Vehicle predictive thermal management method and device

Abstract

The invention provides a vehicle predictive thermal management method and device, relates to the technical field of vehicle intelligent control and thermal management, and aims to predictively adjust thermal management according to a real-time route plan and a battery state so as to significantly improve energy consumption control of a battery and further improve driving experience. The method comprises the following steps: acquiring travel planning information and battery state data of a current vehicle; based on the travel planning information and the battery state data, predicting a first predicted temperature and a predicted residual electric quantity of the battery at the end of the travel; and adjusting operation parameters of thermal management according to the first predicted temperature and the predicted residual electric quantity.

Description

Technical Field

[0001] This application relates to the fields of vehicle intelligent control technology and thermal management technology, and particularly relates to a vehicle predictive thermal management method and device. Background Art

[0002] With the rapid development of the new energy vehicle industry, the battery thermal management systems of pure electric vehicles and hybrid electric vehicles have become the core technologies to ensure the safety and performance of power batteries. Currently, the industry generally adopts a thermal management control strategy triggered based on fixed thresholds, that is, when the battery temperature exceeds the set range, the cooling / heating device is started, and it is immediately turned off after the temperature reaches the standard. This passive control method has significant defects. On the one hand, the frequent start and stop of thermal management components lead to increased energy consumption. Especially in some specific driving situations, the system may perform unnecessary deep temperature regulation. On the other hand, the existing technology cannot optimize the thermal management timing according to the trip characteristics, which not only causes energy waste but also restricts the further improvement of the energy efficiency of the thermal management system. There is an urgent need to provide a vehicle predictive thermal management method to improve the above defects. Summary of the Invention

[0003] To solve the above technical problems, this application provides a vehicle predictive thermal management method and device to solve the technical problems of not only causing energy waste but also restricting the further improvement of the energy efficiency of the thermal management system.

[0004] To achieve the above technical objectives, this application provides the following technical solutions:

[0005] In a first aspect, an embodiment of this specification provides a vehicle predictive thermal management method, including:

[0006] Obtain the trip planning information and battery state data of the current vehicle;

[0007] Based on the trip planning information and battery state data, predict the first predicted temperature and predicted remaining power of the battery at the end of the trip;

[0008] Adjust the operating parameters of the thermal management according to the first predicted temperature and predicted remaining power.

[0009] In a second aspect, an embodiment of this specification provides a vehicle predictive thermal management device, including:

[0010] An obtaining unit, configured to obtain the trip planning information and battery state data of the current vehicle;

[0011] A predicting unit, configured to predict the first predicted temperature and predicted remaining power of the battery at the end of the trip based on the trip planning information and battery state data;

[0012] A processing unit, configured to adjust the operating parameters of the thermal management according to the first predicted temperature and predicted remaining power.

[0013] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for vehicle predictive thermal management according to the first aspect or any corresponding embodiment thereof as described above.

[0014] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the method for vehicle predictive thermal management as described in any one of the above is implemented.

[0015] In a fifth aspect, an embodiment of the present specification provides a computer program product or a computer program. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, the method for vehicle predictive thermal management as described in any one of the above is implemented.

[0016] As can be seen from the above technical solutions, the present application provides a method and a device for vehicle predictive thermal management. The method first obtains the trip planning information and battery state data of the current vehicle, then predicts the predicted temperature and predicted remaining power of the battery at the end of the trip based on the trip planning information and battery state data, and finally adjusts the operating parameters of the thermal management according to the predicted temperature and predicted remaining power. By making predictive adjustments to the thermal management based on real-time trip planning and battery state, the energy consumption control of the battery is significantly improved, thereby enhancing the driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only the embodiments of the present application, and those of ordinary skill in the art can obtain other drawings according to the provided drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of a method for vehicle predictive thermal management provided for the embodiments of the present specification;

[0019] Figure 2 It is a schematic flowchart of a specific process for vehicle predictive thermal management provided for the embodiments of the present specification;

[0020] Figure 3 It is a schematic flowchart of another specific process for vehicle predictive thermal management provided for the embodiments of the present specification;

[0021] Figure 4 A schematic structural diagram of a vehicle predictive thermal management device provided for the embodiments of this specification;

[0022] Figure 5 A schematic structural diagram of an electronic device provided for the embodiments of this specification. Specific embodiments

[0023] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the ordinary meanings understood by those skilled in the art to which this specification pertains. The "first", "second" and similar terms used in the embodiments of this specification do not denote any order, quantity or importance, but are only used to avoid confusion of components.

[0024] Unless otherwise required by the context, throughout this specification, "a plurality" means "at least two", and "including" is interpreted in an open, inclusive sense, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" etc. are intended to indicate that the specific features, structures, materials or characteristics related to the embodiment or example are included in at least one embodiment or example of this specification. The schematic representations of the above terms do not necessarily refer to the same embodiment or example.

