A battery control method, device and related equipment for an electric heavy-duty truck

Abstract

The present application relates to the technical field of electric vehicles, and particularly to a battery control method, device and related equipment for an electric heavy truck. The method includes: in response to receiving a battery control instruction or the current duration of the flameout and stationary state exceeding a first preset duration, controlling the BMS to collect the current battery data of the battery of the electric heavy truck; determining a current battery control plan including at least one target wake-up moment and at least one target charging moment according to the current battery data; wherein the target wake-up moment is the moment when the battery control device wakes up the BMS so that the BMS controls the battery to start discharging; the target charging moment is the moment when the battery control device starts charging the battery through the charging interface; controlling the charging and discharging of the battery according to the current battery control plan; The present application can solve the technical problem that the self-discharge rate of the battery of an idle electric heavy truck is relatively high in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and particularly to a battery control method, device and related equipment for an electric heavy truck. Background Art

[0002] In the field of vehicles, electric heavy trucks are mainly used in engineering construction projects. Usually, only in engineering construction projects, such as electric heavy trucks for dump trucks, mixer trucks, etc., will they be put into use. If there is no engineering construction project, electric heavy trucks will likely be in a long-term idle state without being put into use.

[0003] According to practical experience, if electric heavy trucks are in a long-term idle state, their batteries will continuously self-discharge at a relatively high self-discharge rate, resulting in battery degradation. Especially, since the capacity of the batteries carried by electric heavy trucks is relatively large, the total amount of self-discharge is larger than that of ordinary electric vehicles. Eventually, compared with ordinary electric vehicles, the batteries of electric heavy trucks will show more serious degradation.

[0004] In practical applications, if the batteries of electric heavy trucks show serious degradation, it will lead to performance problems such as a significant decrease in the charge and discharge efficiency of electric heavy trucks, a cliff-like drop in the cruising range, and limited power output. Summary of the Invention

[0005] In view of this, the purpose of the present application is to provide a battery control method, device and related equipment for an electric heavy truck to solve the technical problem of the relatively high self-discharge rate of the batteries of idle electric heavy trucks in the prior art.

[0006] In a first aspect, the present application provides a battery control method for an electric heavy truck, which is applied to a battery control device. The battery control device is connected to both the battery management system (BMS) and the charging interface of the electric heavy truck in an off and stationary state. The method includes:

[0007] In response to receiving a battery control instruction or when the current continuous duration of the off and stationary state exceeds a first preset duration, control the BMS to collect the current battery data of the battery of the electric heavy truck;

[0008] According to the current battery data, determine the current battery control plan including at least one target wake-up time and at least one target charging time;

[0009] Wherein, the target wake-up time is the time when the battery control device wakes up the BMS so that the BMS controls the battery to start discharging; the target charging time is the time when the battery control device starts charging the battery through the charging interface;

[0010] Control the charging and discharging of the battery according to the current battery control plan.

[0011] In a second aspect, the present application provides a battery control device for an electric heavy truck, which is installed in a battery control device. The battery control device is connected to both the battery management system (BMS) and the charging interface of the electric heavy truck in an off and stationary state; the device includes: a control module and a plan formulation module;

[0012] The control module is configured to, in response to receiving a battery control instruction or when the current duration of the off and stationary state exceeds a first preset duration, control the BMS to collect current battery data of the battery of the electric heavy truck;

[0013] The plan formulation module is configured to determine a current battery control plan including at least one target wake-up time and at least one target charging time according to the current battery data;

[0014] Wherein, the target wake-up time is the time when the battery control device wakes up the BMS so that the BMS controls the battery to start discharging; the target charging time is the time when the battery control device starts charging the battery through the charging interface;

[0015] The control module is configured to control the charging and discharging of the battery according to the current battery control plan.

[0016] In a third aspect, the present application provides an electronic device, which includes a processor and a memory. The memory is used to store application programs, and the processor runs or executes the software programs stored in the memory so that the electronic device implements the battery control method of the electric heavy truck as described above.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store program codes executed by a processor. The program codes are used to implement the battery control method of the electric heavy truck as described above.

[0018] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions run on an electronic device, the electronic device implements the battery control method of the electric heavy truck as described above.

[0019] Beneficial effects:

[0020] The present application provides a battery control method for an electric heavy-duty truck, which is applied to a battery control device. The battery control device is connected to both the battery management system (BMS) and the charging interface of the electric heavy-duty truck in an off and stationary state. The method includes: in response to receiving a battery control instruction or when the current duration of the off and stationary state exceeds a first preset duration, controlling the BMS to collect the current battery data of the battery of the electric heavy-duty truck; determining a current battery control plan including at least one target wake-up time and at least one target charging time according to the current battery data, where the target wake-up time is the time when the battery control device wakes up the BMS to enable the BMS to control the battery to start discharging, and the target charging time is the time when the battery control device starts charging the battery through the charging interface; controlling the charging and discharging of the battery according to the current battery control plan. In summary, the battery control device provided by the present application can formulate a current battery control plan including at least one target wake-up time and at least one target charging time to control the charging and discharging of the battery of the electric heavy-duty truck, and reduce the self-discharge rate of the battery of the electric heavy-duty truck during long-term idling. Description of the Drawings

[0021] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. The following drawings only show some embodiments of the present application, so they should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of the battery control method for the electric heavy-duty truck provided by the embodiment of the present application;

[0023] Figure 2 It is a schematic diagram of the battery control plan provided by the embodiment of the present application;

[0024] Figure 3 It is a structural diagram of the battery control device for the electric heavy-duty truck provided by the embodiment of the present application. Detailed Embodiments

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0026] First, this application provides a battery control method for an electric heavy-duty truck, which is applied to a battery control device. The battery control device is connected to both the battery management system (BMS) and the charging interface of the electric heavy-duty truck in an off and stationary state; as Figure 1 shown, Figure 1 is a flowchart of the battery control method for the electric heavy-duty truck provided by the embodiment of this application. The method includes: S110 to S130, details are as follows:

[0027] S110: In response to receiving a battery control instruction or the current duration of the off and stationary state exceeding a first preset duration, control the BMS to collect the current battery data of the battery of the electric heavy-duty truck.

