Charging control method and device and vehicle

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a charging control method and device and a vehicle, the method is applied to the vehicle, and the charging control method comprises the following steps: determining a predicted SOC sequence based on a to-be-solved current sequence; based on the to-be-solved current sequence, the to-be-solved power sequence, the predicted SOC sequence and the environment temperature of the battery, determining a predicted temperature sequence; constructing a target function based on the current sequence to be solved, the predicted SOC sequence and the predicted temperature sequence; taking the to-be-solved current sequence and the to-be-solved power sequence obtained after the target function is minimized as a target current sequence and a target power sequence respectively; and charging the battery based on the target current sequence, and controlling the thermal management system to adjust the temperature of the battery based on the target power sequence. Therefore, the vehicle charging time can be reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more specifically to a charging control method, device, and vehicle. Background Technology

[0002] During travel, the long charging time of vehicles can disrupt users' travel plans. Therefore, shortening charging time and improving vehicle charging speed have become urgent technical challenges. Summary of the Invention

[0003] In view of the above shortcomings, the purpose of this application is to provide a charging control method, device and vehicle, which aims to solve the technical problem of long vehicle charging time.

[0004] In a first aspect, embodiments of this application provide a charging control method, which includes: determining a predicted SOC sequence based on a current sequence to be solved; the current sequence to be solved includes multiple current values ​​to be solved, used to represent the changes in battery charging current at multiple future times; the predicted SOC sequence is used to represent the changes in battery SOC at multiple future times; determining a predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery; the power sequence to be solved includes multiple power demand values ​​to be solved, used to represent the changes in the operating power of the thermal management system when regulating the battery temperature at multiple future times; the predicted temperature sequence... This is used to represent the temperature changes of the battery at multiple future time points. An objective function is constructed based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes a first loss term, a second loss term, and a third loss term. The first loss term is related to the estimated charging time of the battery; the second loss term is related to the battery temperature rise; and the third loss term is related to the capacity decay of the battery during charging. The current sequence and power sequence to be solved obtained after minimizing the objective function are used as the target current sequence and the target power sequence, respectively. The battery is charged based on the target current sequence, and the thermal management system is controlled to adjust the battery temperature based on the target power sequence.

[0005] Based on the current sequence to be solved, a predicted SOC sequence is determined, which can extrapolate the battery's state of charge at multiple future moments according to a preset current change trend, thereby determining the battery's charge accumulation during continuous charging. Subsequently, based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature, a predicted temperature sequence is determined. This sequence can predict the battery's temperature trajectory at future moments based on the battery's temperature changes during charging, the heat exchange caused by the thermal management system's power adjustment, the battery's charge accumulation during continuous charging, and the current ambient temperature, thus generating temperature change data that more closely matches the battery's actual thermal behavior. Finally, an objective function is constructed based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes a first loss term related to the estimated charging time, a second loss term related to battery temperature rise, and a third loss term related to capacity decay. By minimizing the objective function, a balance can be achieved between the current sequence and power sequence to be solved, which are in terms of charging efficiency, temperature safety, and lifespan maintenance. This ensures that the battery temperature is within a suitable range while achieving relatively efficient charging, reducing charging time, and minimizing battery capacity loss during long-term cycling, thereby improving the battery's adaptability and stability under complex operating conditions.

[0006] In one possible embodiment, determining a predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery includes: for the demand power value to be solved at time k in the current sequence to be solved, determining the predicted actual execution power to be solved based on a power prediction model to obtain multiple predicted actual execution powers to be solved, the power prediction model being used to predict the power that the thermal management system can actually execute; determining a target actual power sequence based on the multiple predicted actual execution powers to be solved and a delay correction model, the target actual power sequence including: multiple target actual execution powers to be solved, the delay correction model being used to predict the power that the thermal management system can actually execute under the influence of thermal management response delay, the thermal management response delay being used to indicate the time for the thermal management system to adjust from the power at a first time to the demand power at a second time, the second time being the next time adjacent to the first time; and determining the predicted temperature sequence based on the current sequence to be solved, the target actual power sequence, the predicted SOC sequence, and the ambient temperature of the battery.

[0007] In determining the predicted temperature sequence, firstly, for the demand power value at time k in the current sequence to be solved, the predicted actual execution power is determined based on the power prediction model. This allows for the quantification of the system's actual output power limit and efficiency characteristics based on the current hardware status and environmental conditions of the thermal management system. This eliminates deviations between theoretical demand and actual execution caused by actuator saturation or nonlinear characteristics, ensuring that the energy data input to the temperature calculation stage conforms to physical reality. Subsequently, based on multiple predicted actual execution powers and a delay correction model, the target actual power sequence is determined. This simulates the response lag phenomenon in the thermal management system during the adjustment from the power at time one to the demand power at time two, transforming the idealized step power command into an actual execution trajectory with smooth transition characteristics. This effectively captures transient details in the heat flux change process, avoiding abrupt temperature prediction changes or distortions caused by neglecting dynamic response time. Finally, based on the current sequence to be solved, the target actual power sequence after the above two steps of correction, the predicted SOC sequence, and the ambient temperature of the battery, a predicted temperature sequence is determined. This sequence can comprehensively consider the heat generated by the battery's internal resistance, the actual heat dissipation or heating capacity of the thermal management system, and the environmental thermal boundary conditions to generate temperature change data that closely approximates the actual temperature rise or fall trend of the battery. This high-precision temperature prediction result serves as a key input for constructing the objective function, significantly improving the computational reliability of the second loss term related to battery temperature rise and the third loss term related to capacity decay. This allows the target current sequence and target power sequence obtained by minimizing the objective function to more accurately balance charging speed, temperature safety, and battery life while fully considering the limitations and response delays of thermal management. It prevents the control strategy from being too conservative or aggressive due to temperature prediction deviations, thereby better maintaining the battery within its optimal operating temperature range during actual charging, extending battery life, and improving the safety of the charging process.

[0008] In one possible embodiment, the power prediction model satisfies the following formula: ; in, This represents the predicted actual power of the thermal management system to be solved at time k, where N represents the prediction time domain and i represents the time step index variable. The weight used to represent the influence of the predicted actual execution power at time ki on the power at time k. Used to represent the predicted actual execution power at time ki. This is used to represent the response speed of the demand power value at time k to the predicted actual execution power at time k. Used to represent the power demand value at time k; It is positively correlated with the time step index variable.

[0009] In one possible implementation, the delay correction model satisfies the following formula: ; in, This is used to represent the actual execution power of the target to be solved for in the thermal management system at time k. Used to represent the response coefficient of a thermal management system. This is used to represent the predicted actual power to be solved for the thermal management system at time k. This is used to represent the actual execution power of the target to be solved in the thermal management system at time k-1.

[0010] By employing a delay correction model in the form of a first-order inertial element, the response lag phenomenon in the power adjustment process of the thermal management system can be accurately simulated. This formula uses a response coefficient to weight and fuse the predicted actual power at the current moment with the target actual power at the previous moment, essentially constructing a low-pass filtering mechanism. This transforms theoretically possible step power changes into actual execution trajectories with smooth transition characteristics. This approach realistically reflects the time required for the thermal management device to adjust from the power state at the first moment to the required power state at the second moment, avoiding temperature prediction overshoot or oscillations caused by assumed instantaneous power jumps. The resulting target actual power sequence is highly synchronized with the actual heat flow changes of the thermal system in the time dimension, significantly improving the accuracy of temperature prediction under transient conditions. This allows charging strategies optimized based on this sequence to more effectively avoid the risk of temperature runaway caused by thermal response delays, ensuring that the battery temperature remains within a safe and controllable range.

[0011] In one possible embodiment, the current sequence and power sequence obtained after minimizing the objective function are used as the target current sequence and target power sequence, respectively. This includes: constructing preset constraints; the preset constraints include at least one of the following: power constraints, current constraints, temperature constraints, and / or SOC constraints; the power constraints are used to ensure that the operating power of the thermal management system during battery temperature regulation is within a preset power range; the current constraints are used to ensure that the charging current of the battery is within a preset current range during charging; the temperature constraints are used to ensure that the battery temperature is within a preset temperature range during charging; and the SOC constraints are used to ensure that the battery SOC is within a preset SOC range during charging. The objective function is minimized under the preset constraints, and the solved current sequence and power sequence are used as the target current sequence and target power sequence, respectively.

[0012] By introducing preset constraints including at least one of power, current, temperature, and SOC during the minimization of the objective function, it is ensured that the obtained target current and power sequences are strictly within the safe operating boundaries of the battery and thermal management system. The power constraint prevents the thermal management system from exceeding its hardware limits, avoiding equipment overload damage; the current constraint limits the charging current to within the battery's allowable range, preventing lithium plating or overheating; the temperature constraint directly controls the battery temperature within a preset safe range, eliminating the risk of thermal runaway; and the SOC constraint prevents overcharging or over-discharging, protecting the battery's chemical activity. This constrained optimization mechanism ensures that the final control strategy achieves a mathematically optimal balance between charging efficiency, temperature rise control, and lifespan maintenance, while also guaranteeing absolute system safety at the physical level. It prevents the optimization algorithm from generating dangerous commands that violate physical limits in pursuit of a single metric (such as the shortest charging time), thus significantly improving the safety and reliability of the charging process.

[0013] In one possible embodiment, the preset constraints include power constraints; the preset power range is determined as follows: the required power value to be solved at time k and the historical actual power sequence are input into the power prediction model to obtain the predicted actual execution power at time k; the historical actual power sequence is used to represent the changes in the actual output power of the thermal management system when regulating the battery temperature over multiple past times; the power prediction model is used to predict the actual output power of the thermal management system; the smaller value between the predicted actual execution power at time k and the preset power upper limit threshold is determined as the target power upper limit value in the preset power range. The larger value between the predicted actual execution power at time k and the preset power lower limit threshold is determined as the target power lower limit value in the preset power range; the preset power upper limit threshold is greater than the preset power lower limit threshold, and the preset power range is: the operating power is less than or equal to the target power upper limit value and the operating power is greater than or equal to the target power lower limit value.

[0014] By dynamically determining the upper and lower limits of a preset power range by combining the output of a power prediction model with preset thresholds, the operating boundaries of the thermal management system can be adaptively adjusted. This method first uses the power prediction model combined with historical power sequences to predict the actual power the system can execute at the current moment. Then, it takes the smaller value between this prediction and a fixed preset upper power threshold as the target upper power limit, and the larger value between this prediction and a preset lower power threshold as the target lower power limit. This mechanism ensures that the power constraint range during optimization will neither exceed the absolute limits of the hardware design nor the actual execution capability of the system under the current historical operating conditions. When the actual output capability decreases due to system aging, changes in ambient temperature, or continuous operation, the dynamically adjusted power range can shrink in time, avoiding the issuance of unexecutable power commands, thereby preventing control failure or system oscillation. This dynamic boundary setting method improves the adaptability of the control strategy to system performance fluctuations, ensuring that the thermal management system can operate stably and efficiently under various operating conditions.

[0015] In one possible embodiment, the objective function satisfies the following formula: ; in, The weighting coefficients used to represent the first loss term. Used to represent the first loss term, Used to indicate the length of the preset time window. Used to represent the equivalent time cost at time k. The weighting coefficients used to represent the second loss term, Used to represent the second loss term, Used to represent the temperature of the battery at time k. Used to indicate the initial battery temperature The weighting coefficients used to represent the third loss term. Used to represent the third loss term.