[0025] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by this specification.

[0026] Overview

[0027] As described in the background art, with the rapid development of the new energy vehicle industry, the battery thermal management systems of pure electric vehicles and hybrid electric vehicles have become the core technologies to ensure the safety and performance of power batteries. Currently, the industry generally adopts a thermal management control strategy triggered based on fixed thresholds, that is, when the battery temperature exceeds the set range, the cooling / heating device is started, and it is immediately turned off after the temperature reaches the standard. This passive control method has significant defects. On the one hand, the frequent start and stop of the thermal management components lead to increased energy consumption. Especially in some specific driving situations, the system may perform unnecessary deep temperature regulation. On the other hand, the prior art cannot optimize the thermal management timing according to the driving characteristics, which not only causes energy waste but also restricts the further improvement of the energy efficiency of the thermal management system. Therefore, it is necessary to provide a vehicle predictive thermal management method.

[0028] Therefore, it is necessary to provide a vehicle predictive thermal management method to solve the above problems.

[0029] In order to solve the problems in the prior art, which not only cause energy waste but also restrict the further improvement of the energy efficiency of the thermal management system, in the technical solution of this application, first, the trip planning information and battery state data of the current vehicle are obtained. Then, based on the trip planning information and battery state data, the predicted temperature and predicted remaining power of the battery at the end of the trip are predicted. Finally, the operating parameters of the thermal management are adjusted according to the predicted temperature and predicted remaining power. By making predictive adjustments to the thermal management based on the real-time trip planning and battery state, the energy consumption control of the battery is significantly improved, thereby enhancing the driving experience.

[0030] Based on the above inventive concept, the vehicle predictive thermal management method provided by the embodiments of this specification will be described exemplarily below.

[0031] Exemplary Method

[0032] The embodiments of this specification provide a vehicle predictive thermal management method, as Figure 1 shown, including:

[0033] S101. Obtain the trip planning information and battery state data of the current vehicle.

[0034] In specific implementation, first, the trip planning information of the current vehicle is obtained. The trip planning information needs to include the driving destination of the vehicle. The specific driving destination can be obtained through the vehicle's navigation information or can be inferred by the cloud according to the driver's driving habits. The trip planning information can also include the specific road conditions in the trip path, including the traffic congestion situation, the road surface condition, and the weather information at the passing locations, etc., to further predict a suitable thermal management method.

[0035] S102. Based on the trip planning information and battery state data, predict the first predicted temperature and predicted remaining power of the battery at the end of the trip.

[0036] In specific implementation, first, the predicted change in the battery temperature at the end of the trip, that is, when reaching the navigation end point, is predicted using the trip planning information and battery state data. At the same time, the average power of the driver driving the vehicle to the end point, that is, the trip predicted power, is predicted using the trip planning information. Finally, the first predicted temperature and predicted remaining power of the battery at the end of the trip are predicted according to the predicted change in the battery temperature and the trip predicted power.

[0037] S103. Adjust the operating parameters of the thermal management according to the first predicted temperature and predicted remaining power.

[0038] During specific implementation, first determine the remaining mileage of the current trip to the destination according to the trip planning information, and then compare the remaining mileage with the mileage threshold. If the remaining mileage is less than the mileage threshold, that is, the current trip can be regarded as a short trip. At this time, it is necessary to adjust the operating parameters according to the first predicted temperature and the predicted remaining power to perform predictive thermal management. If the remaining mileage is not less than the mileage threshold, that is, the current trip can be regarded as a long trip. In such a case, the benefit of predictive thermal management is very low and close to conventional thermal management, so predictive thermal management is not performed, that is, the operating parameters are not adjusted. The specific value of the mileage threshold can be comprehensively determined according to specific requirements, vehicle information, driving information, etc. The embodiments of the present invention do not limit this.

[0039] The embodiment of this specification provides a specific process of a vehicle predictive thermal management method, as Figure 2 shown, including:

[0040] S201. Obtain the trip planning information and battery status data of the current vehicle.

[0041] During specific implementation, for the battery status data, the information of the vehicle is obtained in real time through sensors, and then the battery status data is obtained. For the trip planning information, the trip planning information is determined through the cloud and / or navigation information. Specifically, the trip planning information can be obtained by using the navigation information of the vehicle. For example, the driver inputs the navigation location, and according to this location, the planned route, path information on the route, congestion status, etc. can be known, or the cloud estimates the driver's trip destination according to the driver's driving habits, and then obtains other information on the driving path. In this step, as long as one of the cloud or navigation information is available, the subsequent steps can be continued.

[0042] S202. Based on the trip planning information and battery status data, predict the first predicted temperature and the predicted remaining power of the battery at the end of the trip.