[0028] Specifically, in the embodiment of this application, the battery control device is an external device outside the electric heavy-duty truck. This device can be fixedly installed in places such as parking lots, or can be used alone and moved arbitrarily; the battery control device can communicate with the BMS (Battery Management System) of the electric heavy-duty truck and can also be physically connected to the charging interface; when the battery control device is connected to both the BMS and the charging interface of the electric heavy-duty truck, the distance between the battery control device and the electric heavy-duty truck usually does not exceed 10m to 20m. In practical applications, the communication connection between the external device and the BMS and the physical connection to the charging interface are both prior arts and will not be elaborated here.

[0029] In the embodiment of this application, the battery control device can charge the battery of the electric heavy-duty truck through the charging interface; when the battery control device is fixedly installed in places such as parking lots, its power source comes from the fixed power source in places such as parking lots, and when the battery control device is used alone, it can use its own solar function to convert light energy into electrical energy for charging.

[0030] In the embodiments of the present application, the meaning of "the battery control device is connected to both the battery management system (BMS) and the charging interface of the electric heavy truck in the flameout and stationary state" is that when the electric heavy truck is in the running state, the battery control device is connected to both the BMS (Battery Management System) and the charging interface of the electric heavy truck, and then the electric heavy truck can be made to enter the flameout and stationary state; the "current duration of the flameout and stationary state" is the time interval between the moment when the electric heavy truck enters the flameout and stationary state and the current moment; the setting standard of the first preset duration is that when the flameout and stationary duration of the electric heavy truck reaches the first preset duration, it is necessary to manage the charge and discharge of the battery of the electric heavy truck, otherwise the battery of the electric heavy truck may start self-discharging. The specific value of the first preset duration can be determined according to actual needs, and the present application does not make specific limitations in this regard; it should be emphasized that since the present application controls the charge and discharge of the battery of the idle electric heavy truck through an external battery control device, when the electric heavy truck enters the flameout and stationary state, it is default that the BMS has entered the sleep state.

[0031] In actual operation, there are at least two trigger conditions for triggering the battery control device to start controlling the charge and discharge of the battery of the electric heavy truck. The first is receiving a battery control instruction. The scenario of triggering the battery control device to perform battery control through the battery control instruction is usually that the user of the electric heavy truck has clearly known that the electric heavy truck will be in a long-term idle state. Therefore, a battery control instruction is sent to the battery control device through a terminal device or an operation such as pressing a key on the battery control device is performed to generate a battery control instruction; the second is that the current duration of the flameout and stationary state of the electric heavy truck exceeds the first preset threshold. This triggering method is more suitable for the situation where the user of the electric heavy truck himself / herself does not know whether the electric heavy truck will be in a long-term idle state. Therefore, the battery control device is first connected to the BMS and the charging interface. When the current duration of the flameout and stationary state of the electric heavy truck reaches the first preset duration, it indicates that it is necessary to control the charge and discharge of the battery of the electric heavy truck to reduce the self-discharge rate of the battery.

[0032] In actual operation, after the battery control device establishes a communication connection with the BMS, the battery control device can interact with the BMS to determine the moment when the electric heavy truck enters the flameout and stationary state (realized by monitoring the state of the high-voltage contactor in the electric heavy truck). Therefore, when the battery control device receives a battery control instruction or the current duration of the flameout and stationary state exceeds the first preset duration, the battery control device communicates with the BMS to wake up the BMS and make the BMS collect the current battery data of the battery of the electric heavy truck; among them, the current battery data can clearly indicate the performance and state of the battery of the electric heavy truck at the current moment.

[0033] In one implementation, S110 includes: Step (1), details of which are as follows:

[0034] Step (1): In response to receiving a battery control instruction or when the current duration of the flameout and stationary state exceeds a first preset duration, collect the current environmental data of the external environment where the electric heavy truck is located.

[0035] Specifically, in the embodiments of the present application, the current battery data includes current performance data, current state data, and current duration; the current performance data includes data such as battery capacity, battery voltage, battery density, battery resistance, charge-discharge rate, operating range, and self-discharge rate; the current state data includes data such as SOC (State of Charge), SOH (State of Health), SOP (State of Power), and SOF (State of Function).

[0036] In actual operation, after triggering the battery control device to control the charging and discharging of the battery of the electric heavy truck, it is also necessary to collect the current environmental data of the environment where the electric heavy truck is located through various sensors in the battery control device. Since the distance between the battery control device provided in the embodiments of the present application and the electric heavy truck does not exceed 20m, the current environmental data collected by the battery control device can be regarded as the current environmental data of the external environment where the electric heavy truck is located.

[0037] In practical applications, the environmental conditions of the external environment where the electric heavy truck is located will affect the performance and state of the battery of the electric heavy truck. Therefore, timely obtaining the external environmental data where the electric heavy truck is located can be used to predict the impact of the current environmental data on the performance and state of the battery of the electric heavy truck.

[0038] S120: Determine the current battery control plan including at least one target wake-up time and at least one target charging time according to the current battery data.

[0039] Among them, the target wake-up time is the time when the battery control device wakes up the BMS so that the BMS controls the battery to start discharging; the target charging time is the time when the battery control device starts charging the battery through the charging interface.

