Energy consumption control method and device based on vehicle load

Abstract

This application relates to the field of data processing technology, and in particular to a method and device for energy consumption control based on vehicle load. The method includes the following steps: calculating the vehicle's current energy consumption trend based on various vehicle operating data and pre-constructed high-order coupled dynamic equations; selecting the vehicle's target operating point based on the estimated energy consumption when the vehicle's current operating condition is in a bistable range, based on the vehicle's current energy consumption; obtaining the vehicle's actual fuel consumption and generating a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption; and controlling the vehicle to adjust its load and speed based on the target operating point until the vehicle's actual energy consumption reaches the target energy consumption. This solves the problems of related technologies where fuel consumption feedback control ignores the thermodynamic effect of load as a driving force, easily leading to control instability under heavy loads on steep slopes; and is unable to handle real-time disturbances caused by various driving factors, and cannot achieve adaptive optimization of operating conditions.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and apparatus for energy consumption control based on vehicle load. Background Technology

[0002] Vehicles undertaking cargo transfer tasks in complex geological environments have a high proportion of energy consumption in their operating costs. Typical scenarios include heavy-load uphill driving, multi-vehicle platooning, and dynamic road disturbances. For example, in heavy-load uphill driving, when a 30-ton vehicle is driving on a steep slope, the engine torque demand increases sharply, and fuel consumption will be significantly higher than on flat roads. In multi-vehicle platooning, when 10-20 vehicles are working together, uneven load distribution can cause some vehicles to be continuously overloaded, creating localized energy consumption hotspots. Dynamic road disturbances include sudden changes in road slope and fluctuations in the coefficient of slippage, which can cause instability in the operating point of the power system.

[0003] Fuel consumption feedback control methods in related technologies are mainly based on PID control (Proportional, Integral, Differential), which obtains real-time fuel consumption through OBD (On-Board Diagnostics) sensors and adjusts the throttle opening to make the fuel consumption track the set value; some solutions also use a preset "load-gradient-speed" lookup table to select the operating point according to road conditions.

[0004] However, in related technologies, the fuel consumption feedback control method based on PID control only considers second-order vehicle-to-vehicle interaction and ignores the thermodynamic effect of load as driving force, which can easily lead to control instability under steep slopes and heavy loads. The method of querying preset tables relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and is difficult to meet the fuel consumption control needs in actual driving scenarios, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a method and device for energy consumption control based on vehicle load, in order to solve the problems in related technologies, such as the fuel consumption feedback control method based on PID control only considering second-order vehicle-to-vehicle interaction and ignoring the thermodynamic effect of load as driving force, which easily leads to control instability under heavy load on steep slopes; and the method of querying preset tables relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and is difficult to meet the fuel consumption control needs in actual driving scenarios.

[0006] The first aspect of this application provides a vehicle load-based energy consumption control method, comprising the following steps: acquiring various operating data of the vehicle, and calculating the current energy consumption trend of the vehicle based on the various operating data and a pre-constructed high-order coupled dynamic equation; detecting the current operating condition of the vehicle based on the current energy consumption trend, and selecting a target operating point of the vehicle based on the estimated energy consumption of the vehicle when the current operating condition is in a bistable range; acquiring the actual fuel consumption of the vehicle, and generating a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption; and controlling the vehicle to execute the load adjustment command to adjust the load and speed of the vehicle based on the target operating point until the actual energy consumption of the vehicle reaches the target energy consumption.

[0007] A second aspect of this application provides an energy consumption control device based on vehicle load, comprising: an acquisition module for acquiring various operating data of the vehicle, and calculating the current energy consumption trend of the vehicle based on the various operating data and a pre-constructed high-order coupled dynamic equation; a selection module for detecting the current operating condition of the vehicle based on the current energy consumption trend, and selecting a target operating point of the vehicle based on the estimated energy consumption of the vehicle when the current operating condition is in a bistable range; and a control module for acquiring the actual fuel consumption of the vehicle, generating a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption, and controlling the vehicle to execute the load adjustment command to adjust the load and speed of the vehicle based on the target operating point until the actual energy consumption of the vehicle reaches the target energy consumption.

[0008] A third aspect of this application provides a vehicle, including: 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 energy consumption control method based on vehicle load as described in the above embodiments.

[0009] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described energy consumption control method based on vehicle load.

[0010] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described energy consumption control method based on vehicle load.

[0011] This application embodiment can calculate the vehicle's current energy consumption trend based on various vehicle operating data and pre-constructed high-order coupled dynamic equations. When the vehicle's current operating condition is in a bistable range, the target operating point of the vehicle is selected, and the vehicle's load and speed are adjusted until the vehicle's actual energy consumption reaches the target energy consumption. Thus, by introducing a third-order coupling of load-road-engine, torque loss during heavy-load climbing is effectively quantified. Furthermore, by modeling the monotonic response of load-energy consumption, the dual low-energy-consumption attractor induced by road conditions is captured. Based on the attractor switching mechanism and local feedback control law, adaptive optimization of the vehicle's operating conditions is achieved, significantly reducing fuel consumption during heavy-load climbing. Additionally, by utilizing the adaptive switching of the bistable operating point and the chaotic perturbation injection mechanism, this application can suppress speed fluctuations caused by sudden changes in road conditions. Simultaneously, by optimizing the action logic of the hydraulic actuator, fuel waste is reduced, achieving dynamic optimization of the vehicle's energy consumption. This solves the problems in related technologies, such as the fact that the fuel consumption feedback control method based on PID control only considers second-order vehicle-to-vehicle interaction and ignores the thermodynamic effect of load as driving force, which easily leads to control instability under steep slopes and heavy loads; and the method of querying preset tables relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and is difficult to meet the fuel consumption control needs in actual driving scenarios.

[0012] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0013] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating an energy consumption control method based on vehicle load according to an embodiment of this application; Figure 2 This is a schematic diagram of the response curve between load capacity and energy consumption according to one embodiment of this application; Figure 3 This is a schematic diagram of phase evolution under different slopes according to an embodiment of this application; Figure 4 This is a schematic diagram of the energy consumption, Pareto front, and bistable state of phase hysteresis according to an embodiment of this application; Figure 5 This is a schematic diagram of the Lyapunov function decreasing over time according to an embodiment of this application; Figure 6 This is a schematic diagram comparing the Lyapunov exponents before and after chaos detection in one embodiment of this application; Figure 7 This is a schematic diagram comparing the engine speed response before and after disturbance injection according to an embodiment of this application; Figure 8 This is a schematic diagram comparing the energy consumption fluctuations of a conventional PID control method and a vehicle load-based energy consumption control method according to an embodiment of this application. Figure 9 This is a schematic diagram of the energy consumption control device based on vehicle load provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0014] Figure label: 10-Energy consumption control device based on vehicle load; 100-Acquisition module, 200-Selection module and 300-Control module; 1001-Memory, 1002-Processor and 1003-Communication interface. Detailed Implementation

