Hybrid vehicle control method, device and equipment and storage medium
By dynamically adjusting the energy distribution strategy based on vehicle parameter information, the problem of traditional ECMS algorithms ignoring NVH and slope adaptability is solved, thus achieving a comprehensive performance improvement for hybrid vehicles, including NVH performance, power performance, and fuel economy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SAIC GM WULING AUTOMOBILE CO LTD
- Filing Date
- 2025-12-17
- Publication Date
- 2026-04-28
AI Technical Summary
Traditional hybrid vehicle energy management methods, such as the ECMS algorithm, mainly focus on fuel economy, neglecting NVH performance and adaptability to external environmental factors (such as slope), resulting in insufficient driving comfort and power performance.
By acquiring vehicle parameter information, such as current battery level, vehicle speed, and gradient, the energy distribution strategy is dynamically adjusted. The energy distribution is optimized using the Hamiltonian function, and target constraints are formulated by combining lookup table models and weighting coefficients to improve NVH performance and power output.
It improves the vehicle's NVH performance, power performance and fuel economy, provides better driving comfort and power performance, and enhances the user experience.
Smart Images
Figure CN121929124A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle control technology, and in particular to a hybrid vehicle control method, device, equipment, and storage medium. Background Technology
[0002] Currently, hybrid vehicle energy management methods in related technologies mainly include rule-based control and optimization-based control. Among them, the Equivalent Consumption Minimization Strategy (ECMS) algorithm based on optimization is widely used. The ECMS algorithm equates electrical energy consumption to fuel consumption and achieves globally optimal energy allocation through a Hamiltonian function. This method has relatively low computational complexity and is easy to implement.
[0003] However, traditional ECMS algorithms mainly focus on fuel economy and often neglect other important performance indicators, such as NVH (Noise, Vibration, Harshness) performance and adaptability to external environmental factors (such as slope). Summary of the Invention
[0004] In view of this, this application provides a hybrid vehicle control method, apparatus, device, and storage medium to help solve the above-mentioned technical problems.
[0005] In a first aspect, embodiments of this application provide a hybrid vehicle control method, applied to a vehicle, the method comprising: Obtain the vehicle's parameter information; the parameter information includes: current battery level, current vehicle speed, and current gradient; The power difference is obtained based on the current power level and the pre-defined target power level. Based on the parameter information, the target constraints are obtained; When the power difference meets the constraint condition, the target constraint term is substituted into the Hamiltonian function to obtain the energy allocation strategy; The vehicle is controlled according to the energy distribution strategy.
[0006] In this embodiment, the energy distribution strategy is dynamically adjusted based on the vehicle's parameter information. This ensures sufficient power output while maintaining driving comfort, comprehensively improving the vehicle's NVH performance, power performance, and fuel economy. It enhances the user's smooth and comfortable driving experience while also achieving good fuel economy and reliable power performance, thereby improving the user's overall experience.
[0007] In some possible embodiments, obtaining the target constraint based on the parameter information includes: A first limit threshold is obtained based on the current battery level and the current vehicle speed; the first limit threshold represents the degree of limitation on the vehicle's battery level. A second limiting threshold is obtained based on the current slope and the current vehicle speed; the second limiting threshold represents the degree of restriction on the vehicle's slope. Based on the first and second restriction thresholds, a third restriction threshold is obtained; The target restriction item is obtained based on the third restriction threshold.
[0008] In some possible embodiments, obtaining the first limit threshold based on the current battery level and the current vehicle speed includes: The battery level difference and the current vehicle speed are input into the first lookup table model to obtain the first limit threshold output by the first lookup table model.
[0009] In some possible embodiments, obtaining the second limit threshold based on the current slope and the current vehicle speed includes: Obtain the slope curve between the current time and the first time interval before the current time; Integrating the slope curve yields the cumulative slope change. The average slope is obtained by comparing the cumulative slope change with the first duration. The average gradient and the current vehicle speed are input into the second lookup table model to obtain the second limit threshold output by the second lookup table model.
[0010] In some possible embodiments, obtaining the third limiting threshold based on the first limiting threshold and the second limiting threshold includes: The first weighting coefficient and the second weighting coefficient are obtained based on the power difference; the sum of the first weighting coefficient and the second weighting coefficient is 1. The first restriction threshold and the second restriction threshold are weighted and summed based on the first weight coefficient and the second weight coefficient to obtain the third restriction threshold.
