Adaptive rule energy management method and device for hybrid logistics vehicle

Abstract

The invention relates to an adaptive rule energy management method and device for a hybrid logistics vehicle, and the method comprises the steps: constructing a hybrid logistics vehicle model of a target hybrid logistics vehicle; determining a working mode rule and a plurality of adjustable threshold parameters of a target hybrid logistics vehicle based on the hybrid logistics vehicle model; in the outer layer control process, through a finite difference stochastic approximation extremum search algorithm, global reference rule threshold parameters are iteratively optimized in a whole driving period, and periodic working condition changes of a route are dynamically adapted; in the inner-layer control process, through a pre-trained working condition clustering model, an extreme value is independently used for searching fine adjustment parameters for each type of working conditions, and real-time dynamic differences are accurately dealt with. Therefore, the problems that in an existing energy management strategy method, a fixed parameter rule is difficult to adapt to periodic changes of running characteristics of the plug-in hybrid power logistics vehicle, and single-route global parameter optimization cannot accurately cope with changes of dynamic road conditions and environmental conditions are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to an adaptive rule energy management method and device for a hybrid power logistics vehicle. BACKGROUND

[0002] Plug-in Hybrid Electric Logistics Vehicle (PHELV) is a commercial transport vehicle with both fuel and electric drive, and its energy management strategy directly determines the coordination mode of engine and motor, the charging and discharging law of power battery, and the energy consumption and emission performance of the whole vehicle under different working conditions. Logistics transportation has the characteristics of large load variation, complex road conditions, frequent start and stop, etc. If there is no efficient energy management, it may lead to uneven use of fuel and electric energy, excessive discharge of power battery or high load operation of engine, thereby increasing operating cost, shortening battery life and reducing vehicle efficiency. Therefore, the research on energy management strategy optimized for logistics working conditions is of great significance to improve economy, prolong vehicle life and achieve energy saving and emission reduction.

[0003] In the prior art system, the rule-based method has good robustness and real-time performance due to its simple structure and easy implementation, and is widely used in the industry. However, its core relies on fixed logic threshold and empirical rules, and lacks adaptive adjustment capability for complex working conditions and individual differences. Fixed parameters are difficult to continuously approach the global optimal solution in dynamically changing scenarios, resulting in limited energy utilization efficiency.

[0004] On the other hand, although optimization-based and learning-based methods have advantages in adaptability and global optimality, they are subject to their own characteristics: optimization methods often require complex calculation processes and have weak real-time response capability; learning methods are highly dependent on data quality and training scenarios, and their robustness in unknown working conditions is difficult to guarantee. Moreover, they have poor compatibility with existing rule-based engineering systems in the industry, low development and integration efficiency, and are difficult to directly apply to mass production vehicles in engineering practice.

[0005] In the prior art, the energy management strategy using fixed parameter rules has limitations. Specifically, the driving characteristics of plug-in hybrid electric logistics vehicles will change periodically due to traffic, time, environment, etc., and their logistics routes are fixed, so fixed parameter rules are difficult to adapt to such changes. At the same time, in areas with specific geographical characteristics, there are dynamic changes in road conditions and environmental conditions, and single route global parameter optimization cannot accurately respond to the above dynamic differences, resulting in poor energy management effect.

[0006] In summary, the fixed parameter rule in the existing energy management strategy method is difficult to adapt to the periodic changes of the driving characteristics of the plug-in hybrid logistics vehicle, and the single route global parameter optimization cannot accurately respond to the changes of dynamic road conditions and environmental conditions, which needs to be solved urgently. SUMMARY

[0007] The present application provides a kind of adaptive rule energy management method and device of hybrid logistics vehicle, to solve the fixed parameter rule in the existing energy management strategy method is difficult to adapt to the periodic changes of the driving characteristics of the plug-in hybrid logistics vehicle, and single route global parameter optimization cannot accurately respond to the changes of dynamic road conditions and environmental conditions etc.

[0008] The first aspect embodiment of the present application provides an adaptive rule energy management method of hybrid logistics vehicle, comprising the following steps: constructing the longitudinal dynamics model of target hybrid logistics vehicle, and determining the power battery model type, fuel consumption model and energy consumption evaluation model objective function of the target hybrid logistics vehicle, to construct hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the fuel consumption model and the energy consumption evaluation model objective function; based on the hybrid logistics vehicle model, determine the working mode rule and multiple adjustable threshold parameters of the target hybrid logistics vehicle, and through the preset finite difference stochastic approximation algorithm, the multiple adjustable threshold parameters are iteratively optimized in the whole driving cycle of the target hybrid logistics vehicle;Current working condition data of the target hybrid logistics vehicle is collected, and the current working condition data is analyzed by clustering to calculate the incremental parameters of different category working condition data in the current working condition data, and based on the working mode rule, and the incremental parameters and the optimized multiple adjustable threshold parameters are fused to generate current rule parameters, to control the target hybrid logistics vehicle to carry out adaptive energy management operation through the current rule parameters.

[0009] Optionally, in an embodiment of the present application, the longitudinal dynamics model of the target hybrid logistic vehicle is built, and the power battery model type, fuel consumption model and energy consumption evaluation model target function of the target hybrid logistic vehicle are determined, including: calculating the wheel torque of the target hybrid logistic vehicle, and building the longitudinal dynamics model of the target hybrid logistic vehicle based on the wheel torque; determining the open circuit voltage, internal resistance and battery capacity of the power battery of the target hybrid logistic vehicle, and calculating the load power and motor power of the power battery based on the open circuit voltage; calculating the corresponding state of charge according to the load power, the motor power, the open circuit voltage, the internal resistance and the battery capacity, so as to build the power battery model through the state of charge; calculating the engine power of the target hybrid logistic vehicle, and calculating the corresponding compressed natural gas consumption rate according to the engine power, so as to build the fuel consumption model through the compressed natural gas consumption rate; calculating the equivalent energy consumption cost per 100 kilometers of the target hybrid logistic vehicle based on the power battery model type and the fuel consumption model, so as to determine the energy consumption evaluation model target function according to the equivalent energy consumption cost per 100 kilometers.

[0010] Optionally, in an embodiment of the present application, the working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistic vehicle are determined based on the hybrid logistic vehicle model, including: judging whether the remaining power of the target hybrid logistic vehicle is greater than a preset critical remaining power based on the hybrid logistic vehicle model; if the remaining power is greater than the critical remaining power, the working mode rule of the target hybrid logistic vehicle is an electric vehicle-power consumption mode rule, and the demand torque and the current vehicle speed of the target hybrid logistic vehicle are compared and analyzed under the electric vehicle-power consumption mode rule to obtain a corresponding comparison and analysis result, and the target hybrid logistic vehicle is operated according to the comparison and analysis result; if the remaining power is less than the critical remaining power, the working mode rule of the target hybrid logistic vehicle is a power retention mode rule, and the target hybrid logistic vehicle is operated by using a preset electric auxiliary control strategy.