[0015] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0016] The energy consumption control method and apparatus based on vehicle load according to embodiments of this application are described below with reference to the accompanying drawings. In the related technologies mentioned in the background section, the PID-based fuel consumption feedback control method only considers second-order vehicle-to-vehicle interaction, ignoring the thermodynamic effect of load as a driving force, which easily leads to control instability under steep slopes and heavy loads. The method of querying a preset table relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and is difficult to meet the fuel consumption control requirements in actual driving scenarios. This application provides an energy consumption control method based on vehicle load. In this method, the current energy consumption trend of the vehicle can be calculated based on various vehicle operating data and pre-constructed high-order coupled dynamic equations. When the current operating condition of the vehicle is in a bistable range, the target operating point of the vehicle is selected, and the vehicle's load and speed are adjusted until the actual energy consumption of the vehicle reaches the target energy consumption. This invention achieves effective quantification of torque loss during heavy-load hill climbing by introducing a third-order coupling between load, road, and engine. It also captures road-induced low-energy attractors through load-energy monotonic response modeling, and then achieves adaptive optimization of vehicle operating conditions based on attractor switching mechanisms and local feedback control laws, significantly reducing fuel consumption during heavy-load hill climbing. Furthermore, by utilizing bistable operating point adaptive switching and chaotic perturbation injection mechanisms, this application can suppress speed fluctuations caused by sudden changes in road conditions. Simultaneously, by optimizing the hydraulic actuator's action logic, fuel waste is reduced, achieving dynamic optimization of vehicle energy consumption. This solves the problems in related technologies, such as PID-based fuel consumption feedback control methods that only consider second-order vehicle-to-vehicle interactions, ignoring the thermodynamic effects of load as a driving force, easily leading to control instability during steep hills and heavy loads; and methods that rely on pre-defined tables, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and are difficult to meet the fuel consumption control requirements of actual driving scenarios.

[0017] Before explaining the energy consumption control method based on vehicle load in the embodiments of this application, the load-adaptive energy consumption control system involved in the energy consumption control method based on vehicle load in the embodiments of this application will be explained first.

[0018] For example, the load-adaptive energy consumption control system in the embodiments of this application includes, but is not limited to: The load detection module is used to acquire vehicle load information in real time. The road condition sensing module is used to acquire road condition parameters such as slope and friction coefficient; The power status acquisition module is used to acquire power system status information such as engine speed and fuel consumption; The high-order coupled dynamics calculation unit calculates the vehicle's current energy consumption trend based on load information and road condition parameters, according to the pre-constructed high-order coupled dynamics equations. The optimal operating point selection unit automatically selects the optimal steady-state operating point of high load-low speed or low load-high speed based on the load-hysteresis Pareto front diagram, and feeds it back to the feedback control unit. The feedback control unit generates a corresponding load adjustment command based on the deviation between the real-time fuel consumption obtained by the power status acquisition module and the preset target fuel consumption. The load adjustment execution module is used to receive load adjustment commands output by the feedback control unit and control the unloading mechanism or load distribution device to achieve automatic load adjustment. The stability analysis unit, based on the Lyapunov function and Jacobi matrix model, performs stability judgment on the system state under the current operating conditions and determines whether it has entered a chaotic or critical operating condition. The chaos suppression unit generates and injects small-amplitude load disturbances to suppress energy consumption fluctuations when the stability analysis unit determines that the system has entered a chaotic state.

[0019] Specifically, Figure 1 This is a flowchart illustrating an energy consumption control method based on vehicle load provided in an embodiment of this application.

[0020] like Figure 1 As shown, the energy consumption control method based on vehicle load includes the following steps: Step S101: Obtain various operating data of the vehicle, and calculate the current energy consumption trend of the vehicle based on the various operating data and the pre-constructed high-order coupled dynamic equations. Step S102: Based on the current energy consumption trend, detect the current operating condition of the vehicle, and select the target operating point of the vehicle based on the vehicle's estimated energy consumption when the current operating condition is in a bistable range. Step S103: Obtain the vehicle's actual fuel consumption, and generate a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption. Based on the target operating point, control the vehicle to execute the load adjustment command to adjust the vehicle's load and speed until the vehicle's actual energy consumption reaches the target energy consumption.

[0021] As one possible approach, this application can acquire various operational data of the vehicle and, in conjunction with pre-constructed high-order coupled dynamic equations, calculate the vehicle's current energy consumption trend.

[0022] Here, the pre-constructed high-order coupled dynamic equations can be understood as functional expressions based on historical transportation data of vehicles, used to represent the quantitative coupling relationship between vehicle load, road conditions and vehicle dynamics.

[0023] For example, the various vehicle operating data obtained in this application include, but are not limited to, the slope of the road the vehicle is currently traveling on, the coefficient of friction, the current engine speed, the current load, and the current fuel consumption. For instance, a piezoelectric load sensor is installed on the vehicle chassis load-bearing beam to collect the vehicle's current load value in real time; a multi-axis inertial measurement unit and a lidar fusion device are integrated into the top of the cockpit to obtain the road surface slope and coefficient of friction of the road the vehicle is currently traveling on; and an ultrasonic flow meter is embedded in the engine fuel supply line to monitor the vehicle's real-time fuel consumption.

[0024] Furthermore, embodiments of this application can calculate the current energy consumption trend of the vehicle based on these operational data and according to a pre-constructed high-order coupled dynamic equation. Here, the current energy consumption trend refers to the pattern and direction of energy consumption per unit distance (or unit time) of the vehicle changing with driving status (vehicle speed, load, road conditions, driving behavior, etc.), which can effectively reflect the dynamic changes in vehicle energy consumption.

[0025] Next, this embodiment of the application can detect whether the vehicle's current operating condition is in a bistable range based on the vehicle's current energy consumption trend. Here, the current operating condition can be understood as the set of operating states the vehicle is currently in, including but not limited to key parameters such as vehicle speed, engine speed, road resistance, and gradient, which are direct factors determining the energy consumption level.

[0026] The bistable range here refers to a range in the vehicle energy consumption characteristic mapping where the operating parameters change continuously. At each fixed operating parameter point within this range, the energy consumption of the vehicle's powertrain simultaneously exhibits two distinct, locally stable low energy consumption levels. When the vehicle is in this range, even if the operating parameters experience minor disturbances, the vehicle's energy consumption can still stably remain at one of the two low energy consumption levels (specifically determined by historical conditions or initial conditions), and will not fluctuate continuously and gradually with changes in the operating parameters.

[0027] For a given set of fixed operating parameters, the system has two locally asymptotically stable energy consumption equilibrium points (both low energy consumption points). During actual operation, depending on the historical path or initial state, the vehicle can stably converge to and maintain at one of these equilibrium points. When the operating parameters change continuously within a certain range, these two stable energy consumption equilibrium points always coexist; this parameter range is the bistable interval.

[0028] This application's embodiments analyze the variation of vehicle energy consumption with various operating parameters to detect whether the current operating condition allows the vehicle energy consumption to possess the bistable self-sustaining characteristic: that is, under fixed operating parameters, the system has a stable equilibrium point with two low energy consumptions, and the vehicle can self-sustain and return to the original equilibrium point after being subjected to minor disturbances. If the vehicle's current operating condition allows the vehicle energy consumption to possess the bistable self-sustaining characteristic, then the current operating condition is determined to be in the bistable range; otherwise, it is determined that the current operating condition does not fall into the bistable range.

