Commercial vehicle heat pump type whole vehicle integrated heat management system

Abstract

The invention provides a commercial vehicle heat pump type vehicle integrated thermal management system, and relates to the technical field of data processing, the system comprises an environment acquisition module used for obtaining environment temperature data and a vehicle starting signal; the heat source scheduling module is used for controlling the heat pump unit to operate to generate first heat energy flow; the battery pack heat exchange module is used for heating the battery pack through a heat exchange structure; the temperature feedback module is used for judging whether a preset starting threshold value is reached or not, and if not, an adjusting signal is sent to the heat source scheduling module to adjust the first heat energy flow; the heat flow switching module is used for controlling the heat source scheduling module to switch the first heat energy flow to a cabin body heating passage after the temperature of the battery pack reaches a preset starting threshold value, so as to generate a second heat energy flow; the energy distribution module is used for acquiring vehicle driving state data, dynamically distributing the second heat energy flow according to the vehicle driving state data, and respectively sending the second heat energy flow to a cockpit and an electric drive cooling loop; according to the invention, the autonomy and accuracy of thermal management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a heat pump type whole vehicle integrated thermal management system for commercial vehicles. Background Art

[0002] In the existing technology, commercial vehicles usually use distributed thermal management systems to regulate the heat of the entire vehicle, and independently control the temperature of the battery pack, electric drive system and cockpit. The common practice is to heat the cockpit with an electric heater and cool the battery and electric drive assembly through an independent cooling circuit. Some high-end models introduce heat pump systems to recover the heat of the condenser for air conditioning heating to improve energy efficiency. However, these thermal management modules are usually dispersed in layout, independent in control strategies, and lack sharing mechanisms between heat sources, making it difficult to achieve efficient transfer and reuse of heat under different working conditions.

[0003] During winter operations in cold regions, the existing distributed thermal management systems often have a slow response to battery pack preheating due to their dispersed structures and lengthy response paths. For example, before starting in a low-temperature environment, the power battery needs to be heated up by an electric heater. If the initial battery temperature is lower than 0°C, the heating process will last for more than 15 minutes, during which the vehicle may not start normally or the output may be limited, seriously affecting operational efficiency. Especially in operating conditions such as cold chain transportation or urban bus morning rush hour, higher requirements are placed on the rapid activation and stable performance of the vehicle, and existing technologies make it difficult to achieve rapid and coordinated thermal regulation of core components within a limited time. Summary of the invention

[0004] The object of the present invention is to provide a heat pump type integrated thermal management system for commercial vehicles, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A heat pump type integrated thermal management system for a commercial vehicle, the system comprising:

[0007] The environment acquisition module is used to obtain the ambient temperature data and the vehicle start signal to generate the start condition data;

[0008] a temperature judgment module, used to extract the battery pack temperature data according to the startup condition data, and judge whether the battery pack is in a low temperature state, and if so, generate the first heat source request data;

[0009] a heat source scheduling module, for controlling the operation of the heat pump unit to generate a first heat energy flow according to the first heat source request data;

[0010] A battery pack heat exchange module, used to heat the battery pack through the heat exchange structure according to the first heat energy flow, and simultaneously collect temperature change data during the preheating process to generate battery pack preheating data;

[0011] A temperature feedback module, configured to determine whether a preset start threshold is reached according to the battery pack preheating data, and if not, to send an adjustment signal to the heat source scheduling module to adjust the first heat energy flow and continue heating the battery pack;

[0012] A heat flow switching module, used to control the heat source scheduling module to switch the first heat energy flow to the cabin heating path to generate a second heat energy flow after the battery pack temperature reaches a preset start threshold;

[0013] The energy distribution module is used to obtain the vehicle driving status data and dynamically distribute the second thermal energy flow based on it, sending it to the cockpit and electric drive cooling circuit respectively, and completing the thermal energy distribution of the vehicle thermal management system by adjusting the proportional valve.

[0014] Preferably, the heat source scheduling module includes:

[0015] a heat control submodule, for extracting ambient temperature data and battery pack temperature data according to the first heat source request data, determining a target operating frequency and a target exhaust temperature of the heat pump unit, and generating a heat source adjustment parameter;

[0016] An operation instruction generation submodule is used to issue an operation control instruction to the heat pump unit according to the heat source adjustment parameter, so that the heat pump unit generates a first heat energy flow according to a target operation frequency;

[0017] The heat feedback submodule is used to adjust the operation control instruction in real time according to the adjustment signal to generate an adjusted first heat energy flow.

[0018] Preferably, the battery pack heat exchange module includes:

[0019] A heat exchange path selection submodule, used to determine the flow channel enabled in the heat exchange path according to the heat amount of the first heat energy flow and the temperature distribution data of different positions of the battery pack;

[0020] A bypass control submodule, for opening a bypass channel to partially guide the first heat energy flow to bypass the battery pack area in the case of excess heat or local overheating, thereby generating an optimized heat exchange path;

[0021] The temperature monitoring submodule is used to collect temperature change data at different positions of the battery pack during the preheating process and generate battery pack preheating data.

[0022] Preferably, the temperature feedback module comprises:

[0023] The temperature change rate calculation submodule is used to calculate the change rate of the battery pack temperature based on the battery pack preheating data and obtain the temperature rise trend data;

[0024] The threshold prediction submodule is used to predict the remaining time required to reach the preset start threshold according to the temperature rising trend data, and generate a heat source regulation advance signal;

[0025] The control instruction updating submodule is used to send a heat source adjustment advance signal to the heat source scheduling module to optimize the generation strategy of the first heat energy flow in advance.

[0026] Preferably, the energy distribution module comprises:

[0027] The priority evaluation submodule is used to calculate the corresponding thermal energy demand value and generate heat distribution priority data according to the vehicle driving status data, the current cockpit temperature data and the electric drive system temperature data;

[0028] a proportion decision submodule, for determining a distribution ratio of the second heat energy flow between the cockpit and the electric drive cooling circuit according to the heat distribution priority data, and generating a proportion control parameter;

[0029] The execution feedback submodule is used to control the proportional valve to adjust the heat flow according to the proportional control parameters, and monitor the heat flow distribution effect of the two circuits in real time. If there is a deviation, the adjustment feedback signal is output to correct the proportional control parameters.

[0030] Preferably, the heat control submodule comprises:

[0031] An environmental assessment unit, configured to determine whether the current environment belongs to a high-cold interval, a medium-temperature interval, or a warm interval according to the environmental temperature data in the first heat source request data, and generate a corresponding temperature interval label;

[0032] A load mapping unit, configured to determine a target operating frequency and a target exhaust temperature by looking up a table according to the temperature range label and the current temperature data of the battery pack, wherein the table lookup operation is based on a preset parameter mapping table;

[0033] The operating range selection unit is used to match the operating range of the heat pump unit according to the target exhaust temperature, give priority to the operating range with the best thermal efficiency, and generate the heat source adjustment parameters.

[0034] Preferably, the threshold prediction submodule includes:

[0035] A heating trend modeling unit is used to construct a temperature change trend curve based on the multi-period temperature change data in the battery pack preheating data;

[0036] A threshold arrival time estimation unit is used to predict the time required for the battery pack temperature to reach a preset start threshold according to the temperature change trend curve, and obtain the predicted remaining heating time;

[0037] The advance warning judgment unit is used to compare the predicted remaining heating time with the expected vehicle starting time. If the time is insufficient, a heat source adjustment advance signal is generated.

[0038] Preferably, the priority evaluation submodule includes:

[0039] A state recognition unit is used to determine whether the vehicle is currently in a passenger boarding and alighting stage, a high-speed driving stage or an idling and parking stage according to the vehicle driving state data, and generate a corresponding running state label;

[0040] A heat demand calculation unit, used to calculate the heat demand values ​​of the cockpit and the electric drive cooling circuit respectively according to the operation status tag, the current temperature data of the cockpit and the temperature data of the electric drive system;

[0041] The priority generation unit is used to compare the thermal energy demand value with the preset state threshold range according to the thermal energy demand value and the corresponding operating status label, select the target with a higher demand value in the cockpit and the electric drive cooling circuit as the priority allocation object, and generate heat allocation priority data. If the difference between the demand values ​​of the two is within the preset difference range, the priority order is determined and adjusted based on the preset weight factor.