[0040] Specifically, in the prior art, electric vehicles are usually provided with a health management strategy for the battery in an idle state. The health management strategy is to adjust the SOC of the battery to the range of 40% - 60% through the BMS. In actual operation, compared with ordinary electric vehicles, the battery of an electric heavy truck usually includes more battery cells, and the number may even be as high as tens of thousands. Therefore, problems such as differences in self-discharge rates between cells and uneven temperature distribution are much more prominent than those of ordinary electric vehicles. In addition, due to the large number of battery cells included in the battery of an electric heavy truck, the total amount of self-discharge per unit time is much larger than that of ordinary electric vehicles. Therefore, the SOC change rate of the battery of an electric heavy truck is also faster than that of ordinary electric vehicles. As a result, it is more difficult for the BMS of an electric heavy truck to maintain the SOC of the battery within the range of 40% - 60%. Therefore, if the first health management strategy is applied to the control of the battery of an electric heavy truck, it is very likely that the health management strategy for the battery will fail.

[0041] To solve the above technical problems, the embodiments of the present application construct a current battery control plan including at least one target wake-up moment and at least one target charging moment based on the current battery data. The target wake-up moment is the moment when the battery control device wakes up the BMS to enable the BMS to control the battery to start discharging. The target charging moment is the moment when the battery control device starts charging the battery through the charging interface. For example, when the target wake-up moment arrives, the battery control device communicates with the BMS to wake up the BMS, and then commands the BMS to control the battery to start discharging. When the target charging moment arrives, the battery control device starts charging the battery through the charging interface.

[0042] In the prior art, users can also perform deep charge and discharge operations on electric vehicles including electric heavy trucks regularly to reduce the self-discharge rate of the battery. However, according to actual experience, the self-discharge rate of the battery of electric vehicles including electric heavy trucks does not change linearly because the self-discharge rate of the battery is related to factors such as the SOC interval difference and the degree of battery aging. For example, for a certain battery at 25°C and 50% SOC, its monthly self-discharge rate is 2%, but at 40°C and 80% SOC, the self-discharge rate can reach 8%. In addition, according to actual experience, it is also known that for electric vehicles including electric heavy trucks, the impact of multiple small charge and discharge operations on the battery is smaller than that of deep charge and discharge operations on the battery. Therefore, a battery control plan should be formulated according to the self-discharge law of the battery for charge and discharge operations before various self-discharge time points. The self-discharge time point is the time point when the predicted self-discharge rate increases significantly.

[0043] It can be seen from this that the battery control device provided by the embodiments of the present application not only functions as an external power source (charging the battery through the charging interface), but more importantly, it also has the function of formulating a current battery control plan according to the current battery data of the battery, and is used to formulate the most suitable battery control plan according to the actual situation of the battery, so as to minimize the self-discharge rate of the battery.

[0044] In one implementation, S120 includes: step (2), details are as follows:

[0045] Step (2): According to the battery type, current performance data, current state data, current duration of the flameout and stationary state, and current environmental data, determine the current battery control plan including at least one target wake-up moment, the target wake-up duration corresponding to each target wake-up moment, at least one target charging moment, and the target charging duration corresponding to each target charging moment through the first battery control model;

[0046] Among them, the first battery control model is obtained by training an AI large model.

[0047] Specifically, in the embodiments of the present application, the meaning of "the target wake-up duration corresponding to each target wake-up moment" is that each target wake-up moment corresponds to a target wake-up duration, and the target wake-up duration is the wake-up duration after the target wake-up moment; the meaning of the target charging moment can refer to the target wake-up moment, which will not be elaborated here. As Figure 2 shown, Figure 2 is a schematic diagram of the battery control plan provided by the embodiments of the present application. In Figure 2 the target wake-up moment and the target charging moment alternate to make the battery of the electric heavy truck charge and discharge alternately.

[0048] According to actual experience, for electric vehicles including electric heavy trucks, although the impact of multiple small-scale charge and discharge cycles on the battery is smaller than that of deep charge and discharge cycles, if the number of charge and discharge cycles is too large, it will cause battery aging. Therefore, in the process of formulating the battery control plan, not only should we consider how to reduce the self-discharge rate of the battery, but also we should consider that the measures taken should not bring other potential hazards to the use of the battery. Therefore, the target wake-up moment, target wake-up duration, target charging moment, and target charging duration determined by the present application should find a balance between "reducing the self-discharge rate of the battery" and "preventing other adverse effects such as battery aging".

[0049] In actual operation, since the charge and discharge process of the battery is an electrochemical reaction, and in addition, the performance, state, etc. of the battery are also affected by many factors, the first battery control model obtained by training the AI (Artificial Intelligence) large model can be used to determine the current battery control plan.

[0050] In one implementation, before step (2), the method further includes steps (3) to (5), details are as follows:

[0051] Step (3): Build a digital twin model for the battery of the electric heavy truck.

[0052] Specifically, in the embodiments of the present application, by collecting data on various physical characteristics, chemical characteristics, working principles, etc. of various types of batteries, and using computer modeling, simulation and other technologies, a digital twin model that can simulate the actual operating state of the battery can be built. For example, based on the electrochemical model, thermal model, etc. of the battery, combined with the real-time data collected by the sensor, it can more accurately reflect the behavior of the battery at different ambient temperatures.

[0053] Step (4): Determine multiple groups of battery simulation data by running the digital twin model;

[0054] Among them, each group of battery simulation data includes the simulated battery type, simulated performance data, simulated state data, simulated environment data, multiple simulated wake-up times, multiple simulated wake-up durations, multiple simulated charging times, and multiple simulated charging durations; the battery simulation data represents that when charging and discharging the battery of the electric heavy truck according to the simulated wake-up time, simulated wake-up duration, simulated charging time, and simulated wake-up duration, the self-discharge rate of the battery of the electric heavy truck is less than the first preset threshold.