[0011] Optionally, in an embodiment of the present application, the comparing and analyzing operation of the demand torque and the current vehicle speed of the target hybrid logistic vehicle under the electric vehicle-power consumption mode rule to obtain a corresponding comparison and analysis result, and operating the target hybrid logistic vehicle according to the comparison and analysis result, comprises: determining a minimum speed threshold, a cutoff torque, an engine optimal torque and a motor maximum torque corresponding to the target hybrid logistic vehicle; comparing the current vehicle speed of the target hybrid logistic vehicle with the minimum speed threshold, and comparing the demand torque of the target hybrid logistic vehicle with the cutoff torque, the engine optimal torque and the motor maximum torque; when the demand torque is less than the cutoff torque, or the current vehicle speed is less than the minimum speed threshold, controlling the target hybrid logistic vehicle to operate in a pure electric mode; when the demand torque is greater than a preset motor maximum torque, and the current vehicle speed is less than the minimum speed threshold, controlling the target hybrid logistic vehicle to operate in a hybrid drive mode; when the demand torque is greater than the cutoff torque, but the demand torque is less than the engine optimal torque, controlling the engine of the target hybrid logistic vehicle to maintain optimal torque point operation, and charging the battery through the motor; when the demand torque is greater than the cutoff torque, and the demand torque is between the engine optimal torque and the motor maximum torque, driving the target hybrid logistic vehicle through the engine alone; when the demand torque is greater than the cutoff torque, and the demand torque is greater than the motor maximum torque, controlling the target hybrid logistic vehicle to operate in a hybrid drive mode.

[0012] Optionally, in an embodiment of the present application, the iterative optimization of the plurality of adjustable threshold parameters of the target hybrid logistic vehicle in a full driving cycle of the target hybrid logistic vehicle through a preset finite difference stochastic approximation algorithm comprises: determining a minimum hundred-kilometer equivalent energy consumption cost of the target hybrid logistic vehicle, and constructing a corresponding outer optimization objective function based on the minimum hundred-kilometer equivalent energy consumption cost; performing an extreme value search on the outer optimization objective function by using the finite difference stochastic approximation algorithm to optimize the plurality of adjustable threshold parameters.

[0013] The second aspect embodiment of the application provides a self-adaptive rule energy management device of a hybrid logistics vehicle, comprising: a modeling module, configured to construct a longitudinal dynamics model of a target hybrid logistics vehicle, and determine a power battery model type, an oil consumption model and an energy consumption evaluation model objective function of the target hybrid logistics vehicle, so as to construct a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model and the energy consumption evaluation model objective function; an outer optimization module, configured to determine a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle based on the hybrid logistics vehicle model, and iteratively optimize the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid logistics vehicle through a preset finite difference stochastic approximation algorithm; and an inner control module, configured to collect current working condition data of the target hybrid logistics vehicle, and perform cluster analysis on the current working condition data to calculate incremental parameters of different category working condition data in the current working condition data, and generate a current rule parameter based on the working mode rule and by fusing the incremental parameters and the optimized plurality of adjustable threshold parameters, so as to control the target hybrid logistics vehicle to perform a self-adaptive energy management operation through the current rule parameter.

[0014] Optionally, in an embodiment of the application, the modeling module comprises: a first calculation unit, configured to calculate wheel torque of the target hybrid logistics vehicle, and construct a longitudinal dynamics model of the target hybrid logistics vehicle based on the wheel torque; a second calculation unit, configured to determine open circuit voltage, internal resistance and battery capacity of a power battery of the target hybrid logistics vehicle, and calculate load power and motor power of the power battery based on the open circuit voltage; a third calculation unit, configured to calculate corresponding state of charge according to the load power, the motor power, the open circuit voltage, the internal resistance and the battery capacity, so as to construct the power battery model through the state of charge; a construction unit, configured to calculate engine power of the target hybrid logistics vehicle, and calculate corresponding compressed natural gas consumption rate according to the engine power, so as to construct the oil consumption model through the compressed natural gas consumption rate; and a fourth calculation unit, configured to calculate 100-kilometer equivalent energy consumption cost of the target hybrid logistics vehicle based on the power battery model type and the oil consumption model, so as to determine the energy consumption evaluation model objective function according to the 100-kilometer equivalent energy consumption cost.

[0015] Optionally, in an embodiment of the present application, the outer layer optimization module comprises: a judging unit configured to judge whether the remaining electric quantity of the target hybrid logistic vehicle is greater than a preset critical remaining electric quantity based on the hybrid logistic vehicle model; a comparison and analysis unit configured to, if the remaining electric quantity is greater than the critical remaining electric quantity, the working mode rule of the target hybrid logistic vehicle is an electric vehicle-electric quantity consumption mode rule, and the demand torque and the current vehicle speed of the target hybrid logistic vehicle are compared and analyzed under the electric vehicle-electric quantity consumption mode rule to obtain a corresponding comparison and analysis result, and the target hybrid logistic vehicle is run according to the comparison and analysis result; and a running unit configured to, if the remaining electric quantity is less than the critical remaining electric quantity, the working mode rule of the target hybrid logistic vehicle is an electric quantity maintenance mode rule, and the target hybrid logistic vehicle is run by using a preset electric auxiliary control strategy.

[0016] Optionally, in an embodiment of the present application, the comparison and analysis unit comprises: a determination sub-unit configured to determine a minimum speed threshold, a cutoff torque, an engine optimal torque and a motor maximum torque corresponding to the target hybrid logistic vehicle; a comparison sub-unit configured to compare the current vehicle speed of the target hybrid logistic vehicle with the minimum speed threshold, and compare the demand torque of the target hybrid logistic vehicle with the cutoff torque, the engine optimal torque and the motor maximum torque; a first control sub-unit configured to control the target hybrid logistic vehicle to run in a pure electric mode when the demand torque is less than the cutoff torque or the current vehicle speed is less than the minimum speed threshold; a second control sub-unit configured to control the target hybrid logistic vehicle to run in a hybrid drive mode when the demand torque is greater than a preset motor maximum torque and the current vehicle speed is less than the minimum speed threshold; a third control sub-unit configured to control the engine of the target hybrid logistic vehicle to maintain at an optimal torque point and charge the battery through the motor when the demand torque is greater than the cutoff torque but less than the engine optimal torque; a drive sub-unit configured to drive the target hybrid logistic vehicle through the engine alone when the demand torque is greater than the cutoff torque and between the engine optimal torque and the motor maximum torque; and a fourth control sub-unit configured to control the target hybrid logistic vehicle to run in the hybrid drive mode when the demand torque is greater than the cutoff torque and greater than the motor maximum torque.

[0017] Optionally, in an embodiment of the present application, the outer layer optimization module further comprises: a function construction unit, configured to determine a minimum 100-kilometer equivalent energy consumption cost of the target hybrid logistics vehicle, and construct a corresponding outer layer optimization objective function based on the minimum 100-kilometer equivalent energy consumption cost; and an extreme value searching unit, configured to perform extreme value searching on the outer layer optimization objective function by using the finite difference stochastic approximation algorithm, so as to optimize the plurality of adjustable threshold parameters.