[0016] By constructing an objective function formula that includes three losses—equivalent time cost, accumulated temperature deviation, and accumulated capacity decay—a comprehensive quantitative evaluation of the charging process across multiple dimensions can be achieved. The first loss term, by accumulating the equivalent time cost at each moment, directly relates to the total charging time, guiding the optimization algorithm to shorten charging time. The second loss term, by accumulating the deviation of battery temperature from the initial temperature at each moment, quantifies the risks of thermal shock and sustained high temperatures during charging, guiding the algorithm to suppress temperature rise. The third loss term, by accumulating capacity decay, directly relates to battery life loss, guiding the algorithm to reduce irreversible chemical damage. Each of the three loss terms is multiplied by its corresponding weighting coefficient, allowing the optimization objective to flexibly balance different dimensions according to actual needs. This mathematical expression clearly transforms charging efficiency, thermal safety, and durability into calculable numerical indicators, enabling the solver to accurately search for the current and power sequence that achieves the optimal balance among the three, thereby maximizing charging efficiency and extending battery life while ensuring safety.

[0017] In one possible embodiment, when the vehicle is in fast charging mode, the weight coefficient of the first loss term is greater than the weight coefficients of the second and third loss terms; when the vehicle is in temperature control mode, the weight coefficient of the second loss term is greater than the weight coefficients of the first and third loss terms; and when the vehicle is in long-life mode, the weight coefficient of the third loss term is greater than the weight coefficients of the first and second loss terms.

[0018] By dynamically adjusting the weight coefficients of various loss terms in the objective function according to different vehicle operating modes, scenario-based adaptive optimization of the charging strategy can be achieved. In fast charging mode, increasing the weight of the first loss term makes the optimization algorithm prioritize the shortest charging time, meeting users' urgent need for rapid energy replenishment. In temperature control mode, increasing the weight of the second loss term makes the algorithm prioritize the stability of battery temperature, ensuring that the battery can maintain a suitable operating temperature even under extreme ambient temperatures, preventing thermal runaway or low-temperature performance degradation. In long-life mode, increasing the weight of the third loss term makes the algorithm prioritize suppressing capacity decay, sacrificing some charging speed to maximize battery cycle life, suitable for scenarios with high durability requirements. This dynamic weight allocation mechanism allows the same control system to flexibly respond to diverse user needs and usage scenarios, achieving a smooth shift in performance focus without switching between different control architectures, significantly improving the intelligence level of the battery management system and user satisfaction.

[0019] In one possible implementation, the battery temperature at time k+1 in the predicted temperature sequence satisfies the following battery temperature rise model: ; in, Used to represent the temperature of the battery at time k+1. Used to represent the thermal inertia coefficient. Used to represent the temperature of the battery at time k. Used to represent the temperature rise gain coefficient. Used to represent the battery charging current at time k. Used to represent the battery resistance at time k. Used to represent the thermal conversion efficiency coefficient This represents the power demand value to be solved for the thermal management system at time k. Used to represent environmental bias constants.

[0020] By employing a battery temperature rise model formula that incorporates thermal inertia, internal resistance heat generation, thermal management power, and environmental bias, the battery's temperature evolution trend at future moments can be predicted with high accuracy. The thermal inertia coefficient in the formula reflects the hysteresis characteristic of battery temperature changes, avoiding instantaneous jumps in temperature prediction; the temperature rise gain coefficient, combining the product of the square of the current and the resistance, accurately quantifies the heat generated by Joule heating within the battery; the product of the thermal conversion efficiency coefficient and the actual executed power precisely describes the heating or cooling effect of the thermal management system on the battery; and the environmental bias constant compensates for the influence of ambient temperature on the battery's base temperature. This modeling approach, combining mechanism and data, comprehensively considers the key physical factors affecting battery temperature, ensuring that the predicted temperature at time k+1 closely matches the battery's actual thermal behavior. High-precision temperature prediction provides reliable feedback information for the optimization algorithm, ensuring that the charging and thermal management strategies based on this prediction effectively prevent temperature anomalies and improve the foresight and accuracy of control.

[0021] In one possible embodiment, the battery charging method further includes: when the thermal management system needs to switch from a standby state to an operating state, determining whether the thermal management system meets the startup conditions; if the thermal management system meets the startup conditions, then starting the thermal management system; wherein the startup conditions include: the standby time of the thermal management system is greater than or equal to a preset standby time; the power demand of the thermal management system is greater than or equal to a preset power activation threshold; the duration of the activation command of the thermal management system is greater than or equal to a preset activation duration; and / or; when the thermal management system needs to switch from an operating state to a standby state, determining whether the thermal management system meets the standby conditions; if the thermal management system meets the standby conditions, then shutting down the thermal management system; wherein the standby conditions include: the operating time of the thermal management system is greater than or equal to a preset operating time; the power demand of the thermal management system is greater than or equal to a preset power standby threshold; the duration of the activation command of the thermal management system is greater than or equal to a preset standby duration.

[0022] By introducing multiple judgment logics based on time, power threshold, and command duration during the thermal management system's state switching process, equipment wear and energy waste caused by frequent start-stop operations can be effectively prevented. When switching from standby to operating state, the thermal management system is only activated if the standby time, required power, and start command duration simultaneously or individually meet the activation conditions. This avoids invalid starts due to brief power fluctuations or erroneous command triggers, ensuring the necessity and stability of startup. Similarly, when switching from operating to standby state, a comprehensive judgment is made based on operating time, power level, and command duration to determine whether standby conditions are met, preventing the thermal management system from repeatedly oscillating near the critical power point. This state management mechanism with hysteresis and time filtering significantly reduces the number of actuator actions, extends the lifespan of the thermal management system hardware, and reduces additional energy consumption and noise caused by frequent start-stop operations, improving the smoothness and economy of the entire vehicle system.

[0023] Secondly, this application provides a charging control device, which includes: a sequence determination module, a function construction module, a charging control module, and a temperature control module.

[0024] The sequence determination module is used to determine the predicted SOC sequence based on the current sequence to be solved; the current sequence to be solved includes multiple current values ​​to be solved, which are used to represent the changes in battery charging current at multiple future times; the predicted SOC sequence is used to represent the changes in battery SOC at multiple future times.

[0025] The sequence determination module is used to determine the predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery. The power sequence to be solved includes multiple power demand values ​​to be solved, which are used to represent the changes in the operating power of the thermal management system when regulating the battery temperature at multiple future times. The predicted temperature sequence is used to represent the changes in the battery temperature at multiple future times.

[0026] The function construction module is used to construct an objective function based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes: a first loss term, a second loss term, and a third loss term. The first loss term is the loss term related to the estimated charging time of the battery. The second loss term is the loss term related to the battery temperature rise. The third loss term is the loss term related to the capacity decay of the battery during the charging process.

[0027] The charging control module is used to take the current sequence and power sequence to be solved after minimizing the objective function as the target current sequence and target power sequence, respectively.

[0028] The temperature control module is used to charge the battery based on the target current sequence and control the thermal management system to adjust the battery temperature based on the target power sequence.

[0029] Thirdly, this application provides a vehicle including a thermal management system, a battery, and a charging control device as described in the second aspect. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application will be described below.

[0031] Figure 1 This is a schematic diagram of the structure of a charging control system disclosed in an embodiment of this application; Figure 2 This is a schematic flowchart of a charging control method disclosed in an embodiment of this application; Figure 3 This is a schematic diagram illustrating an example of a SOC change curve during room temperature charging as disclosed in an embodiment of this application. Figure 4 This is a schematic diagram illustrating another example of the SOC change curve during room temperature charging disclosed in an embodiment of this application. Figure 5 This is a schematic diagram illustrating an example of a temperature change curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 6 This is a schematic diagram illustrating another example of a temperature change curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 7 This is a schematic diagram illustrating an example of a thermal management power variation curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 8 This is a schematic diagram illustrating another example of the thermal management power variation curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 9 This is a schematic diagram illustrating an example of a current variation curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 10 This is a schematic diagram illustrating another example of a current change curve during charging at room temperature, as disclosed in an embodiment of this application. Figure 11 This is a schematic diagram illustrating an example of a SOC change curve during low-temperature charging as disclosed in an embodiment of this application. Figure 12 This is a schematic diagram illustrating another example of a SOC change curve during low-temperature charging disclosed in an embodiment of this application. Figure 13 This is a schematic diagram illustrating an example of a temperature change curve during low-temperature charging as disclosed in an embodiment of this application. Figure 14This is a schematic diagram illustrating another example of a temperature change curve during low-temperature charging disclosed in this application. Figure 15 This is a schematic diagram illustrating an example of a thermal management power variation curve during low-temperature charging disclosed in an embodiment of this application. Figure 16 This is a schematic diagram illustrating another example of a thermal management power variation curve during low-temperature charging disclosed in an embodiment of this application. Figure 17 This is a schematic diagram illustrating an example of a current change curve during low-temperature charging disclosed in an embodiment of this application; Figure 18 This is a schematic diagram illustrating another example of a current change curve during low-temperature charging disclosed in an embodiment of this application; Figure 19 This is a schematic diagram illustrating an example of a SOC change curve during high-temperature charging as disclosed in an embodiment of this application. Figure 20 This is a schematic diagram illustrating another example of a SOC change curve during high-temperature charging disclosed in an embodiment of this application. Figure 21 This is a schematic diagram illustrating an example of a temperature change curve during high-temperature charging as disclosed in an embodiment of this application. Figure 22 This is a schematic diagram illustrating another example of a temperature change curve during high-temperature charging disclosed in an embodiment of this application. Figure 23 This is a schematic diagram illustrating an example of a thermal management power variation curve during high-temperature charging as disclosed in an embodiment of this application. Figure 24 This is a schematic diagram illustrating another example of a thermal management power variation curve during high-temperature charging disclosed in an embodiment of this application. Figure 25 This is a schematic diagram illustrating an example of a current change curve during high-temperature charging as disclosed in an embodiment of this application. Figure 26 This is a schematic diagram illustrating another example of a current change curve during high-temperature charging disclosed in an embodiment of this application. Figure 27 This is a schematic diagram of the structure of a charging control device disclosed in an embodiment of this application; Figure 28 This is a schematic diagram of another charging control device disclosed in an embodiment of this application. Detailed Implementation

[0032] The terms “first,” “second,” etc., are used for descriptive purposes only and have no sequential or technical meaning, nor should they be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0033] In the embodiments of this application, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0034] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0035] Furthermore, in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0036] This application provides a charging control method. In this method, a predicted State of Charge (SOC) sequence is determined based on the current sequence to be solved. This allows for the extrapolation of the battery's state of charge at multiple future moments based on a preset current change trend, thereby determining the battery's charge accumulation during continuous charging. Subsequently, a predicted temperature sequence is determined based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature. This allows for the prediction of the battery's temperature trajectory at future moments based on the battery's temperature changes during charging, the heat exchange caused by the thermal management system's power adjustment, the battery's charge accumulation during continuous charging, and the current ambient temperature, thus generating temperature change data that more closely matches the battery's actual thermal behavior. Finally, an objective function is constructed based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes a first loss term related to the estimated charging time, a second loss term related to battery temperature rise, and a third loss term related to capacity decay. By minimizing the objective function, a balance can be achieved between the current sequence and power sequence to be solved, which are in terms of charging efficiency, temperature safety, and lifespan maintenance. This ensures that the battery temperature is within a suitable range while achieving relatively efficient charging, reducing charging time, and minimizing battery capacity loss during long-term cycling, thereby improving the battery's adaptability and stability under complex operating conditions.

