temperature database.

9. A thermal management method for an electric commercial vehicle, operating on the basis of a thermal management system for an electric commercial vehicle according to any one of claims 1-8, characterized in that, including: collecting an initial temperature of the battery pack in the target vehicle and weather forecast information of the target charging station; obtaining driving information and charging distance of the target vehicle; calculating a driving duration to the target charging station based on the driving information and the charging distance of the target vehicle; determining a charging environment temperature of the target vehicle based on the predicted charging time and the weather forecast information; A target vehicle is thermally managed based on an initial temperature of a battery pack, a driving duration, and a charging ambient temperature.

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