A new energy vehicle battery system thermal management control method, system and vehicle

Abstract

The application discloses a new energy automobile battery system heat management control method and system and a vehicle, relates to the battery heat management control technical field of a new energy automobile battery management system, and the method comprises the steps of obtaining the current state of a power battery of an electric automobile, calling a control strategy table, and preliminarily determining a battery system heat management control scheme; obtaining a planned driving mileage and a predicted full-range speed curve of this trip, and combining vehicle parameters to obtain a demand power curve; according to the planned driving mileage and the demand power curve, predicting the temperature, SOC and SOE curves of the power battery through a prediction model, and judging whether the demand of this trip is met; if yes, the heat management is not started, otherwise, the heat management control scheme is adjusted according to scenes, and the updated scheme is executed; the temperature, SOC and SOE curves of the prediction power battery are periodically updated, the heat management control scheme is iteratively updated and executed, and the process is continued until the end of this trip.

Description

Technical Field

[0001] This invention relates to the field of thermal management and control technology for new energy vehicle batteries, and in particular to a thermal management and control method, system, and vehicle for a new energy vehicle battery system. Background Technology

[0002] In the field of new energy vehicles, battery thermal management technology is a core technology to ensure battery performance, extend battery life and ensure driving safety. The optimal operating temperature range for power batteries (such as lithium-ion batteries) is 25℃~35℃. When the temperature is below this range, the battery management system (BMS) is used to control the heating device to heat the battery. When the temperature is above this range, cooling is required.

[0003] Existing battery management systems (BMS) mostly employ a graded heating / cooling strategy with fixed temperature thresholds. These thresholds cannot be dynamically adjusted, making it difficult to adapt to the diverse temperature variations under different driving styles, resulting in a lack of flexibility in the management strategy. While existing research attempts to dynamically adjust the thresholds based on user driving style or different routes, this is merely an optimization of fixed scenarios or a simple analysis of driving style using driving data. It fails to consider the actual driving conditions of the user, nor the effects of battery heat generation and natural cooling, making it difficult to meet the requirements of full-process thermal management prediction. The thermal management solutions are crude, with limited energy consumption optimization effects. Summary of the Invention

[0004] To address the shortcomings of the existing technologies, this invention proposes a thermal management control method, system, and vehicle for a new energy vehicle battery system. By predicting and judging the state of the power battery throughout the entire journey and combining it with the user's driving conditions, the thermal management strategy under different scenarios is dynamically adjusted to reduce thermal management energy consumption and improve user experience. This solves the problem that existing solutions have limited applicability to specific scenarios and insufficient data consideration, resulting in limited thermal management energy consumption effects.

[0005] In a first aspect, the present invention provides a thermal management control method for a new energy vehicle battery system.

[0006] A thermal management control method for a new energy vehicle battery system includes: Obtain the current status of the electric vehicle's power battery, call the control strategy table, and initially determine the battery system's thermal management control scheme; Obtain the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve. Based on the planned driving mileage and power demand curve, the temperature, SOC, and SOE curves of the power battery throughout the entire journey are predicted by the prediction model to determine whether the requirements of this trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario and the updated scheme is executed. The system periodically updates and predicts the temperature, SOC, and SOE curves of the power battery, iteratively updates and executes the thermal management control scheme, until the end of this trip.

[0007] A further technical solution is to construct the control strategy table as follows: Within a set temperature range, charge and discharge tests are conducted on the power battery of electric vehicles at different SOCs. The allowable power, SOE change curves, and heat generation rate of the power battery at different temperatures and SOCs are recorded to obtain battery test data. Among them, the allowable power is the discharge power / charge power, SOE is the remaining usable energy, and the heat generation rate is the heat generated by the battery due to charging and discharging per unit time. Based on battery test data, a table of correspondences between battery temperature, SOC and battery system thermal management control scheme is obtained, namely the control strategy table. The battery system thermal management control scheme includes thermal management request type, thermal management target temperature and thermal management flow request. The thermal management request type includes three types: heating, cooling and temperature equalization.

[0008] A further technical solution involves converting the vehicle speed curve into a power demand curve using a power calculation formula based on the vehicle parameters; the power calculation formula is as follows: ; In the above formula, Indicates rolling resistance, Indicates wind resistance, For the overall vehicle quality, Rolling resistance coefficient, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, To reach the mileage The vehicle speed at that time.

[0009] A further technical solution involves constructing a temperature prediction model based on the battery thermal balance equation to predict the battery temperature; the battery thermal balance equation is: ; in, The rate of heat generation during battery charging and discharging. The heat exchange rate between the battery and the environment. For battery quality, For the specific heat capacity of the battery, The rate of change of battery temperature. This is the charging and discharging current. This refers to the battery's internal resistance. For heat dissipation coefficient, For battery heat dissipation area, For battery temperature, Ambient temperature; The SOC prediction model is constructed based on the law of charge conservation and adopts the ampere-hour integration method. It takes the initial SOC, the battery rated capacity, and the charge-discharge current curve as inputs, accumulates the battery discharge capacity, updates the SOC value in real time, and obtains the SOC curve for the entire trip by recursively deriving it segment by segment. When the battery is in a static steady state, the mapping relationship between open circuit voltage and SOC is used to correct the accumulated error. Based on the pre-built mapping relationship between SOC and SOE, the predicted value of SOE is determined by combining the temperature prediction results.

[0010] Further technical solutions, based on the predicted temperature, SOC, and SOE curves of the power battery throughout the entire journey, will determine whether they meet the requirements for this trip, including: Whether the battery's highest temperature exceeds the preset high temperature threshold, whether the battery's lowest temperature meets the power output requirement temperature, whether the battery's available power matches the required power curve, and whether the battery's SOE meets the energy requirements of the planned driving range; among these, matching the required power curve means that the power fluctuation does not exceed the set threshold range.

[0011] Further technical solutions include adjusting the thermal management control scheme according to different scenarios. These scenarios include scenarios where temperature is not met, power is not met, and energy is not met. Among them, scenarios where temperature is not met include short-distance high-temperature scenarios and low-temperature self-heating scenarios.

[0012] Further technical solutions for regulating short-range high-temperature scenarios include: If the remaining mileage for this trip is less than the preset short-distance threshold, and after re-predicting that there is no risk of overheating of the battery temperature, the available power of the battery matches the power required for the remaining mileage, and the battery SOE meets the requirements for the remaining mileage, then thermal management will not be turned on; otherwise, it will be turned on. The regulation of low-temperature self-heating scenarios is as follows: If the re-prediction results show that the battery's own heat generation rate causes the battery temperature to rise continuously, and the temperature rise is sufficient to meet the power output requirements for the remaining range, then the thermal management heating function will be turned off in advance; otherwise, it will not be turned off. The battery's own heat generation rate is calculated by combining the heat generation rate parameter in the battery charge and discharge test data with the current power demand.