[0042] Preferably, the operation interval selection unit includes:

[0043] An interval screening unit, for screening candidate intervals that meet upper and lower limits of the target exhaust temperature from a plurality of preset operating intervals according to the target exhaust temperature;

[0044] The interval evaluation unit is used to perform a weighted evaluation of the historical energy efficiency performance, the current thermal demand level of the battery and the ambient temperature of the vehicle for each candidate interval to calculate a comprehensive score;

[0045] The preferred output unit is used to select the operating interval with the highest comprehensive score from the candidate intervals as the optimal operating interval, and extract the corresponding frequency and operating parameters to generate heat source adjustment parameters.

[0046] Preferably, the heating trend modeling unit comprises:

[0047] A data integration unit is used to extract temperature sampling values ​​at multiple different times based on the battery pack preheating data, and construct a temperature sequence data set in chronological order;

[0048] A local trend analysis unit is used to divide the temperature series data set into several continuous time periods, and determine the local rising trend or platform trend according to the temperature change rate in each time period to form trend data;

[0049] The global trend construction unit is used to combine the local trends of each continuous time period according to the trend data to construct a temperature change trend curve.

[0050] The above solution of the present invention includes at least the following beneficial effects:

[0051] The commercial vehicle heat pump integrated thermal management system provided by the present invention centrally integrates thermal management strategies through a modular architecture, breaking through the decentralized problems of the structural layout and control strategies of the distributed thermal management system in the prior art, and realizing the unified scheduling and on-demand allocation of the heat source of the whole vehicle. In this system, the ambient temperature and vehicle start-up signal are obtained through the environmental acquisition module, which can identify the key working conditions in cold climates in real time and activate the subsequent thermal control process in advance, significantly improving the system's responsiveness in extreme environments.

[0052] The system uses the temperature judgment module to conduct a linkage analysis of the battery pack temperature and environmental conditions. It has the function of intelligently identifying "preheating requirements" and can actively generate the first heat source request data when the temperature is lower than the preset standard, avoiding the passive startup after waiting for the delayed response of the temperature sensor in traditional technology, thereby shortening the preheating startup time from the root.

[0053] Different from the existing solution of using electric heaters to independently heat up, this system controls the heat pump unit to output the first heat energy flow through the heat source scheduling module, introduces the high-efficiency heat pump system into the battery heating path, and enables the heat source to have dynamic adjustment capabilities and rapid response characteristics. At the initial start-up, the battery pack heat exchange module can quickly guide the first heat energy flow to the heat exchange structure, and build a closed-loop adjustment mechanism through the temperature feedback module to continuously monitor and feedback control the temperature changes during the preheating process, thereby avoiding overheating, underheating or energy waste in the traditional single-time heating mode.

[0054] When the battery pack is heated to the preset start threshold, the system quickly switches the heat energy path through the heat flow switching module, so that the heat source originally used for battery preheating is immediately transferred to the cockpit and electric drive cooling system, ensuring uninterrupted use of heat energy and improving the thermal management efficiency of the vehicle. On this basis, the energy distribution module dynamically controls the distribution ratio of heat energy between the cockpit and the electric drive cooling circuit according to the real-time vehicle driving status, so that the heat energy resources are allocated according to the current working conditions, further avoiding the problem of heat redundancy or insufficient local cooling.

[0055] Compared with the prior art, the present invention effectively solves the problems of long response path and low energy allocation efficiency caused by the dispersion of thermal management modules through a unified scheduling mechanism for the thermal energy of the whole vehicle. Especially in early start scenarios in cold areas, such as when urban buses are put into operation in the early morning or when cold chain transportation restarts vehicles in sub-zero temperatures, the system can complete the preheating of the battery pack in a very short time and transfer the heat energy to other subsystems in time, thereby improving the rapid activation capability of the whole vehicle and the thermal stability of the system operation. The entire heating and regulation process is built based on the data flow between modules, with clear logical paths and fast response speeds, and has the significant advantages of high integration, high efficiency and high adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is an architectural diagram of a commercial vehicle heat pump type vehicle integrated thermal management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a heat pump type integrated thermal management system for commercial vehicles, the system comprising:

[0059] The environment acquisition module is used to obtain the ambient temperature data and the vehicle start signal to generate the start condition data;

[0060] a temperature judgment module, used to extract the battery pack temperature data according to the startup condition data, and judge whether the battery pack is in a low temperature state, and if so, generate the first heat source request data;

[0061] a heat source scheduling module, for controlling the operation of the heat pump unit to generate a first heat energy flow according to the first heat source request data;

[0062] A battery pack heat exchange module, used to heat the battery pack through the heat exchange structure according to the first heat energy flow, and simultaneously collect temperature change data during the preheating process to generate battery pack preheating data;

[0063] A temperature feedback module, configured to determine whether a preset start threshold is reached according to the battery pack preheating data, and if not, to send an adjustment signal to the heat source scheduling module to adjust the first heat energy flow and continue heating the battery pack;

[0064] A heat flow switching module, used to control the heat source scheduling module to switch the first heat energy flow to the cabin heating path to generate a second heat energy flow after the battery pack temperature reaches a preset start threshold;

[0065] The energy distribution module is used to obtain the vehicle driving status data and dynamically distribute the second thermal energy flow based on it, sending it to the cockpit and electric drive cooling circuit respectively, and completing the thermal energy distribution of the vehicle thermal management system by adjusting the proportional valve.

[0066] In an embodiment of the present invention, when a commercial vehicle is in a low-temperature environment and is ready to start, the vehicle integrated thermal management system can work together through multiple functional modules to achieve rapid heating of the battery pack and efficient scheduling of the thermal energy resources of the vehicle. The environmental acquisition module first senses the external ambient temperature and the vehicle start-up signal, and generates start-up condition data as a trigger for subsequent judgment and control. The start-up condition data can significantly improve the response speed and adaptability of the entire system to the vehicle start-up conditions, avoiding response delays caused by data lags.

[0067] After receiving the startup condition data, the temperature judgment module extracts the temperature data of the current battery pack and judges whether it is in the low temperature range based on the current ambient temperature and the battery pack status. This judgment process can realize risk prediction in the early startup stage, especially when the ambient temperature is significantly below the freezing point. The system can generate the first heat source request data based on the low temperature state judgment result, providing a clear trigger signal for the subsequent heat pump activation.

[0068] After receiving the first heat source request data, the heat source scheduling module immediately controls the heat pump unit to start and output the first heat energy flow. Since the module has a short response time and a direct control link, the first heat energy flow can be generated quickly at the initial start of the vehicle, which helps to quickly establish effective heat exchange conditions. Subsequently, the battery pack heat exchange module guides the first heat energy flow to the battery heat exchange structure and heats the battery pack using the established heat exchange path. During the preheating process, the module can also continuously collect temperature change information and generate battery pack preheating data to achieve real-time feedback control conditions.

[0069] The system further uses the temperature feedback module to judge the collected battery pack preheating data and determine whether the current temperature of the battery pack has reached the preset startup threshold. If not, the module will actively send an adjustment signal to the heat source scheduling module to drive it to optimize the heat pump operation strategy and then adjust the output level of the first heat energy flow. This feedback process has a closed-loop control feature and can correct the heating strategy in real time according to the actual temperature rise curve, significantly improving the preheating efficiency and temperature stability.

[0070] Once the battery pack reaches the startup threshold, the system uses the heat flow switching module to export the first heat flow from the battery circuit and switch it to the cabin heating path, thereby generating the second heat flow. The heat flow switching action is uniformly controlled by the heat source scheduling module, with simple switching logic and high execution efficiency, avoiding energy waste or thermal shock risks caused by disordered heat source distribution.

[0071] The energy distribution module starts running after the heat flow switching is completed. It dynamically adjusts the distribution path of the second heat flow according to the real-time vehicle driving status data, and distributes it to the cockpit and the electric drive cooling circuit respectively. The actual distribution of heat flow is completed by adjusting the proportional valve, so that the thermal resources of the whole vehicle can be flexibly adjusted according to the status and priority of different working parts, ensuring that the comfort of the cockpit and the thermal stability of the electric drive system are met at the same time. This module enhances the intelligent level of thermal management of the whole vehicle and has a strong adaptability to sudden working conditions.