[0018] The third aspect of the embodiments of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the adaptive rule energy management method of the hybrid logistics vehicle as described in the above embodiments.

[0019] The fourth aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the adaptive rule energy management method of the hybrid logistics vehicle as described above.

[0020] The fifth aspect of the embodiments of the present application provides a computer program product, comprising a computer program, and the computer program is executed to implement the adaptive rule energy management method of the hybrid logistics vehicle as described above.

[0021] Therefore, the embodiments of the present application have the following beneficial effects: Embodiments of the present application can construct a longitudinal dynamics model of a target hybrid logistics vehicle, determine a power battery model type, an oil consumption model, and an energy consumption evaluation model target function of the target hybrid logistics vehicle, construct a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model, and the energy consumption evaluation model target function, determine a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle based on the hybrid logistics vehicle model, and iteratively optimize the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid logistics vehicle through a preset finite difference stochastic approximation algorithm, collect current working condition data of the target hybrid logistics vehicle, perform cluster analysis on the current working condition data to calculate incremental parameters of different category working condition data in the current working condition data, generate current rule parameters based on the working mode rule and by fusing the incremental parameters and the optimized plurality of adjustable threshold parameters, and control the target hybrid logistics vehicle to perform adaptive energy management operation through the current rule parameters. The present application adopts adaptive adjustable rule parameter design, guarantees the real-time performance and robustness of the strategy through a rule-based method, and has adaptability and optimality based on an optimization method. Thus, the problems in the existing energy management strategy method, such as that the fixed parameter rule is difficult to adapt to the periodic changes of the driving characteristics of the plug-in hybrid logistics vehicle, and that the single route global parameter optimization cannot accurately cope with dynamic road conditions and environmental changes, are solved.

[0022] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein: Figure 1 A flowchart of a hybrid logistics vehicle adaptive rule energy management method according to an embodiment of the present application; Figure 2 A structural schematic diagram of a plug-in hybrid logistics vehicle according to an embodiment of the present application; Figure 3 A vehicle mode switching strategy schematic diagram involved in a cluster-assisted mechanism search adaptive rule strategy according to an embodiment of the present application; Figure 4 A working condition data schematic diagram in a hardware-in-the-loop experiment according to an embodiment of the present application; Figure 5 A hardware-in-the-loop experiment platform schematic diagram according to an embodiment of the present application; Figure 6A schematic diagram of SOC trajectory evolution in a hardware-in-the-loop experiment of a vehicle according to an embodiment of the present application; Figure 7 A schematic diagram of control threshold parameters and equivalent cost in a hardware-in-the-loop experiment according to an embodiment of the present application; Figure 8 A schematic diagram of incremental parameter changes of two rule threshold parameters under three types of working conditions in a hardware-in-the-loop experiment according to an embodiment of the present application; Figure 9 A comparison schematic diagram of results with and without inner extremum search in a hardware-in-the-loop experiment according to an embodiment of the present application; Figure 10 An example diagram of an adaptive rule energy management device of a hybrid logistics vehicle according to an embodiment of the present application; Figure 11 A structural schematic diagram of an electronic device according to an embodiment of the present application.

[0024] Among them, 10 is an adaptive rule energy management device of a hybrid logistics vehicle; 100 is a modeling module, 200 is an outer layer optimization module, and 300 is an inner layer control module; 1101 is a memory, 1102 is a processor, and 1103 is a communication interface. DETAILED DESCRIPTION

[0025] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0026] An adaptive rule energy management method and device for a hybrid logistics vehicle are described below with reference to the accompanying drawings. To address the problems mentioned in the background, the present application provides an adaptive rule energy management method for a hybrid logistics vehicle. In the method, a longitudinal dynamics model of a target hybrid logistics vehicle is constructed, and a power battery model type, an oil consumption model, and an energy consumption evaluation model objective function of the target hybrid logistics vehicle are determined, to construct a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model, and the energy consumption evaluation model objective function. Based on the hybrid logistics vehicle model, a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle are determined, and the plurality of adjustable threshold parameters are iteratively optimized in a full driving cycle of the target hybrid logistics vehicle through a pre-set finite difference stochastic approximation algorithm. Current working condition data of the target hybrid logistics vehicle are collected, and cluster analysis is performed on the current working condition data to calculate incremental parameters of different categories of working condition data in the current working condition data. Based on the working mode rule, the incremental parameters, and the optimized plurality of adjustable threshold parameters are fused to generate current rule parameters, to control the target hybrid logistics vehicle to perform adaptive energy management operations through the current rule parameters. The adaptive adjustable rule parameter design is adopted in the present application, which guarantees the real-time and robustness of the strategy through the rule-based method, and has adaptability and optimality based on the optimization method. Thus, the problems that the fixed parameter rule in the existing energy management strategy method is difficult to adapt to the periodic changes of the driving characteristics of the plug-in hybrid logistics vehicle, and the single route global parameter optimization cannot accurately respond to dynamic road conditions and environmental changes are solved.

[0027] Specifically, Figure 1 A flowchart of an adaptive rule energy management method for a hybrid logistics vehicle provided by an embodiment of the present application is shown.

[0028] As Figure 1 shown, the adaptive rule energy management method for a hybrid logistics vehicle includes the following steps: In step S101, a longitudinal dynamics model of a target hybrid logistics vehicle is constructed, and a power battery model type, an oil consumption model, and an energy consumption evaluation model objective function of the target hybrid logistics vehicle are determined, to construct a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model, and the energy consumption evaluation model objective function.

[0029] The embodiment of the application first establishes a longitudinal dynamics model reflecting the relationship between the longitudinal force and the motion of the vehicle according to the driving characteristics of the target hybrid logistics vehicle; secondly, the suitable power battery model type is determined according to the state of charge of the power battery of the logistics vehicle, and an oil consumption model is constructed according to the working principle and fuel consumption characteristics of the engine, and the energy consumption evaluation model objective function for evaluating the energy utilization efficiency of the vehicle is determined; finally, the longitudinal dynamics model, the power battery model, the oil consumption model and the energy consumption evaluation model objective function are integrated to form a complete hybrid logistics vehicle model.

[0030] Therefore, the embodiment of the application builds the hybrid logistics vehicle model step by step to comprehensively characterize the power and energy consumption characteristics of the logistics vehicle, thereby improving the operation economy and energy utilization rate of the logistics vehicle.

[0031] Alternatively, in one embodiment of the application, the longitudinal dynamics model of the target hybrid logistics vehicle is constructed, and the power battery model type, the oil consumption model and the energy consumption evaluation model objective function of the target hybrid logistics vehicle are determined, including: calculating the wheel torque of the target hybrid logistics vehicle, and constructing the longitudinal dynamics model of the target hybrid logistics vehicle based on the wheel torque; determining the open circuit voltage, internal resistance and battery capacity of the power battery of the target hybrid logistics vehicle, and calculating the load power and motor power of the power battery based on the open circuit voltage; calculating the corresponding state of charge according to the load power, motor power, open circuit voltage, internal resistance and battery capacity to construct the power battery model through the state of charge; calculating the engine power of the target hybrid logistics vehicle, and calculating the corresponding compressed natural gas consumption rate according to the engine power to construct the oil consumption model through the compressed natural gas consumption rate; calculating the equivalent energy consumption cost per 100 kilometers of the target hybrid logistics vehicle based on the power battery model type and the oil consumption model, to determine the energy consumption evaluation model objective function according to the equivalent energy consumption cost per 100 kilometers.