[0043] During specific implementation, the predicted change in battery temperature at the end of the journey is calculated using trip planning information and battery status data, which is the temperature difference between the battery temperature at the navigation destination and the current battery temperature. Temperature estimation is an iterative calculation process that continuously updates the predicted end temperature as the vehicle moves. During iteration, starting from the current battery temperature, the end temperature is updated in each round of iteration, and the updated end temperature is used as the input for the next round, and the calculation is repeated until the end of the journey. At the initial stage of estimation, since there are two cases of battery heating and cooling in thermal management, when the battery is heated, the lowest current temperature of the entire battery is selected as the battery temperature, and when the battery is cooled, the highest temperature of the entire battery is selected as the current battery temperature. Then, based on the current battery charge and the current battery temperature, the average equivalent DC resistance value R of the battery in this journey is determined for the first time, and in subsequent iterations, based on the current battery charge, the current battery temperature, and the battery end temperature of the previous iteration cycle, the average equivalent DC resistance value R of the battery in this journey is re-determined. Then, based on the remaining mileage and the estimated remaining time, the average vehicle speed is calculated, and then the average equivalent DC current value I of the battery in this journey is determined. The time is the estimated remaining time t input by the navigation or the cloud, and the total heat generation E1 of the battery itself during the journey can be obtained according to the following formula.

[0044] E1 = I 2 Rt

[0045] Based on the current battery temperature and the battery end temperature of the previous iteration cycle (the current battery temperature is selected for the initial iteration cycle), the average temperature T2 of the battery in this journey is determined. The current ambient temperature T1 is read for heat exchange calculation with temperature difference. The heat transfer coefficient Z and the heat transfer area Ar can be calibrated according to different batteries. The time is the estimated remaining time t input by the navigation or the cloud. The heat exchange amount E2 between the battery and the environment during the journey can be obtained according to the following formula.

[0046] E2 = (T1 - T2)ArZ

[0047] Then, by adding the total heat generation E1 of the battery itself and the heat exchange amount E2 between the battery and the environment, the total energy change of the battery during the journey can be obtained. The specific heat capacity at constant pressure C p and the battery mass m are calibrated according to different batteries.

[0048] According to the following formula, the change in battery temperature ΔT at the end of the journey can be obtained.

[0049]

[0050] Then, by adding the current battery temperature T and the change in battery temperature ΔT at the end of the journey, the battery temperature at the end of the journey can be obtained, which is the first predicted temperature of the battery.

[0051] In addition, the average power of the driver driving the vehicle to the end point, i.e., the trip prediction power, is predicted using the trip planning information. When calculating the trip prediction power, it is necessary to calculate according to the trip planning information. When the navigation is turned on or the cloud prediction information is valid, the predicted driver demand power PredDrvPwrReq can be obtained, which is used for high-voltage battery thermal management temperature control. And the cloud estimated driving demand power MaxDischrgPwPdict and the cloud estimated maximum driving speed LimtedVehSpdPdict are obtained, and then the demand power P-Cloud for driving at a constant speed at this speed is calculated. And the current road speed limit SpdLimOfRoad sent by the navigation is received, and then the demand power P-Nav for driving at a constant speed at this speed is calculated, as follows:

[0052] When the navigation information is valid and the cloud prediction information is valid:

[0053] PredDrvPwrReq = Max[MaxDischrgPwPdict, P-Cloud, P-Nav] + P-reserve(TBD), where P-reserve(TBD) is the "power reserve" that can be changed by itself, representing the additional power value reserved when predicting the driver demand power. Its purpose is to provide safety redundancy for the system to cope with unforeseen situations such as prediction errors, sudden driving demands (such as sudden acceleration, climbing slopes), or environmental changes (such as temperature fluctuations).

[0054] When the navigation information is valid and the cloud prediction information is invalid:

[0055] PredDrvPwrReq = P-Nav + P-reserve(TBD).

[0056] When the navigation information is invalid and the cloud prediction information is valid:

[0057] PredDrvPwrReq = Max[MaxDischrgPwPdict, P-Cloud] + P-reserve(TBD).

[0058] When the navigation information is invalid and the cloud prediction information is invalid:

[0059] PredDrvPwrReq = 0.

[0060] For calculating the predicted remaining power, if the predicted remaining power value is estimated through the cloud, the predicted remaining power is calculated based on the cloud-estimated remaining mileage TotalMileagePdict and the cloud-estimated remaining time TotalDrivTimPdict. The calculation method is as follows: First, the driving conditions can be divided into low speed, medium speed, high speed, and ultra-high speed, and the corresponding effective current I1 is obtained under different conditions. Then, according to the estimated average driving speed TotalMileagePdict / TotalDrivTimPdict, the estimated driving power P-Cloud = I1*HvBattUDc is obtained.