[0011] In some possible embodiments, obtaining the first weighting coefficient and the second weighting coefficient based on the power difference includes: If the power difference is greater than or equal to a preset power threshold, then the first weighting coefficient is the first preset weighting value, and the second weighting coefficient is the second preset weighting value; If the power difference is less than the preset power threshold, the power difference is used as the input of the third lookup table model to obtain the first weight coefficient output by the third lookup table model, and the second weight coefficient is obtained based on the first weight coefficient.
[0012] In some possible embodiments, obtaining the target restriction item based on the third restriction threshold includes: Based on the third restriction threshold and the first restriction threshold, the target restriction threshold is obtained; A sequence of candidate engine speeds is constructed based on a preset speed interval; For each candidate speed in the engine candidate speed sequence, perform the following: based on the target limit threshold and the candidate speed, obtain the speed deviation value corresponding to the candidate speed; The minimum deviation value is determined from the deviation value corresponding to each candidate speed and the upper limit of the deviation value; The target constraint is obtained based on the maximum value between the minimum deviation value and the lower limit of the deviation value.
[0013] Secondly, a hybrid vehicle control device provided in the embodiments of this application is applied to a vehicle, the device comprising: The parameter acquisition module is used to acquire the parameter information of the vehicle; the parameter information includes: current battery level, current vehicle speed, and current gradient; The power difference determination module is used to obtain the power difference based on the current power level and the pre-calibrated target power level; The restriction determination module is used to obtain the target restriction based on the parameter information; The strategy determination module is used to input the target constraint into the Hamiltonian function when the power difference meets the constraint conditions to obtain the energy allocation strategy. A control module is used to control the vehicle according to the energy distribution strategy.
[0014] In some possible embodiments, the restriction determination module is specifically used to: obtain a first restriction threshold based on the current battery level and the current vehicle speed; the first restriction threshold characterizes the degree of restriction on the vehicle's battery level; A second limiting threshold is obtained based on the current slope and the current vehicle speed; the second limiting threshold represents the degree of restriction on the vehicle's slope. Based on the first and second restriction thresholds, a third restriction threshold is obtained; The target restriction item is obtained based on the third restriction threshold.
[0015] In some possible embodiments, the restriction determination module is specifically used to: input the battery difference and the current vehicle speed into a first lookup table model to obtain a first restriction threshold output by the first lookup table model.
[0016] In some possible embodiments, the constraint determination module is specifically used to: obtain the slope curve between the current time and a first time period before the current time; Integrating the slope curve yields the cumulative slope change. The average slope is obtained by comparing the cumulative slope change with the first duration. The average gradient and the current vehicle speed are input into the second lookup table model to obtain the second limit threshold output by the second lookup table model.
[0017] In some possible embodiments, the restriction determination module is specifically used to: obtain a first weighting coefficient and a second weighting coefficient based on the power difference; the sum of the first weighting coefficient and the second weighting coefficient is 1; The first restriction threshold and the second restriction threshold are weighted and summed based on the first weight coefficient and the second weight coefficient to obtain the third restriction threshold.
[0018] In some possible embodiments, the restriction determination module is specifically used to: if the power difference is greater than or equal to a preset power threshold, then the first weight coefficient is a first preset weight value, and the second weight coefficient is a second preset weight value; If the power difference is less than the preset power threshold, the power difference is used as the input of the third lookup table model to obtain the first weight coefficient output by the third lookup table model, and the second weight coefficient is obtained based on the first weight coefficient.
[0019] In some possible embodiments, the restriction determination module is specifically used to: obtain a target restriction threshold based on the third restriction threshold and the first restriction threshold; A sequence of candidate engine speeds is constructed based on a preset speed interval; For each candidate speed in the engine candidate speed sequence, perform the following: based on the target limit threshold and the candidate speed, obtain the speed deviation value corresponding to the candidate speed; The minimum deviation value is determined from the deviation value corresponding to each candidate speed and the upper limit of the deviation value; The target constraint is obtained based on the maximum value between the minimum deviation value and the lower limit of the deviation value.
[0020] Thirdly, another embodiment of this application also provides an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods provided in the first aspect embodiment of this application.
[0021] Fourthly, another embodiment of this application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for causing a computer to perform any of the methods provided in the first aspect of this application.