[0032] Specifically, the process of constructing the plug-in hybrid electric logistics vehicle model according to the embodiment of the application is as follows: (1) Longitudinal dynamics model: The PHELV (plug-in hybrid electric logistics vehicle) adopts a coaxial parallel hybrid power structure, and the engine, motor, clutch, AMT (automated mechanical transmission) transmission and main reducer have the structure as shown in Figure 2 .

[0033] The wheel torque calculation expression of the plug-in hybrid electric logistics vehicle is:

[0034] wherein, represents the wheel torque; is the transmission efficiency; and respectively represent the gear ratio of the AMT transmission and the final drive; and respectively represent the engine torque and the motor torque; is the braking torque acting on the wheels; is the road slope; denotes the vehicle speed; is the vehicle acceleration.

[0035] It should be noted that the specific parameters of the above longitudinal dynamics model are shown in Table 1, and the main parameters of the PHELVs are shown in Table 2: Table 1

[0036] Table 2

[0037] (2) Power battery model type: The embodiments of the present application can adopt a lithium-ion battery, and an internal resistance battery model which does not consider the influence of temperature change and battery aging is modeled, and the state of charge (SOC) is as follows:

[0038]

[0039]

[0040] wherein, , and respectively represent the open-circuit voltage, internal resistance and battery capacity; and respectively represent the load power and the motor power.

[0041] (3) Fuel consumption model: The CNG consumption rate (milliliter / second) of the compressed natural gas (CNG) engine is as follows:

[0042] wherein, is the engine power calculated by is the current engine torque and the corresponding CNG consumption rate of the rotational speed, which can be obtained by calibration test; ​Density of compressed natural gas.

[0043] (4) Energy consumption evaluation model objective function: Equivalent energy consumption cost per 100 km (yuan / 100 km), as shown in the following formula:

[0044] wherein, (yuan / 100 km) ) and (yuan / kW·h) represent the market prices of compressed natural gas and electricity, respectively; ) and (kW·h) are the gas consumption and electricity consumption, respectively; (km) is the driving cycle mileage; and

[0045] Therefore, the embodiment of the present application can construct a hybrid power logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the fuel consumption model and the energy consumption evaluation model objective function, thereby providing reliable technical support for the implementation of adaptive rule energy management of the subsequent hybrid power logistics vehicle.

[0046] In step S102, based on the hybrid power logistics vehicle model, the working mode rule and the plurality of adjustable threshold parameters of the target hybrid power logistics vehicle are determined, and the plurality of adjustable threshold parameters are iteratively optimized in the entire driving cycle of the target hybrid power logistics vehicle through a preset finite difference stochastic approximation algorithm.

[0047] In step S103, the current working condition data of the target hybrid power logistics vehicle is collected, and the current working condition data is subjected to cluster analysis to calculate the incremental parameters of different categories of working condition data in the current working condition data, and based on the working mode rule, the incremental parameters and the optimized plurality of adjustable threshold parameters are fused to generate current rule parameters, so as to control the target hybrid power logistics vehicle to perform adaptive energy management operation through the current rule parameters.

[0048] Further, the embodiment of the present application also needs to determine the working mode rule and the plurality of adjustable threshold parameters of the target hybrid power logistics vehicle according to the above-mentioned hybrid power logistics vehicle model.

[0049] Secondly, the embodiment of the present application can perform adaptive energy management operation through the CAESRB (Cluster-Assisted Extremum Seeking adaptive Rule-Based) strategy, and the specific implementation manner is as follows: ​​1. The outer controller learns the periodic working conditions through the extremum search algorithm of finite difference stochastic approximation (FDSA), iterates in the entire driving cycle to optimize the global benchmark rule threshold parameters, and thus adapts to the working condition changes of the fixed line.

[0050] 2. The inner controller uses the trained working condition clustering model to fine-tune the benchmark threshold parameters for each type of working condition using the extremum search algorithm, so as to cope with real-time dynamic differences under the same working condition.

[0051] Therefore, the embodiments of the present application provide adaptive benchmark parameters through the outer controller, and the inner controller dynamically corrects the parameters based on the clustering results, so that the parameters meet the long-term and short-term fluctuation requirements; in addition, the embodiments of the present application also introduce a disturbance coefficient to avoid interference of local parameter adjustment with the global trend, and introduce a momentum term to reduce the number of iterations and accelerate convergence.

[0052] It can be understood that the embodiments of the present application use the adaptive rule energy management strategy based on the clustering-assisted extremum search method, aiming to achieve near-optimal performance under the premise of low computational complexity and high reliability, and ensure the mass production feasibility; the CAESRB strategy adopts adaptive adjustable rule parameter design, guarantees the real-time performance and robustness of the strategy through the rule-based method, and has adaptability and optimality based on the optimization method.

[0053] Optionally, in an embodiment of the present application, based on the hybrid logistics vehicle model, the working mode rule and the plurality of adjustable threshold parameters of the target hybrid logistics vehicle are determined, including: based on the hybrid logistics vehicle model, determining whether the remaining power of the target hybrid logistics vehicle is greater than a preset critical remaining power; if the remaining power is greater than the critical remaining power, the working mode rule of the target hybrid logistics vehicle is an electric vehicle-power consumption mode rule, and under the electric vehicle-power consumption mode rule, the demand torque and the current vehicle speed of the target hybrid logistics vehicle are compared and analyzed to obtain a corresponding comparison and analysis result, and the target hybrid logistics vehicle is operated according to the comparison and analysis result; if the remaining power is less than the critical remaining power, the working mode rule of the target hybrid logistics vehicle is a power preservation mode rule, and the target hybrid logistics vehicle is operated using a preset electric auxiliary control strategy.

[0054] In actual execution, the embodiments of the present application can perform adaptive rule parameter design, wherein the adaptive rule parameters mainly include the working mode rule and the adjustable key threshold parameters, which are as follows: (1) Working mode rule: The working mode in the embodiment of the application adopts a three-stage rule of "EV (Electric Vehicle), CD (Charge Depleting) combined with CS (Charge Sustaining) ": As shown in Figure 3 , when SOC>SOC L (i.e. critical remaining power), the embodiment of the application will adopt the Electric Vehicle- Charge Depleting mode rule mode, in which mode, the embodiment of the application can compare the demand torque and the current speed of the hybrid power logistics vehicle to determine the corresponding control strategy and run the hybrid power logistics vehicle.