[0037] The implementation environment of the embodiments of this application is described below.

[0038] For example, such as Figure 1 The diagram shows a schematic representation of a charging control system. This system includes a charging control unit, a thermal management system, a battery, and a charging device. The charging control unit is connected to the thermal management system, the battery, and the charging device, respectively. The battery is connected to both the thermal management system and the charging device.

[0039] The charging control device can be a controller within the vehicle. The vehicle can be, but is not limited to, a pure electric vehicle (PEV / BEV), a hybrid electric vehicle (HEV), a range-extended electric vehicle (REEV), a plug-in hybrid electric vehicle (PHEV), or a new energy vehicle (NEV). This application does not limit the specific form of the vehicle. Alternatively, the charging control device can also be an external server connected to the vehicle, or a server cluster consisting of multiple external servers. In some implementations, the server cluster can be a distributed cluster server. This application does not impose any limitations in this regard.

[0040] The battery can be a power battery pack in a vehicle. Alternatively, the battery can be an external mobile energy storage unit detachably connected to the vehicle, or a battery pack system composed of multiple battery modules; this application embodiment does not impose any limitations on this. The charging device can be an on-board charger in the vehicle. Alternatively, the charging device can be an external charging pile connected to the vehicle, or a charging station network composed of multiple charging piles; this application embodiment does not impose any limitations on this.

[0041] In this embodiment, the charging control device is configured to determine a predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted state of charge (SOC) sequence, and the ambient temperature of the battery. The charging control device is further configured to construct an objective function based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence, and to use the current sequence to be solved and the power sequence to be solved obtained by minimizing the objective function as the target current sequence and the target power sequence, respectively. The charging control device is further configured to generate a charging command based on the target current sequence and send the charging command to the charging device, the charging command including the charging current value at the next moment. The charging control device is further configured to generate a power control command based on the target power sequence and send the power control command to the thermal management system, the power control command including the required power value at the next moment.

[0042] In this embodiment of the application, a thermal management system is configured to, in response to receiving a power control command, execute the required power value for the next moment to adjust the temperature of the battery.

[0043] In this embodiment of the application, the charging device is configured to, in response to receiving a charging command, execute the charging current value at the next moment to charge the battery.

[0044] It should be noted that the system architecture and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0045] The charging control method provided in this application can be applied to a charging control device, specifically to the processor of the charging control device. This application uses a controller in a vehicle executing the charging control method as an example to illustrate the charging control method provided in this application.

[0046] like Figure 2 As shown, this application provides a charging control method, which includes: S201. Determine the predicted SOC sequence based on the current sequence to be solved.

[0047] The unsolved current sequence includes multiple current values ​​to represent the changes in battery charging current at multiple future time points. The predicted SOC sequence represents the changes in battery SOC at multiple future time points. Current is a direct physical quantity that causes changes in the amount of charge inside the battery, while SOC characterizes the battery's current state of remaining charge. Since the future SOC evolution trajectory of the battery depends entirely on the initial state and the charging and discharging behavior in subsequent time periods, the predicted SOC sequence can be determined based on the unsolved current sequence.

[0048] As one possible implementation, S201 includes: establishing a SOC state transition equation based on the current SOC and the current sequence to be solved; solving the SOC state transition equation to obtain the predicted SOC sequence. The SOC state transition equation reflects the relationship between the current state of charge and the influence of the input current on the state of charge at the next moment.

[0049] For example, the SOC state transition equation satisfies Formula 1.

[0050] Formula 1.

[0051] Among them, SOCk+1 Used to represent the SOC at time k+1, SOC k Used to represent the SOC at time k. The value is used to indicate the battery's rated capacity, k is used to indicate the time index, and I... k Used to represent the charging current at time k.

[0052] S202. Based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery, determine the predicted temperature sequence.

[0053] The power sequence to be solved includes multiple demand power values ​​to be solved, which are used to represent the changes in the operating power of the thermal management system when regulating the battery temperature at multiple future times; the predicted temperature sequence is used to represent the changes in the battery temperature at multiple future times.

[0054] The future temperature evolution of a battery is essentially the cumulative result of the dynamic balance between internal heat generation and external heat dissipation. Accurate prediction of this state requires a complete definition of the key boundaries of energy generation, parameter correction, and heat dissipation. Current and power, as direct drivers of heat generation, determine the intensity of Joule heating and quantify the additional thermal effect of electrochemical polarization, together determining the instantaneous total heat generation rate. State of Charge (SOC) is a key variable for improving the accuracy of heat generation calculations. Given the significant nonlinearity of battery internal resistance with charge, real-time SOC can map an accurate internal resistance value, thereby reducing heat generation estimation errors caused by parameter drift. Ambient temperature constitutes the physical boundary of the heat dissipation process; its value directly determines the temperature difference driving potential between the battery and the environment, thus affecting the efficiency of heat dissipation to the environment. Therefore, current and power determine the scale of battery heat generation, SOC determines the battery's heat generation baseline, and ambient temperature determines the battery's heat dissipation efficiency. By combining current, power, SOC, and ambient temperature, the future battery temperature can be predicted. Therefore, the predicted temperature sequence can be determined by combining the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery.

[0055] As one possible implementation, S202 includes: determining the predicted battery resistance sequence based on the predicted SOC sequence; establishing a battery temperature rise model based on the thermal inertia coefficient, the current battery temperature, the temperature rise gain coefficient, the current sequence to be solved, the thermal conversion efficiency coefficient, the power sequence to be solved, the predicted battery resistance sequence, and the ambient temperature of the battery; and solving the battery temperature rise model to obtain the predicted temperature sequence.

[0056] Among them, the battery temperature rise model is used to reflect the Joule heating effect of the input current on the battery internal resistance, the convective heat transfer process between the battery and the environment, and the influence of the battery's own thermal capacity characteristics on the dynamic changes of battery temperature.

[0057] As a feasible implementation method, the battery temperature at time k+1 satisfies the following formula 2 (i.e., battery temperature rise model).

[0058] Formula 2.

[0059] in, Used to represent the temperature of the battery at time k+1. Used to represent the thermal inertia coefficient. Used to represent the temperature of the battery at time k. Used to represent the temperature rise gain coefficient. Used to represent the battery charging current at time k. Used to represent the battery resistance at time k. Used to represent the thermal conversion efficiency coefficient This represents the power demand value to be solved for the thermal management system at time k. Used to represent environmental bias constants.

[0060] The thermal inertia coefficient satisfies Formula 3.

[0061] Formula 3.

[0062] in, Used to indicate the sampling period, Used to indicate battery heat capacity Used to indicate the internal resistance of a battery.

[0063] The temperature rise gain coefficient satisfies Formula 4.

[0064] Formula 4.

[0065] Among them, the environmental bias constant satisfies Formula 5.

[0066] Formula 5.

[0067] in, Used to indicate ambient temperature.

[0068] By employing a battery temperature rise model formula that incorporates thermal inertia, internal resistance heat generation, thermal management power, and environmental bias, the battery's temperature evolution trend at future moments can be predicted with high accuracy. The thermal inertia coefficient in the formula reflects the hysteresis characteristic of battery temperature changes, avoiding instantaneous jumps in temperature prediction; the temperature rise gain coefficient, combining the product of the square of the current and the resistance, accurately quantifies the heat generated by Joule heating within the battery; the product of the thermal conversion efficiency coefficient and the actual executed power precisely describes the heating or cooling effect of the thermal management system on the battery; and the environmental bias constant compensates for the influence of ambient temperature on the battery's base temperature. This modeling approach, combining mechanism and data, comprehensively considers the key physical factors affecting battery temperature, ensuring that the predicted temperature at time k+1 closely matches the battery's actual thermal behavior. High-precision temperature prediction provides reliable feedback information for the optimization algorithm, ensuring that the charging and thermal management strategies based on this prediction effectively prevent temperature anomalies and improve the foresight and accuracy of control.

[0069] S203. Construct an objective function based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence.

[0070] The objective function includes: a first loss term, a second loss term, and a third loss term; the first loss term is the loss term related to the estimated charging time of the battery; the second loss term is the loss term related to the battery temperature rise; and the third loss term is the loss term related to the capacity decay of the battery during the charging process.

[0071] As a feasible implementation method, the objective function satisfies Formula 6.

[0072] Formula Six.

[0073] in, Used to represent the minimization objective function. The weighting coefficients used to represent the first loss term. Used to represent the first loss term (i.e., the equivalent time cost at time k). Used to indicate the length of the preset time window. The weighting coefficients used to represent the second loss term, Used to represent the second loss term, Used to represent the temperature of the battery at time k. Used to indicate the initial battery temperature The weighting coefficients used to represent the third loss term. Used to represent the third loss term.

[0074] Thus, by constructing an optimization objective function that includes the first loss term, the second loss term, and the third loss term, the comprehensive cost of charging time, battery temperature change, and battery capacity loss can be taken into account.

[0075] For example, the first loss term satisfies Formula 7.

[0076] Formula 7.

[0077] Among them, t k T is used to represent the equivalent time cost of each step in the prediction time domain. s Used to indicate sampling time, SOC inc,k I is used to represent the charging current at time k in the prediction time domain. k The increase in SOC is the value at which the battery capacity is represented by C.

[0078] For example, the third loss term satisfies Formula 8.

[0079] Formula 8.

[0080] in, Used to represent aging weight, which reflects the degree of influence of SOC at time k in the prediction time domain on the aging rate; Used to indicate the initial aging offset; Used to represent electrochemical aging kinetics, reflecting the degree of influence of temperature and current on the battery aging reaction rate; Used to represent the battery resistance at time k in the prediction time domain. Used to represent the battery temperature at time k in the prediction time domain. Used to represent the charging current at time k in the prediction time domain. Used to indicate the sampling period.

[0081] For example, the aging weights satisfy Formula Nine.

[0082] Formula Nine.

[0083] Specifically, when the SOC is less than or equal to 0.45, the aging weight is 1287.6. When the SOC is greater than 0.45, the aging weight is 1385.5.

[0084] For example, the initial aging offset satisfies Formula 10.

[0085] Formula 10.

[0086] Specifically, when the SOC is less than or equal to 0.45, the initial aging offset value is 6356.3. When the SOC is greater than 0.45, the initial aging offset value is 4193.2.

[0087] By constructing an objective function formula that includes three losses—equivalent time cost, accumulated temperature deviation, and accumulated capacity decay—a comprehensive quantitative evaluation of the charging process across multiple dimensions can be achieved. The first loss term, by accumulating the equivalent time cost at each moment, directly relates to the total charging time, guiding the optimization algorithm to shorten charging time. The second loss term, by accumulating the deviation of battery temperature from the initial temperature at each moment, quantifies the risks of thermal shock and sustained high temperatures during charging, guiding the algorithm to suppress temperature rise. The third loss term, by accumulating capacity decay, directly relates to battery life loss, guiding the algorithm to reduce irreversible chemical damage. Each of the three loss terms is multiplied by its corresponding weighting coefficient, allowing the optimization objective to flexibly balance different dimensions according to actual needs. This mathematical expression clearly transforms charging efficiency, thermal safety, and durability into calculable numerical indicators, enabling the solver to accurately search for the current and power sequence that achieves the optimal balance among the three, thereby maximizing charging efficiency and extending battery life while ensuring safety.

[0088] In some embodiments, passengers can select different charging modes via the in-vehicle infotainment system buttons to adjust the weighting coefficients of the first, second, and third loss terms, thereby achieving optimization for different objectives and meeting the charging needs of different passengers.