[0029] Furthermore, in this embodiment of the application, when the vehicle's current operating condition is in a bistable range, a target operating point for the vehicle can be selected based on the vehicle's estimated energy consumption. Here, the estimated energy consumption refers to the energy consumption calculated under different driving parameter variations; the target operating point can be understood as the driving parameter state where the vehicle achieves the lowest energy consumption among vehicle states with different energy consumption conditions, such as a state of increasing load and decreasing speed, or a state of decreasing load and increasing speed, etc.

[0030] Then, in this embodiment of the application, the actual fuel consumption of the vehicle can be obtained, and a load adjustment command can be generated based on the difference between the actual fuel consumption and the target energy consumption. Based on the target operating point, the vehicle is controlled to execute the load adjustment command to adjust the vehicle's load and speed. Here, the target fuel consumption can be understood as the fuel consumption value that the vehicle is expected to achieve in a pre-set manner.

[0031] By using the difference between the two, embodiments of this application can adopt a state of increasing load and decreasing speed, or a state of decreasing load and increasing speed, corresponding to the target operating point, to reduce or increase the vehicle's load and speed. This can be achieved by controlling the unloading mechanism or load distribution device to realize automatic load adjustment; until the vehicle's fuel consumption can be reduced to a certain level, thereby reducing fuel consumption while simultaneously ensuring that the vehicle's actual energy consumption reaches the target energy consumption. Here, the target energy consumption can be understood as the lowest energy consumption that the vehicle's actual energy consumption can achieve when the vehicle's actual fuel consumption reaches the target fuel consumption.

[0032] This application embodiment can accurately calculate the energy consumption trend of a vehicle and determine the bistable range through a pre-constructed high-order coupled dynamic equation. Finally, it selects the optimal operating point by combining the estimated energy consumption of the vehicle under different driving parameter changes. Then, according to the load adjustment command, it dynamically adjusts the vehicle's load and speed, which can ensure that the vehicle always operates in the low energy consumption steady-state range, effectively reducing the overall vehicle energy consumption and improving the range. It can also optimize the operating stability of the power system, reduce mechanical wear caused by frequent switching of operating conditions, and achieve a dual improvement in energy consumption control and system reliability.

[0033] Optionally, in one embodiment of this application, before calculating the current energy consumption trend of the vehicle based on various operational data and pre-constructed high-order coupled dynamic equations, the method further includes: collecting historical transportation data of the vehicle, and abstracting the energy consumption patterns of the vehicle under different power states into a state network based on the load value and its corresponding energy consumption value in historical transportation, so as to generate a monotonicity model between the vehicle's load and energy consumption based on the state network; constructing a quantitative coupling relationship between the vehicle's load, road conditions and dynamic model based on the monotonicity model; and obtaining a pre-constructed high-order coupled dynamic equation for quantifying the energy consumption change information of the vehicle based on the quantitative coupling relationship between the load, road conditions and dynamic model.

[0034] The expression for the monotonicity model can be, but is not limited to, expressed as:

[0035]

[0036] in, Indicates all It is the root and contains edges The sum of the spanning tree weights, Indicates all Root and does not contain The sum of the spanning tree weights of the edges. ,in , , , , , All are non-negative real numbers.

[0037] In some embodiments, when constructing the pre-constructed high-order coupled dynamic equations, this application may, but is not limited to, first collect historical transportation data of the vehicle, and based on the load value and its corresponding energy consumption value in historical transportation, abstract the energy consumption pattern of the vehicle under different power states into a state network, thereby generating a monotonicity model between the vehicle's load and energy consumption based on the state network.

[0038] In short, the embodiments of this application can establish load parameters based on the principle of non-equilibrium thermodynamic constraints. Vehicle energy consumption status The quantitative response model is a monotonicity model of the relationship between vehicle load and energy consumption.

[0039] For example, firstly, this application can collect the load value and corresponding energy consumption value from historical transportation data, and abstract the vehicle power system into a Markov state network. This abstraction process will correspond each state of the vehicle power system to different energy consumption modes, such as empty, half-loaded, and fully loaded.

[0040] For example, this application can treat changes in vehicle load as an external driving force and vehicle energy consumption as a probabilistic weighted sum of system states. Specifically, in treating changes in load as an external driving force, embodiments of this application may, but are not limited to, employ state transition rate formulas and unbalanced driving force formulas to establish expressions for how load affects the transition rates between vehicle power system states.

[0041] The formulas for state transition rate and non-equilibrium driving force can be, but are not limited to, expressed as follows: Formula for state transition rate: ; Formula for unbalanced driving force: ; in, Representing the state transition rate, the formula for the state transition rate can effectively describe the transition of a vehicle's power system from state to state. transition to state The rate; Representing state Energy level (unit: dimensionless or energy-related unit) In vehicle power systems, different states represent different operating modes (such as idling, constant speed driving, climbing, etc.). Energy level can indicate the stability of that state; the lower the energy, the more stable the state. Represents from state transition to state The energy barrier (unit: dimensionless) is the highest energy barrier, and the more difficult the transition is to occur. Represented by load capacity The non-equilibrium driving force contributed (unit: dimensionless) can disrupt the delicate balance and drive the power system away from the equilibrium state; This represents an unbalanced driving force. The formula for unbalanced driving forces can incorporate the load as an external driving force into the model. This represents the fundamental unbalanced driving force (such as the inherent torque characteristics of an engine). Represents the load value (unit: tons), which, as an adjustable external driving force, directly affects the transition rate.

[0042] According to the state transition rate formula and the non-equilibrium driving force formula, when the load... When increasing, Increase, thereby changing the transition rate Specifically, An increase in will promote or inhibit a particular transition, depending on ... The sign and magnitude of the sign change the probability distribution of the dynamic system in each state.

[0043] Assuming state The state is "uniform speed on a flat road". To "climb the hill", the load is increased ( Increase) leading to Increase → transition rate The tendency to transition from flat ground to incline increases. This makes it easier for the power system to enter an incline state, and the probability of a steady state in this state increases, resulting in higher energy consumption.

[0044] In the process of treating energy consumption values ​​as a probabilistic weighted sum of the states of the dynamic system, the steady-state probability distribution of the system is... The master equation that is satisfied can be, but is not limited to, expressed as follows:

[0045] in, Represents the state probability vector; This indicates that the power system is in a certain state. probability ; Represents the transition rate matrix, which is composed of composition; This indicates that the probability distribution no longer changes, meaning the dynamical system has reached a steady state.

[0046] This master equation can describe the stable state reached by the vehicle's power system during long-term operation.

[0047] Energy consumption It can be defined, but is not limited to, as a weighted sum of state probabilities, as shown in the following formula. This formula defines the long-term average fuel consumption as the statistical expectation of fuel consumption for each state, which can be expressed, but is not limited to, as follows:

[0048] in, For state Fuel consumption per unit time (unit: L / h); For state The steady-state probability; This is the total fuel consumption of the system (unit: L / h).

[0049] Subsequently, embodiments of this application may, but are not limited to, using the matrix tree theorem to analyze the network topology and prove that the partial derivative of energy consumption with respect to load is always positive. That is, energy consumption will inevitably increase monotonically as the load increases. For example... Figure 2 As shown, Figure 2 This is a schematic diagram of the response curve between load and energy consumption according to one embodiment of this application.