[0055] Specifically, in the embodiments of the present application, the simulated battery type is the type of the simulated battery when performing bionic calculation through the digital twin model; the simulated performance data and simulated state data are the performance data and state data of the simulated battery recorded when performing bionic calculation through the digital twin model; the simulated environment data is the environmental data of the external environment where the simulated battery is located input to the digital twin model; the meanings of the simulated wake-up time and the simulated charging time are the simulated target wake-up time and target charging time, which will not be elaborated here.

[0056] In the embodiments of the present application, the meaning of "a plurality of simulated wake-up moments and a plurality of simulated charging moments arranged in sequence" is that the plurality of simulated wake-up moments and the plurality of simulated charging moments are arranged in sequence, and the arrangement method can be an alternating arrangement; the meaning of "the battery simulation data represents that when charging and discharging the battery of an electric heavy truck according to the simulated wake-up moments and simulated charging moments, the self-discharge rate of the battery of the electric heavy truck is less than a first preset threshold" is that when charging and discharging the battery according to the simulated wake-up moments and simulated charging moments arranged in sequence, the self-discharge of the battery can be maximally suppressed, that is, the self-discharge speed of the battery is relatively low at this time. Among them, whether it is "relatively low" is confirmed by comparing with the first preset threshold. If it is less than the first preset threshold, it is considered "relatively low" and meets the expectation. If it is greater than or equal to the first preset threshold, it is considered not "relatively low" and does not meet the expectation.

[0057] In practical applications, in the digital twin model of the battery, the simulated battery type, the initial performance data of the simulated battery, and the simulated environmental data of the battery are input, and then various types of simulation runs are carried out. First, after the simulated battery type is determined, multiple simulation runs need to be carried out under the condition that the "simulated environmental data" remains unchanged to determine how to set the simulated wake-up moments, simulated wake-up durations, simulated charging moments, and simulated charging durations to reduce the self-discharge speed of the simulated battery to less than the first preset threshold under the condition that the "simulated environmental data" remains unchanged; among them, the first preset threshold can be determined according to actual needs, and the present application does not make specific limitations on this.

[0058] Secondly, multiple simulation runs also need to be carried out under the condition that the "simulated environmental data" changes to determine how to set the simulated wake-up moments, simulated wake-up durations, simulated charging moments, and simulated charging durations to reduce the self-discharge speed of the simulated battery to less than the first preset threshold under the condition that the "simulated environmental data" remains unchanged; among them, the specific changed content such as the "degree of change" of the "simulated environmental data" should be determined according to the needs of the actual external environment for simulation, such as the temperature change in an open-air parking lot where an electric heavy truck needs to be idle within half a year.

[0059] Finally, the above simulations also need to be repeated for each battery type until multiple sets of battery simulation data corresponding to each battery type, etc. are obtained.

[0060] Step (5): Use multiple sets of battery simulation data as multiple training samples to train the AI large model to obtain the first battery control model.

[0061] Specifically, according to the foregoing discussion, in order to determine multiple sets of battery simulation data, a large number of simulation tests are usually required. Each simulation test using the digital twin model takes a relatively long time. Coupled with the parameter tuning process, if the digital twin model is directly used when formulating the current battery control plan, it will affect the formulation efficiency of the current battery control plan. Therefore, in the embodiments of the present application, only the digital twin model is used to determine multiple sets of battery simulation data, and then the battery simulation data is used as training samples to train the AI large model, so as to transfer the task of predicting the current battery control plan to the trained first battery control model. Although the first battery control model is a large model, its computational workload is still greatly reduced compared to that of the digital twin model. Therefore, the prediction efficiency of predicting the current battery control plan can be effectively improved.

[0062] S130: Control the charging and discharging of the battery according to the current battery control plan.

[0063] Specifically, after the current battery control plan is determined, the charging and discharging of the battery can be controlled according to the current battery control plan.

[0064] In one implementation, S130 includes steps (6) to (9), details are as follows:

[0065] Step (6): During the process of controlling the charging and discharging of the battery according to the current battery control plan, in response to the interval duration between the current moment and the next unachieved target charging moment exceeding the second preset duration, execute the step of collecting the current environmental data of the external environment where the electric heavy truck is located to obtain the updated current environmental data.

[0066] Specifically, in the embodiments of the present application, the purpose of re-collecting the current environmental data when "the interval duration between the current moment and the next unachieved target charging moment exceeds the second preset duration" is to determine whether the current battery control plan needs to be updated.

[0067] In actual operation, if there are significant changes in the external environment where the electric heavy truck is located, it is very likely to affect the performance and / or state of the battery of the electric heavy truck. Therefore, it is necessary to re-determine the current environmental data before reaching the next target charging moment.

[0068] Step (7): Determine the updated current battery control plan through the first battery control model according to the current performance data, current state data, current duration, and updated current environmental data.

[0069] Specifically, in the embodiments of the present application, the impact of the change in the current environmental data on the performance and / or state of the battery needs to be determined by relying on the updated current environmental data and the corresponding updated current battery control plan.

[0070] Step (8): If the differences between the target wake-up time, target wake-up duration, target charging time, and target charging duration included in the current battery control plan before update and those included in the current battery control plan after update are all less than their respective second preset thresholds, control the battery according to the current battery control plan before update.

[0071] Specifically, when the updated current battery control plan is determined, compare the updated current battery control plan with the current battery control plan before update. If the differences between the key parameters such as the target wake-up time, target wake-up duration, target charging time, and target charging duration are all less than their respective second preset thresholds, it indicates that the difference between the battery control plans before and after update is small. Therefore, it may have little impact on the performance and / or state of the battery. Therefore, the battery control device will choose to control the battery according to the current battery control plan before update. So the replacement of the current environmental data is often time-sensitive. It is possible that the current environmental data tomorrow or next month will become similar to that of yesterday and last month again. Therefore, if the change in the current environmental data cannot cause a significant change in the current battery control plan, the instability and unnecessary computational overhead caused by frequent switching of the control plan should be avoided to ensure the smoothness and reliability of the battery control process.