[0022] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description
[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 This is a schematic diagram of the overall process of a hybrid vehicle control method provided in an embodiment of this application; Figure 2 A flowchart illustrating the process of obtaining target constraints in a hybrid vehicle control method provided in this application embodiment; Figure 3 A schematic flowchart illustrating the process of obtaining a second limiting threshold in a hybrid vehicle control method provided in this application embodiment; Figure 4 A schematic diagram of a slope curve for a hybrid vehicle control method provided in an embodiment of this application; Figure 5 A schematic flowchart illustrating the process of obtaining a third limiting threshold in a hybrid vehicle control method provided in this application embodiment; Figure 6 A flowchart illustrating the process of obtaining a target restriction item based on a third restriction threshold in a hybrid vehicle control method provided in this application embodiment; Figure 7 A schematic diagram of an apparatus for a hybrid vehicle control method provided in an embodiment of this application; Figure 8 This is a schematic diagram of an electronic device for a hybrid vehicle control method provided in an embodiment of this application. Detailed Implementation
[0025] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0027] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0028] It should be understood that the term "and / or" used in this article 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 article generally indicates that the preceding and following related objects have an "or" relationship.
[0029] Currently, hybrid vehicle energy management methods in related technologies mainly include rule-based control and optimization-based control. Among them, the Equivalent Consumption Minimization Strategy (ECMS) algorithm based on optimization is widely used. The ECMS algorithm equates electrical energy consumption to fuel consumption and achieves globally optimal energy allocation through a Hamiltonian function. This method has relatively low computational complexity and is easy to implement.
[0030] However, traditional ECMS algorithms mainly focus on fuel economy and often neglect other important performance indicators, such as NVH (Noise, Vibration, Harshness) performance and adaptability to external environmental factors (such as slope).
[0031] NVH (Noise, Vibration, and Harshness) performance is a key factor affecting ride comfort. Engines generate high noise and vibration levels within certain RPM ranges, negatively impacting the user experience. Therefore, it's necessary to consider how to limit engine operation within unsuitable RPM ranges. Furthermore, the demands on the powertrain vary depending on the slope. When climbing hills, greater power output is required, and overly strict NVH limits may impair the vehicle's climbing ability. Therefore, the vehicle needs to adapt to changes in slope, adjusting strategies appropriately to ensure sufficient power output during hill climbs.
[0032] To address the aforementioned problems, embodiments of this application provide a hybrid vehicle control method, apparatus, device, and storage medium to solve these problems. The inventive concept of this application can be summarized as follows: acquiring vehicle parameter information; the parameter information includes: current battery level, current vehicle speed, and current gradient; obtaining the battery level difference based on the current battery level and a pre-calibrated target battery level; obtaining a target constraint based on the parameter information; when the battery level difference meets the constraint conditions, substituting the target constraint value into a Hamiltonian function to obtain an energy allocation strategy; and controlling the vehicle according to the energy allocation strategy.
[0033] In this embodiment, the energy distribution strategy is dynamically adjusted based on the vehicle's parameter information. This ensures sufficient power output while maintaining driving comfort, comprehensively improving the vehicle's NVH performance, power performance, and fuel economy. It enhances the user's smooth and comfortable driving experience while also achieving good fuel economy and reliable power performance, thereby improving the user's overall experience.
[0034] For ease of understanding, the following detailed description of a hybrid vehicle control method provided in the embodiments of this application is provided in conjunction with the accompanying drawings: like Figure 1 The diagram shown is a schematic overall flow chart of a hybrid vehicle control method provided in an embodiment of this application, wherein: In step 101: Obtain the vehicle's parameter information; the parameter information includes: current battery level, current vehicle speed, and current gradient.
[0035] In this embodiment of the application, the current battery level is the power level during execution. Figure 1 The steps shown correspond to the remaining battery power in the vehicle at the current moment; the current speed is the vehicle speed at the current moment; and the current gradient is the gradient value of the road surface detected at the current moment.
[0036] In step 102: the power difference is obtained based on the current power level and the pre-calibrated target power level.
[0037] In this embodiment of the application, in order to determine whether the vehicle's current battery level is sufficient, a target battery level is set. The battery level difference can be obtained using Formula 1, where: , (Formula 1) in, This is the difference in battery power. This is the current battery level. The target power is pre-calibrated.