[0050] For example, according to the matrix tree theorem, the steady-state probability of a Markov chain can be expressed as the sum of all nodes... Let be the sum of the weights of the directed spanning tree of the root. Specifically, let Here is the state transition graph of a Markov chain, with the number of nodes being... In this application embodiment, a Laplace matrix can be defined. for:

[0051] Then steady state probability It can be represented as:

[0052] in, for Delete the Line number Submatrix after column, Indicates the vehicle powertrain system from state transition to state rate, Indicates the vehicle powertrain system from state transition to state rate, Laplace matrix Delete the Line number Submatrix after column, Let be the number of nodes in the state transition graph of the Markov chain.

[0053] Furthermore, considering the load-bearing capacity For transition rate and Due to the influence of the unbalanced driving force formula, the embodiments of this application can be derived from the unbalanced driving force formula. Substituting into the transition rate formula, we can obtain, but are not limited to, the following formula:

[0054]

[0055] right Taking the partial derivative yields the following formula:

[0056]

[0057] According to the matrix tree theorem, It can be expressed as about and rational functions:

[0058] Among them, coefficient All are non-negative real numbers, and are consistent with... , It is irrelevant; it depends only on the topology and other transition rates, i.e., the coefficients. It is determined by topology and other transition rates.

[0059] Again beg The partial derivatives of can be used to obtain the following formula:

[0060] Substituting the partial derivatives and simplifying, we get the following formula:

[0061] Further expansion of the molecules yields the following formula: molecule =

[0062] That is: molecule =

[0063] therefore:

[0064] in, .

[0065] Graph theory analysis reveals that in strongly connected state networks, the spanning tree structure between nodes exhibits symmetry; therefore, the coefficients... , , They have the same sign (both positive or both negative).

[0066] Specifically, the embodiments of this application may be defined, but are not limited to, the following: For all It is the root and contains edges The sum of the spanning tree weights; For all Root and does not contain The weights of the spanning tree edges.

[0067] The proportional relationship can then be expressed as follows:

[0068]

[0069] This proportional relationship is the load-energy consumption monotonicity model.

[0070] because (The weight of the spanning tree is positive), therefore Same sign. Combined with energy consumption. The definition, and ,final .

[0071] The load-energy consumption monotonicity model established in this application is the physical foundation of the entire control system and provides a key theoretical basis for the vehicle's energy consumption feedback controller. Simultaneously, this model strongly demonstrates the controllability of energy consumption through load adjustment, making it possible to optimize energy consumption through subsequent load adjustments. Furthermore, the Markov state network abstraction provides a state-space description framework for higher-order dynamic modeling in Part II.

[0072] Then, embodiments of this application may, but are not limited to, construct a quantitative coupling relationship between the vehicle's load, road conditions, and dynamics model based on a monotonicity model, thereby obtaining a pre-constructed high-order coupled dynamic equation for quantifying the vehicle's energy consumption variation information. Before establishing the pre-constructed high-order coupled dynamic equations, the embodiments of this application first explain the relevant formula parameters, which can be, but are not limited to, expressed as follows: Representative vehicle The engine output phase, measured in rad, is obtained from a crankshaft angle sensor and normalized. ; Other cooperating vehicles (such as the lead vehicle) in the vehicle platoon. Engine output phase; Represents the road excitation phase (such as the vibration phase caused by road surface unevenness); Represents the engine's natural frequency, measured in rad / s, and is obtained by measuring idle speed. The calculation formula is: ; Represents road condition hysteresis, measured in rad, estimated using a slope sensor and friction coefficient. The calculation formula is as follows: ; It represents the mechanical synchronization strength between vehicles and is calibrated through the torque transmission efficiency of the drive shaft. Represents the load-road coupling strength, through load It was calculated.

[0073] Therefore, the high-order coupled dynamic equations pre-constructed in the embodiments of this application can be expressed, but are not limited to, as follows:

[0074] Among them, the second-order synchronization term middle, Indicates vehicle With vehicles The crankshaft angle difference, if > It indicates that the leading vehicle has a faster engine speed. In practical applications, it describes the mutual pulling effect of the engine speeds of the vehicles in front and behind when the vehicles are traveling in a convoy. For example, when the lead vehicle slows down, the following vehicles will slow down in sync to maintain the coordination of the convoy.

[0075] Figure 3 This is a schematic diagram illustrating the phase evolution under different slopes according to an embodiment of this application. Figure 3 As shown, road conditions are stagnant. Its purpose is to quantify the degree to which road conditions (such as gradient) affect the delay in torque transmission; for example, an 8° gradient corresponds to... This indicates that there is a delay in torque transmission when going uphill.

[0076] Furthermore, in this dynamic equation, its third-order coupling term middle, This refers to the second harmonic of the engine in a heavy-duty vehicle. This indicates the road excitation frequency, such as vibrations caused by road surface unevenness. For example, when a vehicle is climbing a hill under heavy load, the load... Increase, heavy-duty vehicles Second harmonic With road incentives Resonance exacerbates torque demand, while also increasing the third-order coupling strength. It is directly proportional to the load and the slope, that is For example, a load capacity of 30 tons + a slope of 10° → .

[0077] Furthermore, hysteresis Primarily determined by road condition parameters (slope and coefficient of friction), it can be expressed, but is not limited to, as follows:

[0078] in, Represents slope sensor data (unit: degrees); Represents the coefficient of friction of the road surface, such as dry asphalt pavement. muddy road ; Represents fundamental hysteresis; Represents the slope influence coefficient; This represents the friction influence coefficient.

[0079] Furthermore, the third-order coupling strength Depending on the load capacity and community structure, it can be represented, but is not limited to, as follows:

[0080] in, Represents the community coupling decay factor, within the same community Different communities ; Represents the interaction order, for example, when three cars are in the same community. When two cars are in the same community and one car is in a different community. ; Representing load-bearing gain, the example values ​​in this application embodiment are calibrated through actual measurements. That is, for every 1 ton increase in load capacity, Increase by 3%.

[0081] Furthermore, the community coupling attenuation factor here refers to a parameter used to describe the degree of attenuation of mutual influence between various components of a vehicle's power system (such as engine, motor, and hydraulic system) when they work together. For example, when the engine and motor are coupled for drive, the impact of a load change on one side on the other side will be corrected by this factor.

[0082] In this application embodiment, the pre-constructed higher-order coupled dynamic equations are presented in the extended Kuramoto-Sakaguchi model because it can describe the synchronization behavior of the phase oscillator and supports higher-order coupling terms, including the introduction of third-order coupling terms. It can be used to capture load-related resonance effects and map road condition parameters (slope, friction coefficient) into hysteresis. Establish a quantitative relationship and load Mapped to third-order coupling strength The gain factor, through the above parts, the embodiments of this application construct a high-order model that can accurately describe the coupling of load-road condition-dynamics, laying the foundation for subsequent control strategy design.

[0083] For example, in practical applications, this application can first have the vehicle perform a flat road cruise test under no-load conditions to determine the vehicle's engine reference speed; then, through a formation coordination test, the mechanical synchronization strength between vehicles is calibrated, and the phase hysteresis compensation coefficient, i.e., the friction influence coefficient and the slope influence coefficient, is determined based on friction tests of different road surface materials; finally, the vehicle performs a stepped load test on a slope, fits the load gain function, and thus establishes the actual third-order coupling strength of the vehicle.