[0072] Among them, the specific values of the second preset thresholds corresponding to the target wake-up time, target wake-up duration, target charging time, and target charging duration may be the same or different, and specifically need to be determined according to actual requirements. The present application does not make specific limitations on this.

[0073] Step (9): If there are a preset number of differences among the differences between the target wake-up time, target wake-up duration, target charging time, and target charging duration included in the current battery control plan before update and those included in the current battery control plan after update that are greater than their respective second preset thresholds, control the battery according to the current battery control plan after update.

[0074] Specifically, after determining the updated current battery control plan, compare the updated current battery control plan with the previous one. If among the differences between the key parameters such as the target wake-up time, target wake-up duration, target charging time, and target charging duration, a preset number of differences are greater than their respective second preset thresholds, it indicates that the difference between the battery control plans before and after the update is relatively large. Therefore, it may have a greater impact on the performance and / or state of the battery. Thus, the battery control device will choose to control the battery according to the updated current battery control plan to ensure the accuracy of the battery control process.

[0075] In one implementation, the current battery control plan further includes: the target charging power; after S130, the method further includes steps (10) to (13), details are as follows:

[0076] Step (10): After adding at least one electric heavy-duty truck that needs battery control to the battery control device that is already connected to at least one electric heavy-duty truck that needs battery control, determine the current battery control plan of the newly added electric heavy-duty truck.

[0077] Specifically, in the embodiment of the present application, the target charging power is the power that needs to be charged into the battery of the electric heavy-duty truck within the target charging duration.

[0078] According to the foregoing discussion, the battery control device provided in the embodiment of the present application can be fixedly installed in places such as parking lots, or can be used alone and moved arbitrarily; in actual use, when the battery control device is fixedly installed in places such as parking lots, the battery control device can continuously obtain electric energy from the parking lot, so it has sufficient power to charge the electric heavy-duty truck. However, when the battery control device is used alone and moved arbitrarily, it indicates that the power of the battery control device is not continuous. Especially when it is placed where solar energy cannot be obtained to convert solar energy into electric energy, the power stored in the battery control device is even more limited. Therefore, it needs to be "used according to a plan".

[0079] In the embodiment of the present application, the situations discussed in steps (10) to (13) are that when the battery control device used alone has limited stored power, if the number of electric heavy-duty trucks connected to the battery control device is relatively large, it is very likely that the power of the battery control device will be consumed relatively quickly. In actual operation, the number of electric heavy-duty trucks connected to the battery control device used alone can be limited to reduce the charging pressure of the battery control device and try to increase the usage duration of the battery control device.

[0080] In actual operation, when the battery control device is already connected and controlling the batteries of at least one electric heavy-duty truck, if it is necessary to newly connect an electric heavy-duty truck to the battery control device, for the newly connected electric heavy-duty truck, perform S110~S120 to determine its current battery control plan.

[0081] Step (11): According to the target charging power in multiple current battery control plans, determine the total charging power required for charging of multiple electric heavy-duty trucks that need battery control within the target time period.

[0082] Specifically, in the embodiment of the present application, the target time period is the time period starting from the current moment, that is, the time period between the current moment and a certain future moment. In actual operation, the target time period can be determined as one week or one month, etc.

[0083] In actual operation, by summarizing the target charging powers in the current battery control plans of all electric heavy-duty trucks (including the originally connected ones and the newly connected ones) that need battery control, the total charging power can be obtained; by summarizing the above target charging powers, the total charging power required by multiple electric heavy-duty trucks within the target time period can be obtained, so as to evaluate whether the power of the battery control device is sufficient to support these charging requirements in the subsequent process.

[0084] In actual application, the current battery control plan corresponding to each electric heavy-duty truck is the most favorable battery control plan for it. Ideally, by performing charge and discharge control on the electric heavy-duty truck according to the current battery control plan, the self-discharge rate of each electric heavy-duty truck can be reduced to the lowest. However, if the power stored in the battery control device is not sufficient to support the charging requirements of multiple electric heavy-duty trucks, the battery control device will directly lose its function after the power is exhausted, and the battery control device will not be able to play any role in the process of reducing the self-discharge rate of the electric heavy-duty truck subsequently. Therefore, when the power stored in the battery control device is limited, the requirement for the accuracy of the battery control of the electric heavy-duty truck can be relaxed, and it is not expected that the self-discharge rate of the electric heavy-duty truck can be reduced to the lowest, but only to reduce the self-discharge rate of the electric heavy-duty truck by a part, so as to provide the service of reducing the self-discharge rate for as many electric heavy-duty trucks as possible.

[0085] Step (12): If the current remaining power of the battery control device is less than the total charging power and the difference between the current remaining power and the total charging power is greater than or equal to the third preset threshold, among the multiple electric heavy-duty trucks that need battery control, determine the electric heavy-duty truck whose difference between the target charging moment and the current moment is greater than the fourth preset threshold as the target electric heavy-duty truck.

[0086] Specifically, in the embodiment of the present application, first, it is determined whether the current remaining power of the battery control device is less than the total charging power calculated previously. If it is less and the difference between the two reaches or exceeds the third preset threshold, it indicates that the current remaining power of the battery control device may not be sufficient to meet the charging requirements of all electric heavy-duty trucks. Accordingly, from all the electric heavy-duty trucks that need battery control, the electric heavy-duty trucks with the difference between the target charging time and the current time greater than the fourth preset threshold are screened out and determined as the target electric heavy-duty trucks, and the current battery control plan of the target electric heavy-duty trucks may be updated subsequently; wherein, both the third preset threshold and the fourth preset threshold are preset values. The third preset threshold is used to measure the size of the power gap, and the fourth preset threshold is used to determine which electric heavy-duty trucks have a relatively late charging time.