[0089] As a feasible implementation method, when the vehicle is in fast charging mode, the weight coefficient of the first loss term is greater than the weight coefficients of the second and third loss terms; when the vehicle is in temperature control mode, the weight coefficient of the second loss term is greater than the weight coefficients of the first and third loss terms; and when the vehicle is in long-life mode, the weight coefficient of the third loss term is greater than the weight coefficients of the first and second loss terms. Thus, in fast charging mode, charging time can be minimized; and in long-life mode, battery capacity degradation and fast charging time and capacity can be balanced, reducing battery capacity loss during fast charging.

[0090] By dynamically adjusting the weight coefficients of various loss terms in the objective function according to different vehicle operating modes, scenario-based adaptive optimization of the charging strategy can be achieved. In fast charging mode, increasing the weight of the first loss term makes the optimization algorithm prioritize the shortest charging time, meeting users' urgent need for rapid energy replenishment. In temperature control mode, increasing the weight of the second loss term makes the algorithm prioritize the stability of battery temperature, ensuring that the battery can maintain a suitable operating temperature even under extreme ambient temperatures, preventing thermal runaway or low-temperature performance degradation. In long-life mode, increasing the weight of the third loss term makes the algorithm prioritize suppressing capacity decay, sacrificing some charging speed to maximize battery cycle life, suitable for scenarios with high durability requirements. This dynamic weight allocation mechanism allows the same control system to flexibly respond to diverse user needs and usage scenarios, achieving a smooth shift in performance focus without switching between different control architectures, significantly improving the intelligence level of the battery management system and user satisfaction.

[0091] S204. The current sequence and power sequence to be solved obtained after minimizing the objective function are taken as the target current sequence and target power sequence, respectively.

[0092] As a feasible implementation method, S204 includes: S301. Construct preset constraints.

[0093] The preset constraints include at least one of the following: power constraint, current constraint, temperature constraint, or SOC constraint; the power constraint is used to constrain the operating power of the thermal management system when regulating the battery temperature to be within a preset power range; the current constraint is used to constrain the charging current of the battery to be within a preset current range when charging the battery; the temperature constraint is used to constrain the temperature of the battery to be within a preset temperature range when charging the battery; and the SOC constraint is used to constrain the SOC of the battery to be within a preset SOC range when charging the battery.

[0094] The preset power range is as follows: the operating power is less than or equal to the upper limit of the target power, and the operating power is greater than or equal to the lower limit of the target power; the lower limit of the target power is less than the upper limit of the target power. The lower limit of the target power can be -50 kW, -30 kW, -20 kW, -10 kW, or 0, and the upper limit of the target power can be 100 kW, 80 kW, 50 kW, 20 kW, or 10 kW. This application does not impose any restrictions on the specific values ​​of the lower and upper limits of the target power.

[0095] The preset current range is as follows: the charging current is less than or equal to the upper limit of the target current, and the charging current is greater than or equal to the lower limit of the target current; the lower limit of the target current is less than the upper limit of the target current. The lower limit of the target current can be 0 amperes (A), 1A, 2A, 3A, or 4A, and the upper limit of the target current can be 200A, 150A, 100A, 50A, or 20A. This application does not limit the specific values ​​of the lower limit and the upper limit of the target current.

[0096] The preset temperature range is as follows: the battery temperature is less than or equal to the upper limit of the target temperature, and the battery temperature is greater than or equal to the lower limit of the target temperature; the lower limit of the target temperature is less than the upper limit of the target temperature. The lower limit of the target temperature can be -30 degrees Celsius (°C), -10°C, 0°C, 10°C, or 20°C, or 243.15 Kelvin (K), 263.15 K, 273.15 K, 283.15 K, or 293.15 K; the upper limit of the target temperature can be 60°C, 55°C, 50°C, 45°C, or 40°C, or 333.15 K, 328.15 K, 323.15 K, 318.15 K, or 313.15 K. This application does not limit the specific values ​​of the lower and upper limits of the target temperature.

[0097] The preset power range is as follows: the battery's SOC is less than or equal to the target SOC upper limit, and the battery's SOC is greater than or equal to the target SOC lower limit; the target SOC lower limit is less than the target SOC upper limit. The target SOC lower limit can be 0%, 5%, 10%, 20%, or 30%, and the target SOC upper limit can be 100%, 95%, 90%, 80%, or 70%. This application does not limit the specific values ​​of the target SOC lower limit and the target SOC upper limit.

[0098] For example, the power constraint is: the power of the thermal management system is greater than or equal to -20kW and less than or equal to 50kW. The current constraint is: the charging current is greater than or equal to 0 and less than or equal to 50A; the temperature constraint is: the battery temperature is greater than or equal to 263.15K and less than or equal to 313.15K; the SOC constraint is: the SOC is greater than or equal to 0 and less than or equal to 100%.

[0099] As one possible implementation, the target current upper limit is determined as follows: based on a preset current correspondence, the preset current threshold corresponding to the current SOC and the current battery temperature is determined as the target current upper limit.

[0100] The preset current correspondence refers to the relationship between the preset current threshold, the preset SOC, and the preset battery temperature. This preset current correspondence can be determined through experimental testing (e.g., by recording the maximum charging current at different battery temperatures and SOCs using actual cell data).

[0101] For example, the upper limit of the target current satisfies Formula 11.

[0102] Formula 11.

[0103] in, Used to indicate the upper limit of the target current. Used to indicate the preset current correspondence.

[0104] S302. Minimize the objective function under preset constraints, and use the current sequence and power sequence obtained by the solution as the target current sequence and target power sequence, respectively.

[0105] As can be seen from S301-S302, the solution provided in this application, by introducing preset constraints including at least one of power, current, temperature, and SOC during the minimization of the objective function, ensures that the obtained target current sequence and target power sequence are strictly within the safe operating boundaries of the battery and thermal management system. The power constraint prevents the thermal management system from exceeding its hardware capacity limits, avoiding equipment overload damage; the current constraint limits the charging current to within the battery's allowable range, preventing lithium plating or overheating; the temperature constraint directly controls the battery temperature within a preset safe range, eliminating the risk of thermal runaway; and the SOC constraint avoids overcharging or over-discharging, protecting the battery's chemical activity. This constrained optimization mechanism ensures that the final generated control strategy not only achieves a mathematically optimal balance between charging efficiency, temperature rise control, and lifespan maintenance, but also guarantees absolute system safety at the physical level. It prevents the optimization algorithm from generating dangerous instructions that violate physical limits in pursuit of a single indicator (such as the shortest charging time), thereby significantly improving the safety and reliability of the charging process.

[0106] S205. Charge the battery based on the target current sequence, and control the thermal management system to adjust the battery temperature based on the target power sequence.

[0107] As one possible implementation, S205 includes: determining a target charging current based on a target current sequence, wherein the target charging current is the battery charging current at the next moment in the current sequence to be solved; determining a target power of the thermal management system based on a target power sequence, wherein the target power is the required power value at the next moment in the power sequence to be solved; charging the battery based on the target charging current; and controlling the thermal management system to adjust the battery temperature based on the target power.

[0108] For example, the target current sequence is The target power sequence is The target current is Target power is .

[0109] This allows for state control during battery charging. For example, in fast charging mode, by adjusting the current and thermal management power, the battery can adaptively find the high-rate fast charging region from its initial state to the target SOC.

[0110] As can be seen from S201-S205, the solution provided in this application determines the predicted SOC sequence based on the current sequence to be solved, and can extrapolate the battery's state of charge at multiple future moments based on a preset current change trend, thereby determining the battery's charge accumulation during continuous charging. Subsequently, a predicted temperature sequence is determined based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature. This sequence can predict the battery's temperature change trajectory at future moments based on the battery's temperature change during charging, the heat exchange caused by the thermal management system's power adjustment, the battery's charge accumulation during continuous charging, and the current ambient temperature, thereby generating temperature change data that better matches the battery's actual thermal behavior. Subsequently, an objective function is constructed based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes a first loss term related to the estimated charging time, a second loss term related to the battery's temperature rise, and a third loss term related to capacity decay. By minimizing the objective function, a balance can be achieved between the current sequence and power sequence to be solved, which are in terms of charging efficiency, temperature safety, and lifespan maintenance. This ensures that the battery temperature is within a suitable range while achieving relatively efficient charging, reducing charging time, and minimizing battery capacity loss during long-term cycling, thereby improving the battery's adaptability and stability under complex operating conditions.

[0111] As a feasible implementation method, S202 includes: S401. For the demand power value to be solved at time k in the current sequence to be solved, the predicted actual execution power to be solved is determined based on the power prediction model, so as to obtain multiple predicted actual execution powers to be solved.

[0112] Among them, the power prediction model is used to predict the actual power that the thermal management system can perform.

[0113] As a feasible implementation method, the power prediction model satisfies the following formula twelve: Formula twelve.

[0114] in, This represents the predicted actual power of the thermal management system to be solved at time k, where N represents the prediction time domain and i represents the time step index variable. The parameters are used to represent the first power model parameters, indicating the degree of influence of the predicted actual execution power at time ki on the predicted actual execution power to be solved at time k. Used to represent the predicted actual execution power at time ki. This is used to represent the parameters of the second power model, indicating the response speed of the demand power value at time k to the predicted actual execution power at time k. Used to represent the power demand value at time k; It is positively correlated with the time step index variable.

[0115] By constructing a power prediction model using a formula that incorporates historical power feedback and current demand power response, the dynamic inertial characteristics of the power execution of a thermal management system can be quantified. The formula utilizes multiple historical moments within the prediction time domain to predict the actual power execution and their corresponding influence weights, reflecting the system output's dependence on past states. Furthermore, the design of a positive correlation between the influence weights and the time step index variable ensures that historical data closer to the current moment has a greater impact on the current prediction result, consistent with the recent-state-dominated nature of the thermal management system's physical response. Simultaneously, the introduction of a demand power response rate coefficient accurately describes the system's immediate response capability upon receiving new instructions. This modeling approach ensures that the predicted actual power execution at the k-th moment not only considers the current theoretical demand but also fully integrates the system's historical operating trajectory and dynamic response characteristics. This effectively eliminates power prediction abrupt changes or deviations caused by neglecting system inertia, providing smoother and more physically realistic power input data for subsequent temperature predictions and improving the robustness of the overall control strategy under dynamic conditions.

[0116] In some embodiments, the parameters of the first power model satisfy the normalization constraint. That is, the N parameters of the first power model satisfy Formula Thirteen.

[0117] Formula Thirteen.

[0118] In some embodiments, the first power model parameter and the second power model parameter in the power prediction model can be determined by: acquiring historical expected power data and historical actual power data; and performing parameter identification on the power prediction model based on the historical expected power data and historical actual power data to determine the first power model parameter and the second power model parameter in the power prediction model.

[0119] Among them, historical expected power data is used to represent the changes in the power demand of the thermal management system for battery temperature regulation at multiple past moments, and historical actual power data is used to represent the changes in the actual output power of the thermal management system for battery temperature regulation at multiple past moments.

[0120] For example, during battery charging, historical data from the previous N moments can be collected and stored in real time, forming a sliding time window. The historical expected power data is as follows: Historical actual power data is as follows: .

[0121] When identifying parameters of a power prediction model based on historical expected power data and historical actual power data, the parameters can be identified using either the least squares method or the gradient descent method. This application does not limit the specific algorithm used for parameter identification of the power prediction model.

[0122] In some embodiments, the first power model parameters and the second power model parameters can be updated online.