[0013] A further technical solution for adjusting power in scenarios where it is insufficient is as follows: When the minimum battery temperature is lower than the power output requirement temperature, the thermal management cutoff temperature is increased, and the SOE of the battery after the increased cutoff temperature is re-predicted. If the predicted SOE meets the remaining range requirement, the increased cutoff temperature is used as the thermal management target temperature; if the predicted SOE does not meet the remaining range requirement, the battery allowable power is dynamically matched; when the maximum battery temperature exceeds the preset high temperature threshold, the thermal management target temperature is reduced, and the thermal management flow request is increased. Regulation in scenarios where energy is insufficient is as follows: When the available battery power does not match the demand power curve or the battery SOE does not meet the energy requirements of the planned driving range, the difference between the battery SOE increase through heating and the power consumed by the thermal management process is calculated. If the difference is positive, thermal management is activated according to the predicted target temperature to heat the battery to the target temperature. If the difference is negative or the battery SOE cannot meet the remaining range requirements, and it is predicted that the battery temperature will still be within the safe boundary range after the thermal management is turned off in advance, the thermal management is turned off in advance.

[0014] Secondly, the present invention provides a thermal management and control system for a new energy vehicle battery system.

[0015] A thermal management and control system for a new energy vehicle battery system, comprising: The preliminary solution determination module is used to obtain the current status of the electric vehicle's power battery, call the control strategy table, and preliminarily determine the battery system thermal management control scheme. The power demand acquisition module is used to acquire the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve. The thermal management control module is used to predict the temperature, SOC, and SOE curves of the power battery throughout the trip based on the planned driving mileage and power demand curve, and to determine whether the requirements of the trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario and the updated scheme is executed. The predicted temperature, SOC, and SOE curves of the power battery are periodically updated, and the thermal management control scheme is iteratively updated and executed until the trip ends.

[0016] Thirdly, the present invention also provides a vehicle.

[0017] A vehicle includes the above-described new energy vehicle battery system thermal management control system, or performs the above-described new energy vehicle battery system thermal management control method.

[0018] The above one or more technical solutions have the following beneficial effects: This invention proposes a thermal management control method, system, and vehicle for new energy vehicle battery systems. This method achieves reduced thermal management energy consumption and improved user experience through strategies such as precise anchoring of the initial control scheme, multi-dimensional prediction throughout the entire process, dynamic adjustment in different scenarios, and periodic iterative optimization. Specifically, a control strategy table is constructed based on charge-discharge test data across the entire battery temperature range and SOC range, initially determining a basic thermal management scheme adapted to the current battery state, providing a safe and battery-characteristic-appropriate starting point for subsequent optimization. Combining the planned mileage and vehicle speed curves obtained from navigation, a power formula is used to convert it into a demand power curve. Relying on battery temperature prediction models, SOC prediction models, and SOE prediction models, the battery state throughout the entire journey without thermal management is accurately predicted. Then, based on the judgment results of the three core requirements—temperature, power, and energy—differentiated control strategies are formulated for four scenarios: insufficient power, insufficient energy, short-distance high temperature, and low-temperature self-heating. The battery state and travel needs are updated periodically with a set time interval, iteratively optimizing the control scheme. By using this full-process prediction and dynamic adaptation approach, the problems of existing solutions being limited to a single scenario and insufficient data application are solved. While meeting the core power and mileage requirements under different travel conditions, thermal management energy consumption is significantly reduced, and the vehicle performance is not limited due to fixed thermal management parameters, thus greatly improving the user's driving experience.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is an overall flowchart of the thermal management control method for new energy vehicle battery systems proposed in this embodiment of the invention; Figure 2 This is a flowchart of the thermal management control in a low-temperature scenario in the control method proposed in the embodiments of the present invention; Figure 3 This is a flowchart of thermal management control in short-range scenarios in the control method proposed in the embodiments of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed descriptions are exemplary and are intended only to describe specific embodiments and to provide further explanation of the invention, and are not intended to limit the scope of exemplary embodiments of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Example 1 As the background section points out, existing BMS battery thermal management strategies are mostly designed for fixed scenarios, simply differentiating driving styles based on user data to adjust temperature thresholds or target temperatures. These thermal management schemes are simplistic and crude, relying solely on the difference between the maximum and target temperatures throughout the journey for basic control and optimization, resulting in limited energy savings. Therefore, this embodiment proposes a thermal management control method for new energy vehicle battery systems. This method predicts battery temperature, SOC, and SOE curves throughout the entire journey, determines whether to activate thermal management based on the prediction results, and adjusts the thermal management control scheme according to the specific scenario when activated, thereby effectively reducing thermal management energy consumption and improving user experience.

[0024] like Figure 1 As shown, the thermal management control method for new energy vehicle battery systems proposed in this embodiment specifically includes the following steps: Step S1: Obtain the current status of the electric vehicle's power battery, call the control strategy table, and initially determine the battery system thermal management control scheme.

[0025] Specifically, the current state of the electric vehicle's power battery is first obtained, such as temperature, SOC, SOE, allowable power, and heat generation rate. Based on this, a pre-built battery thermal management control strategy table is called to initially determine the thermal management request type (such as heating / cooling / temperature equalization), target temperature, flow rate request, etc.

[0026] In this embodiment, a control strategy table is pre-built. This table is called during the actual control process to initially determine the initial control scheme, laying the foundation for subsequent optimization and adjustment. As one implementation method, the construction process of the control strategy table is as follows: First, within a set temperature range, charge-discharge tests are conducted on the electric vehicle power battery at different temperatures and SOCs. The allowable power (discharge power / charge power), SOE (remaining available energy) change curves, and heat generation rate (heat generated by the battery during charging and discharging per unit time) are recorded at different temperatures and SOCs to obtain battery test data. In this embodiment, within a temperature range of -30℃ to 60℃, charge-discharge tests are conducted on target batteries such as ternary lithium-ion batteries and lithium iron phosphate batteries at different SOCs (0% to 100%). The allowable power, SOE change curves, and heat generation rate of the battery at different temperatures and SOCs are recorded. Simultaneously, the heating / cooling efficiency (e.g., the rate of battery temperature increase / decrease per unit time) of the thermal management system at different flow rates (e.g., 5L / min to 20L / min) and different target temperatures is tested.

[0027] By constructing a control strategy table based on full-condition test data, the initial thermal management scheme can be highly matched with the current state of the battery, avoiding overcooling, overheating, or energy waste caused by fixed threshold strategies. Multi-dimensional state acquisition and table lookup calculations can complete scheme anchoring within 100ms, meeting the real-time requirement of temperature control upon vehicle power-on. Furthermore, this embodiment pre-locks a safe and reasonable temperature control starting point, reducing the magnitude of subsequent dynamic adjustments and improving the stability of thermal management throughout the entire process.