[0072] In summary, through this integrated thermal management system, it is possible to achieve high-responsiveness and rapid heating of the battery pack under extremely cold conditions, while dynamically switching the heat energy utilization path to ensure the stable operation of the battery, electric drive and cabin temperature control system under different working conditions, thereby effectively solving problems such as slow startup response and rigid heat energy distribution, and improving the operating efficiency and system reliability of the entire vehicle.

[0073] Among them, the environment acquisition module is mainly used to timely sense the temperature changes of the current external environment when the vehicle is stationary or about to start, and synchronously receive the signal information whether the vehicle is ready to start. This module is usually composed of two main functional units: an ambient temperature sensing unit and a start signal receiving unit.

[0074] The ambient temperature sensing unit is installed on the vehicle shell or in an area that is easily exposed to the atmospheric temperature, preferably at the front of the vehicle or outside the battery compartment. Its core is a digital or thermistor temperature sensor, which is used to detect the external temperature of the vehicle's current environment. This temperature information serves as the basic judgment parameter for the vehicle's thermal management strategy. In terms of structural layout, the sensor should avoid interference from residual heat sources in the engine to ensure that the collected temperature data is representative and accurate.

[0075] The start signal receiving unit is connected to the vehicle's power management system and can monitor multiple signal sources such as the vehicle key switch status, remote start command, battery ignition controller output, etc., to determine whether the vehicle is in the start preparation stage. The signal receiving logic circuit can be set to edge trigger mode. When the signal status changes from "not started" to "ready to start", the status is immediately reported to the control unit to trigger the activation of subsequent modules.

[0076] After the two units are combined and run, the environment acquisition module will simultaneously output the external environment temperature data and the vehicle start signal, and integrate them to generate "starting condition data". This data serves as the basic input for subsequent temperature judgment and heat source scheduling, and plays a key role as a trigger source in the entire thermal management control process. In system design, it is recommended to set the sampling period to less than 1 second to ensure timely response to sudden temperature changes or sudden start operations.

[0077] The temperature judgment module is used to judge whether the current battery pack is in a state that requires heating, and to generate the first heat source request data when necessary. The core function of this module is to perform a logical comparison between the startup condition data and the battery pack temperature data, and to set a set of judgment conditions to make a heating decision.

[0078] This module usually includes three key processing steps: battery pack temperature extraction, low temperature state identification and request generation.

[0079] First, the system extracts the current battery pack temperature data from the temperature collection point in the battery pack thermal management loop. This data can be obtained through multiple temperature sensors arranged inside the battery pack shell or housing, and representative temperature is extracted through temperature averaging or taking the lowest temperature algorithm, thereby avoiding misjudgment of the overall state due to local temperature rise.

[0080] Secondly, the module compares the extracted battery pack temperature with a set of preset low temperature judgment thresholds. For example, in an area where the ambient temperature is below 5°C, if the battery pack temperature is below 0°C or below the system-defined safe operating lower limit (such as 5°C), it is judged to be in a "low temperature state". The judgment process can be adjusted according to the specific vehicle type, battery type and operating area.

[0081] When the low temperature state is confirmed, the module will immediately generate the "first heat source request data" and output it to the heat source scheduling module. The request data contains information such as the current temperature state of the battery, the required heat energy level, and the external ambient temperature level. The control strategy can use this information to determine whether the heat pump needs to be activated immediately and at what power level to output heat.

[0082] The temperature judgment module has the characteristics of high judgment accuracy, fast response speed, and dynamic configuration. It is suitable for a variety of commercial vehicle battery system structures, and is particularly suitable for power battery pack application environments with zoned temperature distribution and complex thermal capacity.

[0083] In a preferred embodiment of the present invention, the heat source scheduling module includes:

[0084] a heat control submodule, for extracting ambient temperature data and battery pack temperature data according to the first heat source request data, determining a target operating frequency and a target exhaust temperature of the heat pump unit, and generating a heat source adjustment parameter;

[0085] An operation instruction generation sub-module, configured to issue an operation control instruction to the heat pump unit according to the heat source adjustment parameter, so that the heat pump unit generates a first heat energy flow at a target operation frequency;

[0086] A heat quantity feedback sub-module, configured to adjust the operation control instruction in real time according to the adjustment signal, and generate an adjusted first heat energy flow.

[0087] In an embodiment of the present invention, in the vehicle integrated thermal management system, the heat source scheduling module is further refined into three sub-modules, each responsible for parameter determination, control signal generation, and output adjustment functions of the heat pump unit. Through the internal division of labor and cooperation of the modules, the system can achieve precise heat source control under different working conditions and improve the heating response efficiency.

[0088] The heat quantity control sub-module first extracts the current ambient temperature data and battery pack temperature data according to the first heat source request data, and determines the target operation frequency and target exhaust temperature of the heat pump unit with these as inputs. Compared with the traditional scheme with fixed operation parameters, this method realizes the dynamic adaptation of the operation strategy by integrating external and internal thermal environment information. Especially when the ambient temperature changes rapidly or the battery has strong thermal inertia, the adaptive setting of the target frequency and exhaust temperature can significantly improve the heat pump energy efficiency and reduce the ineffective operation time.

[0089] The operation instruction generation sub-module constructs a specific control instruction based on the heat source adjustment parameter and issues it to the heat pump unit. This control link has high timeliness in response time, can quickly trigger the change of the heat pump heating state, and enable the first heat energy flow to be accurately output according to the expected parameters. At the same time, this instruction generation mechanism also has a certain fault tolerance design, and can perform fault detection according to the response state of the control instruction, enhancing the stability and safety of the system.

[0090] The heat quantity feedback sub-module is used to adjust the original operation control instruction in real time according to the subsequent adjustment signal from the temperature feedback module. When it is detected that the heating effect is insufficient or the target temperature is not reached for a long time, the system can quickly reconstruct the heat pump operation strategy, increase the heat output or extend the heating time, and realize the continuous optimization of the heat pump working state.

[0091] This structured heat source scheduling module makes the heat pump unit no longer a fixed device driven by a single logic, but an intelligent heating component that can be dynamically scheduled and real-time controlled. Compared with the traditional heat pump control system, this method can greatly improve the adaptability of the system to complex thermal environments without increasing additional hardware costs, and is especially suitable for the rapid start-up requirements of the vehicle in cold environments.

[0092] In a preferred embodiment of the present invention, the battery pack heat exchange module includes:

[0093] A heat exchange path selection submodule, used to determine the flow channel enabled in the heat exchange path according to the heat amount of the first heat energy flow and the temperature distribution data of different positions of the battery pack;

[0094] A bypass control submodule, for opening a bypass channel to partially guide the first heat energy flow to bypass the battery pack area in the case of excess heat or local overheating, thereby generating an optimized heat exchange path;

[0095] The temperature monitoring submodule is used to collect temperature change data at different positions of the battery pack during the preheating process and generate battery pack preheating data.

[0096] In the embodiment of the present invention, in order to achieve more efficient heat conduction of the first heat flow to the battery pack, the battery pack heat exchange module in the system is further subdivided into three submodules, which are responsible for the three functions of heat exchange path selection, heat conduction and temperature monitoring. The core design of this module is to improve the uniformity and stability of heat distribution, reduce the risk of local thermal shock or overheating, and enhance the safety and efficiency of the battery system during the heating process.

[0097] The heat exchange path selection submodule dynamically selects the currently enabled flow channel based on the heat amount of the first heat flow and the temperature distribution data at different locations inside the battery pack. This path selection basis can preferentially direct heat to the lower temperature area to accelerate the consistency of the overall temperature rise of the battery. In addition, the channel selection mechanism has a hierarchical priority structure, which can automatically complete the optimal path combination based on the target temperature gradient and path impedance, thereby reducing heat transfer losses.

[0098] The bypass control submodule is activated when excess heat is detected or the temperature in a local area of ​​the battery is abnormally high. By opening the bypass channel, the module diverts part of the heat energy flow out of the main heating path, bypasses the high-temperature area and enters the bypass heat exchange loop, thereby achieving dynamic dispersion of the heat flow. This operation not only avoids the performance degradation of local battery cells due to excessive heating, but also enhances the thermal stability of the entire heating process.

[0099] The temperature monitoring submodule collects temperature change data at different locations of the battery pack in real time during the entire preheating process, and generates battery pack preheating data, providing basic information for subsequent judgment by the temperature feedback module. This submodule has high-frequency sampling and multi-point monitoring capabilities, can quickly perceive the trend of thermal field changes, and identify potential abnormal areas, thereby improving the accuracy of preheating control.