[0123] As one possible implementation, after determining the first power model parameters and the second power model parameters, the first power model parameters and the second power model parameters can be updated based on the real-time operating data of the thermal management system to obtain the updated first power model parameters and the updated second power model parameters.

[0124] In this way, the output value of the power prediction model at the next moment can be predicted based on the updated first power model parameters and the updated second power model parameters, so as to realize rolling prediction and dynamic correction of the power prediction model.

[0125] S402. Based on multiple unsolved predicted actual execution power and delay correction models, determine the target actual power sequence.

[0126] The target actual power sequence includes: multiple target actual execution powers to be solved; a delay correction model to predict the actual power that the thermal management system can execute under the influence of thermal management response delay; and a thermal management response delay to indicate the time it takes for the thermal management system to adjust its power from the power at the first moment to the required power at the second moment, where the second moment is the next moment adjacent to the first moment. The first moment is any moment within the prediction time domain.

[0127] As a feasible implementation method, the delay correction model satisfies the following formula fourteen: Formula Fourteen.

[0128] in, This is used to represent the actual execution power of the target to be solved for in the thermal management system at time k. The response coefficient used to represent the thermal management system is greater than 0 and less than 1. Used to represent the predicted actual power to be solved for the thermal management system at time k; This is used to represent the actual execution power of the target to be solved in the thermal management system at time k-1.

[0129] By employing a delay correction model in the form of a first-order inertial element, the response lag phenomenon in the power adjustment process of the thermal management system can be accurately simulated. This formula uses a response coefficient to weight and fuse the predicted actual power at the current moment with the target actual power at the previous moment, essentially constructing a low-pass filtering mechanism. This transforms theoretically possible step power changes into actual execution trajectories with smooth transition characteristics. This approach realistically reflects the time required for the thermal management device to adjust from the power state at the first moment to the required power state at the second moment, avoiding temperature prediction overshoot or oscillations caused by assumed instantaneous power jumps. The resulting target actual power sequence is highly synchronized with the actual heat flow changes of the thermal system in the time dimension, significantly improving the accuracy of temperature prediction under transient conditions. This allows charging strategies optimized based on this sequence to more effectively avoid the risk of temperature runaway caused by thermal response delays, ensuring that the battery temperature remains within a safe and controllable range.

[0130] In some embodiments, the Battery Management System (BMS) can pre-set the response coefficient of the thermal management system based on the way the thermal management system adjusts the battery temperature (heating or cooling), and obtain the correspondence between the preset response coefficient and the preset temperature adjustment method.

[0131] As one possible implementation, the response coefficient of the thermal management system is determined as follows: based on the preset coefficient correspondence, the preset response coefficient corresponding to the current temperature adjustment mode of the thermal management system is determined as the response coefficient of the thermal management system.

[0132] For example, if the current temperature adjustment method of the thermal management system is to heat the PTC inside the battery, then the corresponding preset response coefficient is determined to be 0.95, that is, the response coefficient of the thermal management system is 0.95; if the current temperature adjustment method of the thermal management system is liquid cooling or direct cooling, then the corresponding preset response coefficient is determined to be 0.7, that is, the response coefficient of the thermal management system is 0.7.

[0133] S403. Based on the current sequence to be solved, the target actual power sequence, the predicted SOC sequence, and the ambient temperature of the battery, determine the predicted temperature sequence.

[0134] As can be seen from S401-S403, the solution provided in this application, in determining the predicted temperature sequence, firstly, for the demand power value to be solved at time k in the current sequence to be solved, the predicted actual execution power to be solved is determined based on the power prediction model. This allows for the quantification of the upper limit of the system's actual output power and efficiency characteristics based on the current hardware state and environmental conditions of the thermal management system, thereby eliminating deviations between theoretical demand and actual execution caused by actuator saturation or nonlinear characteristics, ensuring that the energy data input to the temperature calculation stage conforms to physical reality. Subsequently, based on multiple predicted actual execution powers to be solved and a delay correction model, the target actual power sequence is determined. This can simulate the response lag phenomenon in the thermal management system during the adjustment of power from the first time to the demand power at the second time, transforming the idealized step power command into an actual execution trajectory with smooth transition characteristics. This effectively captures transient details in the heat flow change process and avoids temperature prediction abrupt changes or distortions caused by ignoring dynamic response time. Finally, based on the current sequence to be solved, the target actual power sequence after the above two steps of correction, the predicted SOC sequence, and the ambient temperature of the battery, a predicted temperature sequence is determined. This sequence can comprehensively consider the heat generated by the battery's internal resistance, the actual heat dissipation or heating capacity of the thermal management system, and the environmental thermal boundary conditions to generate temperature change data that closely approximates the actual temperature rise or fall trend of the battery. This high-precision temperature prediction result serves as a key input for constructing the objective function, significantly improving the computational reliability of the second loss term related to battery temperature rise and the third loss term related to capacity decay. This allows the target current sequence and target power sequence obtained by minimizing the objective function to more accurately balance charging speed, temperature safety, and battery life while fully considering the limitations and response delays of thermal management. It prevents the control strategy from being too conservative or aggressive due to temperature prediction deviations, thereby better maintaining the battery within its optimal operating temperature range during actual charging, extending battery life, and improving the safety of the charging process.

[0135] In some embodiments, the power that the thermal management system can actually execute at time k can be compared with the maximum and minimum power that the thermal management system can physically execute to determine the preset power range of the thermal management system.

[0136] As a feasible implementation method, the preset constraints include power constraints; the preset power range is determined in the following way: S501. Input the demand power to be solved at time k and the historical actual power sequence into the power prediction model to obtain the predicted actual execution power at time k.

[0137] The historical actual power sequence represents the changes in the actual output power of the thermal management system when regulating the battery temperature over multiple past periods. The historical actual power sequence and the historical actual power data can be the same or different data, and can be set according to actual conditions; this application does not impose any restrictions on this.

[0138] S502. The smaller value between the predicted actual execution power at time k and the preset power upper limit threshold is determined as the target power upper limit value in the preset power range.

[0139] The preset power upper limit threshold is the physical maximum power limit of the thermal management system.

[0140] For example, the target power upper limit satisfies Formula 15.

[0141] Formula 15.

[0142] in, Used to indicate the upper limit of the target power. Used to indicate a preset power upper limit threshold. Used to represent the predicted actual execution power at time k.

[0143] S503. The larger of the predicted actual execution power at time k and the preset power lower limit threshold is determined as the target power lower limit value in the preset power range.

[0144] Among them, the preset power upper limit threshold is greater than the preset power lower limit threshold, and the preset power range is: the operating power is less than or equal to the target power upper limit value and the operating power is greater than or equal to the target power lower limit value.

[0145] The preset power lower limit threshold is the physical minimum power limit of the thermal management system.

[0146] For example, the target power lower limit satisfies Formula Sixteen.

[0147] Formula Sixteen.

[0148] in, Used to indicate the lower limit of the target power. Used to indicate the preset lower power threshold.

[0149] In some embodiments, the battery management system (BMS) requests thermal management power from the vehicle with a lag. Because the BMS typically has significant thermal inertia, it cannot respond to battery demands promptly, and temperature changes are delayed relative to power adjustments. Therefore, a prediction error margin can be added or reduced based on the predicted actual power output to compensate for the inherent thermal inertia and control lag of the BMS. This allows the BMS to appropriately increase the upper limit based on the current actual power level within a short period when there are sudden load changes or environmental disturbances, thereby improving the system's dynamic performance and energy utilization efficiency.

[0150] As one possible implementation, the target power upper limit is determined as follows: the actual corrected power is determined based on the predicted actual execution power and the prediction error margin at time k; the smaller value between the actual corrected power and the preset power upper limit threshold is determined as the target power upper limit within the preset power range.

[0151] For example, the target power upper limit satisfies Formula 17.

[0152] Formula 17.

[0153] in, Used to indicate the upper limit of the target power. Used to indicate a preset power upper limit threshold. Used to represent the predicted actual execution power at time k. Used to represent the prediction error margin.

[0154] As another possible implementation, the target power lower limit is determined by taking the larger of the actual corrected power and the preset power lower limit threshold as the target power lower limit within the preset power range.

[0155] For example, the target power lower limit satisfies Formula 18.

[0156] Formula 18.

[0157] in, Used to indicate the lower limit of the target power. Used to indicate the preset lower power threshold.

[0158] The target power upper limit is used as the upper limit constraint for thermal management power in MPC optimization, and the target power lower limit is used as the lower limit constraint for thermal management power in MPC optimization. The upper limit constraint and the lower limit constraint for thermal management power are substituted into the objective function predictive control optimization solution to ensure that the thermal management power obtained in the next moment does not exceed the power range that the vehicle thermal management system can currently respond to.

[0159] As can be seen from S501-S503, the solution provided in this application dynamically determines the upper and lower limits of the preset power range by combining the output of the power prediction model with preset thresholds, thereby enabling adaptive adjustment of the operating boundary of the thermal management system. This method first uses the power prediction model combined with historical actual power sequences to predict the power that the system can actually execute at the current moment. Then, it takes the smaller value between this prediction and a fixed preset power upper limit threshold as the target power upper limit, and the larger value between this prediction and a preset power lower limit threshold as the target power lower limit. This mechanism ensures that the power constraint range during the optimization process will neither exceed the absolute limits of the hardware design nor exceed the actual execution capability of the system under the current historical operating conditions. When the actual output capability of the system decreases due to aging, changes in ambient temperature, or continuous operation, the dynamically adjusted power range can shrink in time, avoiding the issuance of unexecutable power commands, thereby preventing control failure or system oscillation. This dynamic boundary setting method improves the adaptability of the control strategy to system performance fluctuations, ensuring that the thermal management system can operate stably and efficiently under various operating conditions.

[0160] In some embodiments, the model coefficients of the battery temperature rise model can be determined through offline identification.

[0161] As one possible approach, regression analysis of the experimental state-of-charge (SOC) data using the least squares method is employed to determine the model coefficients and baseline efficiency of the battery temperature rise model. The experimental SOC data, obtained experimentally, includes battery temperature, charging current, battery resistance, power, ambient temperature, and battery capacitance. The model coefficients of the battery temperature rise model include thermal inertia, temperature rise gain, and thermal conversion efficiency. Battery resistance can be determined by looking up tables based on SOC and battery temperature.

[0162] In this way, the model coefficients of the battery temperature rise model can be determined, thereby determining the battery temperature rise model.

[0163] In some embodiments, the model coefficients of the battery temperature rise model can be updated through online identification.

[0164] As one possible implementation, the charging control method further includes: iteratively updating the model coefficients of the battery temperature rise model based on real-time charging state data to obtain updated model coefficients. The real-time charging battery state data includes: real-time battery temperature data, real-time charging current data, real-time battery resistance data, real-time power data, real-time ambient temperature data, and real-time battery capacitance data.

[0165] When iteratively updating the model coefficients of the battery temperature rise model based on real-time charging state data, the model coefficients can be iteratively updated using the recursive least squares method with a forgetting factor, or using the gradient descent method. This application does not limit the specific algorithm for iteratively updating the model coefficients of the battery temperature rise model.

[0166] For example, the battery management system collects real-time battery temperature data, real-time charging current data, real-time SOC data, and real-time power data from the thermal management system. Based on the collected real-time data, it updates the battery's current state information and calculates the deviation between the actual state and the predicted state from the previous cycle. Then, based on the calculated state deviation, the model coefficients of the battery temperature rise model are updated (i.e., corrected online) to reduce model errors and improve the prediction accuracy for the next control cycle. In this way, the battery temperature rise model can adaptively adjust, reducing the error between the predicted and actual battery temperatures.