[0084] The high-order coupled dynamic equations pre-constructed in this application are the core link connecting load parameters and vehicle state. The hysteresis parameters included in these equations can introduce road condition disturbances (fluctuations in vehicle running resistance caused by changes in road slope and friction coefficient, such as increased disturbances on uphill or icy roads) into the power system, thus providing input variables for subsequent stability analysis of the vehicle under different road conditions. The variation in the equations' value range directly determines the existence of a bistable region in the energy consumption optimization process. Furthermore, the state evolution trajectory output by these equations can provide an effective data foundation for chaos detection during vehicle operation.

[0085] Optionally, in one embodiment of this application, the method further includes: calculating the phase hysteresis of the vehicle's current driving road based on various operating data; calculating the critical phase of the vehicle based on the community coupling attenuation factor and the speed deviation between the vehicle's current speed and the target speed; and determining whether the vehicle's current operating condition is in a bistable range by combining the phase hysteresis and the critical phase.

[0086] In actual implementation, before generating the load adjustment command, this application can also calculate the phase hysteresis of the vehicle's current driving road through various vehicle operating data, and calculate the vehicle's critical phase based on the community coupling attenuation factor and the speed deviation between the vehicle's current speed and the target speed. Then, by combining the phase hysteresis and the critical phase, it can determine whether the vehicle's current operating condition is in a bistable range. When the vehicle's current operating condition is in a bistable range, the energy consumption optimization control process, such as selecting the vehicle's target operating point and generating the vehicle's load adjustment command, is then executed.

[0087] Here, hysteresis refers to the quantification of the influence of road conditions (such as slope) on torque transmission. As can be seen from other embodiments, the hysteresis in this application embodiment can be calculated from the slope and friction coefficient of the current driving road. The calculation formula is hysteresis. The calculation formula is as follows: .

[0088] The target speed here refers to the optimal speed of the vehicle under the current driving conditions, which can be set or adjusted by those skilled in the art according to the actual situation. The embodiments in this application are only illustrative and do not impose any specific limitations.

[0089] The critical phase here refers to the threshold parameter used to determine whether the current operating condition of a vehicle is stable based on its rotational speed. If the calculated result (critical phase) is within the pre-set critical phase range, it can be said that the current operating condition of the vehicle is stable.

[0090] Specifically, the formula for calculating the critical phase can be, but is not limited to, expressed as follows:

[0091] in, This represents the critical phase obtained from the calculation; Represents the community coupling attenuation factor, used to characterize the basic coupling strength when various components of the power system (such as the engine and the motor) work together; Represents the speed deviation between the vehicle's current speed and the target speed (unit: rpm); This represents the preset speed deviation influence coefficient, used to quantify the degree to which speed fluctuations weaken the system's synchronization stability.

[0092] For example, substituting the community coupling attenuation factor of 0.7 (indicating high coupling efficiency between the engine and the motor), and assuming a pre-calibrated speed deviation influence coefficient... Given a value of 0.002, and considering the vehicle's current speed deviation of 50 rpm, substituting this into the above formula yields a critical phase value of 0.6 (i.e., ...). At this point, the value is within the pre-set critical phase stability threshold range.

[0093] Only when both road condition hysteresis and critical phase meet their respective conditions can the vehicle's current operating condition be determined to be in a bistable range. At this point, the vehicle can be allowed to output an optimizable signal, triggering the subsequent control system to switch to energy consumption optimization mode to further reduce energy consumption. That is, if the hysteresis of the current driving road is within a pre-set hysteresis range and the critical phase is within a pre-set critical phase range, the vehicle's current operating condition can be determined to be in a bistable range.

[0094] In this context, the pre-defined hysteresis interval refers to a pre-defined range of hysteresis values ​​that defines the current driving path of the vehicle as a stable state; the pre-defined critical phase range refers to a pre-defined range of critical phase values ​​that defines the critical phase of the vehicle as a stable state. The specific pre-defined hysteresis interval and pre-defined critical phase range can be set or adjusted by those skilled in the art according to actual circumstances. The embodiments in this application are merely illustrative and do not constitute specific limitations.

[0095] The embodiments of this application can accurately quantify the real-time road condition disturbances and operating condition stability (critical phase) of the current driving road of the vehicle, quickly and accurately determine whether the vehicle is in a low-energy-consumption bistable range and output an optimizable signal, providing a real-time and reliable trigger basis for subsequent energy consumption optimization strategies. This avoids energy waste caused by ineffective adjustments and ensures the timeliness and accuracy of operating condition judgment, helping the vehicle to continuously maintain a high-efficiency and low-consumption operating state.

[0096] Optionally, in one embodiment of this application, when the current operating condition is in a bistable range, the target operating point of the vehicle is selected based on the vehicle's estimated energy consumption, including: when the current operating condition is in a bistable range, calculating the first attractor and its corresponding estimated fuel consumption of the vehicle under high load and low speed conditions based on the target potential function, and calculating the second attractor and its corresponding estimated fuel consumption of the vehicle under low load and high speed conditions; detecting the current road condition of the vehicle, and selecting the second attractor as the target operating point when the current road condition is an uphill condition, otherwise selecting the first attractor as the target operating point.

[0097] Based on the descriptions of other embodiments, this application allows the vehicle to select its target operating point when the vehicle's current operating condition is in a bistable range, according to the estimated energy consumption under different driving parameter states. In short, embodiments of this application can utilize the bistable characteristics induced by road disturbances to dynamically select the optimal operating point for vehicle energy consumption, thereby achieving coordinated optimization of vehicle load and speed.

[0098] In actual implementation, this application may, but is not limited to, when the current operating condition is in a bistable range, calculate the first attractor of the vehicle under high load and low speed conditions and its corresponding estimated fuel consumption based on the target potential function, and calculate the second attractor of the vehicle under low load and high speed conditions and its corresponding estimated fuel consumption.

[0099] Here, the target potential function can be understood as a predefined mathematical expression representing the mechanical energy of the vehicle's power system in the dimension of rotational speed (change state), which in this embodiment can be obtained, but is not limited to, by a quasi-static approximation of the dynamic equations.

[0100] For example, embodiments of this application may define a potential function. Potential function Essentially, it is the projection of the system's mechanical energy into the rotational speed dimension. In this embodiment, the power system is simplified based on practical considerations, i.e., vehicle-to-vehicle coupling is ignored, and only the load-road term is retained. The potential function at this point... It can be expressed as follows, but is not limited to:

[0101] in, , , , These represent the rate of change of vehicle speed, the deviation from the reference speed, and the real-time speed of the vehicle (dimensionless state), respectively. This represents the load-road coupled interference term modulated by road condition lag (where... (This indicates the load-road coupling strength).

[0102] Under steady state Definition of equivalence Then the potential function also satisfies the following formula:

[0103] therefore:

[0104] Integrating this formula yields the following formula: .

[0105] If the vehicle's current operating condition is to be maintained within the bistable range, mathematically speaking, this requires... It has three real roots.

[0106] by Taking (linear approximation) as an example, then:

[0107] The discriminant of the cubic equation is: .when There exist three real roots, at which point:

[0108] Corresponding hysteresis Must meet Therefore, critical hysteresis From the equation The solution is determined.