[0038] It should be noted that the specific value of the target power is not limited in the embodiments of this application. In actual implementation, the user can set it according to the vehicle body conditions, or the preset calibration value built into the vehicle before it leaves the factory can be used.
[0039] In step 103: Based on the parameter information, the target constraints are obtained.
[0040] In this embodiment, a target restriction is set. When the engine is running in an unsuitable speed range and a specific triggering condition is met, the target restriction increases the cost of the system, thereby prompting the ECMS controller to adjust the energy distribution strategy to avoid or reduce the engine running in this speed range.
[0041] For detailed instructions on this step, please refer to [link / reference]. Figure 2 The details mentioned above will not be repeated here.
[0042] In step 104: when the power difference meets the constraint conditions, the target constraint term is substituted into the Hamiltonian function to obtain the energy allocation strategy.
[0043] In this embodiment, the ECMS algorithm (i.e. Hamiltonian function) continuously evaluates the current operating conditions based on the target constraints, seeking an optimal solution that minimizes the overall Hamiltonian function value. The ECMS controller outputs a power distribution (i.e., energy distribution strategy) between the engine and the electric motor, as well as the corresponding speed and torque.
[0044] In step 105: Control the vehicle according to the energy distribution strategy.
[0045] In this embodiment of the application, after obtaining the energy distribution strategy, the power of the engine and generator is adjusted according to the energy distribution strategy, and the engine speed and output torque of the vehicle are adjusted according to the speed and torque in the energy distribution strategy. The specific implementation of this step is the same as the process of using the ECMS algorithm to control the vehicle in related technologies, and will not be described again here.
[0046] To facilitate a further understanding of the hybrid vehicle control method provided in the embodiments of this application, the following will describe the above-mentioned method. Figure 1 The middle part of the steps is explained in detail: In some possible embodiments, the target constraint item is obtained based on the parameter information in step 103 above, which can be specifically implemented as follows: Figure 2 The steps shown are as follows: In step 201: A first limit threshold is obtained based on the current battery level and the current vehicle speed; the first limit threshold represents the degree of limitation on the vehicle's battery level.
[0047] In this embodiment, the first limiting threshold has a positive proportional relationship with the current vehicle speed; that is, the higher the current vehicle speed, the larger the first limiting threshold. Since noise increases with increasing vehicle speed, the first limiting threshold needs to be increased. The first limiting threshold also has an inverse proportional relationship with the current battery level; that is, when the current battery level is high (i.e., the vehicle has sufficient battery power), the first limiting threshold will be low; when the current battery level is low (the vehicle has insufficient battery power), the first limiting threshold will be high.
[0048] In some possible embodiments, a first lookup table model can be pre-built, with the input of the battery difference and the current vehicle speed, and the output being the first limit threshold.
[0049] Specifically, the first lookup table model can be a Lookup Table model. The process of constructing the first lookup table model in this application is the same as the method of constructing a Lookup Table model in related technologies, and will not be described in detail here.
[0050] In step 202: the second limit threshold is obtained based on the current slope and the current vehicle speed; the second limit threshold represents the degree of restriction on the vehicle's slope.
[0051] In this embodiment, the second limiting threshold has a positive proportional relationship with the current slope. When the current slope is large, the second limiting threshold is increased to make the engine run at a higher speed to ensure that the vehicle can climb the slope smoothly. When the current slope is small (downhill or flat road), the second limiting threshold is reduced to ensure user comfort.
[0052] In some possible embodiments, a second limit threshold is obtained based on the current slope and the current vehicle speed, specifically implemented as follows: Figure 3 The steps shown are as follows: In step 301: Obtain the slope curves for the current time and the first time interval before the current time.
[0053] In this embodiment of the application, the first duration is the time window set in this embodiment of the application, and the slope curve is a curve constructed based on the relationship between slope and time collected within the first duration before the current moment.
[0054] For example, the first duration can be set to 3 seconds or 5 seconds. This application does not limit the specific value of the first duration, and it can be selected by the user in specific implementation.
[0055] For example: the slope curve constructed from data collected within the first time period, such as... Figure 4 As shown, the horizontal axis represents time, and the vertical axis represents the slope value.
[0056] In step 302: Integrate the slope curve to obtain the cumulative slope change.