[0061] The predicted remaining power value SOC-Cloud estimated by the cloud is:

[0062] SOC-Cloud = HvBattSoc - [P-Cloud*TotalDrivTimPdict / high-voltage battery capacity].

[0063] If the predicted remaining power value is estimated through the navigation, the predicted remaining power is calculated based on the remaining mileage DstToDestination of this navigation and the remaining time TiToDestNavRoute of this navigation. The calculation method is as follows: The driving conditions are divided into low speed, medium speed, high speed, and ultra-high speed, corresponding to the effective current I2 respectively. According to the estimated average driving speed DstToDestination / TiToDestNavRoute, the estimated driving power P-Nav = I2*HvBattUDc is obtained.

[0064] The predicted remaining power value PredTarSOC estimated by the navigation is:

[0065] SOC-Nav = HvBattSoc - [P-Nav*TiToDestNavRoute / high-voltage battery capacity].

[0066] In calculating the predicted remaining power, the navigation estimated value is taken as the standard. If it cannot be calculated based on the navigation, the cloud estimated value is used. The specific execution logic is as follows:

[0067] When the navigation information is valid and the cloud prediction information is valid:

[0068] PredTarSOC = Max[SOC-Nav, 10% (TBD)]. Among them, 10% (TBD) is the "power reserve" that can be changed by oneself. Its purpose is to provide safety redundancy for the system to cope with unforeseen situations such as prediction errors, sudden driving demands (such as sudden acceleration, climbing), or environmental changes (such as temperature fluctuations). The specific value can be set according to requirements. Here, 10% is taken as an example.

[0069] When the navigation information is valid and the cloud prediction information is invalid:

[0070] PredTarSOC = Max[SOC - Nav, 10%(TBD)].

[0071] When the navigation information is invalid and the cloud prediction information is valid:

[0072] PredTarSOC = Max[SOC - Cloud, 10%(TBD)].

[0073] When the navigation information is invalid and the cloud prediction information is invalid:

[0074] PredTarSOC = 0.

[0075] In the cases where PredDrvPwrReq = 0 and PredTarSOC = 0, predictive thermal management is not performed. In this step, the status of the vehicle can also be judged. If the conditions for predictive thermal management are not met, predictive thermal management is not performed. Specifically, it is necessary to judge whether it is a traffic jam or a long - term parking. In one example, when all of the following conditions are met, the vehicle is considered to be in long - term parking:

[0076] The vehicle mode is Driving, the vehicle speed is 0 km / h and the time exceeds 3 minutes, and the maximum battery temperature is less than 43°.

[0077] When any of the following conditions is met, the long - term parking of the vehicle becomes invalid:

[0078] The vehicle mode is not equal to Driving for 1 minute, the vehicle speed is greater than 0 km / h for 1 minute, and the maximum battery temperature is greater than 44°.

[0079] When all of the following conditions are met, it is considered a traffic jam:

[0080] The traffic jam flag bit input by the navigation or the cloud is in severe congestion and the time exceeds 1 minute, the vehicle speed is lower than 10 km / h, and the maximum battery temperature is less than 43°.

[0081] When any of the following conditions is met, the traffic jam becomes invalid:

[0082] The traffic jam flag bit input by the navigation or the cloud is maintained for 1 minute, the vehicle speed is greater than 30 km / h for 1 minute, and the maximum battery temperature is greater than 44°.

[0083] It should be noted that the specific data in the above examples can be set according to requirements, and the embodiments of the present invention do not limit this.

[0084] S203. Determine the remaining mileage based on the trip planning information.

[0085] In specific implementation, the travel destination and travel route are determined according to the travel planning information, and then the remaining mileage of the vehicle is determined based on the travel destination and travel route.

[0086] S204. When the remaining mileage is less than the mileage threshold, adjust the operating parameters according to the first predicted temperature and the predicted remaining power.

[0087] In specific implementation, it is determined whether to perform predictive thermal management by comparing the remaining mileage determined in step S203 with the mileage threshold. Specifically, when the remaining mileage is less than the mileage threshold, the operating parameters are adjusted according to the first predicted temperature and the predicted remaining power; when the remaining mileage is not less than the mileage threshold, the operating parameters are not adjusted. The specific mileage threshold depends on factors such as speed and temperature. Combining big data of user habits, the proportion of trips within 30 km for a single trip is 92%. Predictive thermal management based on available energy and available power can cover most user working conditions to obtain benefits. Under a driving mileage of 30 km, the predictive thermal management strategy has benefits regardless of the working conditions. However, in the case of medium and long mileage, due to the decrease in the temperature rise speed, the intervention of energy recovery is postponed and the power is limited, and the overall benefit gradually decreases until it becomes negative. The benefit is strongly correlated with cyclic energy recovery. Therefore, here the mileage threshold is taken as 30 km for illustration. When the remaining mileage is less than 30 km, this trip is identified as a short trip; when the remaining mileage is not less than 30 km, it is identified as a long trip.