[0028] Secondly, based on the battery test data obtained above, a table showing the correspondence between battery temperature, SOC, and the battery system thermal management control scheme was fitted, i.e., a control strategy table. The battery system thermal management control scheme includes thermal management request type, thermal management target temperature, and thermal management flow rate request. The thermal management request type includes three types: heating, cooling, and temperature equalization. For example, when the battery temperature is 20℃ and the SOC is 50%, the thermal management request type is temperature equalization, the target temperature is 25℃, and the flow rate request is 8L / min; when the battery temperature is 38℃ and the SOC is 60%, the thermal management request type is cooling, the target temperature is 32℃, and the flow rate request is 15L / min; when the battery temperature is 15℃ and the SOC is 40%, the thermal management request type is heating, the target temperature is 25℃, and the flow rate request is 12L / min.

[0029] In this embodiment, based on a pre-built control strategy table, the Battery Management System (BMS) reads the current battery state, namely the current battery temperature, SOC, and SOE, and calls the control strategy table (i.e., the correspondence table) to obtain an initial thermal management control scheme, initially determining the thermal management request type, target temperature, and thermal management flow request. Specifically, when calling the control strategy table, the BMS uses the battery's average temperature, SOC, ambient temperature, and current discharge rate as inputs, performing multi-condition table lookups and weighted calculations to output a thermal management type, target temperature, and flow request that better reflects the real-time state. For example, in a low-temperature, high-rate discharge scenario, the heating target temperature is automatically increased; in a high-temperature, low-load scenario, the cooling flow is automatically reduced, achieving refined initial control from state to scheme.

[0030] Step S2: Obtain the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve.

[0031] Specifically, based on the destination imported by the user, the system obtains the trip data through the in-vehicle navigation system, including the planned mileage and the predicted speed curve for the entire journey based on the current traffic conditions. On this basis, combined with vehicle parameters (including vehicle weight, drag coefficient, rolling resistance coefficient, etc.), the speed curve for the entire journey is converted into the power demand curve for the entire journey through the power calculation formula, providing an accurate basis for determining whether thermal management should be activated.

[0032] In this embodiment, based on the planned mileage L and the predicted vehicle speed curve, the predicted vehicle speed for each road segment is obtained through the mileage correspondence. Then, according to the power calculation formula, the power required to drive the vehicle is calculated using the vehicle speed. The specific calculation approach is as follows: when the vehicle travels to a certain mileage segment... The corresponding vehicle speed can be expressed as: This vehicle speed reflects the wind resistance and wheel rolling resistance that the vehicle overcomes while moving. By integrating the force required to reach this speed and multiplying that force by the vehicle speed, the required power can be obtained. Therefore, the power calculation formula can be expressed as: ; In the above formula, Indicates rolling resistance, Indicates wind resistance, For the overall vehicle quality, Rolling resistance coefficient, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, To reach the mileage The vehicle speed at that time.

[0033] Through step S2 above, this embodiment, based on the original planned driving mileage and predicted vehicle speed curve obtained from navigation, further integrates external parameters such as real-time road conditions, traffic light information, congestion level, vehicle load, and wind resistance to smooth and correct the vehicle speed curve, eliminating power calculation distortion caused by sudden acceleration and deceleration. Substituting the corrected vehicle speed curve into the vehicle power calculation formula, and uniformly analyzing driving resistance, air resistance, etc., a continuous power demand curve that closely matches the actual driving conditions is obtained. This curve can accurately reflect the power demand of the vehicle on the battery in different driving stages such as starting, constant speed, acceleration and deceleration, providing high-precision input for subsequent battery status prediction. It can predict power fluctuations throughout the journey in advance, providing a basis for early intervention in thermal management and early suppression of abnormal temperature changes, realizing predictive control rather than passive control.

[0034] Step S3: Based on the planned driving mileage and power demand curve, predict the temperature, SOC, and SOE curves of the power battery throughout the trip using a prediction model to determine whether the requirements of this trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario, and the updated scheme is executed.

[0035] Specifically, after obtaining the power demand curve for this trip, the planned driving mileage, the power demand curve for the entire trip, the current battery status (including initial temperature, SOC, SOE, etc.), and the ambient temperature are respectively input into the temperature prediction model, the SOC prediction model, and the SOE prediction model. The output is the prediction results without thermal management, including the predicted temperature curve, SOC curve, and SOE curve of the power battery for the entire trip.

[0036] As one implementation method, the temperature prediction model in this embodiment employs a mechanistic model, which is constructed based on the battery thermal balance equation and can be used to predict battery temperature. Specifically, the battery thermal balance equation is: ; in, The rate of heat generation during battery charging and discharging. The heat exchange rate between the battery and the environment. For battery quality, For the specific heat capacity of the battery, The rate of change of battery temperature. This is the charging and discharging current. This refers to the battery's internal resistance. For heat dissipation coefficient, For battery heat dissipation area, For battery temperature, The ambient temperature is used. Preferably, the key parameters such as the battery charge / discharge heat generation rate and specific heat capacity are determined by battery charge / discharge tests within a temperature range of -30℃ to 60℃ under different SOC conditions.

[0037] The aforementioned temperature prediction model is based on the first law of thermodynamics (the rate of heat generation inside the battery minus the rate of heat dissipation to the environment equals the thermal power of the battery's own temperature rise). It can calculate the temperature at the next moment using the current, internal resistance, battery temperature, and ambient temperature, achieving full-process prediction. In this embodiment, the full-process demand power curve, the current battery temperature, and the ambient temperature are used as inputs. Specifically, the current battery temperature, ambient temperature, the current corresponding to the demand power, and the battery internal resistance (obtainable through a table) are acquired. Combined with fixed parameters such as the heat dissipation coefficient k, heat dissipation area a, mass m, and specific heat capacity c, the temperature prediction model calculates the ohmic heat generation rate of the battery during charging and discharging and the heat exchange rate with the environment, further obtaining the temperature change rate. By iteratively extrapolating segment by segment with a fixed time step, the full-process temperature curve of the battery without thermal management during this trip is obtained, providing a temperature basis for subsequent demand judgments.

[0038] Furthermore, the SOC prediction model also adopts a mechanistic model based on the law of charge conservation (remaining battery capacity = initial capacity - cumulative discharged capacity). It uses the ampere-hour integration method to track the battery charge and utilizes the resting OCV (open-circuit voltage) correction to eliminate accumulated errors, achieving full-range SOC prediction. Specifically, using the ampere-hour integration method, with the initial SOC, battery rated capacity, and charge / discharge current curve (charge / discharge current corresponding to the required power) as input, it accumulates and calculates the battery discharge capacity, updates the SOC value in real time, and finally obtains the full-range SOC curve for this trip through segmented recursion, thus achieving SOC prediction. The above calculation process can be expressed as: ; in, For the initial SOC, For the battery's rated capacity, This is the charging and discharging current.