[0057] In this embodiment of the application, the change in slope over a first time period can be obtained by integrating the slope curve.
[0058] In step 303: the average slope is obtained by comparing the cumulative slope change with the first time period.
[0059] In this embodiment of the application, the average slope value within the first time period is obtained by comparing the cumulative slope change with the first time period.
[0060] In step 304: the average gradient and the current vehicle speed are input into the second lookup table model to obtain the second limit threshold output by the second lookup table model.
[0061] In some possible embodiments, a second lookup table model can be pre-built, with the average gradient and current vehicle speed as inputs and the second limit threshold as output.
[0062] Specifically, the second lookup table model can be a Lookup Table model. The process of constructing the second lookup table model in this application is the same as the method of constructing a Lookup Table model in related technologies, and will not be described in detail here.
[0063] In some possible embodiments, the above Figure 3 The process of obtaining the average slope can be implemented as shown in Formula 2, where: , (Formula 2) in, The average slope This is the start time of the time window. For the current moment, The slope curves collected during the first time period. This refers to the length of the time window and the length of the first duration.
[0064] In step 203: Based on the first and second restriction thresholds, the third restriction threshold is obtained.
[0065] In this embodiment of the application, in order to comprehensively consider the impact of slope and battery power on the vehicle, a third limiting threshold is set. The third limiting threshold can simultaneously characterize the degree of restriction on battery power and slope.
[0066] In some possible embodiments, a third limiting threshold is obtained based on the first limiting threshold and the second limiting threshold, which can be specifically implemented as follows: Figure 5 The steps shown are as follows: In step 501: the first weighting coefficient and the second weighting coefficient are obtained based on the difference in power consumption; the sum of the first weighting coefficient and the second weighting coefficient is 1.
[0067] In this embodiment of the application, in order to measure whether the vehicle's battery power is sufficient, a preset battery power threshold is set. When the battery power difference is greater than or equal to the preset battery power threshold, it indicates that the vehicle's battery power is sufficient; when the battery power difference is less than the preset battery power threshold, it indicates that the vehicle's battery power is insufficient.
[0068] For example, the preset power threshold can be set to 0. When the power difference is less than or equal to 0, it means that the current power is less than the preset target power, indicating that the power is insufficient. When the power difference is greater than or equal to 0, it means that the current power is greater than or equal to the preset target power.
[0069] In some possible embodiments, step 501 above can be specifically implemented as follows: if the power difference is greater than or equal to a preset power threshold, then the first weight coefficient is the first preset weight value and the second weight coefficient is the second preset weight value; if the power difference is less than the preset power threshold, then the power difference is used as the input of the third lookup table model to obtain the first weight coefficient output by the third lookup table model, and the second weight coefficient is obtained based on the first weight coefficient.
[0070] For example, if the difference in battery power is greater than or equal to the preset battery power threshold, it means that the vehicle has sufficient battery power at this time. Therefore, it is necessary to prioritize the comfort of the user and not worry about insufficient power due to NVH limitations. Therefore, the first weighting coefficient can be set to the first preset weighting value, i.e., set to 1, that is, NVH limitations are based on the first limit threshold. If the battery difference is less than the preset battery threshold, it means that the battery is insufficient. In order to ensure that the vehicle has enough power to drive, the NVH limit is based on the second limit threshold. That is, when the battery difference is less than the preset battery threshold, the first weighting coefficient has an inverse proportional relationship with the absolute value of the battery difference. That is, the larger the absolute value of the battery difference, the smaller the first weighting coefficient.
[0071] In some possible embodiments, a third lookup table model can be pre-built, with the input being the power difference and the output being the first weighting coefficient.
[0072] After obtaining the first weight coefficient, the second weight coefficient can be obtained by subtracting 1 from the first weight coefficient.
[0073] In step 502: the first restriction threshold and the second restriction threshold are weighted and summed based on the first weight coefficient and the second weight coefficient to obtain the third restriction threshold.
[0074] In this embodiment of the application, after obtaining the first weighting coefficient and the second weighting coefficient, a weighted sum can be performed using Formula 3, wherein: , (Formula 3) in, The third limiting threshold, The first limiting threshold, The second limiting threshold, As the first weighting coefficient, This is the second weighting coefficient.
[0075] In step 204: the target restriction item is obtained based on the third restriction threshold.