[0100] Through the above structure, the battery pack heat exchange module not only has the traditional heat flow access function, but also has multiple capabilities such as heat distribution control, thermal risk avoidance and information collection and feedback. It is especially suitable for use in large commercial vehicle application scenarios with large initial temperature differences and complex battery structures. It can effectively improve the intelligence level of vehicle thermal management and the reliability of the battery system.

[0101] Among them, the heat exchange path selection submodule, as the core functional unit in the battery pack heat exchange module, is mainly used to dynamically select the appropriate heat exchange path according to the current heat source output and the temperature distribution characteristics inside the battery pack, so as to achieve efficient transfer and balanced distribution of heat inside the battery pack.

[0102] This submodule includes the following implementation logic steps: thermal energy flow intensity judgment, temperature distribution perception and path switching control.

[0103] First, the system obtains the output parameters of the first heat flow, including heat flow, fluid temperature, flow rate and other indicators. This information can be obtained in real time through the temperature sensor and flow sensor at the outlet of the heat pump unit. Combined with this information, the system evaluates whether the current heat flow has sufficient heating capacity and determines whether it can enter the normal heat exchange process or needs to be bypassed (the bypass control function is completed by the subsequent submodule).

[0104] Next, the system uses temperature sensors placed at multiple locations in the battery pack to establish a battery pack temperature distribution model to identify which areas are still at low temperatures and which areas have reached the target temperature. This distribution information is used as the core reference data for path selection, combined with heat transfer efficiency and path impedance, to select the heat exchange branch or sub-circuit that should be prioritized to guide the heat flow through.

[0105] The path switching control mechanism performs the heat flow path switching operation based on the above judgment results through the electromagnetic valve, commutator or controllable flow guide mechanism inside the heat exchange unit. The specific path configuration can be parallel, series or ring structure, and a flow resistance control device can be set between each path to accurately control the heat energy distribution ratio of each path.

[0106] Through path selection control, the submodule can achieve local temperature difference compensation, avoid hot spot overheating, and improve overall heating uniformity. It is particularly effective in complex multi-module battery structures, helping to extend battery life and improve system heating efficiency.

[0107] Among them, the bypass control submodule is arranged in the battery pack heat exchange module, and is used to divert the heat flow when the heat flow intensity is too high or there is a trend of local overheating inside the battery pack, so as to avoid concentrated heat shock to a certain sub-area of ​​the battery and ensure the balance and safety of the overall heating of the battery pack.

[0108] Structurally, the bypass control submodule includes a heat flow shunt judgment unit, a bypass valve control unit, and a guide path switching unit. The heat flow shunt judgment unit receives the current thermal energy flow information from the heat exchange path selection submodule in real time, including flow rate, temperature, fluid pressure and other data, and monitors the local temperature information of the battery pack from the temperature monitoring submodule. If it is found that the heat flow intensity exceeds the set threshold, or the temperature of certain battery areas continues to rise and approaches the upper limit, the unit will generate a bypass trigger signal.

[0109] The bypass valve control unit responds to the trigger signal and controls the bypass solenoid valve, proportional valve or switching valve on the main heat flow path to switch, and guide part of the heat energy flow into the bypass pipeline or auxiliary heat exchange path. Usually these paths are arranged in parallel or crosswise with the main heating circuit, which can guide the heat flow to bypass the high temperature area and flow to the heat exchange branch with relatively low temperature, playing a buffering role.

[0110] The guide path switching unit is used to perform path stabilization operations after the heat energy is redistributed, such as delaying the bypass shutdown and slowly opening and closing the valve to ensure that the heat flow pressure does not fluctuate violently during the switching process and avoid pulse shocks in the thermal system. At the same time, the maximum opening time can be set in combination with the main controller feedback mechanism to prevent the bypass from continuing to operate.

[0111] Overall, the bypass control submodule effectively improves the thermal safety of the battery heating process by introducing an "active avoidance" mechanism in heat regulation. It has strong practical value and control flexibility, especially in system environments with high power output or uneven temperature control sensing.

[0112] Among them, the core function of the temperature monitoring submodule is to provide high-resolution temperature status data for the entire heat exchange control process, and support dynamic collection and real-time upload functions. It is the basis for building a temperature feedback mechanism and preheating trend judgment.

[0113] Its structure usually includes a sensor layout unit, a data acquisition unit and a data reporting interface unit. The sensor layout unit needs to combine the internal structural characteristics of the battery pack and place multiple temperature sensors in key heat-sensitive locations, such as the middle of the battery cell, the end of the module, and the battery pack shell near the heat exchange interface. These sensors should use high-sensitivity digital temperature sensors or thermocouple arrays, which are required to have a small temperature response lag time and a high sampling frequency.

[0114] The data acquisition unit is responsible for periodic polling or batch synchronous reading of the temperature data of each measuring point, and performs preliminary abnormal value screening and filtering to remove pseudo signals such as instantaneous pulse interference. It is recommended to set the sampling period within 1 second so that the system can obtain the thermal field change trend in time during the heating process.

[0115] The data reporting interface unit formats the sorted temperature data uniformly and transmits it to the system main control unit or temperature feedback module through the bus or serial port protocol. The reported data should include the current value of each measuring point, sampling timestamp, change rate and other information to facilitate subsequent system trend analysis and control response.

[0116] The temperature monitoring submodule not only provides a single temperature value, but also supports the system to realize the thermal state recognition capability of "spatial distribution + time change" through time series changes, providing basic data support for multiple functions such as bypass control, trend modeling and startup judgment.

[0117] In a preferred embodiment of the present invention, the temperature feedback module comprises:

[0118] The temperature change rate calculation submodule is used to calculate the change rate of the battery pack temperature based on the battery pack preheating data and obtain the temperature rise trend data;

[0119] The threshold prediction submodule is used to predict the remaining time required to reach the preset start threshold according to the temperature rising trend data, and generate a heat source regulation advance signal;

[0120] The control instruction updating submodule is used to send a heat source adjustment advance signal to the heat source scheduling module to optimize the generation strategy of the first heat energy flow in advance.

[0121] In an embodiment of the present invention, during the preheating process, in order to avoid the heat pump system being in an inefficient state of blind constant power output or fixed heating cycle, a temperature feedback module is introduced and further refined into three sub-modules with clear functions to achieve dynamic prediction and closed-loop regulation control, thereby enhancing the system's responsiveness to the battery heating state.

[0122] After receiving the battery pack preheating data, the temperature change rate calculation submodule first calculates the time change rate of the battery temperature. This rate value is calculated based on the temperature difference change in a continuous time period, and can accurately reflect the actual response speed of the current heating system. Through this parameter, the system can determine whether the current heat output has reached the expected efficiency, providing a quantitative basis for the subsequent prediction module.

[0123] Based on the temperature change rate, the threshold prediction submodule further estimates the remaining time required to reach the preset start threshold and obtains the predicted remaining heating time. This prediction behavior upgrades the traditional "whether the target temperature is reached" judgment method to a "when the target is expected to be reached" feedforward control method. The prediction mechanism enables the system to identify whether the heating progress is lagging behind before the temperature is reached, effectively avoiding the problem of slow startup. For example, when the heating curve shows a platform trend and the temperature rise rate slows down, the module can quickly sense and issue an early warning signal.

[0124] Once the predicted remaining heating time is found to be too long, the early warning judgment submodule will immediately generate a heat source adjustment advance signal and feed it back to the heat source scheduling module, triggering the heating power increase or operating frequency adjustment, and intervening in the heat pump system output strategy in advance. This strategy can not only shorten the total preheating time, but also avoid the risk of vehicle start failure or delay due to insufficient heating.

[0125] The temperature feedback module builds a dynamic prediction mechanism based on the current state, which essentially breaks the traditional feedback mechanism's single reliance on the real-time temperature threshold, improves the intelligent adjustment capability of the temperature control system, and has strong adaptability and foresight. It is particularly suitable for commercial vehicles to be quickly activated in different ambient temperatures and different battery conditions.