[0039] Preferably, when the battery is in a stable resting state, the cumulative error is corrected using the mapping relationship between open-circuit voltage and SOC. That is, after calculating the SOC according to the above formula, the cumulative error of the ampere-hour integral is corrected every 30 minutes using the correspondence between the battery open-circuit voltage and SOC, so as to make the predicted SOC more accurate.

[0040] In addition, the predicted value of SOE is determined based on the pre-built mapping relationship between SOC and SOE, which is the corresponding curve of SOC and SOE at different temperatures obtained by testing.

[0041] Preferably, considering that temperature changes can affect the battery's usable energy, the SOE prediction value can be dynamically corrected by combining the temperature prediction results to output a more accurate SOE change curve.

[0042] Furthermore, after acquiring the battery temperature curve, SOC curve, and SOE curve for the entire journey, it is determined whether thermal management can be disabled to meet the needs of the current trip based on set thresholds. The determination criteria include: whether the battery's highest temperature exceeds the preset high-temperature threshold, whether the battery's lowest temperature meets the power output requirement temperature, whether the battery's available power matches the required power curve, and whether the battery's SOE meets the energy requirements of the planned driving range. When all the above conditions are met, thermal management can be disabled; otherwise, thermal management must be enabled.

[0043] In this embodiment, a preset high-temperature threshold of 35°C is set, above which the battery's allowable power decreases; a power output requirement temperature threshold of 25°C is set, below which the battery's allowable power decreases; a range energy threshold of 45% is set, meaning the SOE (Sort of Energy) for this trip must be no less than 45%; and a power matching threshold is set, i.e., the allowable battery power is set. It can reserve 10% redundancy. This is the required power.

[0044] Based on the above settings, temperature is first assessed: if the predicted maximum temperature is less than the preset high temperature threshold and the predicted minimum temperature is higher than the power output required temperature threshold, then the temperature requirement is met. Next, power is assessed: based on the predicted temperature and SOC, the battery's allowable power map is queried to obtain the battery's allowable power. If this power meets the power requirement for the planned driving range, then it is determined whether the remaining available SOE curve meets the mileage requirement for this trip. If it does, thermal management is not activated; otherwise, thermal management control is implemented depending on the situation. If the battery's allowable power does not meet the energy requirement for the planned driving range, then it is determined whether the battery's available power matches the required power curve. If it does, thermal management is not activated; otherwise, thermal management control is implemented depending on the situation.

[0045] In summary, thermal management will not be activated when all the requirements of this trip are met. However, if any condition is not met, the battery system thermal management control scheme will be adjusted according to the scenario, and the updated thermal management control scheme will be executed.

[0046] As one implementation method, this embodiment adjusts the thermal management control scheme according to different scenarios, dividing the scenarios into temperature non-compliance scenarios, power non-compliance scenarios, and energy non-compliance scenarios. The temperature non-compliance scenarios include short-range high-temperature scenarios and low-temperature self-heating scenarios. Considering the characteristics of different scenarios, different adjustment strategies for the battery system's thermal management control scheme are set, specifically as follows: (1) Regulation of short-distance high-temperature scenarios like Figure 3As shown, if the remaining mileage for the current trip is less than the preset short-distance threshold, and after re-predicting that there is no risk of battery overheating after turning off thermal management, the available battery power matches the power required for the remaining mileage, and the battery SOE meets the requirements for the remaining mileage, then thermal management will not be activated, and the vehicle will rely on natural heat dissipation during driving to reduce thermal management energy consumption. Otherwise, cooling will be activated, and the thermal management control scheme will be implemented. In this embodiment, the preset short-distance threshold is 5km, and no risk of overheating is defined as a battery temperature not exceeding 38°C.

[0047] (2) Regulation of low-temperature self-heating scenarios Based on the heat generation rate parameters in the battery charge / discharge test data, and combined with the current power demand, the battery's own heat generation rate is calculated, and the battery temperature change trend is re-predicted. If the prediction shows that the battery's own heat generation rate causes the battery temperature to rise continuously, and the temperature rise is sufficient to meet the power output requirements for the remaining range, the thermal management heating function is turned off in advance, utilizing the battery's own heat generation to achieve the heating effect and reduce thermal management energy consumption. If the predicted self-heating cannot meet the power output requirements, the heating function remains on until the temperature meets the requirements.

[0048] (3) Power adjustment for scenarios where the power requirement is not met When the battery's minimum temperature is lower than the power output requirement temperature, such as Figure 2 As shown, heating needs to continue to increase the thermal management cutoff temperature (at the same SOC, the battery's allowable power increases with increasing temperature at low temperatures), and the SOE of the battery after increasing the cutoff temperature is re-predicted. At the same time, the power consumption caused by increasing the thermal management cutoff temperature is judged. If the predicted SOE meets the remaining range requirements, the increased cutoff temperature is used as the thermal management target temperature; if the predicted SOE does not meet the remaining range requirements, the power limit is dynamically adjusted to match the required power with the battery's allowable power, prioritizing range achievability; when the battery's maximum temperature exceeds the preset high temperature threshold, the thermal management efficiency is improved in advance, such as lowering the thermal management target temperature and increasing the thermal management flow request, to quickly suppress the battery temperature rise and avoid entering the high-temperature region that limits power.

[0049] (4) Energy not meeting the needs of the scenario When the available battery power does not match the required power curve or the battery SOE does not meet the energy requirements of the planned driving range, the difference between the increase in battery SOE through heating and the energy consumed during the thermal management process is calculated. If the difference is positive, thermal management is activated based on the predicted target temperature to heat the battery to the target temperature and increase the available battery energy. If the difference is negative or the battery SOE cannot meet the remaining driving range requirements, and it is predicted that the battery temperature will still be within the safe boundary range and the temperature rise rate will not exceed the limit after the thermal management is turned off in advance, then the thermal management is turned off in advance to reduce energy consumption and ensure the driving range requirements. In this embodiment, the safe boundary range is set as follows: the highest battery temperature does not exceed 45°C, and the lowest temperature does not fall below -20°C.

[0050] Preferably, to avoid logical conflicts when multiple scenarios are triggered, scenario execution priorities are set as follows: safety constraints (no overheating, no low-temperature failure) > power output requirements > energy / mileage requirements, with only the highest priority strategy executed at any given time. Simultaneously, thermal management start / stop is configured with a temperature hysteresis range to prevent frequent switching near the threshold, thus improving system operational stability.