[0167] In some embodiments, based on the corrected model parameters and the updated battery state, a rolling optimization algorithm is executed to calculate the optimal thermal management control command for the current moment and issue it for execution. This completes the process of state update, coefficient update, and control command execution within a single operating cycle. Subsequently, the battery management system enters the next control cycle and repeats the above data acquisition, state update, coefficient update, and rolling optimization steps to form a continuous closed-loop control process until the battery reaches the target state of charge or completes full charging.

[0168] In some embodiments, to avoid frequent start-stop responses of the thermal management system for heating or cooling, reduce the lifespan loss of the vehicle or battery actuators, increase the energy consumption of the thermal management system, or affect the vehicle's noise, vibration, and harshness (NVH), the thermal management system can be turned on or off by determining whether it meets the start-stop conditions.

[0169] As a feasible implementation method, charging control methods also include: S601. When the thermal management system needs to switch from standby mode to running mode, determine whether the thermal management system meets the startup conditions.

[0170] The start-up conditions include: the standby time of the thermal management system is greater than or equal to the preset standby time; the power demand of the thermal management system is greater than or equal to the preset power start-up threshold; and the duration of the start-up command of the thermal management system is greater than or equal to the preset start-up duration.

[0171] S602. If the thermal management system meets the startup conditions, then the thermal management system will be started.

[0172] As can be seen from S601-S602, the solution provided in this application, by introducing multiple judgment logic based on time, power threshold, and command duration during the thermal management system state switching process, can effectively prevent equipment wear and energy waste caused by frequent start-stop of the thermal management system. When switching from standby state to running state, the thermal management system is only started when the standby time, required power, and start command duration simultaneously or individually meet the start conditions, avoiding invalid starts caused by brief power fluctuations or erroneous trigger commands, and ensuring the necessity and stability of the start-up.

[0173] As another feasible implementation method, the charging control method also includes: S701. When the thermal management system needs to switch from the running state to the standby state, determine whether the thermal management system meets the standby conditions.

[0174] The standby conditions include: the operating time of the thermal management system is greater than or equal to the preset operating time; the power demand of the thermal management system is greater than or equal to the preset power standby threshold; and the duration of the start command of the thermal management system is greater than or equal to the preset standby duration.

[0175] S702. If the thermal management system meets the standby conditions, then shut down the thermal management system.

[0176] As can be seen from S701-S702, the solution provided in this application, when switching from the running state to the standby state, also comprehensively judges whether the standby conditions are met based on the running time, power level, and command duration, preventing the thermal management system from repeatedly oscillating near the critical power point. This state management mechanism with hysteresis characteristics and time filtering significantly reduces the number of actuator actions, extends the service life of the thermal management system hardware, and reduces the additional energy consumption and noise caused by frequent start-stop, thereby improving the smoothness and economy of the entire vehicle system.

[0177] In some embodiments, the preset constraints further include: thermal management start-stop constraints, which are used to constrain the operating state of the thermal management system to either standby or on. The thermal management start-stop constraints include: if the thermal management system meets the start-up conditions, its operating state is on; if the thermal management system meets the standby conditions, its operating state is standby.

[0178] In some embodiments, the first element of the target current sequence corresponds to the target current at the current moment, and the first element of the target power sequence corresponds to the target power at the current moment. After obtaining the target current sequence and target power sequence, the first current value in the target current sequence is sent to the charger as the target current command at the current moment for charging, and the first power value in the target power sequence is sent to the thermal management system as the target power command at the current moment to drive the actuator. After one control cycle, the updated actual state of the battery is collected again, the time window is rolled forward by one step, the historical data already executed in the original sequence is discarded, and the optimization problem is reconstructed and solved based on the new initial state to generate new current and power sequences. This process is repeated in each control cycle, with state acquisition, rolling solution, command issuance, and execution feedback. By continuously correcting the prediction deviation, it is ensured that the battery always follows the dynamically optimized trajectory for charging and temperature control throughout the entire charging process.

[0179] This application employs a rolling optimization mechanism based on model predictive control, eliminating the need for pre-calculation of the global state space and offering significant advantages in terms of low computational complexity and strong real-time performance. In terms of specific control strategies, this application implements stepless control for demand current and thermal management, dynamically determining the optimal charging current and thermal management power demand during fast charging based on the real-time battery state, demonstrating stronger adaptability compared to rule-based strategies. Simultaneously, this application introduces a feedback correction module to correct battery model parameter deviations, thereby improving the algorithm's robustness under complex conditions such as battery aging and temperature variations. Furthermore, this application is widely applicable to various types of lithium-ion batteries, including ternary lithium batteries and lithium iron phosphate batteries, exhibiting excellent versatility. Compared to traditional solutions (such as rule-based thermal management strategies and demand-based fast charging methods), the technical solution of this application can improve battery charging speed and enhance battery charging performance.

[0180] For example, as shown in Table 1, it illustrates the completion charging time of the conventional solution and the technical solution of this application.

[0181] Table 1

[0182] Specifically, with the battery initially at room temperature (25°C) and 20% SOC, the conventional solution completes charging in 870 seconds, while the present application completes charging in 813 seconds, representing a 6.6% performance improvement. For details regarding the charging times of the conventional and present application solutions under other battery initial states, as well as the improved performance of the present application, please refer to the description of the charging times of the conventional and present application solutions at room temperature (25°C) and 20% SOC, and the improved performance of the present application; these details will not be repeated here.

[0183] Simulation comparisons show that the method proposed in this application is effective in improving the fast charging rate of batteries from 20% to 80% across multiple temperature ranges, with improvements of 6.6%, 19.2%, and 0.4% at normal, low, and high temperatures, respectively.

[0184] The following is combined Figures 3-10 This paper describes how the battery is charged under normal temperature conditions, as described in this application and in conventional methods.

[0185] For example, such as Figure 3 As shown, Figure 3 The graph shows the state of charge (SOC) of the battery under normal temperature conditions as a function of time. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value of the battery. As can be seen from the graph, the SOC starts to rise slowly from an initial value of about 0.2, then the rate of increase gradually accelerates, and after about 500 seconds it tends to level off, finally monotonically increasing to 0.8 at about 800 seconds.

[0186] For example, such as Figure 4 As shown, Figure 4 The graph shows the state of charge (SOC) of a conventional battery under normal temperature conditions as a function of time. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value of the battery. As can be seen from the graph, the SOC starts from an initial value of about 0.2 and steadily increases in an approximately linear manner, continuing to increase within the range of 0 to 800 seconds without any significant acceleration, and finally reaching 0.8 at around 800 seconds.

[0187] For example, such as Figure 5 As shown in the figure, this graph illustrates the temperature change of the battery of this application over time under normal temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents battery temperature (in °C). As can be seen from the figure, the battery temperature starts to rise continuously from between 20 °C and 30 °C, and the rate of increase slows down and tends to stabilize after about 300 seconds, eventually reaching a temperature above 40 °C.

[0188] For example, such as Figure 6 As shown in the figure, this graph illustrates the temperature change over time for a traditional battery solution under normal temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents battery temperature (in °C). The graph shows that the battery temperature begins to rise continuously from below 30 °C, but the rise is more linear and lacks a clear inflection point. The battery temperature reaches its highest point around 500 seconds, after which it first decreases and then rises again.

[0189] For example, such as Figure 7As shown in the figure, this graph illustrates the curve of the power (i.e., thermal management power) of the thermal management system of this application changing over time under normal temperature conditions. The horizontal axis represents time (unit: seconds), and the vertical axis represents the power of the thermal management system (unit: kW). As can be seen from the figure, the power of the thermal management system remains constant within the 0–100 second range, gradually increases slightly around 100 seconds, remains constant between 300 and 500 seconds, and exhibits smaller power variations after 500 seconds.

[0190] For example, such as Figure 8 As shown in the figure, this graph illustrates the power (i.e., thermal management power) of a conventional thermal management system under ambient temperature conditions as a function of time. The horizontal axis represents time (in seconds), and the vertical axis represents the power of the thermal management system (in kW). The graph shows that the thermal management system was not operating before 400 seconds, suddenly started operating around 500 seconds, causing a sharp increase in power, and then a sharp decrease in power around 700 seconds. After 700 seconds, the thermal management system remained inactive.

[0191] For example, such as Figure 9 As shown in the figure, this graph compares the relationship between the battery charging current and the maximum allowable battery charging current of this application under normal temperature conditions over time. The horizontal axis represents time (unit: seconds), and the vertical axis represents battery charging current (unit: A). The solid line represents the battery charging current, and the dashed line represents the maximum allowable battery charging current. As can be seen from the figure, the curve of the battery charging current of this application basically overlaps with the curve of the maximum allowable battery charging current, showing a step-decreasing trend. This indicates that the algorithm dynamically adjusts the charging / discharging current while ensuring safety, in order to achieve a balance between efficiency and lifespan.

[0192] For example, such as Figure 10 As shown in the figure, this graph illustrates the battery charging current limiting curve of a traditional solution under normal temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents battery charging current (in A). The graph reveals that the current fluctuates in a sawtooth pattern, rising and then decreasing in segments, without a smooth transition. This indicates that the rule-based strategy relies on preset thresholds for on / off control, which may lead to system response lag or insufficient energy utilization.

[0193] The following is combined Figures 11-18 This paper describes the charging process of the battery in this application and conventional methods under low-temperature conditions.

[0194] For example, such as Figure 11 As shown in the figure, this curve illustrates the change of the state of charge (SOC) of the battery in this application over time under low-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value. It can be seen from the figure that the slope of the tangent line to the SOC increases monotonically with time.

[0195] For example, such as Figure 12 As shown in the figure, this graph illustrates the change of SOC (State of Charge) of a conventional battery over time in a low-temperature environment. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value. The graph shows that the slope of the tangent line to the SOC initially increases with time, then gradually decreases.

[0196] For example, such as Figure 13 As shown in the figure, this curve illustrates the temperature change of the battery in this application over time under low-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents temperature (in °C). The figure shows that the battery temperature rises steadily and continuously from -20°C, with the slope of the curve remaining essentially constant, indicating a uniform rate of temperature increase. After reaching 40°C at approximately 1400 seconds, the battery temperature experiences a slight decrease followed by a rebound.

[0197] For example, such as Figure 14 As shown in the figure, this graph illustrates the temperature change over time for a traditional battery in a low-temperature environment. The horizontal axis represents time (in seconds), and the vertical axis represents temperature (in °C). The graph shows that the battery temperature initially rises from -20°C, reaching its peak at approximately 25°C after about 1000 seconds. After reaching its peak around 1000 seconds, the battery temperature stops rising, begins to slowly decrease, and eventually stabilizes at around 20°C.

[0198] For example, such as Figure 15 As shown in the figure, this graph illustrates the curve of the power (i.e., thermal management power) of the thermal management system of this application changing over time under low-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents the power of the thermal management system (in kW). As can be seen from the figure, the power of the thermal management system remains constant before 1200 seconds, begins to increase in a stepwise manner around 1400 seconds, reaches its peak at 1600 seconds, and then begins to decrease in a stepwise manner.

[0199] For example, such as Figure 16 As shown in the figure, this graph illustrates the change in power (i.e., thermal management power) of a conventional thermal management system over time in a low-temperature environment. The horizontal axis represents time (in seconds), and the vertical axis represents the power of the thermal management system (in kW). As can be seen from the graph, the power of the thermal management system remains constant until about 1000 seconds. Around 1000 seconds in, the power of the thermal management system jumps instantaneously to 0 kW and remains at 0 thereafter, indicating that the thermal management system is shut down.