[0055] When SOC<SOC L , the embodiment of the application will switch to the CS (Charge Sustaining) mode and adopt the Electric Assist Control Strategy (EACS) ; at this time, the engine is the main power source, and the electric machinery (EM) maintains the state of charge of the battery while assisting the engine to run; considering that the engine efficiency improves with the increase of the load rate, its operation is limited within the lower limit T emin to the upper limit T emax of the high-efficiency working zone, and the additional charging torque T chg is used to charge the battery.

[0056] (2) Adjustable key threshold parameters: In the embodiment of the application, as shown in Table 3, the above-mentioned adjustable key threshold parameters include the speed threshold V and the engine cutoff torque coefficient K : Table 3

[0057] Further, the embodiment of the application also needs to dynamically optimize the adjustable key threshold parameters through extreme value search, and the calculation expression is as follows:

[0058] Optionally, in one embodiment of this application, under the electric vehicle-electric power consumption mode rule, a comparative analysis operation is performed on the required torque and current speed of the target hybrid logistics vehicle to obtain the corresponding comparative analysis results, and the target hybrid logistics vehicle is operated according to the comparative analysis results, including: determining the minimum speed threshold, cutoff torque, optimal engine torque, and maximum motor torque corresponding to the target hybrid logistics vehicle; comparing the current speed of the target hybrid logistics vehicle with the minimum speed threshold, and comparing the required torque of the target hybrid logistics vehicle with the cutoff torque, optimal engine torque, and maximum motor torque; when the required torque is less than the cutoff torque, or the current speed is less than the minimum speed threshold, controlling the target hybrid... The hybrid logistics vehicle operates in pure electric mode. When the required torque is greater than the preset maximum motor torque and the current vehicle speed is less than the minimum speed threshold, the target hybrid logistics vehicle is controlled to operate in hybrid drive mode. When the required torque is greater than the cutoff torque but less than the engine's optimal torque, the engine of the target hybrid logistics vehicle is controlled to operate at the optimal torque point, and the battery is charged through the motor. When the required torque is greater than the cutoff torque and the required torque is between the engine's optimal torque and the motor's maximum torque, the target hybrid logistics vehicle is driven solely by the engine. When the required torque is greater than the cutoff torque and greater than the motor's maximum torque, the target hybrid logistics vehicle is controlled to operate in hybrid drive mode.

[0059] It should be noted that, as Figure 3 As shown, in the electric vehicle-electric consumption mode rule mode, if the vehicle speed is lower than the minimum speed threshold V L Or the required torque T r Less than the cutoff torque T eoff The vehicle will operate in pure electric mode; when the vehicle speed is below V... L And the required torque T r Exceeding the motor's maximum torque T mmax In this case, the embodiments of this application can automatically switch to hybrid driving mode.

[0060] When the required torque T r Greater than the cutoff torque T eoff At this point, it is necessary to further determine its relationship with the engine's optimal torque T. eopt Maximum torque T emax The relationship between the required torque T: r Less than the engine's optimal torque T eopt The engine will maintain operation at its optimal torque point while simultaneously charging the battery via the electric motor; if a torque T is required... r In T eopt With T emax Between (i.e., greater than T) eopt And less than T emax), the vehicle is driven by the engine alone; if the demand torque T r exceeds T emax , the embodiment of the application can enable the hybrid drive mode.

[0061] Thus, the embodiment of the application effectively guarantees the reliability of the controller design by designing adaptive rule parameters.

[0062] Optionally, in an embodiment of the application, a plurality of adjustable threshold parameters are iteratively optimized in the entire driving cycle of the target hybrid logistics vehicle by a preset finite difference stochastic approximation algorithm, including: determining the minimum equivalent energy consumption cost per 100 kilometers of the target hybrid logistics vehicle, and constructing a corresponding outer optimization objective function based on the minimum equivalent energy consumption cost per 100 kilometers; performing extreme value search on the outer optimization objective function by using the finite difference stochastic approximation algorithm to optimize the plurality of adjustable threshold parameters.

[0063] As understood by those skilled in the art, in the prior art, the rule-based method relies on fixed parameters, and the optimization learning-based method is a single-layer structure, while the embodiment of the application can adapt to the periodic changes of fixed routes and the dynamic differences of single routes by using the double-layer optimization adaptive rule architecture of outer global benchmark parameter optimization combined with inner cluster auxiliary incremental parameter fine-tuning, and can balance real-time performance, robustness and adaptability, thereby realizing adaptive rule energy management of the hybrid logistics vehicle, as described in detail below. 1. Outer control: Extreme value search based on improved SPSA (Simultaneous Perturbation Stochastic Approximation).

[0064] (1) Optimization objective: minimizing the equivalent energy consumption cost per 100 kilometers, and the control variable is the benchmark parameter. (2) Gradient estimation: calculate the gradient by simultaneous perturbation stochastic approximation, and introduce a momentum term and noise to avoid local optimum:

[0065] wherein, denote the 2 rule library parameters to be optimized; sequence denotes the perturbation magnitude; is a scaling factor considering the different dimensions of each threshold; = [ , ] is a unit vector that determines the perturbation direction in SPSA.

[0066] (3) Parameter update:

[0067] wherein, represents a momentum coefficient; represents noise.

[0068] 2. Inner layer control: cluster-assisted extremum search (1) Working condition clustering: based on historical data (vehicle speed, altitude, air temperature, air pressure), the driving working condition is divided into three categories (I, II, III), and the real-time classification is realized through pre-training of the clustering model; (2) Incremental parameter optimization: calculate the incremental parameter for each type of working condition ; (3) Gradient estimation:

[0069] wherein, is a reduction factor, and the actual rule parameter input is:

[0070] It can be understood that in the embodiments of the application, the outer layer of the overall process iteratively optimizes the benchmark parameter in the whole cycle through the FDSA (Finite Difference Stochastic Approximation) algorithm ; the inner layer collects working condition data in real time, classifies through the clustering model, and calculates the incremental parameter for each type of working condition through the extremum search ; the real-time rule parameter is output by fusing and , and the power distribution is controlled, so as to realize adaptive energy management of dynamic working conditions under a fixed route through double-layer optimization, and significantly improve energy efficiency.

[0071] Therefore, unlike the existing fixed logic threshold based on rules, the outer layer of the embodiments of the application iteratively optimizes the global benchmark rule threshold parameter in the whole driving cycle through the finite difference stochastic approximation extremum search algorithm, dynamically adapts to the periodic working condition changes of the route, and breaks through the limitations of fixed parameters; in addition, unlike the uniform processing of working conditions in the prior art, the inner layer of the embodiments of the application fine-tunes the parameters for each type of working condition through the pre-trained working condition clustering model (based on vehicle speed, altitude, etc. Data is divided into three categories), and accurately responds to real-time dynamic differences.

[0072] The execution process and results of the adaptive rule energy management method of the hybrid power logistics vehicle of the application are described below through a specific embodiment.

[0073] To verify the effectiveness and real-time performance of the embodiments of the application, a HIL simulation platform is built for experimental evaluation. As Figure 4As shown, the Speedgoat real-time control platform is equipped with a Celeron 2.0 GHz quad-core CPU, 4.0 GB memory and 32.0 GB solid state disk, and the host computer is configured with 8.0 GB memory and a 1.8 GHz Core i5 processor, as Figure 5 shown.