[0051] Step S4: Periodically update the predicted temperature, SOC, and SOE curves of the power battery, iteratively update and execute the thermal management control scheme until the end of this trip.

[0052] Specifically, by setting a cycle iteration time, the battery's real-time status, remaining mileage, and real-time power demand are updated at fixed intervals. The battery status is then re-predicted and the thermal management scheme is iteratively adjusted until the end of the current trip. Through this scenario-based control strategy and periodic update mechanism, it can adapt to various travel conditions and significantly reduce thermal management energy consumption while ensuring mileage and power requirements. This provides an efficient and flexible control scheme for the thermal management of new energy vehicle batteries.

[0053] As another implementation method, the original fixed-cycle iteration can be optimized into a variable-cycle adaptive iteration mechanism. For example, dangerous working conditions such as high speed, high multiplier, and extreme temperature use a short cycle of 30 seconds for rapid updates; stable working conditions such as low speed, uniform speed, and medium temperature in urban areas use a long cycle of 2-5 minutes for updates, achieving the optimal balance between real-time control and computing power consumption.

[0054] Furthermore, each iteration re-collects real-time battery status, remaining range, real-time power, and real-time road conditions, and re-runs the prediction model and satisfaction assessment, dynamically correcting the thermal management target temperature, flow rate, and start / stop timing, forming a complete closed loop of perception-prediction-decision-execution-feedback. Preferably, a three-layer safety protection strategy is set simultaneously: policy protection, hardware protection, and fault degradation, namely: immediate forced cooling upon predicting over-temperature, switching to a fixed threshold for sensor failure, and entering a power-limited safety mode upon thermal management component failure, ensuring no risk of thermal runaway throughout the process.

[0055] The above process is continuously iterated until the vehicle reaches its destination and goes into hibernation. The thermal management strategy is continuously optimized according to changes in road conditions, power, and temperature, so as to always maintain optimal energy consumption, best performance, and highest safety, achieve stable control throughout the process, and extend battery cycle life.

[0056] This embodiment utilizes the combined prediction of the three-dimensional states of temperature, SOC, and SOE throughout the entire journey, along with dynamic control across four scenarios: insufficient power, insufficient energy, short-distance high temperature, and low-temperature self-heating. This allows for precise matching of different driving conditions, mileage, and real-time power requirements, avoiding the crude control of traditional fixed temperature thresholds and achieving full-process adaptive iterative adjustment of the thermal management strategy. While ensuring the battery remains within its optimal operating range of 25℃~35℃, the solution prioritizes mileage and power output, preventing power limitations, power degradation, or range reduction due to improper temperature control. Simultaneously, it fully leverages the battery's self-heating during charging and discharging, as well as natural heat dissipation during driving, significantly reducing unnecessary heating and cooling actions and fundamentally lowering the energy consumption of the thermal management system. Compared to traditional strategies, this solution effectively reduces thermal management energy consumption, increases overall vehicle range, controls battery temperature fluctuations within ±3℃, effectively mitigates battery polarization, internal resistance increases, and capacity decay, and extends cycle life.

[0057] Furthermore, this embodiment can adapt to all operating conditions in real time, including urban commuting, highway driving, short-distance travel, low-temperature start-up, and high-temperature exposure. It can automatically switch to the optimal control mode without manual intervention, which not only improves battery safety and reliability and reduces the risk of thermal runaway, but also improves the driving experience. It achieves a high degree of unity in power, range, safety, and economy. The overall control accuracy, scenario adaptability, and energy-saving effect are significantly better than existing technologies.

[0058] Example 2 This embodiment proposes a thermal management and control system for a new energy vehicle battery system, including: The preliminary solution determination module is used to obtain the current status of the electric vehicle's power battery, call the control strategy table, and preliminarily determine the battery system thermal management control scheme. The power demand acquisition module is used to acquire the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve. The thermal management control module is used to predict the temperature, SOC, and SOE curves of the power battery throughout the trip based on the planned driving mileage and power demand curve, and to determine whether the requirements of the trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario and the updated scheme is executed. The predicted temperature, SOC, and SOE curves of the power battery are periodically updated, and the thermal management control scheme is iteratively updated and executed until the trip ends.

[0059] In this embodiment, the power battery system of the new energy vehicle adopts a ternary lithium / lithium iron phosphate battery pack, which is equipped with temperature sampling, voltage sampling, and current sampling units to obtain the current state of the electric vehicle's power battery, such as temperature, SOC, SOE, allowable power, and heat generation rate. Based on this, a pre-built battery thermal management control strategy table is called to initially determine the thermal management request type (such as heating / cooling / temperature equalization), target temperature, flow rate request, etc. In this embodiment, the control strategy table is pre-built, and its construction process is as follows: First, within a set temperature range, charge-discharge tests are conducted on the electric vehicle power battery at different temperatures and SOCs. The allowable power (discharge power / charge power), SOE (remaining available energy) change curves, and heat generation rate (heat generated by the battery during charging and discharging per unit time) are recorded at different temperatures and SOCs to obtain battery test data. In this embodiment, within a temperature range of -30℃ to 60℃, charge-discharge tests are conducted on target batteries such as ternary lithium-ion batteries and lithium iron phosphate batteries at different SOCs (0% to 100%). The allowable power, SOE change curves, and heat generation rate of the battery at different temperatures and SOCs are recorded. Simultaneously, the heating / cooling efficiency (e.g., the rate of battery temperature increase / decrease per unit time) of the thermal management system at different flow rates (e.g., 5L / min to 20L / min) and different target temperatures is tested.

[0060] Secondly, based on the battery test data obtained above, a table showing the correspondence between battery temperature, SOC, and the battery system thermal management control scheme was fitted, i.e., a control strategy table. The battery system thermal management control scheme includes thermal management request type, thermal management target temperature, and thermal management flow rate request. The thermal management request type includes three types: heating, cooling, and temperature equalization. For example, when the battery temperature is 20℃ and the SOC is 50%, the thermal management request type is temperature equalization, the target temperature is 25℃, and the flow rate request is 8L / min; when the battery temperature is 38℃ and the SOC is 60%, the thermal management request type is cooling, the target temperature is 32℃, and the flow rate request is 15L / min; when the battery temperature is 15℃ and the SOC is 40%, the thermal management request type is heating, the target temperature is 25℃, and the flow rate request is 12L / min.

[0061] Based on this, the Battery Management System (BMS) reads the current battery status, namely the current battery temperature, SOC, and SOE, calls the above-mentioned corresponding relationship table, obtains the initial thermal management control scheme, and preliminarily determines the thermal management request type, thermal management target temperature, and thermal management flow request.