[0126] In a preferred embodiment of the present invention, the energy distribution module comprises:

[0127] The priority evaluation submodule is used to calculate the corresponding thermal energy demand value and generate heat distribution priority data according to the vehicle driving status data, the current cockpit temperature data and the electric drive system temperature data;

[0128] a proportion decision submodule, for determining a distribution ratio of the second heat energy flow between the cockpit and the electric drive cooling circuit according to the heat distribution priority data, and generating a proportion control parameter;

[0129] The execution feedback submodule is used to control the proportional valve to adjust the heat flow according to the proportional control parameters, and monitor the heat flow distribution effect of the two circuits in real time. If there is a deviation, the adjustment feedback signal is output to correct the proportional control parameters.

[0130] In the embodiment of the present invention, after the battery pack reaches the startable state and the heat energy is switched to the cabin heating path, the focus of vehicle thermal energy control is shifted to the coordination between the cockpit comfort and the electric drive cooling requirements. The energy distribution module has been systematically designed for this and divided into three functional sub-modules, from demand identification, proportion decision-making to adjustment feedback, to build a complete set of dynamic distribution logic.

[0131] The priority assessment submodule obtains the vehicle's driving status data, the current cockpit temperature data, and the electric drive system temperature data, calculates the thermal energy demand values ​​of the two circuits, and generates heat distribution priority data accordingly. Compared with the static weight or fixed proportion distribution mechanism, this module can dynamically determine the distribution center of gravity based on real-time data. For example, when the vehicle is first started in the morning, the cockpit is still in a low temperature state, and the system prioritizes directing heat energy to the cockpit; after a long period of operation, when the temperature of the electric drive system rises to a safe boundary, the priority can automatically switch to electric drive cooling.

[0132] The proportion decision submodule calculates the distribution ratio of the second heat flow between the two targets based on the priority data, generates a proportion control parameter, and passes the parameter as input to the execution unit. The proportion parameter supports high-precision continuous adjustment and can achieve stepless switching of different proportions (such as 70%-30%, 50%-50%, etc.) to adapt to actual working conditions.

[0133] The execution feedback submodule serves as the closed-loop control node of the entire heat distribution process, monitoring the actual distribution effect in real time. When valve control deviation, uneven heat distribution or slow loop response occur, the module can immediately generate an adjustment feedback signal and correct the current proportional control parameters to ensure that the heat regulation meets the expected strategy and avoid problems such as uneven cooling, overheating or insufficient cooling.

[0134] Through the above division of labor and cooperation, the energy distribution module can not only realize the dual-path allocation of thermal energy flow, but also continuously optimize the distribution strategy under various driving and working conditions, effectively improving the adaptive ability and comfort guarantee ability of the vehicle's thermal management system.

[0135] Among them, the proportion decision submodule is used to dynamically determine the distribution ratio of heat energy between the cockpit heating circuit and the electric drive cooling circuit according to the heat distribution priority data under the condition that the second heat energy flow has been generated, and output the corresponding proportion control parameters.

[0136] The workflow of this module includes three steps: priority interpretation unit, ratio calculation unit and ratio parameter generation unit. The priority interpretation unit first receives and reads the heat distribution priority data output by the priority evaluation submodule to determine whether the current heat demand of the cockpit is dominant or the cooling load of the electric drive system is higher. The priority information may be expressed in the form of fixed value, weight factor, scoring interval, etc. The system can make judgments by comparing high and low values ​​or referring to weight factors.

[0137] The ratio calculation unit divides the second heat flow into different ratios according to the identified priority. It is recommended that the ratio be set to a continuously adjustable mode, such as any value between 0% and 100%, rather than only supporting fixed gears (such as 70 / 30, 50 / 50). The calculation process can be determined by referring to multiple factors, such as the difference in heat load, heat demand trend, vehicle speed or driving status, etc., to further improve the rationality of heat resource allocation.

[0138] The proportional parameter generation unit generates proportional control parameters with the calculated distribution ratio in a standard data format and outputs them to the execution feedback submodule. This parameter can correspond to the electric proportional valve control signal in the physical execution component, or be used to control the opening and closing time of the reversing valve, thereby actually completing the heat flow direction and flow distribution adjustment.

[0139] The proportional decision submodule realizes the precise distribution of thermal energy, allowing the vehicle to dynamically respond to the different thermal demands of the cabin and electric drive during driving. It has high thermal adaptability and improves the thermal balance regulation performance of the vehicle's thermal management system in complex operating scenarios.

[0140] Among them, the execution feedback submodule is responsible for implementing the control parameters of the proportional decision submodule into the heat flow physical pathway, and for real-time monitoring and dynamic correction of the execution effect. It is an important part of building a complete closed-loop control structure.

[0141] The module structure consists of three parts: execution control unit, feedback acquisition unit and regulation output unit. The execution control unit is responsible for receiving the proportional control parameters and converting them into specific control instructions to send to the proportional valve, electric commutator or other actuators. The control instructions can be in the form of PWM pulse width modulation signals, current drive signals or digital regulation commands, depending on the actuator.

[0142] The feedback collection unit collects the actual flow direction, flow rate and temperature change of the corresponding area of ​​the current heat flow in real time through flow sensors, temperature sensors or pressure difference sensors installed in the heat flow path. These feedback information are compared with the preset expected state to determine whether there is a control deviation.

[0143] When a significant deviation is detected between the heat flow distribution and the expected ratio (such as the error exceeds the allowable range), the regulation output unit will generate a regulation feedback signal based on the error size and change trend, and re-correct the control parameters to make the valve control action tend to the target ratio value, thereby achieving rapid closed-loop adjustment.

[0144] This module ensures that proportional control instructions can not only be issued, but also form real-time response and effective verification at the execution end, forming a complete loop of "decision-making-execution-feedback-correction" in the control chain, effectively solving the problem of thermal flow control distortion caused by valve response lag, unstable execution accuracy and other factors.

[0145] In a preferred embodiment of the present invention, the heat control submodule includes:

[0146] An environmental assessment unit, configured to determine whether the current environment belongs to a high-cold interval, a medium-temperature interval, or a warm interval according to the environmental temperature data in the first heat source request data, and generate a corresponding temperature interval label;

[0147] A load mapping unit, configured to determine a target operating frequency and a target exhaust temperature by looking up a table according to the temperature range label and the current temperature data of the battery pack, wherein the table lookup operation is based on a preset parameter mapping table;

[0148] The operating range selection unit is used to match the operating range of the heat pump unit according to the target exhaust temperature, give priority to the operating range with the best thermal efficiency, and generate the heat source adjustment parameters.

[0149] In an embodiment of the present invention, in the heat source scheduling module, in order to make the operation of the heat pump closer to the actual working conditions and improve energy efficiency, the system further designs the internal structure of the heat control submodule, and completes the entire process of environmental judgment, parameter table lookup and operating range selection through three subunits.

[0150] The environmental assessment unit is used to parse the ambient temperature data in the first heat source request data, determine the current external temperature environment, and divide it into a high-cold interval, a medium-temperature interval, or a warm interval. This classification operation provides the basic conditions for the subsequent switching of control strategies. For example, in the high-cold interval, the heat pump system usually requires a higher initial frequency to quickly output heat energy, while in the medium-temperature or warm interval, it pays more attention to the balance between efficiency and energy consumption.

[0151] The load mapping unit combines the temperature range label and the current temperature data of the battery pack to determine the target operating frequency and target exhaust temperature of the heat pump system. The mapping table is preset by the system and built based on experience and test data. The table lookup process has a fast response feature and can complete the operation parameter matching within milliseconds, avoiding the response delay caused by the long operation time of traditional algorithms.

[0152] The operation range selection unit matches multiple operation ranges of the heat pump system according to the determined target exhaust temperature, and preferentially selects the operation range with the best thermal efficiency as the current activation target, and outputs the frequency and exhaust temperature corresponding to the range as heat source adjustment parameters. This selection process is not only based on the principle of energy efficiency priority, but also can be combined with the current current, voltage and other safety factors of the system to further improve the operation stability.

[0153] Through the coordinated cooperation of the three units, the heat control submodule can dynamically build the optimal operating parameters in the face of complex and changing ambient temperature and battery status, achieving the dual goals of rapid response and efficient heating. This mechanism avoids the operating deviation caused by fixed parameters of the heat pump system in different scenarios, and has significant adaptability to working conditions and control flexibility.