[0074] The hardware configuration of the experimental platform of the specific embodiments of the present application is shown in Table 1, the main parameters of the experimental vehicle are shown in Table 2, and the range setting of the adjustable threshold in the control strategy is shown in Table 3.

[0075] During the experiment, after the control algorithm and the model are compiled via the Simulink platform, they are downloaded to the Speedgoat controller to carry out real-time simulation operation, and the results generated by the simulation are recorded by the host computer.

[0076] It can be understood that a driving cycle is defined as a standardized test period, which is used to evaluate the fuel efficiency or dynamic performance of a vehicle; based on the data collected from the corresponding national road logistics vehicle route, a typical driving cycle condition is constructed, as Figure 4 shown. The data of the driving cycle is derived from three days of data selected from 135 experimental curves, and the characteristic parameters cover vehicle speed, altitude, air temperature and air pressure.

[0077] 1. CAESRB experiment under cycle condition: First, in the specific embodiments of the present application, the experiment is carried out using the actual driving condition as Figure 4 shown, and each time the disturbance is applied, a group is randomly selected from the 135 recorded conditions; the hardware-in-the-loop experimental results of the SOC trajectory are as Figure 6 shown, which Figure 6 presents the initial trajectory, the intermediate trajectory, the experiment preset termination point trajectory and the optimal DP reference trajectory; under the 91 online changing conditions, the SOC trajectory gradually approaches the solution obtained based on the rule control from the suboptimal state. This result shows that the adaptability of the present application to the driving condition is improved, a more efficient power distribution is achieved, and the fuel consumption is significantly reduced.

[0078] As the disturbance progresses, Figure 7 the adjustment curves of the two rule threshold parameters (reference parameters) and the equivalent cost per 100 kilometers of the driving route are shown: from 159.1 yuan to 148.2 yuan, with a decrease of about 6.9%.

[0079] For the complex condition changes of the fixed logistics vehicle route, the Foton national road route is divided into three types of independent optimization RB threshold parameters. The incremental parameter changes of the two rule threshold parameters under the three types of conditions are as Figure 8 shown.

[0080] 2. With or without inner layer extremum search comparison experiment: To verify the effectiveness of the inner layer clustering assisted extremum search, the effects of the presence and absence of the inner layer ES are compared based on the above-mentioned RB benchmark parameters (where the increment parameters are the optimized solution and zero, respectively), and the relevant results are as shown in Figure 9 The experiment shows that the inner layer ES can adapt to the complex working conditions of the Fukuda National Highway, and can reduce the equivalent fuel cost by 2.3 yuan.

[0081] The final result comparison part shows the differences between the fixed parameter rule strategy (Rule Based, RB), the single layer ES adaptive rule strategy (SESRB), the clustering assisted ES adaptive rule strategy (CAESRB) and the global optimal dynamic programming (DP), and the comparison parameters involved include the initial SOC, the termination SOC, the equivalent energy consumption cost per 100 kilometers and the cost reduction percentage compared with the rule strategy. The above comparison data are shown in Table 4: Table 4

[0082] From the above experiment, it can be known that the embodiments of the application can effectively adapt to the working condition changes in the vehicle energy management process, realize adaptive adjustment of the control parameters under the premise of low calculation amount and high reliability, so that the vehicle energy management system can adapt to various driving conditions, and effectively reduce the energy consumption during vehicle operation.

[0083] According to the adaptive rule energy management method of the hybrid power logistics vehicle provided by the embodiments of the application, a longitudinal dynamics model of the target hybrid power logistics vehicle is constructed, a power battery model type, an oil consumption model and an energy consumption evaluation model objective function of the target hybrid power logistics vehicle are determined, and a hybrid power logistics vehicle model is constructed based on the longitudinal dynamics model, the power battery model type, the oil consumption model and the energy consumption evaluation model objective function. Based on the hybrid power logistics vehicle model, the working mode rule and the plurality of adjustable threshold parameters of the target hybrid power logistics vehicle are determined, and the plurality of adjustable threshold parameters are iteratively optimized in the entire driving cycle of the target hybrid power logistics vehicle through a pre-set finite difference stochastic approximation algorithm. The current working condition data of the target hybrid power logistics vehicle are collected, and the current working condition data are analyzed by clustering to calculate the increment parameters of the different category working condition data in the current working condition data. Based on the working mode rule, the increment parameters and the plurality of optimized adjustable threshold parameters are fused to generate the current rule parameters, so as to control the target hybrid power logistics vehicle to perform adaptive energy management operation through the current rule parameters. The adaptive adjustable rule parameter design is adopted in the application, the real-time and robustness of the strategy are guaranteed through the rule-based method, and the adaptability and optimality based on the optimization method are also possessed.

[0084] Secondly, the adaptive rule energy management device of the hybrid logistics vehicle is described with reference to the drawings.

[0085] Figure 10 is a block schematic diagram of the adaptive rule energy management device of the hybrid logistics vehicle of the embodiment of the present application.

[0086] As Figure 10 shown, the adaptive rule energy management device 10 of the hybrid logistics vehicle includes a modeling module 100, an outer optimization module 200, and an inner control module 300.

[0087] The modeling module 100 is configured to construct a longitudinal dynamics model of the target hybrid logistics vehicle, determine a power battery model type, a fuel consumption model, and an energy consumption evaluation model objective function of the target hybrid logistics vehicle, and construct a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the fuel consumption model, and the energy consumption evaluation model objective function.

[0088] The outer optimization module 200 is configured to determine a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle based on the hybrid logistics vehicle model, and iteratively optimize the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid logistics vehicle through a preset finite difference stochastic approximation algorithm.

[0089] The inner control module 300 is configured to collect current working condition data of the target hybrid logistics vehicle, perform cluster analysis on the current working condition data, calculate incremental parameters of different category working condition data in the current working condition data, generate current rule parameters based on the working mode rule and by fusing the incremental parameters and the optimized plurality of adjustable threshold parameters, and control the target hybrid logistics vehicle to perform adaptive energy management operation through the current rule parameters.

[0090] Optionally, in an embodiment of the present application, the modeling module 100 includes a first calculation unit, a second calculation unit, a third calculation unit, a construction unit, and a fourth calculation unit.

[0091] The first calculation unit is configured to calculate wheel torque of the target hybrid logistics vehicle, and construct the longitudinal dynamics model of the target hybrid logistics vehicle based on the wheel torque.

[0092] The second calculation unit is configured to determine open circuit voltage, internal resistance, and battery capacity of a power battery of the target hybrid logistics vehicle, and calculate load power and motor power of the power battery based on the open circuit voltage.

[0093] The third calculation unit is configured to calculate corresponding state of charge according to the load power, the motor power, the open circuit voltage, the internal resistance, and the battery capacity, and construct the power battery model through the state of charge.

[0094] The constructing unit is configured to calculate an engine power of the target hybrid logistic vehicle, and calculate a corresponding compressed natural gas consumption rate according to the engine power, so as to construct a fuel consumption model by the compressed natural gas consumption rate.