[0062] Furthermore, the demand power acquisition module is integrated into the vehicle controller (VCU). The in-vehicle navigation system acquires the trip data based on the destination imported by the user, including the planned mileage and the predicted speed curve based on the current traffic conditions. On this basis, combined with vehicle parameters (including vehicle mass, drag coefficient, rolling resistance coefficient, etc.), the speed curve is converted into a demand power curve for the entire trip using a power calculation formula, providing an accurate basis for determining whether thermal management should be activated.

[0063] In this embodiment, based on the planned mileage L and the predicted vehicle speed curve, the predicted vehicle speed for each road segment is obtained through the mileage correspondence. Then, according to the power calculation formula, the power required to drive the vehicle is calculated using the vehicle speed. The specific calculation approach is as follows: when the vehicle travels to a certain mileage segment... The corresponding vehicle speed can be expressed as: This vehicle speed reflects the wind resistance and wheel rolling resistance that the vehicle overcomes while moving. By integrating the force required to reach this speed and multiplying that force by the vehicle speed, the required power can be obtained. Therefore, the power calculation formula can be expressed as: ; In the above formula, Indicates rolling resistance, Indicates wind resistance, For the overall vehicle quality, Rolling resistance coefficient, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, To reach the mileage The vehicle speed at that time.

[0064] Furthermore, the thermal management control module is set in the battery management system (BMS). Based on the planned driving mileage and power demand curve, it uses a prediction model to predict the temperature, SOC, and SOE curves of the power battery throughout the journey to determine whether the requirements of the current trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario, and the updated scheme is executed.

[0065] Specifically, after obtaining the power demand curve for this trip, the planned driving mileage, the power demand curve for the entire trip, the current battery status (including initial temperature, SOC, SOE, etc.), and the ambient temperature are respectively input into the temperature prediction model, the SOC prediction model, and the SOE prediction model. The output is the prediction results without thermal management, including the predicted temperature curve, SOC curve, and SOE curve of the power battery for the entire trip.

[0066] As one implementation method, the temperature prediction model in this embodiment employs a mechanistic model, which is constructed based on the battery thermal balance equation and can be used to predict battery temperature. Specifically, the battery thermal balance equation is: ; in, The rate of heat generation during battery charging and discharging. The heat exchange rate between the battery and the environment. For battery quality, For the specific heat capacity of the battery, The rate of change of battery temperature. This is the charging and discharging current. This refers to the battery's internal resistance. For heat dissipation coefficient, For battery heat dissipation area, For battery temperature, The ambient temperature is used. Preferably, the key parameters such as the battery charge / discharge heat generation rate and specific heat capacity are determined by battery charge / discharge tests within a temperature range of -30℃ to 60℃ under different SOC conditions.

[0067] The aforementioned temperature prediction model is based on the first law of thermodynamics (the rate of heat generation inside the battery minus the rate of heat dissipation to the environment equals the thermal power of the battery's own temperature rise). It can calculate the temperature at the next moment using the current, internal resistance, battery temperature, and ambient temperature, achieving full-process prediction. In this embodiment, the full-process demand power curve, the current battery temperature, and the ambient temperature are used as inputs. Specifically, the current battery temperature, ambient temperature, the current corresponding to the demand power, and the battery internal resistance (obtainable through a table) are acquired. Combined with fixed parameters such as the heat dissipation coefficient k, heat dissipation area a, mass m, and specific heat capacity c, the temperature prediction model calculates the ohmic heat generation rate of the battery during charging and discharging and the heat exchange rate with the environment, further obtaining the temperature change rate. By iteratively extrapolating segment by segment with a fixed time step, the full-process temperature curve of the battery without thermal management during this trip is obtained, providing a temperature basis for subsequent demand judgments.

[0068] Furthermore, the SOC prediction model also adopts a mechanistic model based on the law of charge conservation (remaining battery capacity = initial capacity - cumulative discharged capacity). It uses the ampere-hour integration method to track the battery charge and utilizes the resting OCV (open-circuit voltage) correction to eliminate accumulated errors, achieving full-range SOC prediction. Specifically, using the ampere-hour integration method, with the initial SOC, battery rated capacity, and charge / discharge current curve (charge / discharge current corresponding to the required power) as input, it accumulates and calculates the battery discharge capacity, updates the SOC value in real time, and finally obtains the full-range SOC curve for this trip through segmented recursion, thus achieving SOC prediction. The above calculation process can be expressed as: ; in, For the initial SOC, For the battery's rated capacity, This is the charging and discharging current.

[0069] Preferably, when the battery is in a stable resting state, the cumulative error is corrected using the mapping relationship between open-circuit voltage and SOC. That is, after calculating the SOC according to the above formula, the cumulative error of the ampere-hour integral is corrected every 30 minutes using the correspondence between the battery open-circuit voltage and SOC, so as to make the predicted SOC more accurate.

[0070] In addition, the predicted value of SOE is determined based on the pre-built mapping relationship between SOC and SOE, which is the corresponding curve of SOC and SOE at different temperatures obtained by testing.

[0071] Preferably, considering that temperature changes can affect the battery's usable energy, the SOE prediction value can be dynamically corrected by combining the temperature prediction results to output a more accurate SOE change curve.

[0072] Furthermore, after acquiring the battery temperature curve, SOC curve, and SOE curve for the entire journey, it is determined whether thermal management can be disabled to meet the needs of the current trip based on set thresholds. The determination criteria include: whether the battery's highest temperature exceeds the preset high-temperature threshold, whether the battery's lowest temperature meets the power output requirement temperature, whether the battery's available power matches the required power curve, and whether the battery's SOE meets the energy requirements of the planned driving range. When all the above conditions are met, thermal management can be disabled; otherwise, thermal management must be enabled.

[0073] In this embodiment, a preset high-temperature threshold of 35°C is set, above which the battery's allowable power decreases; a power output requirement temperature threshold of 25°C is set, below which the battery's allowable power decreases; a range energy threshold of 45% is set, meaning the SOE (Sort of Energy) for this trip must be no less than 45%; and a power matching threshold is set, i.e., the allowable battery power is set. It can reserve 10% redundancy. This is the required power.

[0074] Based on the above settings, temperature is first assessed: if the predicted maximum temperature is less than the preset high temperature threshold and the predicted minimum temperature is higher than the power output required temperature threshold, then the temperature requirement is met. Next, power is assessed: based on the predicted temperature and SOC, the battery's allowable power map is queried to obtain the battery's allowable power. If this power meets the power requirement for the planned driving range, then it is determined whether the remaining available SOE curve meets the mileage requirement for this trip. If it does, thermal management is not activated; otherwise, thermal management control is implemented depending on the situation. If the battery's allowable power does not meet the energy requirement for the planned driving range, then it is determined whether the battery's available power matches the required power curve. If it does, thermal management is not activated; otherwise, thermal management control is implemented depending on the situation.