[0200] For example, such as Figure 17As shown in the figure, this graph compares the relationship between the battery charging current and the maximum allowable battery charging current (i.e., the maximum allowable current) over time under low-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents the battery charging current (in A). The solid line represents the battery charging current of this application, and the dashed line represents the maximum allowable battery charging current. As can be seen from the figure, in the interval from 0 to 1200 seconds, both lines show a monotonically increasing trend. The solid line (i.e., the battery charging current) always closely follows the lower edge of the dashed line (i.e., the maximum allowable battery charging current), indicating that the battery charging current tracks and approaches the current thermal safety boundary of the battery in real time. Between 1200 and 1400 seconds, the battery charging current rapidly climbs to a peak plateau of approximately 200A. The solid and dashed lines highly overlap during this stage, showing that the system achieves continuous maximum power output under safety constraints. After 1400 seconds, affected by changes in battery state, the dashed line begins to decline in a stepped manner, and the solid line subsequently follows suit, maintaining a high degree of consistency between the actual load and the dynamic upper limit throughout the process.

[0201] For example, such as Figure 18 As shown in the figure, this graph illustrates the current limiting curve of a traditional solution under low-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents the battery charging current (in A). The graph shows that from 0 to 900 seconds, the battery charging current increases linearly and slowly. From 900 to 1000 seconds, the battery charging current undergoes a step-like abrupt change, rising sharply from approximately 120A to a peak of approximately 180A. From 1000 seconds onwards, battery charging enters a continuous, stepped decay mode, with the charging current periodically decreasing according to a preset logic, passing through multiple discrete levels, and finally stabilizing at a low level of approximately 80A after 2000 seconds.

[0202] The following is combined Figures 19-26 This paper describes how the battery is charged under high-temperature conditions, as described in this application and in conventional solutions.

[0203] For example, such as Figure 19 As shown in the figure, this curve illustrates the change of the state of charge (SOC) of the battery under high temperature conditions over time. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value. As can be seen from the figure, the initial SOC is 0.2, and then the SOC increases rapidly in an approximately linear manner from 0 to 800 seconds, before plateauing to a slope of approximately 0.8 after 800 seconds.

[0204] For example, such as Figure 20 As shown in the figure, this graph illustrates the SOC (State of Charge) curve of a traditional battery under high-temperature conditions as a function of time. The horizontal axis represents time (in seconds), and the vertical axis represents the SOC value. The graph shows that the initial SOC is 0.2, and then, within the range of 0 to 800 seconds, the SOC exhibits an approximately linear and rapid increase, before plateauing to a slope of approximately 0.8 after 800 seconds.

[0205] For example, such as Figure 21 As shown in the figure, this graph illustrates the temperature change of the battery of this application over time under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents temperature (in °C). As can be seen from the graph, the temperature rises rapidly from 40°C, reaches its peak at approximately 300 seconds, and then fluctuates and decreases.

[0206] For example, such as Figure 22 As shown in the figure, this graph illustrates the temperature change over time of a traditional battery under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents temperature (in °C). The graph shows that the temperature rises rapidly from 40°C, reaches its peak in approximately 200 seconds, and then drops rapidly.

[0207] For example, such as Figure 23 As shown in the figure, this curve illustrates the power variation of the thermal management system of this application over time under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents power (in kW). As can be seen from the figure, the power of the thermal management system remains at 4 kW from 0 to 100 seconds. Afterward, from approximately 150 to 300 seconds, the power of the thermal management system fluctuates between 4 kW and 5 kW. Then, the power of the thermal management system rises back to 4 kW, reaching 5 kW at approximately 350 seconds. Subsequently, the power of the thermal management system decreases in a stepwise manner between 500 and 700 seconds. Between 700 and 900 seconds, the power of the thermal management system first rises to 5 kW, then fluctuates between 4 kW and 5 kW once, before decreasing in a stepwise manner from 4 kW back to 0.

[0208] For example, such as Figure 24 As shown in the figure, this graph illustrates the power variation over time of a traditional thermal management system under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents power (in kW). The graph shows that the power of the thermal management system jumps instantaneously from 0 kW to 5 kW between 0 and 100 seconds, and then remains constant at 5 kW after 100 seconds.

[0209] For example, such as Figure 25 As shown in the figure, this graph compares the relationship between battery charging current and maximum permissible battery charging current over time under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents battery charging current (in A). The solid line represents battery charging current, and the dashed line represents the maximum permissible battery charging current. The graph shows that the battery charging current remains constant between 0 and 100 seconds, then decreases stepwise between 100 and 400 seconds. Between 400 and 600 seconds, it first increases stepwise, then decreases stepwise, then increases stepwise again, before remaining constant, and then decreases stepwise again after 600 seconds.

[0210] For example, such as Figure 26 As shown in the figure, this graph illustrates the battery charging current limitation curve of the traditional solution under high-temperature conditions. The horizontal axis represents time (in seconds), and the vertical axis represents the battery charging current (in A). The graph shows that the battery charging current remains constant for the first 100 seconds, then begins to decrease, and then gradually decreases in a stepwise manner until it reaches zero around 200 seconds.

[0211] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the charging control device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0212] This application embodiment can divide the charging control device into functional modules according to the above method. For example, the charging control device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. The module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0213] Reference Figure 27 The charging control device includes a sequence determination module 2701, a function construction module 2702, a charging control module 2703, and a temperature control module 2704.

[0214] The sequence determination module 2701 is used to determine the predicted SOC sequence based on the current sequence to be solved; the current sequence to be solved includes multiple current values ​​to be solved, which are used to represent the changes in battery charging current at multiple future times; the predicted SOC sequence is used to represent the changes in battery SOC at multiple future times.

[0215] The sequence determination module 2701 is also used to determine the predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery. The power sequence to be solved includes multiple demand power values ​​to be solved, which are used to represent the changes in the operating power of the thermal management system when regulating the temperature of the battery at multiple future times. The predicted temperature sequence is used to represent the changes in the temperature of the battery at multiple future times.

[0216] The function construction module 2702 is used to construct an objective function based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence. The objective function includes a first loss term, a second loss term, and a third loss term. The first loss term is a loss term related to the estimated charging time of the battery. The second loss term is a loss term related to the temperature rise of the battery. The third loss term is a loss term related to the capacity decay of the battery during the charging process.

[0217] The charging control module 2703 is used to take the current sequence and power sequence to be solved after minimizing the objective function as the target current sequence and target power sequence, respectively.

[0218] Temperature control module 2704 is used to charge the battery based on a target current sequence and control the thermal management system to adjust the battery temperature based on a target power sequence.

[0219] In some embodiments, the charging control device further includes a power determination module.

[0220] The power determination module is used to determine the predicted actual execution power at time k in the current sequence to be solved, based on the power prediction model, so as to obtain multiple predicted actual execution powers. The power prediction model is used to predict the actual power that the thermal management system can execute.

[0221] The sequence determination module is used to determine the target actual power sequence based on multiple unsolved predicted actual execution power and delay correction models. The target actual power sequence includes: multiple unsolved target actual execution power, delay correction models used to predict the actual power that the thermal management system can execute under the influence of thermal management response delay, and thermal management response delay used to indicate the time for the thermal management system to adjust from the power at the first moment to the required power at the second moment, where the second moment is the next moment adjacent to the first moment.

[0222] The sequence determination module is also used to determine the predicted temperature sequence based on the current sequence to be solved, the target actual power sequence, the predicted SOC sequence, and the ambient temperature of the battery.

[0223] In some embodiments, the power prediction model satisfies the following formula: ; in, This represents the predicted actual power of the thermal management system to be solved at time k, where N represents the prediction time domain and i represents the time step index variable. The weight used to represent the influence of the predicted actual execution power at time ki on the power at time k. Used to represent the predicted actual execution power at time ki. This is used to represent the response speed of the demand power value at time k to the predicted actual execution power at time k. Used to represent the power demand value at time k; It is positively correlated with the time step index variable.

[0224] In some embodiments, the delay correction model satisfies the following formula: ; in, This is used to represent the actual execution power of the target to be solved for in the thermal management system at time k. Used to represent the response coefficient of a thermal management system. This is used to represent the predicted actual power to be solved for the thermal management system at time k. This is used to represent the actual execution power of the target to be solved in the thermal management system at time k-1.

[0225] In some embodiments, the charging control device further includes a constraint construction module.

[0226] The constraint construction module is used to construct preset constraints. The preset constraints include at least one of the following: power constraint, current constraint, temperature constraint and / or SOC constraint. The power constraint is used to constrain the operating power of the thermal management system when regulating the battery temperature to be within a preset power range. The current constraint is used to constrain the charging current of the battery when charging the battery to be within a preset current range. The temperature constraint is used to constrain the temperature of the battery when charging the battery to be within a preset temperature range. The SOC constraint is used to constrain the SOC of the battery when charging the battery to be within a preset SOC range. The sequence determination module is used to minimize the objective function under preset constraints, and the solved current sequence and power sequence are used as the target current sequence and target power sequence, respectively.

[0227] In some embodiments, the power determination module is used to preset constraints including power constraints; the preset power range is determined in the following manner: The power determination module is also used to input the demand power value to be solved at time k and the historical actual power sequence into the power prediction model to obtain the predicted actual execution power at time k; the historical actual power sequence is used to represent the changes in the actual output power of the thermal management system when regulating the battery temperature at multiple past times; the power prediction model is used to predict the actual output power of the thermal management system. The power determination module is also used to determine the smaller of the predicted actual execution power at time k and the preset power upper limit threshold as the target power upper limit value in the preset power range.

[0228] The power determination module is also used to determine the larger of the predicted actual execution power at time k and the preset power lower limit threshold as the target power lower limit value in the preset power range; the preset power upper limit threshold is greater than the preset power lower limit threshold, and the preset power range is: the operating power is less than or equal to the target power upper limit value and the operating power is greater than or equal to the target power lower limit value.

[0229] In some embodiments, the objective function satisfies the following formula: ; in, The weighting coefficients used to represent the first loss term. Used to represent the first loss term, Used to indicate the length of the preset time window. Used to represent the equivalent time cost at time k. The weighting coefficients used to represent the second loss term, Used to represent the second loss term, Used to represent the temperature of the battery at time k. Used to indicate the initial battery temperature The weighting coefficients used to represent the third loss term. Used to represent the third loss term.

[0230] In some embodiments, when the vehicle is in fast charging mode, the weight coefficient of the first loss term is greater than the weight coefficients of the second and third loss terms; when the vehicle is in temperature control mode, the weight coefficient of the second loss term is greater than the weight coefficients of the first and third loss terms; when the vehicle is in longevity mode, the weight coefficient of the third loss term is greater than the weight coefficients of the first and second loss terms.

[0231] In some embodiments, the battery temperature at time k+1 in the predicted temperature sequence satisfies the following battery temperature rise model: ; in, Used to represent the temperature of the battery at time k+1. Used to represent the thermal inertia coefficient. Used to represent the temperature of the battery at time k. Used to represent the temperature rise gain coefficient. Used to represent the battery charging current at time k. Used to represent the battery resistance at time k. Used to represent the thermal conversion efficiency coefficient This represents the power demand value to be solved for the thermal management system at time k. Used to represent environmental bias constants.