[0109] Therefore, the bistable condition for maintaining the vehicle's current operating condition within the bistable range is as follows: First-order condition: Extreme points satisfy ; Second-order condition: Local minimum requires .

[0110] Double well condition: when When, equation There are three real roots ,in and This is a minimum point (stable). This is a maximum point (unstable).

[0111] For critical phase hysteresis To solve this problem, at the bifurcation point, the potential function saddle point satisfies:

[0112] Therefore, through The critical phase hysteresis can then be solved. .in, , These represent the rotational speed state variable and road condition hysteresis variable, respectively, when calculating the Hessian matrix.

[0113] For example, suppose , , Then the bistable requirement ,Right now .

[0114] because ,have to In practical applications, the actual value can be solved numerically. Perform calibration.

[0115] In specific road condition lag intervals ( Under these conditions, the vehicle's powertrain system has two stable attractors, namely: Attractor A (high load - low speed state), i.e., the first attractor in the embodiment of this application, can be expressed as follows, but is not limited to: Attractor A and its corresponding vehicle energy consumption.

[0116] in, This indicates the target load corresponding to the first attractor; Indicates the vehicle's maximum physical design load capacity; This indicates the preset load safety offset. This indicates the target rotational speed corresponding to the first attractor; This represents the coefficient for adjusting the speed to compensate for phase lag.

[0117] This attractor is suitable for use on flat roads. At this point, the vehicle's load is close to its maximum, and friction loss can be reduced by decreasing the engine speed.

[0118] Attractor B (low load - high speed state), i.e., the second attractor in the embodiments of this application, can be expressed as follows, but is not limited to:

[0119] in, This indicates the target load corresponding to the second attractor; Indicates the vehicle's minimum physical design load (e.g., unloaded weight). This indicates the preset low-load safety bias. This indicates the target rotational speed corresponding to the second attractor.

[0120] This attractor is suitable for use in scenarios such as climbing hills. Torque overload can be avoided by reducing the load to increase the vehicle's speed.

[0121] Figure 4 This is a schematic diagram illustrating the energy consumption, Pareto front, and bistable state of phase hysteresis according to an embodiment of this application. Figure 4 As shown, the embodiments of this application can detect the current road conditions of the vehicle, and when the current road conditions of the vehicle are climbing conditions, the second attractor (attractor B) corresponding to the low load-high speed state is selected as the target operating point through Pareto front analysis; otherwise, the first attractor (attractor A) is selected as the target operating point.

[0122] Specifically, embodiments of this application can select the target operating point under bistable operating conditions through Pareto front analysis to generate optimal load-speed commands. The analysis process can be, but is not limited to, represented as follows: First, construct the objective function:

[0123] Set constraints, including but not limited to: (1) Dynamic steady-state constraints: ,in, This indicates that the system speed change rate is 0. It represents the rate of change of phase difference. When it is 0, it indicates that the components or vehicles have reached a steady state of phase synchronization. (2) Heavy load boundary: ; (3) Speed ​​safety: ; Subsequently, an efficient solution based on attractors can be represented, but is not limited to, as follows: (1) Traverse the attractor: for each Calculate all attractors (A / B states); (2) Assess fuel consumption: , ; (3) Select the optimal:

[0124] in, and These represent the optimal target load and optimal target speed at the selected target operating point, respectively. and These represent the load and rotational speed corresponding to the first attractor (high load - low speed state); and These represent the load and rotational speed corresponding to the second attractor (low load - high speed state); and These represent the estimated fuel consumption of the vehicle under the first and second attractor states, respectively. Additionally, when When the fuel consumption is measured in rad, the embodiments of this application may, but are not limited to, alternately evaluate the fuel consumption in the two states with a period of 2 seconds, and dynamically switch to the low-energy-consumption operating point.

[0125] This application embodiment can be based on the minimum point analysis of the potential function, derived from the stability theory of part four. Its output attractor can provide a target operating point for the vehicle's powertrain controller. Furthermore, this application embodiment can also utilize Pareto optimization based on the vehicle's load-energy consumption monotonicity model, under specific conditions... The optimal steady state of the vehicle is dynamically selected within the interval, ensuring that the vehicle can achieve energy consumption optimization based on dual stability optimization.

[0126] Furthermore, in order to achieve vehicle load self-adaptation, this application embodiment also designs a corresponding local feedback controller when generating a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption, and controlling the vehicle to execute the load adjustment command to adjust the vehicle's load and speed based on the target operating point.

[0127] For example, this application can be based on the load-energy consumption monotonic relationship verified in other embodiments. (That is, energy consumption will inevitably increase monotonically as the load increases), design a load adaptive control law based on real-time energy consumption deviation, wherein the expression of the load adaptive control law can be, but is not limited to, the following:

[0128] in, Represents the load adjustment rate (unit: tons / second); Represents real-time fuel consumption (unit: L / h); Represents the target fuel consumption (economic cruising threshold). The feedback gain coefficient (unit: tons-hour / liter / second) can be determined by the monotonicity constraint of load-energy consumption.

[0129] This adjustment law can be based on target energy consumption. As input, calculate the measured energy consumption deviation. Generate load adjustment command .

[0130] when When energy consumption exceeds the limit, the controller outputs a negative adjustment amount. This triggers the unloading mechanism to reduce the load in a gradient manner; conversely, it allows for increased load. This control relies solely on local energy consumption sensors and does not require global fleet communication.

[0131] Furthermore, The physical calibration of the feedback gain coefficient is shown below:

[0132] in, Represents the system response time constant; This represents the load-fuel consumption sensitivity.

[0133] In actual execution, the load adjustment command at the logical level ultimately needs to act on the vehicle's physical unloading mechanism (such as the hydraulic system). Therefore, after receiving the load adjustment command output by the feedback controller, the vehicle's control center will parse it and generate the corresponding underlying hydraulic execution command.

[0134] Specifically, when the load deviation between the vehicle's current load and the target load (the target load here refers to the vehicle's optimal load, which can be obtained by technical personnel in this field by modifying the vehicle's factory-calibrated optimal load through bench testing in combination with actual needs and application scenarios) is greater than the 3-ton threshold, the vehicle can drive the unloading mechanism to unload at a maximum speed of 2.5 tons / second.

[0135] Meanwhile, the embodiments of this application can also adjust the unloading rate according to the fuel consumption deviation between the vehicle's real-time fuel consumption and the target fuel consumption: for example, by opening the cargo compartment solenoid valve, controlling the loading belt speed to make the material enter the cargo compartment at a rate of 1.8 tons / second, and activating the 45° tilt hydraulic unloading plate, in conjunction with the vibrator to achieve an unloading rate of 3 tons / second.

[0136] This application embodiment can design a certain load-adaptive control law based on the monotonicity between load and energy consumption to adjust the vehicle's unloading and unloading rate in real time. By converting the state variables output by the pre-constructed high-order coupled dynamic equations into execution commands, when the vehicle's power system is in the bistable range, the local feedback controller in this application embodiment will receive the optimal operating point (target operating point) command to achieve a smooth switch. Furthermore, during the control process, this application embodiment also introduces a feedback gain coefficient within the stability boundary, thereby achieving flexible load adjustment based on the vehicle's system response time and load-fuel consumption sensitivity. When the system approaches a bifurcation point, the chaos suppression mechanism in this application embodiment will be triggered to ensure that the vehicle can remain stable in the bistable range.