[0075] In summary, thermal management will not be activated when all the requirements of this trip are met. However, if any condition is not met, the battery system thermal management control scheme will be adjusted according to the scenario, and the updated thermal management control scheme will be executed.

[0076] As one implementation method, this embodiment adjusts the thermal management control scheme according to different scenarios, dividing the scenarios into temperature non-compliance scenarios, power non-compliance scenarios, and energy non-compliance scenarios. The temperature non-compliance scenarios include short-range high-temperature scenarios and low-temperature self-heating scenarios. Considering the characteristics of different scenarios, different adjustment strategies for the battery system's thermal management control scheme are set, specifically as follows: (1) Regulation of short-distance high-temperature scenarios like Figure 3 As shown, if the remaining mileage for the current trip is less than the preset short-distance threshold, and after re-predicting that there is no risk of battery overheating after turning off thermal management, the available battery power matches the power required for the remaining mileage, and the battery SOE meets the requirements for the remaining mileage, then thermal management will not be activated, and the vehicle will rely on natural heat dissipation during driving to reduce thermal management energy consumption. Otherwise, cooling will be activated, and the thermal management control scheme will be implemented. In this embodiment, the preset short-distance threshold is 5km, and no risk of overheating is defined as a battery temperature not exceeding 38°C.

[0077] (2) Regulation of low-temperature self-heating scenarios Based on the heat generation rate parameters in the battery charge / discharge test data, and combined with the current power demand, the battery's own heat generation rate is calculated, and the battery temperature change trend is re-predicted. If the prediction shows that the battery's own heat generation rate causes the battery temperature to rise continuously, and the temperature rise is sufficient to meet the power output requirements for the remaining range, the thermal management heating function is turned off in advance, utilizing the battery's own heat generation to achieve the heating effect and reduce thermal management energy consumption. If the predicted self-heating cannot meet the power output requirements, the heating function remains on until the temperature meets the requirements.

[0078] (3) Power adjustment for scenarios where the power requirement is not met When the battery's minimum temperature is lower than the power output requirement temperature, such as Figure 2As shown, heating needs to continue to increase the thermal management cutoff temperature (at the same SOC, the battery's allowable power increases with increasing temperature at low temperatures), and the SOE of the battery after increasing the cutoff temperature is re-predicted. At the same time, the power consumption caused by increasing the thermal management cutoff temperature is judged. If the predicted SOE meets the remaining range requirements, the increased cutoff temperature is used as the thermal management target temperature; if the predicted SOE does not meet the remaining range requirements, the power limit is dynamically adjusted to match the required power with the battery's allowable power, prioritizing range achievability; when the battery's maximum temperature exceeds the preset high temperature threshold, the thermal management efficiency is improved in advance, such as lowering the thermal management target temperature and increasing the thermal management flow request, to quickly suppress the battery temperature rise and avoid entering the high-temperature region that limits power.

[0079] (4) Energy not meeting the needs of the scenario When the available battery power does not match the required power curve or the battery SOE does not meet the energy requirements of the planned driving range, the difference between the increase in battery SOE through heating and the energy consumed during the thermal management process is calculated. If the difference is positive, thermal management is activated based on the predicted target temperature to heat the battery to the target temperature and increase the available battery energy. If the difference is negative or the battery SOE cannot meet the remaining driving range requirements, and it is predicted that the battery temperature will still be within the safe boundary range and the temperature rise rate will not exceed the limit after the thermal management is turned off in advance, then the thermal management is turned off in advance to reduce energy consumption and ensure the driving range requirements. In this embodiment, the safe boundary range is set as follows: the highest battery temperature does not exceed 45°C, and the lowest temperature does not fall below -20°C.

[0080] Preferably, to avoid logical conflicts when multiple scenarios are triggered, scenario execution priorities are set as follows: safety constraints (no overheating, no low-temperature failure) > power output requirements > energy / mileage requirements, with only the highest priority strategy executed at any given time. Simultaneously, thermal management start / stop is configured with a temperature hysteresis range to prevent frequent switching near the threshold, thus improving system operational stability.

[0081] Example 3 This embodiment proposes a vehicle that includes the thermal management control system for the new energy vehicle battery system proposed in Embodiment 2, or implements the thermal management control method for the new energy vehicle battery system proposed in Embodiment 1, to achieve optimal coordinated control of battery temperature, power, energy, and safety.

[0082] In this embodiment, the vehicle includes a power battery pack, a battery management system (BMS), a vehicle control unit (VCU), an onboard navigation module, a thermal management actuator, and multi-dimensional sensor units, among other components. These components work together to complete the entire process of thermal management control. The power battery pack uses a ternary lithium or lithium iron phosphate system and is equipped with temperature, voltage, and current sampling modules to collect real-time data on key states such as cell / module / assembly temperature, SOC, SOH, and charging / discharging current. The onboard navigation module not only provides planned mileage but also outputs real-time road conditions, vehicle speed prediction, road gradient, and traffic light information, providing input support for demand power calculation and overall state prediction. The VCU is responsible for converting vehicle speed demand into a battery-side demand power curve and dynamically adjusting the output power limit according to the instructions from the thermal management control module, maintaining a balance between power demand and energy safety. The thermal management actuator includes a PTC heater, a water-cooled heat dissipation system, and a liquid-cooled temperature equalization circuit, which can quickly adjust the target temperature and coolant flow rate according to control commands, ensuring timely and effective temperature control.

[0083] Specifically, after the vehicle is powered on, the system automatically reads the current battery status and calls the control strategy table to quickly determine the initial thermal management scheme. During driving, it predicts the battery temperature, SOC, and SOE curves in real time, automatically identifies scenarios such as short-distance high temperature, low temperature self-heating, insufficient power, and insufficient energy, and dynamically adjusts the heating, cooling, and temperature equalization strategies. It adopts variable cycle iterative updates throughout the process, and can quickly correct the prediction results and refresh the control strategy when encountering sudden changes in road conditions, sudden changes in power, or drastic changes in ambient temperature.

[0084] Compared to vehicles that use traditional fixed-threshold thermal management strategies, this embodiment can effectively reduce thermal management energy consumption, control battery temperature fluctuations within ±3℃, achieve more stable power output, more realistic range, and longer battery life. It can fully adapt to the needs of various driving scenarios such as urban commuting, long-distance highway driving, cold start in low temperatures, and exposure to high temperatures, thereby improving the overall vehicle economy, safety, and user driving experience.

[0085] The steps and methods involved in Examples 2 and 3 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0086] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0087] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention has been described in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative effort are still within the scope of protection of the present invention.