[0076] In some possible embodiments, the target restriction item is obtained based on the third restriction threshold, which can be specifically implemented as follows: Figure 6 The steps shown are as follows: In step 601: the target limit threshold is obtained based on the third limit threshold and the first limit threshold.
[0077] In this embodiment of the application, in order to ensure stable driving when the vehicle requires high power, the maximum value of the third limiting threshold and the first limiting threshold is used as the target limiting threshold.
[0078] That is, it can be implemented as shown in Formula 4, where: , (Formula 4) in, To limit the threshold for the target, The first limiting threshold, This is the third limiting threshold.
[0079] In step 602: The engine select speed sequence is constructed according to the preset speed interval.
[0080] In this embodiment of the application, the engine candidate speed sequence is a set of engine operating condition points specified by the engine universal characteristic MAP and energy flow. For example, if the preset speed interval is set to 200 rpm, the engine candidate speed sequence includes: 200 rpm, 400 rpm, 600 rpm, 800 rpm, etc.
[0081] In step 603: For each candidate speed in the engine candidate speed sequence, perform the following: Based on the target limit threshold and the candidate speed, obtain the speed deviation value corresponding to the candidate speed.
[0082] In this embodiment of the application, the rotational speed deviation value obtained from Formula 5 can be used, wherein: , (Formula 5) in, This is the speed deviation value. For the selectable speed, Set a threshold for the target. The maximum engine speed was determined after comprehensively considering the effects of NVH and slope. When the engine speed exceeds this maximum speed, it will have a negative impact on NVH performance.
[0083] In step 604: the target constraint is obtained based on the deviation value corresponding to each candidate speed.
[0084] In some possible embodiments, the target constraint is obtained based on the deviation value corresponding to each candidate speed. Specifically, this can be implemented by: determining the minimum deviation value from the deviation value corresponding to each candidate speed and the upper limit of the deviation value; and obtaining the target constraint based on the maximum value between the minimum deviation value and the lower limit of the deviation value.
[0085] Specifically, the process of obtaining the target constraint can be implemented as formula 6, where: , (Formula 6) in, For the target constraints at the current moment, The pre-set gain coefficient (i.e., calibration value). The deviation value corresponding to each candidate speed. The upper limit of the preset deviation value, The lower limit of the preset deviation value.
[0086] In this embodiment of the application, an upper limit and a lower limit for the deviation value are set, which requires ensuring that the deviation value selected from the deviation values corresponding to each candidate speed is between 0 and... Between; if the deviation value corresponding to each candidate speed is greater than the upper limit of the deviation value. Then the upper limit of the deviation value will be set. The deviation value is determined as follows: if the deviation value corresponding to each candidate speed is less than the lower limit of the deviation value 0, then the lower limit of the deviation value 0 is taken as the determined deviation value, and the product of the finally determined deviation value and the preset gain coefficient is taken as the target limit.
[0087] It should be noted that using 0 as the lower limit of the deviation value is only one embodiment and does not limit the value of the lower limit of the deviation value. In specific implementation, the specific values corresponding to the upper limit and lower limit of the deviation value can be set according to the requirements.
[0088] In some other possible embodiments, after obtaining the target constraint, before substituting the target constraint into the Hamiltonian function in step 104 above, it is necessary to determine whether the constraint condition is met. In this embodiment, a first constraint condition and a second constraint condition are set. If either constraint condition is met, execution is performed. Figure 1 Steps 104 and 105 in the process.
[0089] The first limiting condition is that the difference in battery power is greater than or equal to the first battery power limit value, and the speed deviation value corresponding to each selectable speed is greater than 0. Under this condition, it indicates that the battery power is sufficient, so user comfort needs to be ensured. The second limiting condition is: the battery charge difference is less than the second battery charge limit value, and the speed deviation value corresponding to each candidate speed is greater than the speed deviation threshold; under this condition, it indicates that the battery charge is low, so the engine needs to provide more power.
[0090] In this embodiment of the application, in order to avoid the waste of resources caused by repeatedly adjusting the energy adjustment strategy in the vehicle, if the calculated energy difference is between the first energy limit value and the second energy limit value, the energy adjustment strategy determined last time will continue to be used.