[0137] Optionally, in one embodiment of this application, controlling the vehicle to execute a load adjustment command to adjust the vehicle's load and speed until the vehicle's actual energy consumption reaches the target energy consumption includes: analyzing the stability region of the vehicle's control system based on an objective function; determining whether the vehicle's current operating condition is chaotic based on the stability region; generating a fine-tuning gradient for the load and speed based on the Lyapunov exponent of the actual energy consumption when the current operating condition is chaotic; and adjusting the load and speed according to the fine-tuning gradient to adjust the vehicle's current operating condition from a chaotic state to a bistable range until the vehicle's actual energy consumption reaches the target energy consumption.

[0138] In some embodiments, in the process of adjusting the vehicle's load to achieve optimal energy consumption control, this application can also design a certain gradient perturbation strategy to address the chaotic fluctuations in energy consumption caused by the vehicle under adverse road conditions.

[0139] For example, this application can first analyze the stability region of a vehicle's control system based on an objective function. Here, the objective function can be understood as a mathematical expression describing the energy of the vehicle's power system, constructed based on Lyapunov theory. Based on this function, this application can analyze the stability region of the vehicle's control system.

[0140] First, the embodiments of this application can analyze the stability region of the control system using Lyapunov functions.

[0141] (1) Choose Lyapunov functions This is a quadratic form of the deviation between the current state of the vehicle's power system and the target operating point, because the vehicle's power system is constructed in accordance with the principle of energy decay.

[0142] In control law Under its influence, proof is required. .

[0143] In this embodiment of the application, the construction process of the Lyapunov function used to describe the state of the vehicle powertrain can be, but is not limited to, represented as follows: Define the system state vector ,in, Let load, rotational speed 1, rotational speed 2, and phase difference represent these respectively. Based on this, an energy function can be constructed, which can be, but is not limited to, expressed as follows:

[0144] in, It is the deviation between real-time fuel consumption and target fuel consumption; and These are the deviations between the real-time rotational speeds of the two communities (or vehicles) and the target rotational speed; Quantify the "energy deviation" between the system state and the target operating point; (2) Figure 5 This is a schematic diagram illustrating the decrease of the Lyapunov function over time according to one embodiment of this application. Figure 5 As shown, the embodiments of this application can then proceed to... The time derivative can be expressed, but is not limited to, as follows:

[0145] in, Control Law Substituting the values ​​and using the chain rule, we get:

[0146] After unfolding, we get:

[0147] As demonstrated in other embodiments The first term can be, but is not limited to, written as the following expression:

[0148] The remaining terms need to be described in conjunction with the constraints of the dynamic equations.

[0149] Rate of change of rotational speed under normal operating conditions and the rate of change of phase difference Bounded, and when the system approaches the target speed ( Higher-order terms have a smaller impact. By setting a sufficiently large value... This can ensure (When far from the equilibrium point) or (Equilibrium point).

[0150] As demonstrated in other embodiments The following expressions can be derived, but are not limited to:

[0151] Among them, the first term in the above formula This demonstrates the impact of load adjustment on fuel consumption deviation. Because... and control law Substituting the terms into the equation yields the negative definite term. This is the main source of system stability.

[0152] Higher-order terms represent the influence of other vehicle states (such as engine speed), but with proper design, these terms will not compromise stability. Therefore, in the embodiments of this application, it can be assumed that when... At that time, load control item Provides stability as the primary factor.

[0153] (3) Subsequently, the embodiments of this application can construct a Jacobian matrix. This describes the coupling relationship between vehicle load, energy consumption, and road conditions. In this process, the embodiments of this application consider the stability of the power system in an anti-synchronous state, and the system state vector is... However, due to the anti-phase synchronization state and Therefore, embodiments of this application may, but are not limited to, use the system state vector. Simplified to three independent variables: Jacobian matrix It can then be defined as the partial derivative matrix of the system's dynamic equations, and this Jacobian matrix... It can be expressed as follows, but is not limited to:

[0154] According to the derivation of the characteristic equation, the eigenvalues ​​of the Jacobian matrix are... Satisfies the characteristic equation: ; For the critical condition of phase lag From the derivation, when When, stability boundary And satisfy the following formula:

[0155] The solution is:

[0156] in, Represents the ability to effectively synchronize between communities. It represents the load-road resonance intensity.

[0157] By constructing a Lyapunov function to describe the energy of a vehicle's powertrain, embodiments of this application can analyze the stability domain of the entire vehicle's powertrain, thereby providing a safe operating boundary for the entire control system. When road conditions are disturbed... This can directly trigger the load change rate constraint, thereby directly protecting the local feedback controller from unstable operation. Meanwhile, the Lyapunov exponent calculation can provide a reliable criterion for chaos detection during vehicle operation, and the Jacobian matrix eigenvalue analysis further effectively proves the formation mechanism of the bistable interval.

[0158] Next, embodiments of this application can design a gradient perturbation strategy to address the chaotic fluctuations in energy consumption under adverse road conditions.

[0159] First, the embodiments of this application can determine whether the current operating condition of the vehicle is chaotic based on the stability domain. When the current operating condition of the vehicle is chaotic, a fine-tuning gradient of load and speed is generated based on the Lyapunov exponent of actual energy consumption, and a load fine-tuning command is generated. The load and speed of the vehicle are then fine-tuned according to the fine-tuning gradient to adjust the current operating condition of the vehicle from chaotic to bistable.

[0160] Here, fine-tuning gradient refers to the single adjustment value of the vehicle's load and speed when the vehicle is in a chaotic state.

[0161] (4) Real-time calculation of Lyapunov index : Initialization: Set the time window Initial perturbation vector (Small perturbation).

[0162] Iterative calculation: a. Current state Calculate the Jacobian matrix .

[0163] b. Solve the linear differential equation: .

[0164] c. Integration step one: .

[0165] d. Calculate the index: .

[0166] e. Renormalization: .

[0167] Output: Take the moving average .

[0168] (5) Fine-tuning the gradient The calculation can be performed using, but is not limited to, the central difference method, and the calculation formula can be, but is not limited to, expressed as follows:

[0169] in This is obtained by temporarily modifying the road condition hysteresis value and repeating step (4).

[0170] (6) Figure 6 This is a schematic diagram comparing the Lyapunov exponents before and after chaos detection in one embodiment of this application. Figure 6 As shown, if the Lyapunov index Furthermore, if the duration is greater than or equal to 200ms, it can be determined that the vehicle's current operating condition has entered a chaotic state, and a load perturbation can be injected. That is, by fine-tuning the vehicle's load and speed according to the fine-tuning gradient, the vehicle's current operating condition is adjusted from a chaotic state to a bistable state.

[0171] set up Rotational speed If the oscillation amplitude (standard deviation) is:

[0172] That is, the larger the oscillation amplitude, the stronger the injected perturbation, and the greater the perturbation strength. The system adapts to the oscillation amplitude, driving it back to a periodic state.