[0095] The fourth calculating unit is configured to calculate an equivalent energy consumption cost per 100 kilometers of the target hybrid logistic vehicle based on the power battery model type and the fuel consumption model, so as to determine an energy consumption evaluation model objective function according to the equivalent energy consumption cost per 100 kilometers.

[0096] Optionally, in an embodiment of the present application, the outer layer optimization module 200 comprises a judging unit, a comparison and analysis unit, and a running unit.

[0097] The judging unit is configured to judge whether a residual power of the target hybrid logistic vehicle is greater than a preset critical residual power based on the hybrid logistic vehicle model.

[0098] The comparison and analysis unit is configured to, if the residual power is greater than the critical residual power, set a working mode rule of the target hybrid logistic vehicle as an electric vehicle-power consumption mode rule, and perform a comparison and analysis operation on a demand torque and a current vehicle speed of the target hybrid logistic vehicle under the electric vehicle-power consumption mode rule to obtain a corresponding comparison and analysis result, and run the target hybrid logistic vehicle according to the comparison and analysis result.

[0099] The running unit is configured to, if the residual power is less than the critical residual power, set the working mode rule of the target hybrid logistic vehicle as a power maintaining mode rule, and run the target hybrid logistic vehicle by using a preset electric auxiliary control strategy.

[0100] Optionally, in an embodiment of the present application, the comparison and analysis unit comprises a determining subunit, a comparison subunit, a first control subunit, a second control subunit, a third control subunit, a driving subunit, and a fourth control subunit.

[0101] The determining subunit is configured to determine a lowest speed threshold value, a cutoff torque, an engine optimal torque, and a motor maximum torque corresponding to the target hybrid logistic vehicle.

[0102] The comparison subunit is configured to compare the current vehicle speed of the target hybrid logistic vehicle with the lowest speed threshold value, and compare the demand torque of the target hybrid logistic vehicle with the cutoff torque, the engine optimal torque, and the motor maximum torque.

[0103] The first control subunit is configured to control the target hybrid logistic vehicle to run in a pure electric mode when the demand torque is less than the cutoff torque, or the current vehicle speed is less than the lowest speed threshold value.

[0104] The second control subunit is configured to control the target hybrid logistic vehicle to operate in a hybrid driving mode when the demand torque is greater than a preset maximum torque of the motor and a current vehicle speed is less than a minimum speed threshold.

[0105] The third control subunit is configured to control the engine of the target hybrid logistic vehicle to maintain operation at an optimal torque point and charge the battery through the motor when the demand torque is greater than the cutoff torque but less than an optimal torque of the engine. The driving subunit is configured to drive the target hybrid logistic vehicle through the engine alone when the demand torque is greater than the cutoff torque and the demand torque is between the optimal torque of the engine and a maximum torque of the motor.

[0106] The fourth control subunit is configured to control the target hybrid logistic vehicle to operate in a hybrid driving mode when the demand torque is greater than the cutoff torque and the demand torque is greater than the maximum torque of the motor.

[0107] Optionally, in an embodiment of the present application, the outer layer optimization module 200 further comprises a function construction unit and an extreme value searching unit.

[0108] The function construction unit is configured to determine a minimum 100 km equivalent energy consumption cost of the target hybrid logistic vehicle, and construct a corresponding outer layer optimization objective function based on the minimum 100 km equivalent energy consumption cost.

[0109] The extreme value searching unit is configured to perform extreme value search on the outer layer optimization objective function by using a finite difference stochastic approximation algorithm to optimize a plurality of adjustable threshold parameters.

[0110] It should be noted that the foregoing explanation and description of the adaptive rule energy management method embodiment of the hybrid logistic vehicle also applies to the adaptive rule energy management device of the hybrid logistic vehicle of the embodiment, which will not be described herein again.

[0111] The adaptive rule energy management device of the hybrid power logistics vehicle provided by the embodiment of the application comprises a modeling module 100, which is configured to construct a longitudinal dynamics model of a target hybrid power logistics vehicle, and determine a power battery model type, a fuel consumption model and an energy consumption evaluation model target function of the target hybrid power logistics vehicle, so as to construct a hybrid power logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the fuel consumption model and the energy consumption evaluation model target function; an outer optimization module 200, which is configured to determine a working mode rule and a plurality of adjustable threshold parameters of the target hybrid power logistics vehicle based on the hybrid power logistics vehicle model, and iteratively optimize the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid power logistics vehicle through a preset finite difference stochastic approximation algorithm; and an inner control module 300, which is configured to collect current working condition data of the target hybrid power logistics vehicle, and perform cluster analysis on the current working condition data to calculate incremental parameters of different categories of working condition data in the current working condition data, and generate current rule parameters based on the working mode rule and by fusing the incremental parameters and the optimized plurality of adjustable threshold parameters, so as to control the target hybrid power logistics vehicle to perform adaptive energy management operation through the current rule parameters. The adaptive adjustable rule parameter design is adopted, the real-time performance and the robustness of the strategy are guaranteed through the rule-based method, and the adaptability and the optimality based on the optimization method are also achieved.

[0112] Figure 11 A structural schematic diagram of an electronic device is provided for the embodiment of the application. The electronic device can include: The memory 1101, the processor 1102, and a computer program stored in the memory 1101 and executable on the processor 1102.

[0113] The processor 1102 implements the adaptive rule energy management method of the hybrid power logistics vehicle provided in the above embodiment when executing the program.

[0114] Further, the electronic device further includes: The communication interface 1103 is configured to communicate between the memory 1101 and the processor 1102.

[0115] The memory 1101 is configured to store the computer program executable on the processor 1102.

[0116] The memory 1101 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0117] If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 11 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0118] Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102 and the communication interface 1103 can complete communication between each other through an internal interface.

[0119] The processor 1102 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0120] The embodiment of the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement the adaptive rule energy management method of the hybrid power logistics vehicle.

[0121] The embodiment of the present application further provides a computer program product, which includes a computer program, and the computer program is executed to implement the adaptive rule energy management method of the hybrid power logistics vehicle.

[0122] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0123] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0124] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein are not necessarily performed in the order shown or discussed, including, for example, as performed by a computer processor. Alternate implementations can perform functions or steps described in different orders, including simultaneously or in reverse order.

[0125] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0126] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] Those of skill in the art would understand that the steps carried out by the above-mentioned embodiments can be implemented by a program instructing the relevant hardware to complete all or part of the steps, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiments or a combination thereof.

[0128] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0129] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An adaptive rule-based energy management method for a hybrid delivery vehicle, characterized by, The method comprises the following steps: constructing a longitudinal dynamics model of a target hybrid logistics vehicle, determining a power battery model type, an oil consumption model and an energy consumption evaluation model objective function of the target hybrid logistics vehicle, and constructing a hybrid logistics vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model and the energy consumption evaluation model objective function; based on the hybrid logistics vehicle model, determining a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle, and iteratively optimizing the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid logistics vehicle through a preset finite difference stochastic approximation algorithm; collecting current working condition data of the target hybrid logistics vehicle, performing cluster analysis on the current working condition data to calculate incremental parameters of different category working condition data in the current working condition data, and generating current rule parameters based on the working mode rule and fusing the incremental parameters and the optimized plurality of adjustable threshold parameters to control the target hybrid logistics vehicle to perform adaptive energy management operation through the current rule parameters.