[0091] In some possible embodiments, the energy allocation strategy obtained in step 104 above can be specifically implemented as shown in Formula 7, wherein: , (Formula 7) in, This refers to the engine's instantaneous fuel consumption rate. This refers to the battery-equivalent instantaneous fuel consumption rate of the engine. The target constraint at the current moment is to adjust the engine speed at the current moment to achieve... The energy allocation strategy that minimizes energy consumption is the optimal energy allocation strategy. In this embodiment, by setting a target constraint in the Hamiltonian function, the system cost is increased when the engine operates in an unsuitable speed range and meets specific triggering conditions. This prompts the ECMS controller to adjust its strategy to avoid or reduce engine operation in this speed range. This penalizes engine operating points that, while potentially fuel-efficient, have poor NVH performance or negatively impact power response during hill climbing. In this way, the ECMS algorithm, while pursuing minimum fuel consumption, is also guided towards operating points with better NVH performance and stronger hill-climb adaptability, ultimately improving overall performance.
[0092] Based on the same inventive concept, after introducing a hybrid vehicle control method provided by the embodiments of this application, as follows... Figure 7 As shown, the following describes a hybrid vehicle control device 700 provided in an embodiment of this application. The device includes: The parameter acquisition module 7001 is used to acquire the parameter information of the vehicle; the parameter information includes: current battery level, current vehicle speed, and current gradient; The power difference determination module 7002 is used to obtain the power difference based on the current power level and the pre-calibrated target power level; The restriction determination module 7003 is used to obtain the target restriction based on the parameter information; The strategy determination module 7004 is used to input the target constraint term into the Hamiltonian function when the power difference meets the constraint conditions to obtain the energy allocation strategy; The control module 7005 is used to control the vehicle according to the energy distribution strategy.
[0093] In some possible embodiments, the restriction determination module 7003 is specifically used to: obtain a first restriction threshold based on the current battery level and the current vehicle speed; the first restriction threshold characterizes the degree of restriction on the vehicle's battery level; A second limiting threshold is obtained based on the current slope and the current vehicle speed; the second limiting threshold represents the degree of restriction on the vehicle's slope. Based on the first and second restriction thresholds, a third restriction threshold is obtained; The target restriction item is obtained based on the third restriction threshold.
[0094] In some possible embodiments, the restriction determination module 7003 is specifically used to: input the battery difference and the current vehicle speed into a first lookup table model to obtain a first restriction threshold output by the first lookup table model.
[0095] In some possible embodiments, the constraint determination module 7003 is specifically used to: obtain the slope curve between the current time and a first time period before the current time; Integrating the slope curve yields the cumulative slope change. The average slope is obtained by comparing the cumulative slope change with the first duration. The average gradient and the current vehicle speed are input into the second lookup table model to obtain the second limit threshold output by the second lookup table model.
[0096] In some possible embodiments, the restriction determination module 7003 is specifically used to: obtain a first weighting coefficient and a second weighting coefficient based on the power difference; the sum of the first weighting coefficient and the second weighting coefficient is 1; The first restriction threshold and the second restriction threshold are weighted and summed based on the first weight coefficient and the second weight coefficient to obtain the third restriction threshold.
[0097] In some possible embodiments, the restriction determination module 7003 is specifically used to: if the power difference is greater than or equal to a preset power threshold, then the first weight coefficient is a first preset weight value, and the second weight coefficient is a second preset weight value; If the power difference is less than the preset power threshold, the power difference is used as the input of the third lookup table model to obtain the first weight coefficient output by the third lookup table model, and the second weight coefficient is obtained based on the first weight coefficient.
[0098] In some possible embodiments, the restriction determination module 7003 is specifically used to: obtain a target restriction threshold based on the third restriction threshold and the first restriction threshold; A sequence of candidate engine speeds is constructed based on a preset speed interval; For each candidate speed in the engine candidate speed sequence, perform the following: based on the target limit threshold and the candidate speed, obtain the speed deviation value corresponding to the candidate speed; The minimum deviation value is determined from the deviation value corresponding to each candidate speed and the upper limit of the deviation value; The target constraint is obtained based on the maximum value between the minimum deviation value and the lower limit of the deviation value.
[0099] Corresponding to the above embodiments, this application also provides an electronic device. Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 800 may include a processor 801, a memory 802, and a communication unit 803. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0100] The communication unit 803 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It receives user data sent by other devices or sends user data to other devices.