[0088] Thermal management is divided into two cases: heating and cooling. When the thermal management is heating, when the remaining mileage is less than the mileage threshold, that is, it is identified as a short trip. At this time, the heating target temperature and the heating start temperature are determined according to the first predicted temperature and the predicted remaining power. Specifically, the heating target temperature and the heating start temperature need to be judged according to the following three indicators: the first temperature, the second temperature, and the third temperature. The first temperature is determined according to the predicted travel power and is used to meet the battery discharge demand; the second temperature is determined according to the first predicted temperature and the predicted remaining power and is used to meet the battery energy demand; the third temperature is determined according to the predicted travel power and is used to meet the battery discharge ambient temperature. Then, the maximum temperature value among the first temperature, the second temperature, and the third temperature is selected as the heating target temperature, and the minimum temperature value is selected as the heating start temperature. That is, when the temperature of the battery is lower than the heating start temperature, the battery is heated, and when the temperature of the battery is not lower than the heating target temperature, the heating is stopped. In practical applications, membrane heating is mostly used for the battery. Since membrane heating is mostly constant-power heating, the power of the membrane heating can be set to 0 after the battery temperature reaches the target. In a liquid-cooled battery system, the control targets of components such as the battery water pump are set to the predicted heating target temperature for control.

[0089] The main purpose of predictive battery heating with a short driving range is to determine the heating target temperature of the battery. Usually, battery heating starts after a battery heating request and continues until it stops at minus 10 degrees Celsius. By identifying the working conditions, the heating target temperature can be reduced to end battery heating in advance, achieving the purpose of saving energy consumption.

[0090] When the thermal management is for cooling, when the remaining mileage is less than the mileage threshold, the first cooling target temperature and the cooling start temperature are determined according to the first predicted temperature and the cooling temperature threshold. The cooling temperature threshold includes a first cooling temperature threshold and a second cooling temperature threshold, and the first cooling temperature threshold is less than the second cooling temperature threshold. The cooling temperature threshold is used to preset two temperature thresholds, high and low, to distinguish the urgency of battery cooling, and an entry cooling threshold (i.e., the cooling start temperature) and a cooling target temperature are set for each battery cooling level. The higher the cooling level, the lower the entry stop threshold. When the first predicted temperature is less than the first cooling temperature threshold, the cooling level is determined to be level one; when the first predicted temperature is greater than or equal to the first cooling temperature threshold and less than the second cooling temperature threshold, the cooling level is determined to be level two; when the first predicted temperature is greater than or equal to the second cooling temperature threshold, the cooling level is determined to be level three; the first cooling target temperature and the cooling start temperature are determined according to the cooling level, and then the battery is cooled when the temperature of the battery is higher than the cooling start temperature, and the cooling stops when the temperature of the battery is not higher than the first cooling target temperature.

[0091] The main purpose of predictive battery cooling with a short driving range is to determine the first cooling target temperature of the battery. In conventional thermal management, the cooling target is relatively fixed, and the thermal management components are repeatedly or continuously turned on. The energy consumption is relatively high. In predictive thermal management, according to the user's set purpose, the temperature when reaching the destination can be predicted, and then the battery thermal management range is set in a higher temperature area to reduce the number of turn-ons, and the battery thermal management is directly turned off after the predicted end temperature is within the appropriate battery operating range. The predicted first cooling target temperature is the compressor closed-loop control target.

[0092] In this step, the energy consumption difference after determining and feedback-adjusting the operating parameters and before adjusting the operating parameters can also be determined, so that the driver can clearly know the energy-saving situation before and after turning on the predictive thermal management. Based on the thermal formula, according to the temperature rise of the battery, the energy corresponding to these temperature rises can be calculated:

[0093] E = m * c * (T - T0)

[0094] Through this formula, the temperature rise can be converted into energy. Among them, T is the current temperature, T0 is the set temperature when the thermal management starts to work, m is the mass, c is the specific heat capacity of the battery, and E is the energy.