Claims

1. A thermal management control method for a new energy vehicle battery system, characterized in that, include: Obtain the current status of the electric vehicle's power battery, call the control strategy table, and initially determine the battery system's thermal management control scheme; Obtain the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve. Based on the planned driving mileage and power demand curve, the temperature, SOC, and SOE curves of the power battery throughout the entire journey are predicted by the prediction model to determine whether the requirements of this trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario and the updated scheme is executed. The system periodically updates and predicts the temperature, SOC, and SOE curves of the power battery, iteratively updates and executes the thermal management control scheme, until the end of this trip.

2. The thermal management control method for a new energy vehicle battery system as described in claim 1, characterized in that, The control strategy table is constructed as follows: Within a set temperature range, charge and discharge tests are conducted on the power battery of electric vehicles at different SOCs. The allowable power, SOE change curves, and heat generation rate of the power battery at different temperatures and SOCs are recorded to obtain battery test data. Among them, the allowable power is the discharge power / charge power, SOE is the remaining usable energy, and the heat generation rate is the heat generated by the battery due to charging and discharging per unit time. Based on battery test data, a table of correspondences between battery temperature, SOC and battery system thermal management control scheme is obtained, namely the control strategy table. The battery system thermal management control scheme includes thermal management request type, thermal management target temperature and thermal management flow request. The thermal management request type includes three types: heating, cooling and temperature equalization.

3. The thermal management control method for a new energy vehicle battery system as described in claim 1, characterized in that, Based on the vehicle parameters, the vehicle speed curve is converted into a power demand curve using a power calculation formula; the power calculation formula is as follows: ； In the above formula, Indicates rolling resistance, Indicates wind resistance, For the overall vehicle quality, Rolling resistance coefficient, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, To reach the mileage The vehicle speed at that time.

4. The thermal management control method for a new energy vehicle battery system as described in claim 1, characterized in that, The temperature prediction model is constructed based on the battery thermal balance equation to predict the battery temperature; the battery thermal balance equation is: ； in, The rate of heat generation during battery charging and discharging. The heat exchange rate between the battery and the environment. For battery quality, For the specific heat capacity of the battery, The rate of change of battery temperature. This is the charging and discharging current. This refers to the battery's internal resistance. For heat dissipation coefficient, For battery heat dissipation area, For battery temperature, Ambient temperature; The SOC prediction model is constructed based on the law of charge conservation and adopts the ampere-hour integration method. It takes the initial SOC, the battery rated capacity, and the charge-discharge current curve as inputs, accumulates the battery discharge capacity, updates the SOC value in real time, and obtains the SOC curve for the entire trip by recursively deriving it segment by segment. When the battery is in a static steady state, the mapping relationship between open circuit voltage and SOC is used to correct the accumulated error. Based on the pre-built mapping relationship between SOC and SOE, the predicted value of SOE is determined by combining the temperature prediction results.

5. The thermal management control method for a new energy vehicle battery system as described in claim 1, characterized in that, Based on the predicted temperature, SOC, and SOE curves of the power battery throughout the entire journey, determine whether the requirements for this trip are met, including: Whether the battery's highest temperature exceeds the preset high temperature threshold, whether the battery's lowest temperature meets the power output requirement temperature, whether the battery's available power matches the required power curve, and whether the battery's SOE meets the energy requirements of the planned driving range; among these, matching the required power curve means that the power fluctuation does not exceed the set threshold range.

6. The thermal management control method for a new energy vehicle battery system as described in claim 1, characterized in that, The thermal management control scheme is adjusted according to different scenarios. These scenarios include scenarios where temperature is not met, power is not met, and energy is not met. Among them, scenarios where temperature is not met include short-distance high-temperature scenarios and low-temperature self-heating scenarios.

7. The thermal management control method for a new energy vehicle battery system as described in claim 6, characterized in that, The control measures for short-distance high-temperature scenarios are as follows: If the remaining mileage for this trip is less than the preset short-distance threshold, and after re-predicting that there is no risk of overheating of the battery temperature, the available power of the battery matches the power required for the remaining mileage, and the battery SOE meets the requirements for the remaining mileage, then thermal management will not be turned on; otherwise, it will be turned on. The regulation of low-temperature self-heating scenarios is as follows: If the re-prediction results show that the battery's own heat generation rate causes the battery temperature to rise continuously, and the temperature rise is sufficient to meet the power output requirements for the remaining range, then the thermal management heating function will be turned off in advance; otherwise, it will not be turned off. The battery's own heat generation rate is calculated by combining the heat generation rate parameter in the battery charge and discharge test data with the current power demand.

8. The thermal management control method for a new energy vehicle battery system as described in claim 6, characterized in that, The adjustment method for scenarios where power is insufficient is as follows: When the minimum battery temperature is lower than the power output requirement temperature, the thermal management cutoff temperature is increased, and the SOE of the battery after the increased cutoff temperature is re-predicted. If the predicted SOE meets the remaining range requirement, the increased cutoff temperature is used as the thermal management target temperature; if the predicted SOE does not meet the remaining range requirement, the battery allowable power is dynamically matched; when the maximum battery temperature exceeds the preset high temperature threshold, the thermal management target temperature is reduced, and the thermal management flow request is increased. Regulation in scenarios where energy is insufficient is as follows: When the available battery power does not match the demand power curve or the battery SOE does not meet the energy requirements of the planned driving range, the difference between the battery SOE increase through heating and the power consumed by the thermal management process is calculated. If the difference is positive, thermal management is activated according to the predicted target temperature to heat the battery to the target temperature. If the difference is negative or the battery SOE cannot meet the remaining range requirements, and it is predicted that the battery temperature will still be within the safe boundary range after the thermal management is turned off in advance, the thermal management is turned off in advance.

9. A thermal management and control system for a new energy vehicle battery system, characterized in that, include: The preliminary solution determination module is used to obtain the current status of the electric vehicle's power battery, call the control strategy table, and preliminarily determine the battery system thermal management control scheme. The power demand acquisition module is used to acquire the planned mileage and predicted speed curve for this trip, and combine them with the vehicle parameters to obtain the power demand curve. The thermal management control module is used to predict the temperature, SOC, and SOE curves of the power battery throughout the trip based on the planned driving mileage and power demand curve, and to determine whether the requirements of the trip are met. If they are met, thermal management is not activated; otherwise, the thermal management control scheme is adjusted according to the scenario and the updated scheme is executed. The predicted temperature, SOC, and SOE curves of the power battery are periodically updated, and the thermal management control scheme is iteratively updated and executed until the trip ends.

10. A vehicle, characterized in that, It includes the thermal management control system for a new energy vehicle battery system as described in claim 9, or the thermal management control method for a new energy vehicle battery system as described in any one of claims 1-8.

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