[0101] The processor 801 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs and / or modules stored in the memory 802, and calls data stored in the memory to perform various functions and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 801 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.
[0102] The memory 802 is used to store the execution instructions of the processor 801. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0103] When the execution instructions in memory 802 are executed by processor 801, the electronic device 800 is able to perform operations. Figure 1 Some or all of the steps in the illustrated embodiments.
[0104] In a specific implementation, the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps of the various embodiments of the hybrid vehicle control method provided by the present invention. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0105] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.
[0106] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.
Claims
1. A hybrid vehicle control method, characterized in that, Applied to vehicles, the method includes: Obtain the vehicle's parameter information; the parameter information includes: current battery level, current vehicle speed, and current gradient; The power difference is obtained based on the current power level and the pre-defined target power level. Based on the parameter information, the target constraints are obtained; When the power difference meets the constraint condition, the target constraint term is substituted into the Hamiltonian function to obtain the energy allocation strategy; The vehicle is controlled according to the energy distribution strategy.
2. The method according to claim 1, characterized in that, The step of obtaining the target constraint based on the parameter information includes: A first limit threshold is obtained based on the current battery level and the current vehicle speed; the first limit threshold represents the degree of limitation on the vehicle's battery level. A second limiting threshold is obtained based on the current slope and the current vehicle speed; the second limiting threshold represents the degree of restriction on the vehicle's slope. Based on the first and second restriction thresholds, a third restriction threshold is obtained; The target restriction item is obtained based on the third restriction threshold.
3. The method according to claim 2, characterized in that, The step of obtaining the first limit threshold based on the current battery level and the current vehicle speed includes: The battery level difference and the current vehicle speed are input into the first lookup table model to obtain the first limit threshold output by the first lookup table model.
4. The method according to claim 2, characterized in that, The step of obtaining the second limit threshold based on the current slope and the current vehicle speed includes: Obtain the slope curve between the current time and the first time interval before the current time; Integrating the slope curve yields the cumulative slope change. The average slope is obtained by comparing the cumulative slope change with the first duration. The average gradient and the current vehicle speed are input into the second lookup table model to obtain the second limit threshold output by the second lookup table model.
5. The method according to claim 2, characterized in that, The process of obtaining a third limiting threshold based on the first limiting threshold and the second limiting threshold includes: The first weighting coefficient and the second weighting coefficient are obtained based on the power difference; the sum of the first weighting coefficient and the second weighting coefficient is 1. The first restriction threshold and the second restriction threshold are weighted and summed based on the first weight coefficient and the second weight coefficient to obtain the third restriction threshold.
6. The method according to claim 5, characterized in that, The step of obtaining the first weighting coefficient and the second weighting coefficient based on the power difference includes: If the power difference is greater than or equal to a preset power threshold, then the first weighting coefficient is the first preset weighting value, and the second weighting coefficient is the second preset weighting value; If the power difference is less than the preset power threshold, the power difference is used as the input of the third lookup table model to obtain the first weight coefficient output by the third lookup table model, and the second weight coefficient is obtained based on the first weight coefficient.
7. The method according to claim 2, characterized in that, The step of obtaining the target restriction item based on the third restriction threshold includes: Based on the third restriction threshold and the first restriction threshold, the target restriction threshold is obtained; A sequence of candidate engine speeds is constructed based on a preset speed interval; For each candidate speed in the engine candidate speed sequence, perform the following: based on the target limit threshold and the candidate speed, obtain the speed deviation value corresponding to the candidate speed; The minimum deviation value is determined from the deviation value corresponding to each candidate speed and the upper limit of the deviation value; The target constraint is obtained based on the maximum value between the minimum deviation value and the lower limit of the deviation value.
8. A hybrid vehicle control device, characterized in that, Applied to vehicles, the device includes: The parameter acquisition module is used to acquire the parameter information of the vehicle; the parameter information includes: current battery level, current vehicle speed, and current gradient; The power difference determination module is used to obtain the power difference based on the current power level and the pre-calibrated target power level; The restriction determination module is used to obtain the target restriction based on the parameter information; The strategy determination module is used to input the target constraint into the Hamiltonian function when the power difference meets the constraint conditions to obtain the energy allocation strategy. A control module is used to control the vehicle according to the energy distribution strategy.
9. An electronic device, characterized in that, It includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1-7.