[0232] In some embodiments, the charging control device further includes: a start-up condition judgment module, a thermal management system start-up control module, a standby condition judgment module, and a thermal management system shutdown control module.

[0233] The startup condition judgment module is used to determine whether the thermal management system meets the startup conditions when the thermal management system needs to switch from standby to running state.

[0234] The thermal management system startup control module is used to start the thermal management system if the startup conditions are met; wherein the startup conditions include: the standby time of the thermal management system is greater than or equal to a preset standby time; the power demand of the thermal management system is greater than or equal to a preset power activation threshold; the duration of the startup command of the thermal management system is greater than or equal to a preset activation duration; and / or; The standby condition judgment module is used to determine whether the thermal management system meets the standby conditions when the thermal management system needs to switch from the running state to the standby state.

[0235] The thermal management system shutdown control module is used to shut down the thermal management system if the thermal management system meets the standby conditions. The standby conditions include: the thermal management system's operating time is greater than or equal to a preset operating time; the thermal management system's required power is greater than or equal to a preset power standby threshold; and the duration of the thermal management system's start command is greater than or equal to a preset standby duration.

[0236] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0237] In an exemplary embodiment, this application also provides a computing device, which may include a processor and a memory. The processor may be a computing cluster composed of multiple computing nodes, and the memory may adopt a distributed memory architecture. The processor integrated into the computing device is configured to execute the charging control method of any of the above embodiments.

[0238] Figure 28 This is a schematic diagram of the architecture of a charging control device provided in an embodiment of this application. Figure 28 As shown, the charging control device includes: one or more memories 2820, one or more processors 2810, a communication bus 2840, and a communication interface 2830. The processors 2810 and memories 2820 are connected via the communication bus 2840; the one or more memories 2820 are used to store computer program code, which includes computer instructions; when the one or more processors 2810 execute the computer instructions, the computing device performs the charging control method provided in this embodiment.

[0239] Optionally, the memory 2820 may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc., and the embodiments of this application do not impose any restrictions on this.

[0240] The processor 2810 may be a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof, and the embodiments of this application do not impose any limitations on this.

[0241] The communication bus 2840 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus. This communication bus 2840 can be divided into an address bus, a data bus, and a control bus. For ease of representation, Figure 28 It is represented by a single thick line, but this does not mean that there is only one bus or one type of communication bus.

[0242] The communication interface 2830 uses any transceiver-like device for communicating with other devices or communication networks, such as control systems, radio access networks (RAN), wireless local area networks (WLAN), etc.

[0243] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware; for example, the related hardware can be a processor of a computing device. The program instructions can be stored in the above-mentioned computer-readable storage medium, and when executed, the processes of the above method embodiments can be implemented. The computer-readable storage medium can be memory. The above-mentioned computer-readable storage medium can also be an external storage device, such as a hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Further, the above-mentioned computer-readable storage medium can include both memory and external storage devices. The above-mentioned computer-readable storage medium is used to store the above-mentioned computer program instructions and other programs and data required by the above-mentioned charging control method.

[0244] This application also provides a vehicle that includes a thermal management system, a battery, and a charging control device. The vehicle can perform the methods described in the above embodiments through the charging control device.

[0245] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0246] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0247] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A charging control method, characterized in that, The charging control method includes: A predicted SOC sequence is determined based on the current sequence to be solved; the current sequence to be solved includes multiple current values ​​to be solved, which are used to represent the changes in battery charging current at multiple future times; the predicted SOC sequence is used to represent the changes in battery SOC at multiple future times. Based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery, a predicted temperature sequence is determined; the power sequence to be solved includes multiple demand power values ​​to be solved, which are used to represent the changes in the operating power of the thermal management system when regulating the temperature of the battery at multiple future times; the predicted temperature sequence is used to represent the changes in the temperature of the battery at multiple future times. An objective function is constructed based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence; the objective function includes: a first loss term, a second loss term, and a third loss term; the first loss term is a loss term related to the estimated charging time of the battery; the second loss term is a loss term related to the temperature rise of the battery; and the third loss term is a loss term related to the capacity decay of the battery during the charging process. The current sequence and power sequence to be solved obtained after minimizing the objective function are taken as the target current sequence and target power sequence, respectively. The battery is charged based on the target current sequence, and the thermal management system is controlled to adjust the battery temperature based on the target power sequence.

2. The charging control method according to claim 1, characterized in that, The step of determining the predicted temperature sequence based on the unsolved current sequence, the unsolved power sequence, the predicted SOC sequence, and the ambient temperature of the battery includes: For the demand power value to be solved at time k in the current sequence to be solved, the predicted actual execution power to be solved is determined based on the power prediction model, so as to obtain multiple predicted actual execution powers to be solved. The power prediction model is used to predict the power that the thermal management system can actually execute; the time k is any time in the prediction time domain. Based on multiple predicted actual execution power and delay correction models to be solved, a target actual power sequence is determined. The target actual power sequence includes multiple target actual execution powers to be solved. The delay correction model is used to predict the actual power that the thermal management system can execute under the influence of thermal management response delay. The predicted temperature sequence is determined based on the current sequence to be solved, the target actual power sequence, the predicted SOC sequence, and the ambient temperature of the battery.

3. The charging control method according to claim 2, characterized in that, The power prediction model satisfies the following formula: ； in, The value is used to represent the predicted actual execution power of the thermal management system to be solved at time k, where N represents the prediction time domain and i represents the time step index variable. The weight used to represent the influence of the predicted actual execution power at time ki on the influence of the predicted actual execution power at time ki. Used to represent the predicted actual execution power at the ki-th time point, This is used to represent the response speed of the demand power value at time k to the predicted actual execution power at time k. Used to represent the power demand value at time k; the It is positively correlated with the time step index variable.

4. The charging control method according to claim 2, characterized in that, The delay correction model satisfies the following formula: ； in, This represents the actual execution power of the target to be solved by the thermal management system at time k. The response coefficients used to represent the thermal management system This is used to represent the predicted actual execution power of the thermal management system to be solved at time k. This represents the actual execution power of the target to be solved by the thermal management system at time k-1.

5. The charging control method according to claim 1, characterized in that, The step of minimizing the objective function to obtain the current sequence and power sequence to be solved, and using them as the target current sequence and target power sequence respectively, includes: Establish preset constraints; the preset constraints include at least one of the following: power constraint, current constraint, temperature constraint, and / or SOC constraint; the power constraint is used to constrain the operating power of the thermal management system when regulating the temperature of the battery to be within a preset power range, the current constraint is used to constrain the charging current of the battery when charging the battery to be within a preset current range, the temperature constraint is used to constrain the temperature of the battery when charging the battery to be within a preset temperature range, and the SOC constraint is used to constrain the SOC of the battery when charging the battery to be within a preset SOC range; The objective function is minimized under the preset constraints, and the current sequence and power sequence obtained are used as the target current sequence and target power sequence, respectively.

6. The charging control method according to claim 5, characterized in that, The preset constraints include power constraints; the preset power range is determined in the following way: The required power value at time k and the historical actual power sequence are input into the power prediction model to obtain the predicted actual execution power at time k; the historical actual power sequence is used to represent the changes in the actual output power of the thermal management system when regulating the temperature of the battery at multiple past times; the power prediction model is used to predict the actual power that the thermal management system can execute. The smaller value between the predicted actual execution power at time k and the preset power upper limit threshold is determined as the target power upper limit value within the preset power range; The larger of the predicted actual execution power at time k and the preset power lower limit threshold is determined as the target power lower limit value in the preset power range; the preset power upper limit threshold is greater than the preset power lower limit threshold, and the preset power range is: the operating power is less than or equal to the target power upper limit value and the operating power is greater than or equal to the target power lower limit value.

7. The charging control method according to claim 1, characterized in that, The objective function satisfies the following formula: ； in, The weighting coefficients used to represent the first loss term, Used to represent the first loss term, Used to represent the prediction time domain, Used to represent the equivalent time cost at time k. The weighting coefficients used to represent the second loss term, Used to represent the second loss term, Used to represent the temperature of the battery at time k. Used to indicate the initial battery temperature The weighting coefficients used to represent the third loss term, Used to represent the third loss term.

8. The charging control method according to claim 6, characterized in that, When the vehicle is in fast charging mode, the weighting coefficient of the first loss term is greater than the weighting coefficients of the second loss term and the third loss term; When the vehicle is in temperature control mode, the weighting coefficient of the second loss term is greater than the weighting coefficients of the first loss term and the third loss term; When the vehicle is in long-life mode, the weighting coefficient of the third loss term is greater than the weighting coefficients of the first loss term and the second loss term.

9. The charging control method according to claim 1, characterized in that, In the predicted temperature sequence, the battery temperature at time k+1 satisfies the following battery temperature rise model: ； in, Used to represent the temperature of the battery at time k+1. Used to represent the thermal inertia coefficient. Used to represent the temperature of the battery at time k. Used to represent the temperature rise gain coefficient. Used to represent the battery charging current at time k. Used to represent the battery resistance at the k-th time. Used to represent the thermal conversion efficiency coefficient This represents the power demand value to be solved for the thermal management system at time k. Used to represent environmental bias constants.

10. The charging control method according to claim 1, characterized in that, The battery charging method further includes: When the thermal management system needs to switch from standby to running state, it is determined whether the thermal management system meets the startup conditions; if the thermal management system meets the startup conditions, the thermal management system is started; wherein, the startup conditions include: the standby time of the thermal management system is greater than or equal to a preset standby time; the power demand of the thermal management system is greater than or equal to a preset power activation threshold; the duration of the activation command of the thermal management system is greater than or equal to a preset activation duration; and / or; When the thermal management system needs to switch from running state to standby state, it is determined whether the thermal management system meets the standby conditions; if the thermal management system meets the standby conditions, the thermal management system is turned off; wherein, the standby conditions include: the running time of the thermal management system is greater than or equal to a preset running time; the power demand of the thermal management system is greater than or equal to a preset power standby threshold; the duration of the start command of the thermal management system is greater than or equal to a preset standby duration.

11. A charging control device, characterized in that, The charging control device further includes: a sequence determination module, a function construction module, a charging control module, and a temperature control module; The sequence determination module is used to determine a predicted SOC sequence based on the current sequence to be solved; the current sequence to be solved includes multiple current values ​​to be solved, which are used to represent the changes in battery charging current at multiple future times; the predicted SOC sequence is used to represent the changes in battery SOC at multiple future times. The sequence determination module is used to determine a predicted temperature sequence based on the current sequence to be solved, the power sequence to be solved, the predicted SOC sequence, and the ambient temperature of the battery. The power sequence to be solved includes multiple power demand values ​​to be solved, which are used to represent the changes in the operating power of the thermal management system when regulating the temperature of the battery at multiple future times. The predicted temperature sequence is used to represent the changes in the temperature of the battery at multiple future times. The function construction module is used to construct an objective function based on the current sequence to be solved, the predicted SOC sequence, and the predicted temperature sequence; the objective function includes: a first loss term, a second loss term, and a third loss term; the first loss term is a loss term related to the estimated charging time of the battery; the second loss term is a loss term related to the temperature rise of the battery; and the third loss term is a loss term related to the capacity decay of the battery during the charging process. The charging control module is used to take the current sequence and power sequence to be solved obtained after minimizing the objective function as the target current sequence and target power sequence, respectively. The temperature control module is used to charge the battery based on the target current sequence and control the thermal management system to adjust the temperature of the battery based on the target power sequence.

12. A vehicle, characterized in that, The vehicle includes: a thermal management system, a battery, and a charging control device as described in claim 11.

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