[0095] When the thermal management is for cooling, the main heat sources of the battery are reversible heat and irreversible heat. The battery heat generation model can be expressed by the following formula:

[0096] q = I 2 *R - I * T * dU ocv / dT

[0097] As the vehicle runs, the heat generated by the battery can be calculated:

[0098] E = ∫q * dt

[0099] For the entire journey, the energy consumed is divided into the energy E drived consumed by the thermal management for the mileage already traveled and the energy E predict consumed by the thermal management for the mileage yet to be traveled. The total energy E NoPredict of the entire driving process drived = E predict + E drived . Then, the temperature rise T of the battery without thermal management can be obtained through the above formula, as well as the total energy E

[0100] consumed by the overall thermal management work during this traveled mileage. predict At the same time, based on the remaining driving mileage, remaining driving time, and vehicle weight fed back by the navigation, the average power required to drive the vehicle is calculated. Then, based on the predicted power for the journey, the average discharge current I of the battery is calculated, and further the total energy E

[0101] consumed by the thermal management work for the remaining mileage is calculated, thereby obtaining the energy consumption difference.

[0102] When the thermal management is for heating, the heating method used for thermal management is battery film heating or PTC heating. Conventional thermal management control algorithms are non - predictive, turning on when the temperature is below a certain temperature (e.g., - 15°C) and turning off when reaching the set temperature (e.g., - 10°C).

[0103] This embodiment of the specification provides another specific process for vehicle predictive thermal management, as follows Figure 3 shown, including:

[0104] S301. Obtain the trip planning information and battery status data of the current vehicle.

[0105] Step S301 is the same as step S201 and will not be elaborated here.

[0106] S302. When the thermal management is cooling and the current vehicle is in the charging state, based on the trip planning information and battery status data, predict the second predicted temperature of the battery at the end of the trip without thermal management.

[0107] Specifically in implementation, through the calculation method as described in the foregoing embodiment, obtain the second predicted temperature of the battery at the end point without thermal management.

[0108] S303. Determine the second cooling target temperature of the battery according to the second predicted temperature and the maximum acceptable temperature for the battery to operate.

[0109] Specifically in implementation, by setting the maximum acceptable temperature for the battery to operate and a temperature compensation (which can be marked), and then according to the second predicted temperature, it can be predicted at what battery temperature the trip starts without thermal management will cause the end point temperature to be too high. At this time, the second cooling target temperature of the predictive battery cooling can be calculated. Preferably, a temperature compensation value can also be set, then the maximum acceptable temperature for the battery to operate - the battery temperature difference before and after receiving the trip + the temperature compensation value = the second cooling target temperature.

[0110] S304. Cool the temperature of the battery to the second cooling target temperature.

[0111] Specifically in implementation, through the above variables, the threshold for starting battery cooling is also confirmed. There is a need to start cooling only when there is a sufficient temperature difference from the cooling target temperature. The maximum acceptable temperature for the battery to operate - the battery temperature difference before and after receiving the trip - the temperature compensation value = the battery cooling start threshold. When the battery temperature is greater than the battery cooling start threshold, start cooling until the battery temperature drops to the second cooling target temperature and then stop.

[0112] The predictive thermal management during plug-in charging is mainly to determine the second cooling target temperature of the battery. During plug-in charging, the thermal management components can borrow the power supply of the charging pile to work. Therefore, after the user sets the destination, the battery temperature when arriving at the destination can be predicted. If the temperature is high, battery cooling can be turned on during charging to cool the battery to a suitable temperature in advance. Reduce the power consumption of thermal management during the trip. Increase the endurance. The predicted second cooling target temperature is the target temperature for the compressor closed-loop control.

[0113] In this embodiment, first, the trip planning information and battery status data of the current vehicle are obtained. Then, based on the trip planning information and battery status data, the predicted temperature and predicted remaining power of the battery at the end of the trip are predicted. Finally, the operating parameters of the thermal management are adjusted according to the predicted temperature and predicted remaining power. According to the real-time trip planning and battery status, the predictive adjustment of the thermal management significantly improves the energy consumption control of the battery, thereby enhancing the driving experience.

[0114] Exemplary device

[0115] In an exemplary embodiment of the present specification, a vehicle predictive thermal management device 400 is further provided, as Figure 4 shown, including:

[0116] An acquisition unit 401 that acquires the trip planning information and battery status data of the current vehicle;

[0117] A prediction unit 402 that predicts the first predicted temperature and predicted remaining power of the battery at the end of the trip based on the trip planning information and battery status data;

[0118] A processing unit 403 that adjusts the operating parameters of the thermal management according to the first predicted temperature and predicted remaining power.

[0119] In one implementation, the prediction unit 402 is specifically configured to:

[0120] Determine the predicted change in battery temperature at the end of the trip based on the trip planning information and battery status data;

[0121] Determine the predicted trip power of the vehicle based on the trip planning information;

[0122] Predict the first predicted temperature and predicted remaining power of the battery at the end of the trip according to the predicted change in battery temperature and the predicted trip power.