[0154] In a preferred embodiment of the present invention, the threshold prediction submodule includes:

[0155] A heating trend modeling unit is used to construct a temperature change trend curve based on the multi-period temperature change data in the battery pack preheating data;

[0156] A threshold arrival time estimation unit is used to predict the time required for the battery pack temperature to reach a preset start threshold according to the temperature change trend curve, and obtain the predicted remaining heating time;

[0157] The advance warning judgment unit is used to compare the predicted remaining heating time with the expected vehicle starting time. If the time is insufficient, a heat source adjustment advance signal is generated.

[0158] In the embodiment of the present invention, when the system performs battery pack preheating control, judging only by real-time temperature often results in lag or insufficient judgment accuracy. In particular, when the battery temperature changes slowly or there is thermal inertia, it is difficult for traditional control strategies to accurately predict the arrival time of the target threshold. To this end, the system refines the threshold prediction submodule into three subunits to improve the time prediction capability and control response efficiency of the thermal management system.

[0159] The heating trend modeling unit receives the battery pack preheating data from the temperature monitoring submodule and extracts the temperature changes at multiple time nodes. By establishing a time series and analyzing multiple continuous sampling points, the unit can construct a battery temperature change trend curve to form a basic model for reflecting the current temperature rise trajectory. Compared with the simple linear estimation method, this trend curve has the ability to smooth fluctuations, platform judgment and rate tracking, and is suitable for nonlinear temperature rise scenarios.

[0160] Based on the trend curve, the threshold arrival time estimation unit further calculates the remaining time required for the battery temperature to reach the preset start threshold. The estimation process combines the current temperature rise rate, the slope of the trend curve, the target temperature difference distance and other factors to comprehensively judge and give a more engineering feasible remaining time value. This time estimation not only has a certain degree of accuracy, but also can be periodically corrected during the long heating process to improve the stability and reliability of the prediction.

[0161] The early warning judgment unit compares the estimated remaining heating time with the expected vehicle start time. If it is found that the remaining time is greater than the expected response time of the system, it immediately generates a heat source adjustment advance signal to activate the heat source scheduling module to perform heating intensity enhancement operations or operating frequency adjustment strategies. This early trigger control logic has obvious feedforward control characteristics and can drive the execution of actions without relying on the target value reaching the state. It is an innovative extension of the traditional closed-loop logic.

[0162] Through the above structural design, the battery heating control process is upgraded from "based on real-time status judgment" to "based on trend prediction intervention", which can effectively improve the system heating response efficiency, especially suitable for commercial vehicle operation scenarios where the battery temperature changes slowly or the ambient temperature fluctuates drastically.

[0163] In a preferred embodiment of the present invention, the priority evaluation submodule includes:

[0164] A state recognition unit is used to determine whether the vehicle is currently in a passenger boarding and alighting stage, a high-speed driving stage or an idling and parking stage according to the vehicle driving state data, and generate a corresponding running state label;

[0165] The heat demand calculation unit is used to calculate the heat demand values ​​of the cockpit and the electric drive cooling circuit respectively according to the operation status label, the current temperature data of the cockpit and the temperature data of the electric drive system; wherein, ,

[0166] is the thermal energy demand value, which indicates the current heat compensation demand value of the corresponding module (cockpit or electric drive). To set the target temperature, the desired operating temperature of the cockpit or electric drive, The current temperature is the current temperature of the cockpit or electric drive collected in real time. is the rate of change of the current temperature over time, indicating the response speed of the system heating or cooling. is the current heat load factor, which indicates the load intensity of the system's response to heat flow in the current state. , , are weight coefficients, corresponding to the influence of temperature difference, response rate and load factor on the thermal energy demand value, satisfying ;

[0167] The priority generation unit is used to compare the thermal energy demand value with the preset state threshold range according to the thermal energy demand value and the corresponding operating status label, select the target with a higher demand value in the cockpit and the electric drive cooling circuit as the priority allocation object, and generate heat allocation priority data. If the difference between the demand values ​​of the two is within the preset difference range, the priority order is determined and adjusted based on the preset weight factor.

[0168] In an embodiment of the present invention, in order to achieve accurate heat distribution between the cockpit and the electric drive system, the system has refined the structure of the priority evaluation submodule and constructed a priority generation strategy based on state perception and demand judgment to achieve more flexible and intelligent thermal energy scheduling control.

[0169] The state recognition unit first reads the vehicle's driving state data to determine whether the vehicle is currently in a stage such as passengers boarding or alighting, high-speed driving, or idling, and generates an operating state label based on the judgment result. This recognition result, as an input condition, not only reflects the system's ability to understand the operating situation, but also provides different processing paths for the thermal energy distribution strategy. For example, the high-speed driving stage focuses more on electric drive cooling, while the cockpit comfort is more emphasized when boarding or alighting or idling.

[0170] Based on the status label, the thermal energy demand calculation unit further combines the current temperature data of the cockpit and the temperature data of the electric drive system to calculate the thermal energy demand values ​​of the two systems respectively. This demand value is not only based on the temperature difference, but also combined with factors such as temperature rise rate and load level to calculate, constructing a comprehensive indicator reflecting the real-time thermal load, ensuring the accuracy and dynamics of demand assessment.

[0171] The priority generation unit compares the two thermal energy demand values ​​and makes a judgment based on the preset threshold range under the current status label to determine which subsystem has a higher allocation priority at the current moment. When the two demand values ​​are close and the difference falls within the preset threshold range, the unit will call the built-in weight factor for judgment and dynamically adjust the allocation order to ensure the fairness and stability of resource allocation. This mechanism avoids the oscillation phenomenon of frequent switching of allocation priorities due to slight numerical differences, and at the same time improves the allocation flexibility of the system.

[0172] In general, the design logic of this module has three control dimensions: working condition identification, demand quantification, and priority adjustment. It not only improves the rationality of heat allocation, but also enhances the execution stability of the control strategy. It is particularly suitable for changeable and dynamic driving scenarios.

[0173] Among them, the state recognition unit is used to identify the current driving condition of the vehicle and generate the corresponding operating state label to provide a decision basis for the subsequent thermal energy demand calculation and priority judgment. This unit constructs an intelligent recognition mechanism based on "vehicle condition perception". The core task is to determine whether the vehicle is in the starting stage, high-speed driving stage, low-speed cruising stage, idling waiting state, or in a short stop before and after passengers get on and off the vehicle.

[0174] The input of this unit mainly includes vehicle speed data, acceleration data, transmission gear information, parking status signal, door switch status, mileage change, etc. The above data can be collected through the existing CAN bus system of the vehicle, and can also be connected to the body control module (BCM) and the powertrain control module (VCU) for linkage.

[0175] The judgment logic is generally processed based on a set of state judgment rules. For example, when the vehicle speed is 0 km / h and the transmission is in P gear, and the door is open for more than a set time, it can be judged as the stage of passengers getting on and off the vehicle; when the vehicle speed is continuously and stably above 60 km / h and the acceleration remains within a low fluctuation range, it can be judged as a high-speed driving state; and if the vehicle is stationary for a long time but the engine is not turned off and the air conditioner is running, it is identified as an idling waiting state.

[0176] Once the recognition result is generated, the state recognition unit will output a set of discrete state labels (such as "start", "high speed", "parked", etc.) as the basis for adjusting the weight factor in the subsequent thermal energy allocation strategy. This working condition judgment mechanism can significantly improve the environmental adaptability of thermal energy scheduling and achieve dynamic synchronization between thermal energy resource allocation and vehicle operation intention.

[0177] Among them, the priority generation unit is the core decision-making unit in the entire thermal energy distribution logic. Its main task is to determine the thermal energy distribution priority based on the current operating status on the basis of obtaining the thermal energy demand values ​​of the cockpit and the electric drive system, thereby guiding the proportional decision module to complete the reasonable allocation of heat flow resources.

[0178] In terms of functional structure, the unit first compares the two thermal energy demand values. If the demand value of one side is significantly higher than the other side (such as exceeding the set difference threshold), it will directly mark the side as a high-priority target; if the difference between the two demands is small and within the preset "critical interval", it will refer to a set of preset weight factor rules based on the operating status label for further judgment. For example, in the "idling stop" state, the system may preset the cockpit priority; in the "high-speed driving" state, the electric drive system has a higher priority.