2. The method of claim 1, wherein, The method of constructing a longitudinal dynamics model of a target hybrid logistics vehicle and determining a power battery model type, an oil consumption model and an energy consumption evaluation model objective function of the target hybrid logistics vehicle comprises: calculating wheel torque of the target hybrid logistics vehicle and constructing a longitudinal dynamics model of the target hybrid logistics vehicle based on the wheel torque; determining open circuit voltage, internal resistance and battery capacity of a power battery of the target hybrid logistics vehicle, and calculating load power and motor power of the power battery based on the open circuit voltage; calculating corresponding state of charge according to the load power, the motor power, the open circuit voltage, the internal resistance and the battery capacity to construct the power battery model through the state of charge; calculating engine power of the target hybrid logistics vehicle and calculating corresponding compressed natural gas consumption rate according to the engine power to construct the oil consumption model through the compressed natural gas consumption rate; calculating 100 km equivalent energy consumption cost of the target hybrid logistics vehicle based on the power battery model type and the oil consumption model to determine the energy consumption evaluation model objective function according to the 100 km equivalent energy consumption cost.

3. The method of claim 1, wherein, The method of determining a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistics vehicle based on the hybrid logistics vehicle model comprises: based on the hybrid logistics vehicle model, judging whether the remaining power of the target hybrid logistics vehicle is greater than a preset critical remaining power; if the remaining power is greater than the critical remaining power, the working mode rule of the target hybrid logistics vehicle is an electric vehicle-power consumption mode rule, and a demand torque and a current vehicle speed of the target hybrid logistics vehicle are compared and analyzed under the electric vehicle-power consumption mode rule to obtain a corresponding comparison and analysis result, and the target hybrid logistics vehicle is operated according to the comparison and analysis result; If the remaining power is less than the critical remaining power, a working mode rule of the target hybrid logistic vehicle is a power retention mode rule, and a preset electric auxiliary control strategy is adopted to operate the target hybrid logistic vehicle.

4. The method of claim 3, wherein, The comparison and analysis operation on the demand torque and the current vehicle speed of the target hybrid logistic vehicle under the electric vehicle-power consumption mode rule is performed to obtain a corresponding comparison and analysis result, and the target hybrid logistic vehicle is operated according to the comparison and analysis result, including: determining a minimum speed threshold, a cutoff torque, an engine optimal torque and a motor maximum torque corresponding to the target hybrid logistic vehicle; comparing the current vehicle speed of the target hybrid logistic vehicle with the minimum speed threshold, and comparing the demand torque of the target hybrid logistic vehicle with the cutoff torque, the engine optimal torque and the motor maximum torque; when the demand torque is less than the cutoff torque or the current vehicle speed is less than the minimum speed threshold, controlling the target hybrid logistic vehicle to operate in a pure electric mode; when the demand torque is greater than a preset motor maximum torque and the current vehicle speed is less than the minimum speed threshold, controlling the target hybrid logistic vehicle to operate in a hybrid drive mode; when the demand torque is greater than the cutoff torque but less than the engine optimal torque, controlling the engine of the target hybrid logistic vehicle to operate at an optimal torque point, and charging the battery through the motor; when the demand torque is greater than the cutoff torque and the demand torque is between the engine optimal torque and the motor maximum torque, driving the target hybrid logistic vehicle through the engine alone; when the demand torque is greater than the cutoff torque and the demand torque is greater than the motor maximum torque, controlling the target hybrid logistic vehicle to operate in the hybrid drive mode.

5. The method of claim 1, wherein, The iterative optimization of the plurality of adjustable threshold parameters in the entire driving cycle of the target hybrid logistic vehicle through the preset finite difference stochastic approximation algorithm includes: determining a minimum 100-kilometer equivalent energy consumption cost of the target hybrid logistic vehicle, and constructing a corresponding outer optimization objective function based on the minimum 100-kilometer equivalent energy consumption cost; performing extreme value search on the outer optimization objective function by using the finite difference stochastic approximation algorithm to optimize the plurality of adjustable threshold parameters.

6. An adaptive rule energy management device for a hybrid delivery vehicle, characterized by, including: a modeling module configured to construct a longitudinal dynamics model of a target hybrid logistic vehicle, and determine a power battery model type, an oil consumption model and an energy consumption evaluation model objective function of the target hybrid logistic vehicle, so as to construct a hybrid logistic vehicle model based on the longitudinal dynamics model, the power battery model type, the oil consumption model and the energy consumption evaluation model objective function; An outer optimization module is configured to determine a working mode rule and a plurality of adjustable threshold parameters of the target hybrid logistic vehicle based on the hybrid logistic vehicle model, and iteratively optimize the plurality of adjustable threshold parameters in a full driving cycle of the target hybrid logistic vehicle by using a preset finite difference stochastic approximation algorithm. An inner control module is configured to collect current working condition data of the target hybrid logistic vehicle, perform cluster analysis on the current working condition data to calculate incremental parameters of different categories of working condition data in the current working condition data, and generate current rule parameters based on the working mode rule and by fusing the incremental parameters and the optimized plurality of adjustable threshold parameters, so as to control the target hybrid logistic vehicle to perform adaptive energy management operation by using the current rule parameters.

7. The apparatus of claim 6, wherein, The modeling module comprises: A first calculation unit is configured to calculate wheel torque of the target hybrid logistic vehicle, and construct a longitudinal dynamics model of the target hybrid logistic vehicle based on the wheel torque; A second calculation unit is configured to determine open circuit voltage, internal resistance and battery capacity of a power battery of the target hybrid logistic vehicle, and calculate load power and motor power of the power battery based on the open circuit voltage; A third calculation unit is configured to calculate corresponding state of charge according to the load power, the motor power, the open circuit voltage, the internal resistance and the battery capacity, so as to construct the power battery model by using the state of charge; A construction unit is configured to calculate engine power of the target hybrid logistic vehicle, and calculate corresponding compressed natural gas consumption rate according to the engine power, so as to construct the fuel consumption model by using the compressed natural gas consumption rate; A fourth calculation unit is configured to calculate 100 km equivalent energy consumption cost of the target hybrid logistic vehicle based on the power battery model type and the fuel consumption model, so as to determine the energy consumption evaluation model objective function according to the 100 km equivalent energy consumption cost.

8. An electronic device, comprising: The computer program is executed by the processor to implement the adaptive rule energy management method of the hybrid logistic vehicle. The computer program is executed by the processor to implement the adaptive rule energy management method of the hybrid logistic vehicle.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the adaptive rule energy management method of the hybrid logistic vehicle.

10. A computer program product comprising a computer program, characterized in that, ​

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