[0123] In one implementation, the processing unit 403 is specifically configured to:

[0124] Determine the remaining mileage based on the trip planning information;

[0125] When the remaining mileage is less than the mileage threshold, adjust the operating parameters according to the first predicted temperature and predicted remaining power;

[0126] When the remaining mileage is not less than the mileage threshold, do not adjust the operating parameters.

[0127] In one implementation, when the thermal management is heating, the processing unit 403 is specifically configured to:

[0128] When the remaining mileage is less than the mileage threshold, determine the heating target temperature and heating start temperature according to the first predicted temperature and predicted remaining power;

[0129] Heat the battery when the temperature of the battery is lower than the heating start temperature, and stop heating when the temperature of the battery is not lower than the heating target temperature.

[0130] In one embodiment, the processing unit 403 is specifically configured to:

[0131] Determine a first temperature that meets the battery discharge demand according to the predicted power of the trip;

[0132] Determine a second temperature at which the battery meets the energy demand according to the first predicted temperature and the predicted remaining power;

[0133] Determine a third temperature that meets the battery discharge ambient temperature according to the predicted power of the trip;

[0134] Select the maximum temperature value among the first temperature, the second temperature, and the third temperature as the heating target temperature;

[0135] Select the minimum temperature value among the first temperature, the second temperature, and the third temperature as the heating start temperature.

[0136] In one embodiment, when the thermal management is cooling, the processing unit 403 is specifically configured to:

[0137] When the remaining mileage is less than the mileage threshold, determine a first cooling target temperature and a cooling start temperature according to the first predicted temperature and the cooling temperature threshold;

[0138] Cool the battery when the temperature of the battery is higher than the cooling start temperature, and stop cooling when the temperature of the battery is not higher than the first cooling target temperature.

[0139] In one embodiment, the cooling temperature threshold includes a first cooling temperature threshold and a second cooling temperature threshold, where the first cooling temperature threshold is less than the second cooling temperature threshold, then the processing unit 403 is specifically configured to:

[0140] When the first predicted temperature is less than the first cooling temperature threshold, determine that the cooling level is level one;

[0141] When the first predicted temperature is greater than or equal to the first cooling temperature threshold and less than the second cooling temperature threshold, determine that the cooling level is level two;

[0142] When the first predicted temperature is greater than or equal to the second cooling temperature threshold, determine that the cooling level is level three;

[0143] Determine the first cooling target temperature and the cooling start temperature according to the cooling level.

[0144] In one embodiment, when the thermal management is cooling and the current vehicle is in a charging state, the processing unit 403 is further configured to:

[0145] Predict the second predicted temperature of the battery at the end of the trip without thermal management based on the trip planning information and the battery state data;

[0146] Determine the second cooling target temperature of the battery according to the second predicted temperature and the maximum acceptable temperature for the battery to operate;

[0147] Cool the temperature of the battery to the second cooling target temperature.

[0148] In one embodiment, the processing unit 403 is further configured to:

[0149] Determine and feedback the difference in energy consumption after adjusting the operating parameters and before adjusting the operating parameters.

[0150] In one embodiment, the obtaining unit 401 is specifically configured to:

[0151] Determine the trip planning information of the vehicle through the cloud and / or navigation information;

[0152] Obtain the battery state data of the vehicle in real time.

[0153] The vehicle predictive thermal management device provided in this embodiment belongs to the same inventive concept as the vehicle predictive thermal management method provided in the above embodiments of the present application, and can execute the vehicle predictive thermal management method provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects for executing the vehicle predictive thermal management method. For the technical details not described in detail in this embodiment, reference may be made to the specific processing content of the vehicle predictive thermal management method provided in the above embodiments of the present application, which will not be elaborated here.

[0154] Exemplary Device

[0155] In an exemplary embodiment of this specification, an electronic device is further provided, as Figure 5 shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the vehicle predictive thermal management method, and the method includes:

[0156] Obtain the trip planning information and battery state data of the current vehicle;

[0157] Based on the trip planning information and the battery state data, predict the first predicted temperature and the predicted remaining power of the battery at the end of the trip;

[0158] Adjust the operating parameters of the thermal management according to the first predicted temperature and the predicted remaining power.

[0159] In addition, when the logic instructions in the above-mentioned memory 530 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0160] Exemplary Computer Program Product and Storage Medium

[0161] In addition to the above methods and devices, the vehicle predictive thermal management method provided in the embodiments of this specification can also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the vehicle predictive thermal management method according to various embodiments of this specification described in the "Exemplary Method" section above.

[0162] The computer program product can be written in any combination of one or more programming languages for the program code to execute the operations in the embodiments of this specification. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages.

[0163] In addition, the embodiments of this specification also provide a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to perform the steps in the vehicle predictive thermal management method according to various embodiments of this specification described in the "Exemplary Method" section above.