[0179] The judgment mechanism is based on a simple interval decision and condition matching logic. It does not rely on complex calculation models in engineering implementation, is easy to deploy in embedded systems, and has high real-time performance. The output of the priority generation unit is usually a set of priority labels or numerical levels (such as "electric drive priority: high, cockpit priority: low"), which serves as a direct input for the heat distribution ratio decision.

[0180] By introducing this unit, the vehicle thermal management system has achieved a complete closed loop from "real-time perception" to "demand calculation" to "strategy decision-making", effectively improving the utilization efficiency of heat flow resources. It has significant practical value, especially in scenarios where the vehicle's operating status frequently switches and the thermal demand changes rapidly.

[0181] In a preferred embodiment of the present invention, the operation interval selection unit includes:

[0182] An interval screening unit, for screening candidate intervals that meet upper and lower limits of the target exhaust temperature from a plurality of preset operating intervals according to the target exhaust temperature;

[0183] The interval evaluation unit is used to perform a weighted evaluation of the historical energy efficiency performance, the current thermal demand level of the battery and the ambient temperature of the vehicle for each candidate interval, and calculate a comprehensive score; ,

[0184] Candidate operating interval The comprehensive score is used to select the working range that best suits the current thermal management strategy. Candidate operating interval The historical average thermal efficiency reflects the energy-saving effect in its historical operation. and are the thermal energy requirements of the cockpit and electric drive system, Candidate operating interval The maximum thermal power that can be provided indicates the upper limit of its heating capacity. is the current external ambient temperature, Candidate operating interval The most suitable working environment temperature, is the allowable ambient temperature difference tolerance, indicating the tolerance range for temperature deviation. , , are weight coefficients, corresponding to thermal efficiency, demand matching, and environmental adaptability, respectively, to meet ;

[0185] The preferred output unit is used to select the operating interval with the highest comprehensive score from the candidate intervals as the optimal operating interval, and extract the corresponding frequency and operating parameters to generate heat source adjustment parameters.

[0186] In an embodiment of the present invention, after the heat source scheduling module completes the preliminary determination of the target exhaust temperature and frequency, the operating interval selection unit screens and evaluates the optional operating intervals based on multi-dimensional evaluation factors to ensure that the system operates in the interval with the best efficiency and the highest matching degree, thereby maximizing the overall energy efficiency performance of the heat pump unit.

[0187] Based on the target exhaust temperature, the interval screening unit first screens out all candidate intervals whose upper and lower limits contain the target value from multiple operating intervals preset by the system. This operation not only reduces the scale of subsequent calculations, but also provides boundary conditions for centralized evaluation and improves the system response speed. The formation of candidate intervals is based on the heat pump structure, compressor refrigeration curve and historical operation data, and has stable physical boundary properties.

[0188] After the candidate intervals are determined, the interval evaluation unit weights and scores each interval according to multiple evaluation dimensions, and calculates the comprehensive score of each interval. Among them, the evaluation factors include three core parameters: the average thermal efficiency of the interval in historical operation, the current thermal demand level of the battery, and the current ambient temperature. Thermal efficiency reflects the amount of heat that can be output per unit input power in the interval, the demand level reflects whether the output capacity of the heat source matches the current load, and the adaptability of the ambient temperature reflects the energy efficiency stability of the interval under current external conditions. The multi-factor comprehensive scoring mechanism effectively avoids the system judging the interval based on a single thermal efficiency, and improves the adaptability of actual operation.

[0189] The optimal output unit finally selects the operating range with the highest comprehensive score from the scoring results as the optimal operating range, and extracts its corresponding frequency and operating parameters as heat source adjustment parameters and outputs them to the control logic layer. Through this selection mechanism, the heat pump unit can always operate in an operating state that takes into account both responsiveness and efficiency, significantly reducing high-energy consumption operation time and improving the overall control quality of the system.

[0190] In a preferred embodiment of the present invention, the heating trend modeling unit comprises:

[0191] A data integration unit is used to extract temperature sampling values ​​at multiple different times based on the battery pack preheating data, and construct a temperature sequence data set in chronological order;

[0192] A local trend analysis unit is used to divide the temperature series data set into several continuous time periods, and determine the local rising trend or platform trend according to the temperature change rate in each time period to form trend data;

[0193] The global trend construction unit is used to combine the local trends of each continuous time period according to the trend data to construct a temperature change trend curve.

[0194] In an embodiment of the present invention, in order to enhance the analysis capability of battery temperature variation trend, the heating trend modeling unit constructs a heat rise trajectory through a multi-stage data processing process, thereby providing data support for heating prediction and energy scheduling.

[0195] The data integration unit first extracts temperature sampling values ​​at multiple different times based on the battery pack preheating data, and constructs a temperature sequence data set in chronological order. This sequence not only reflects the heat rise rate, but also captures small changes such as temperature anomalies and fluctuation inflection points, and is the basic data source for trend modeling.

[0196] Subsequently, the local trend analysis unit divides the sequence into multiple continuous time periods and calculates the temperature change rate in each time period to determine whether it presents an upward trend, a plateau trend, or a downward trend. This stage not only realizes the quantitative analysis of the thermal rate, but also improves the model's adaptability to complex curves through the division of trend labels, avoiding error accumulation or overfitting in the overall modeling.

[0197] The global trend construction unit combines the local trends of each time period to form a complete temperature change trend curve. This trend curve is output as a dynamic feature, which can be updated in real time and adapt to the changes in temperature sampling intervals, becoming an important basic information for predicting the remaining heating time and the system dynamic control strategy.

[0198] Through the multi-stage analysis process of the above data collection, local recognition, and overall integration, the heating trend modeling unit provides the system with a complete, accurate, and continuously updated expression of temperature changes, with good scalability and system compatibility, and can significantly improve the prediction accuracy and control response effect during actual operation.

[0199] Among them, the data integration unit, as the basic component of heating trend modeling, its main responsibility is to summarize, organize, and structure the original temperature data from the battery pack temperature monitoring system, providing an available time series data set for subsequent trend analysis.

[0200] Specifically, this unit receives the temperature sampling values from the temperature monitoring sub-module and arranges them in chronological order. The sampling data usually includes the temperature readings of multiple monitoring points at specific timestamps. To meet the needs of trend analysis, the data integration unit will select one or more representative sampling points as the basis for trend reference, such as selecting the location with the lowest temperature in the battery pack, the location with the most significant temperature change, or calculating the representative temperature value through a weighted method.

[0201] In actual engineering implementation, this unit can be designed as a circular buffer or queue structure to store the continuous temperature values and corresponding timestamps in the recent period. The system can set a fixed time window (such as the last 5 minutes or 30 sets of sampling values) and continuously update the data content to ensure that the analysis is based on the latest thermal state.

[0202] In addition, the data integration unit can have a preliminary data quality screening function, such as removing error readings, filling in missing values, smoothing extreme fluctuations, etc., to improve the accuracy and stability of subsequent trend analysis. The output result is a set of ordered, continuous, and non-missing temperature-time pair data sets.

[0203] Among them, the local trend analysis unit is responsible for segmenting the temperature time series provided by the data integration unit to identify the phased change characteristics during the heating process. Its goal is to capture the local change trend of the battery pack temperature during the preheating process, such as continuous temperature rise, plateau, or brief decline, etc., and convert it into structured trend information.

[0204] This unit usually segments the temperature-time series by time or quantity (for example, every 5 sets of data as an analysis segment), and analyzes the temperature change direction and rate within each segment. If the temperature continuously rises and fluctuates little within a certain segment, it is marked as an "upward trend segment"; if the temperature fluctuates little and remains stable as a whole, it is marked as a "plateau trend segment"; if the temperature drops, it is marked as a "downward trend segment" or "abnormal segment".

[0205] This analysis process does not rely on complex algorithm models, but is based on simple trend recognition rules, which is easy to implement in embedded controllers. Thresholds can be set to determine whether the rise and fall rate is significant, ensuring that the identified trend has practical engineering significance.

[0206] After identification, the system encapsulates each trend result into trend tag data, including trend type, duration, temperature change range, etc., for summary analysis in the global trend construction unit. The design of this module improves the responsiveness of the entire system to nonlinear heating behavior and avoids misjudgment of the temperature change process. It is particularly suitable for complex heating scenarios with battery thermal inertia or external disturbances.