[0087] According to the foregoing discussion, when performing charge and discharge control on electric heavy-duty trucks according to the current battery control plan, the self-discharge rate of each electric heavy-duty truck can be reduced to the lowest. Therefore, if the difference between the current remaining power and the total charging power is small, that is, not greater than or equal to the third preset threshold, there is no need to update the current battery control plan for the time being, and then wait for the user to charge the battery control device to support the current battery control plans of the connected multiple electric heavy-duty trucks. However, if the difference between the current remaining power and the total charging power is large, that is, exceeds the third preset threshold, it is considered that it is very likely that the user will not be able to charge the battery control device, so it is necessary to update the current battery control plans of some electric heavy-duty trucks.

[0088] In actual operation, it is not necessary to update the current battery control plans of all electric heavy-duty trucks, but only need to update the current battery control plans of some electric heavy-duty trucks. This is because updating the battery control plan involves a series of operations and calculations, and the above processes all consume certain resources and time. If the current battery control plans of all electric heavy-duty trucks are updated, it will increase a large amount of computing costs. In fact, the target charging times of some electric heavy-duty trucks are relatively close, and it may not be necessary to immediately update the current battery control plans under the current power conditions. Therefore, only the target electric heavy-duty trucks can be adjusted, which can avoid unnecessary resource waste and improve the operation efficiency of the battery control system.

[0089] Step (13): Update the current battery control plans respectively corresponding to at least one target electric heavy-duty truck through the second battery control model according to the current remaining power and the total charging power.

[0090] Among them, the second battery control model is obtained by training the AI large model.

[0091] Specifically, in the embodiments of the present application, the training samples required for training the second battery control model are also obtained through the digital twin model. The process of obtaining the training samples of the second battery control model through the digital twin model can refer to the process of obtaining the training samples of the first battery control model through the digital twin model mentioned above, which will not be elaborated here.

[0092] In actual operation, the second battery control model is used to update the current battery control plan determined for the target electric heavy-duty trucks. The purpose of the update is to re-plan the current battery control plan of these electric heavy-duty trucks according to the power status of the battery control device and other relevant factors to ensure the reasonable distribution of the power stored in the battery control device.

[0093] In one implementation manner, step (13) includes: steps (13.1) to (13.2), details are as follows:

[0094] Step (13.1): Determine the battery control level corresponding to each of the at least one target electric heavy-duty truck according to the current battery data corresponding to each of the at least one target electric heavy-duty truck.

[0095] Specifically, in the embodiments of the present application, for each target electric heavy-duty truck, according to its current battery data (including battery type, current performance data, current status data, etc.), its battery control level is determined according to certain rules or algorithms. The battery control level can be understood as a comprehensive evaluation of the battery status and requirements of the electric heavy-duty truck. Different levels may correspond to different battery control strategies. For example, electric heavy-duty trucks with poor battery performance and poor battery status may be given a higher battery control level to pay more attention to the power distribution.

[0096] In actual operation, the battery control level corresponding to the target electric heavy-duty truck can be set on the battery control device.

[0097] Step (13.2): Update the current battery control plan corresponding to each of the at least one target electric heavy-duty truck through the second battery control model according to the current remaining power, total charging power, current environmental data, the battery control level corresponding to each of the at least one target electric heavy-duty truck, battery type, current performance data, current status data, and the current duration of the flameout and stationary state.

[0098] Specifically, in the embodiments of the present application, the current remaining power, total charging power, current environmental data (such as factors that may affect battery performance, like temperature, humidity, etc.), the battery control level, battery type, current performance data, current status data of each target electric heavy-duty truck, and the current duration of the flameout and stationary state, etc. are used as input data and input into the second battery control model.

[0099] The second battery control model updates the current battery control plan for each target electric heavy-duty truck according to the above input data; the update process comprehensively considers various factors such as the current remaining power, total charging power, the state of the battery itself, environmental factors, and the overall power distribution requirements, etc., to achieve more reasonable and optimized battery charge and discharge control.

[0100] Second, the present application provides a battery control device for an electric heavy-duty truck, which is installed in the battery control equipment. The battery control equipment is connected to both the battery control system BMS and the charging interface of the electric heavy-duty truck in the flameout and stationary state; as Figure 3 shown, Figure 3 is the structural diagram of the battery control device provided by the embodiments of the present application. The device includes: a control module 210 and a plan formulation module 220;

[0101] The control module 210 is configured to control the BMS to collect the current battery data of the battery of the electric heavy-duty truck in response to receiving a battery control instruction or when the current duration of the flameout and stationary state exceeds a first preset duration;

[0102] The plan formulation module 220 is configured to determine the current battery control plan including at least one target wake-up moment and at least one target charging moment according to the current battery data;

[0103] Wherein, the target wake-up moment is the moment when the battery control equipment wakes up the BMS to enable the BMS to control the battery to start discharging; the target charging moment is the moment when the battery control equipment starts charging the battery through the charging interface;

[0104] The control module 210 is configured to control the charge and discharge of the battery according to the current battery control plan.

[0105] In one implementation, the current battery data includes: battery type, current performance data, and current status data; the device further includes: a data acquisition module;

[0106] The data acquisition module is configured to collect the current environmental data of the external environment where the electric heavy-duty truck is located.

[0107] In one implementation, the control module 210 is further configured to determine a current battery control plan including at least one target wake-up time, the target wake-up duration corresponding to each target wake-up time, at least one target charging time, and the target charging duration corresponding to each target charging time through a first battery control model according to the battery type, current performance data, current status data, the current duration of the flameout and stationary state, and current environmental data;

[0108] Wherein, the first battery control model is obtained by training an AI large model.