[0164] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this specification can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0165] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0166] The above-described embodiments merely represent several implementation manners of this specification. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the solutions provided by the embodiments of this specification. It should be noted that for those of ordinary skill in the art, without departing from the concept of this specification, several modifications and improvements can still be made, and these all belong to the protection scope of this specification. Therefore, the protection scope of the patent of this specification should be subject to the appended claims.

Claims

1. A vehicle predictive thermal management method, characterized in that: include: Get the current vehicle's trip planning information and battery status data; Based on the trip planning information and the battery status data, predicting a first predicted temperature and a predicted remaining power of the battery at the end of the trip; The operating parameters of the thermal management are adjusted according to the first predicted temperature and the predicted remaining power.

2. The method according to claim 1, characterized in that The predicting, based on the trip planning information and the battery status data, a first predicted temperature and a predicted remaining power of the battery at the end of the trip includes: determining a predicted change in battery temperature at the end of a trip based on the trip planning information and the battery status data; Determining a predicted trip power of the vehicle based on the trip planning information; A first predicted temperature and a predicted remaining power of the battery at the end of the trip are predicted based on the predicted battery temperature change and the predicted trip power.

3. The method according to claim 2, characterized in that The adjusting the operating parameters of the thermal management according to the first predicted temperature and the predicted remaining power includes: determining the remaining mileage based on the trip planning information; When the remaining mileage is less than a mileage threshold, adjusting the operating parameter according to the first predicted temperature and the predicted remaining power; When the remaining mileage is not less than the mileage threshold, the operating parameter is not adjusted.

4. The method according to claim 3, characterized in that When the thermal management is heating, when the remaining mileage is less than a mileage threshold, adjusting the operating parameter according to the first predicted temperature and the predicted remaining power includes: When the remaining mileage is less than a mileage threshold, determining a heating target temperature and a heating start temperature according to the first predicted temperature and the predicted remaining power; The battery is heated when the temperature of the battery is lower than the heating start temperature, and the heating is stopped when the temperature of the battery is not lower than the heating target temperature.

5. The method according to claim 4, characterized in that The determining of the heating target temperature and the heating start temperature according to the first predicted temperature and the predicted remaining power includes: Determining a first temperature that meets a battery discharge requirement according to the predicted travel power; Determine a second temperature at which the battery meets energy demand based on the first predicted temperature and the predicted remaining power; Determining a third temperature that satisfies the battery discharge environment temperature according to the trip predicted power; Selecting the maximum temperature value among the first temperature, the second temperature and the third temperature as the heating target temperature; The minimum temperature value among the first temperature, the second temperature and the third temperature is selected as the heating start temperature.

6. The method according to claim 3, characterized in that When the thermal management is cooling, when the remaining mileage is less than a mileage threshold, adjusting the operating parameter according to the first predicted temperature and the predicted remaining power includes: When the remaining mileage is less than a mileage threshold, determining a first cooling target temperature and a cooling start temperature according to the first predicted temperature and a cooling temperature threshold; The battery is cooled when the temperature of the battery is higher than the cooling start temperature, and the cooling is stopped when the temperature of the battery is not higher than the first cooling target temperature.

7. The method according to claim 6, characterized in that The cooling temperature threshold includes a first cooling temperature threshold and a second cooling temperature threshold, wherein the first cooling temperature threshold is less than the second cooling temperature threshold, and determining a first cooling target temperature and a cooling start temperature according to the first predicted temperature and the cooling temperature threshold includes: When the first predicted temperature is less than the first cooling temperature threshold, determining the cooling level to be level one; When the first predicted temperature is greater than or equal to the first cooling temperature threshold, and the first predicted temperature is less than the second cooling temperature threshold, determining that the cooling level is level 2; When the first predicted temperature is greater than or equal to the second cooling temperature threshold, determining the cooling level to be level three; The first cooling target temperature and the cooling start temperature are determined according to the cooling level.

8. The method according to claim 1, characterized in that When the thermal management is cooling and the vehicle is currently in a charging state, the method further includes: predicting, based on the trip planning information and the battery status data, a second predicted temperature of the battery at the end of a trip without thermal management; determining a second cooling target temperature of the battery according to the second predicted temperature and a maximum acceptable operating temperature of the battery; The temperature of the battery is cooled to the second cooling target temperature.

9. The method according to claim 1, characterized in that: The method further comprises: The energy consumption difference between the energy consumption after adjusting the operating parameters and the energy consumption before adjusting the operating parameters is determined and fed back.

10. The method according to claim 1, characterized in that The obtaining of the current vehicle's trip planning information and battery status data includes: Determining the trip planning information of the vehicle through the cloud and / or navigation information; The battery status data of the vehicle is obtained in real time.

11. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle predictive thermal management method according to any one of claims 1 to 10 by executing the computer instructions.

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