[0207] Among them, the role of the global trend construction unit is to integrate multiple local trend fragments into a complete temperature change trend curve that can describe the entire battery pack heating process, thereby providing reliable trend reference information for the prediction model and control strategy.

[0208] This unit receives multiple trend tag data from the local trend analysis unit and splices and fuses them in chronological order. During the fusion process, the system can make continuity judgments on adjacent trend segments. If a short-term platform trend appears between two rising trends, the system can fuse it to form a more representative overall rising trend segment. Similarly, if there is an abnormal trend segment (such as a short-term temperature drop), the system can mark it as an area of ​​concern to determine whether there is abnormal heat flow distribution or sensor error.

[0209] The constructed global trend curve is not a function in the mathematical sense, but a structured data chain composed of several trend segments, which can clearly describe the trend direction, temperature rise, duration and fluctuation state of the current heating process. This data chain can be referenced by the temperature feedback module and the heat source scheduling module as an important basis for predicting whether the preset temperature threshold can be reached within the expected time.

[0210] In addition, the global trend construction unit can also set up a trend update mechanism, that is, whenever new sampling data enters the integration unit and completes local trend identification, the system automatically refreshes the global trend curve, giving it dynamic update capabilities, ensuring that the system control logic always makes judgments and decisions based on the latest thermal status.

[0211] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A heat pump type integrated thermal management system for commercial vehicles, characterized in that: The system comprises: The environment acquisition module is used to obtain the ambient temperature data and the vehicle start signal to generate the start condition data; a temperature judgment module, used to extract the battery pack temperature data according to the startup condition data, and judge whether the battery pack is in a low temperature state, and if so, generate the first heat source request data; a heat source scheduling module, for controlling the operation of the heat pump unit to generate a first heat energy flow according to the first heat source request data; A battery pack heat exchange module, used to heat the battery pack through the heat exchange structure according to the first heat energy flow, and simultaneously collect temperature change data during the preheating process to generate battery pack preheating data; A temperature feedback module, configured to determine whether a preset start threshold is reached according to the battery pack preheating data, and if not, to send an adjustment signal to the heat source scheduling module to adjust the first heat energy flow and continue heating the battery pack; A heat flow switching module, used to control the heat source scheduling module to switch the first heat energy flow to the cabin heating path to generate a second heat energy flow after the battery pack temperature reaches a preset start threshold; The energy distribution module is used to obtain the vehicle driving status data and dynamically distribute the second thermal energy flow based on it, sending it to the cockpit and electric drive cooling circuit respectively, and completing the thermal energy distribution of the vehicle thermal management system by adjusting the proportional valve.

2. A commercial vehicle heat pump integrated thermal management system according to claim 1, characterized in that: The heat source scheduling module includes: a heat control submodule, for extracting ambient temperature data and battery pack temperature data according to the first heat source request data, determining a target operating frequency and a target exhaust temperature of the heat pump unit, and generating a heat source adjustment parameter; An operation instruction generation submodule is used to issue an operation control instruction to the heat pump unit according to the heat source adjustment parameter, so that the heat pump unit generates a first heat energy flow according to a target operation frequency; The heat feedback submodule is used to adjust the operation control instruction in real time according to the adjustment signal to generate an adjusted first heat energy flow.

3. A commercial vehicle heat pump integrated thermal management system according to claim 2, characterized in that: The battery pack heat exchange module comprises: A heat exchange path selection submodule, used to determine the flow channel enabled in the heat exchange path according to the heat amount of the first heat energy flow and the temperature distribution data of different positions of the battery pack; A bypass control submodule, for opening a bypass channel to partially guide the first heat energy flow to bypass the battery pack area in the case of excess heat or local overheating, thereby generating an optimized heat exchange path; The temperature monitoring submodule is used to collect temperature change data at different positions of the battery pack during the preheating process and generate battery pack preheating data.

4. A commercial vehicle heat pump integrated thermal management system according to claim 3, characterized in that: The temperature feedback module comprises: The temperature change rate calculation submodule is used to calculate the change rate of the battery pack temperature based on the battery pack preheating data and obtain the temperature rise trend data; The threshold prediction submodule is used to predict the remaining time required to reach the preset start threshold according to the temperature rising trend data, and generate a heat source regulation advance signal; The control instruction updating submodule is used to send a heat source adjustment advance signal to the heat source scheduling module to optimize the generation strategy of the first heat energy flow in advance.

5. A commercial vehicle heat pump integrated thermal management system according to claim 4, characterized in that: The energy distribution module comprises: The priority evaluation submodule is used to calculate the corresponding thermal energy demand value and generate heat distribution priority data according to the vehicle driving status data, the current cockpit temperature data and the electric drive system temperature data; a proportion decision submodule, for determining a distribution ratio of the second heat energy flow between the cockpit and the electric drive cooling circuit according to the heat distribution priority data, and generating a proportion control parameter; The execution feedback submodule is used to control the proportional valve to adjust the heat flow according to the proportional control parameters, and monitor the heat flow distribution effect of the two circuits in real time. If there is a deviation, the adjustment feedback signal is output to correct the proportional control parameters.

6. A commercial vehicle heat pump integrated thermal management system according to claim 2, characterized in that: The heat control submodule includes: An environmental assessment unit, configured to determine whether the current environment belongs to a high-cold interval, a medium-temperature interval, or a warm interval according to the environmental temperature data in the first heat source request data, and generate a corresponding temperature interval label; A load mapping unit, configured to determine a target operating frequency and a target exhaust temperature by looking up a table according to the temperature range label and the current temperature data of the battery pack, wherein the table lookup operation is based on a preset parameter mapping table; The operating range selection unit is used to match the operating range of the heat pump unit according to the target exhaust temperature, give priority to the operating range with the best thermal efficiency, and generate the heat source adjustment parameters.

7. A commercial vehicle heat pump integrated thermal management system according to claim 4, characterized in that: The threshold prediction submodule comprises: A heating trend modeling unit is used to construct a temperature change trend curve based on the multi-period temperature change data in the battery pack preheating data; A threshold arrival time estimation unit is used to predict the time required for the battery pack temperature to reach a preset start threshold according to the temperature change trend curve, and obtain the predicted remaining heating time; The advance warning judgment unit is used to compare the predicted remaining heating time with the expected vehicle starting time. If the time is insufficient, a heat source adjustment advance signal is generated.

8. A commercial vehicle heat pump integrated thermal management system according to claim 5, characterized in that: The priority evaluation submodule includes: A state recognition unit is used to determine whether the vehicle is currently in a passenger boarding and alighting stage, a high-speed driving stage or an idling and parking stage according to the vehicle driving state data, and generate a corresponding running state label; A heat demand calculation unit, used to calculate the heat demand values ​​of the cockpit and the electric drive cooling circuit respectively according to the operation status tag, the current temperature data of the cockpit and the temperature data of the electric drive system; The priority generation unit is used to compare the thermal energy demand value with the preset state threshold range according to the thermal energy demand value and the corresponding operating status label, select the target with a higher demand value in the cockpit and the electric drive cooling circuit as the priority allocation object, and generate heat allocation priority data. If the difference between the demand values ​​of the two is within the preset difference range, the priority order is determined and adjusted based on the preset weight factor.

9. A commercial vehicle heat pump integrated thermal management system according to claim 6, characterized in that: The operation interval selection unit includes: An interval screening unit, for screening candidate intervals that meet upper and lower limits of the target exhaust temperature from a plurality of preset operating intervals according to the target exhaust temperature; The interval evaluation unit is used to perform a weighted evaluation of the historical energy efficiency performance, the current thermal demand level of the battery and the ambient temperature of the vehicle for each candidate interval to calculate a comprehensive score; The preferred output unit is used to select the operating interval with the highest comprehensive score from the candidate intervals as the optimal operating interval, and extract the corresponding frequency and operating parameters to generate heat source adjustment parameters.

10. A commercial vehicle heat pump integrated thermal management system according to claim 7, characterized in that: The heating trend modeling unit comprises: A data integration unit is used to extract temperature sampling values ​​at multiple different times based on the battery pack preheating data, and construct a temperature sequence data set in chronological order; A local trend analysis unit is used to divide the temperature series data set into several continuous time periods, and determine the local rising trend or platform trend according to the temperature change rate in each time period to form trend data; The global trend construction unit is used to combine the local trends of each continuous time period according to the trend data to construct a temperature change trend curve.

Images