[0109] In one implementation, the device further includes: a training module;

[0110] The training module is configured to construct a digital twin model of the battery of the electric heavy truck;

[0111] The training module is configured to determine multiple sets of battery simulation data by running the digital twin model;

[0112] Wherein, each set of battery simulation data includes simulated battery type, simulated performance data, simulated status data, simulated environmental data, multiple simulated wake-up times, multiple simulated wake-up durations, multiple simulated charging times, and multiple simulated charging durations; the battery simulation data represents that when charging and discharging the battery of the electric heavy truck according to the simulated wake-up time, simulated wake-up duration, simulated charging time, and simulated wake-up duration, the self-discharge rate of the battery of the electric heavy truck is less than a first preset threshold;

[0113] The training module is configured to use multiple sets of battery simulation data as multiple training samples to train the AI large model to obtain the first battery control model.

[0114] In one implementation, the control module 210 is further configured to, in the process of controlling the charging and discharging of the battery according to the current battery control plan, in response to the interval duration between the current moment and the next unachieved target charging time exceeding a second preset duration, execute the step of collecting the current environmental data of the external environment where the electric heavy truck is located to obtain updated current environmental data;

[0115] The plan formulation module 220 is further configured to determine an updated current battery control plan through the first battery control model according to the current performance data, current status data, current duration, and updated current environmental data;

[0116] The control module 210 is further configured to control the battery according to the current battery control plan before the update if the differences between the target wake-up time, the target wake-up duration, the target charging time, and the target charging duration included in the current battery control plan before the update and those included in the current battery control plan after the update are all less than their respective second preset thresholds.

[0117] In one implementation, the control module 210 is further configured to control the battery according to the current battery control plan after the update if a preset number of the differences between the target wake-up time, the target wake-up duration, the target charging time, and the target charging duration included in the current battery control plan before the update and those included in the current battery control plan after the update are greater than their respective second preset thresholds.

[0118] In one implementation, the current battery control plan further includes: a target charging power.

[0119] The plan formulation module 220 is further configured to determine the current battery control plan of the newly connected at least one electric heavy truck after adding at least one electric heavy truck that needs battery control to the battery control device connected to at least one electric heavy truck that needs battery control.

[0120] The plan formulation module 220 is further configured to determine the total charging power required for charging of the at least one electric heavy truck that needs battery control within the target time period according to the target charging powers in the multiple current battery control plans.

[0121] The plan formulation module 220 is further configured to determine the target electric heavy trucks among the at least one electric heavy truck that needs battery control, for which the difference between the target charging time and the current time is greater than a fourth preset threshold, if the current remaining power of the battery control device is less than the total charging power and the difference between the current remaining power and the total charging power is greater than or equal to a third preset threshold.

[0122] The plan formulation module 220 is further configured to update the current battery control plans corresponding to the at least one target electric heavy truck through a second battery control model according to the current remaining power and the total charging power.

[0123] Wherein, the second battery control model is obtained by training an AI large model.

[0124] In one implementation, the plan formulation module 220 is further configured to determine the battery control level corresponding to each of at least one target electric heavy-duty truck according to the current battery data corresponding to each of at least one target electric heavy-duty truck;

[0125] The plan formulation module 220 is further configured to update the current battery control plan corresponding to each of at least one target electric heavy-duty truck through a second battery control model according to the current remaining power, the total charging power, the current environmental data, the battery control level corresponding to each of at least one target electric heavy-duty truck, the battery type, the current performance data, the current status data, and the current duration of the flameout and stationary state.

[0126] Third, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of S110~S130 provided in the above embodiments are implemented.

[0127] Fourth, the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of S110~S130 in the above embodiments are executed.

[0128] Fifth, the computer program product provided by the present application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the foregoing method embodiments. For specific implementation, reference can be made to the steps of S110~S130 in the method embodiments, which will not be elaborated herein.

[0129] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0130] In addition, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] Furthermore, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0132] It should be noted that if a function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0133] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0134] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A battery control method for an electric heavy-duty truck, characterized in that, Applied to a battery control device, the battery control device is connected to both the battery management system (BMS) and the charging interface of an electric heavy-duty truck in an off and stationary state; the method includes: In response to receiving a battery control instruction or the current duration of the off and stationary state exceeding a first preset duration, controlling the BMS to collect the current battery data of the battery of the electric heavy-duty truck; Wherein, the current battery data includes: battery type, current performance data, and current state data; According to the current battery data, determining a current battery control plan including at least one target wake-up moment and at least one target charging moment; Wherein, the target wake-up moment is the moment when the battery control device wakes up the BMS to enable the BMS to control the battery to start discharging; the target charging moment is the moment when the battery control device starts charging the battery through the charging interface; Controlling the charging and discharging of the battery according to the current battery control plan; After receiving the battery control instruction or the current duration of the off and stationary state exceeding the first preset duration, the method further includes: Collecting the current environmental data of the external environment where the electric heavy-duty truck is located; The determining the current battery control plan including at least one target wake-up moment and at least one target charging moment according to the current battery data includes: According to the battery type, the current performance data, the current state data, the current duration of the off and stationary state, and the current environmental data, determining the current battery control plan including at least one target wake-up moment, the target wake-up duration corresponding to each target wake-up moment, at least one target charging moment, and the target charging duration corresponding to each target charging moment through a first battery control model; Wherein, the first battery control model is obtained by training an AI large model; Before the current battery control plan determined by the first battery control model, the method further includes: Constructing a digital twin model of the battery of the electric heavy-duty truck; Determining multiple sets of battery simulation data by running the digital twin model; Wherein, each set of battery simulation data includes a simulated battery type, simulated performance data, simulated state data, simulated environmental data, multiple simulated wake-up moments, multiple simulated wake-up durations, multiple simulated charging moments, and multiple simulated charging durations; the battery simulation data represents that when charging and discharging the battery of the electric heavy-duty truck according to the simulated wake-up moment, the simulated wake-up duration, the simulated charging moment, and the simulated wake-up duration, the self-discharge rate of the battery of the electric heavy-duty truck is less than a first preset threshold; Using multiple sets of the battery simulation data as multiple training samples to train the AI large model to obtain the first battery control model.

2. The method according to claim 1, wherein The controlling the charging and discharging of the battery according to the current battery control plan includes: During the process of controlling the charging and discharging of the battery according to the current battery control plan, in response to the interval duration between the current moment and the next unachieved target charging moment exceeding a second preset duration, perform the step of collecting the current environmental data of the external environment where the electric heavy truck is located to obtain updated current environmental data; Determine an updated current battery control plan through the first battery control model according to the current performance data, the current state data, the current duration, and the updated current environmental data; If the differences between the target wake-up moment, the target wake-up duration, the target charging moment, and the target charging duration included in the current battery control plan before update and those included in the updated current battery control plan are all less than their respective second preset thresholds, control the battery according to the current battery control plan before update.

3. The method according to claim 1, wherein After determining the updated current battery control plan through the first battery control model, the method further includes: If among the differences between the target wake-up moment, the target wake-up duration, the target charging moment, and the target charging duration included in the current battery control plan before update and those included in the updated current battery control plan, a preset number of the differences are greater than their respective second preset thresholds, control the battery according to the updated current battery control plan.

4. The method according to claim 2, wherein The current battery control plan further includes: a target charging power; after controlling the charging and discharging of the battery according to the current battery control plan, the method further includes: after adding at least one electric heavy truck that needs to be battery-controlled to the battery control device already connected with at least one electric heavy truck that needs to be battery-controlled, determining the current battery control plan of the newly added electric heavy truck; Determine the total charging power required for charging of multiple electric heavy trucks that need to be battery-controlled within a target time period according to the target charging power in multiple current battery control plans; If the current remaining power of the battery control device is less than the total charging power and the difference between the current remaining power and the total charging power is greater than or equal to a third preset threshold, determine the electric heavy trucks among the multiple electric heavy trucks that need to be battery-controlled, for which the difference between the target charging moment and the current moment is greater than a fourth preset threshold, as target electric heavy trucks; Update the current battery control plan corresponding to at least one of the target electric heavy trucks according to the current remaining power and the total charging power through a second battery control model; Wherein, the second battery control model is obtained by training an AI large model.

5. The method according to claim 4, characterized in that, The updating of the current battery control plan corresponding to at least one of the target electric heavy trucks through the second battery control model includes: Determine the battery control levels corresponding to at least one of the target electric heavy-duty trucks according to the current battery data corresponding to each of the at least one target electric heavy-duty trucks; Update the current battery control plans corresponding to at least one of the target electric heavy-duty trucks through the second battery control model according to the current remaining power, the total charging power, the current environmental data, the battery control levels corresponding to at least one of the target electric heavy-duty trucks, the battery type, the current performance data, the current status data, and the current duration of the flameout and stationary state.

6. A battery control device for an electric heavy-duty truck, characterized in that, Installed in the battery control device, the battery control device is connected to both the battery control system BMS and the charging interface of the electric heavy-duty truck in the flameout and stationary state; the device includes: a control module, a plan formulation module, a data acquisition module, and a training module; The control module is configured to control the BMS to collect the current battery data of the battery of the electric heavy-duty truck in response to receiving a battery control instruction or when the current duration of the flameout and stationary state exceeds a first preset duration; wherein the current battery data includes: battery type, current performance data, and current status data; The plan formulation module is configured to determine the current battery control plan including at least one target wake-up time and at least one target charging time according to the current battery data; Wherein, the target wake-up time is the time when the battery control device wakes up the BMS to enable the BMS to control the battery to start discharging; the target charging time is the time when the battery control device starts charging the battery through the charging interface; The control module is configured to control the charging and discharging of the battery according to the current battery control plan; The data acquisition module is configured to collect the current environmental data of the external environment where the electric heavy-duty truck is located; The current battery data includes: battery type, current performance data, and current status data; the device further includes: a data acquisition module; The data acquisition module is configured to collect the current environmental data of the external environment where the electric heavy-duty truck is located; The control module is further configured to determine, according to the battery type, the current performance data, the current status data, the current duration of the flameout and stationary state, and the current environmental data, the current battery control plan including at least one target wake-up time, the target wake-up duration corresponding to each target wake-up time, at least one target charging time, and the target charging duration corresponding to each target charging time through the first battery control model; Wherein, the first battery control model is obtained by training an AI large model; The training module is configured to construct a digital twin model of the battery of the electric heavy-duty truck; The training module is configured to determine multiple sets of battery simulation data by running the digital twin model; Among them, each set of battery simulation data includes the simulated battery type, simulated performance data, simulated state data, simulated environmental data, multiple simulated wake-up times, multiple simulated wake-up durations, multiple simulated charging times, and multiple simulated charging durations; the battery simulation data characterizes that when charging and discharging the battery of the electric heavy truck according to the simulated wake-up time, simulated wake-up duration, simulated charging time, and simulated wake-up duration, the self-discharge rate of the battery of the electric heavy truck is less than the first preset threshold. The training module is configured to use multiple sets of battery simulation data as multiple training samples to train the AI large model to obtain the first battery control model.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory. The memory is used to store application programs. The processor runs or executes the software programs stored in the memory to enable the electronic device to implement the battery control method for the electric heavy truck according to any one of claims 1 to 5.

Images