[0173] as well as, Figure 7 This is a schematic diagram comparing the engine speed response before and after disturbance injection according to an embodiment of this application. Figure 8 This diagram illustrates a comparison of energy consumption fluctuations between a conventional PID controller and the energy consumption optimization control method based on vehicle load in one embodiment of this application. Figure 7 and Figure 8 As shown, the chaotic state suppression mechanism introduced in this embodiment can reduce the energy consumption fluctuation of the vehicle by 70% and avoid overload damage to the power system.

[0174] It should be noted that when the vehicle load exceeds the limit or the speed loses synchronization, the embodiments of this application can immediately cut off the local feedback control loop and switch to the backup PID controller.

[0175] This application embodiment can, after the vehicle load is adjusted, utilize the Lyapunov index based on the stability domain of the vehicle's power system. The calculation detects whether the vehicle's current operating condition is in a chaotic state, and upon detection... At that time, the current operating condition of the vehicle injects a perturbation command into the local feedback controller, thereby affecting the state variables of the higher-order coupled dynamic equations, enabling the vehicle's power system to escape the chaotic attraction domain and return to the vehicle's bistable operating area, providing a solid and reliable safety protection layer for the vehicle's power system.

[0176] The energy consumption control method based on vehicle load proposed in this application can calculate the current energy consumption trend of the vehicle based on various vehicle operating data and pre-constructed high-order coupled dynamic equations. When the current operating condition of the vehicle is in a bistable range, the target operating point of the vehicle is selected, and the vehicle load and speed are adjusted until the actual energy consumption of the vehicle reaches the target energy consumption. This achieves effective quantification of torque loss during heavy-load hill climbing by introducing a third-order coupling of load-road-engine, and captures the road-induced low-energy-consumption attractor through load-energy-consumption monotonic response modeling. Then, based on the attractor switching mechanism and local feedback control law, adaptive optimization of vehicle operating conditions is achieved, significantly reducing fuel consumption during heavy-load hill climbing. Furthermore, by using the adaptive switching of the bistable operating point and the chaotic perturbation injection mechanism, this application can suppress speed fluctuations caused by sudden changes in road conditions. Simultaneously, by optimizing the action logic of the hydraulic actuator, fuel waste is reduced, achieving dynamic optimization of vehicle energy consumption. This solves the problems in related technologies, such as the fact that the PID-based fuel consumption feedback control method only considers second-order vehicle-to-vehicle interaction and ignores the thermodynamic effect of load as driving force, which easily leads to control instability under steep slopes and heavy loads; and the method of querying preset tables relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and cannot meet the fuel consumption control needs in actual driving scenarios.

[0177] Next, referring to the accompanying drawings, an energy consumption control device based on vehicle load proposed according to an embodiment of this application is described.

[0178] Figure 9 This is a schematic diagram of the energy consumption control device based on vehicle load according to an embodiment of this application.

[0179] like Figure 9 As shown, the energy consumption control device based on vehicle load includes: a calculation module 100, a selection module 200, and a control module 300.

[0180] The acquisition module 100 is used to acquire various operating data of the vehicle, and to calculate the current energy consumption trend of the vehicle based on the various operating data and the pre-constructed high-order coupled dynamic equations. The selection module 200 is used to detect the current operating condition of the vehicle based on the current energy consumption trend, so as to select the target operating point of the vehicle based on the estimated energy consumption of the vehicle when the current operating condition is the bistable interval. The control module 300 is used to acquire the actual fuel consumption of the vehicle, generate a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption, and control the vehicle to execute the load adjustment command to adjust the load and speed of the vehicle based on the target operating point until the actual energy consumption of the vehicle reaches the target energy consumption.

[0181] Optionally, in one embodiment of this application, it further includes: a first generation module, configured to collect historical transportation data of the vehicle before calculating the current energy consumption trend of the vehicle based on the multiple operating data and the pre-constructed high-order coupled dynamic equation, and abstract the energy consumption patterns of the vehicle under different power states into a state network based on the load value and its corresponding energy consumption value in the historical transportation, so as to generate a monotonicity model between the load and energy consumption of the vehicle based on the state network; a construction module, configured to construct a quantitative coupling relationship between the load, road conditions and dynamic model of the vehicle based on the monotonicity model; and a determination module, configured to obtain the pre-constructed high-order coupled dynamic equation for quantifying the energy consumption change information of the vehicle based on the quantitative coupling relationship between the load, road conditions and dynamic model.

[0182] Optionally, in one embodiment of this application, the expression for the monotonicity model is:

[0183]

[0184] in, Indicates all It is the root and contains edges The sum of the spanning tree weights, Indicates all Root and does not contain The sum of the spanning tree weights of the edges. ,in, , , , , , All are non-negative real numbers.

[0185] Optionally, in one embodiment of this application, the selection module 200 includes: a calculation unit, configured to calculate, based on a target potential function, the first attractor and its corresponding estimated fuel consumption of the vehicle under high load and low speed conditions, and the second attractor and its corresponding estimated fuel consumption of the vehicle under low load and high speed conditions when the current operating condition is a bistable interval; and a selection unit, configured to detect the current road condition of the vehicle, and select the second attractor as the target operating point when the current road condition is an uphill condition, otherwise select the first attractor as the target operating point.

[0186] Optionally, in one embodiment of this application, it further includes: a first calculation module, used to calculate the phase hysteresis of the current driving road of the vehicle based on the multiple operating data; a second calculation module, used to calculate the critical phase of the vehicle based on the community coupling attenuation factor and the speed deviation between the current speed and the target speed of the vehicle; and a first judgment module, used to combine the phase hysteresis and the critical phase to determine whether the current operating condition of the vehicle is in the bistable range.

[0187] Optionally, in one embodiment of this application, the control module 300 includes: an analysis module, configured to analyze the stability domain of the vehicle's control system based on an objective function; a second judgment module, configured to determine whether the vehicle's current operating condition is chaotic based on the stability domain; a second generation module, configured to generate a fine-tuning gradient of the load and the rotational speed based on the Lyapunov exponent of the actual energy consumption when the current operating condition is chaotic; and an adjustment module, configured to adjust the load and the rotational speed according to the fine-tuning gradient to adjust the vehicle's current operating condition from a chaotic state to the bistable range, until the vehicle's actual energy consumption reaches the target energy consumption.

[0188] It should be noted that the foregoing explanation of the embodiment of the energy consumption control method based on vehicle load also applies to the energy consumption control device based on vehicle load in this embodiment, and will not be repeated here.

[0189] The energy consumption control method based on vehicle load proposed in this application can calculate the current energy consumption trend of the vehicle based on various vehicle operating data and pre-constructed high-order coupled dynamic equations. When the current operating condition of the vehicle is in a bistable range, the target operating point of the vehicle is selected, and the vehicle load and speed are adjusted until the actual energy consumption of the vehicle reaches the target energy consumption. This achieves effective quantification of torque loss during heavy-load hill climbing by introducing a third-order coupling of load-road-engine, and captures the road-induced low-energy-consumption attractor through load-energy-consumption monotonic response modeling. Then, based on the attractor switching mechanism and local feedback control law, adaptive optimization of vehicle operating conditions is achieved, significantly reducing fuel consumption during heavy-load hill climbing. Furthermore, by using the adaptive switching of the bistable operating point and the chaotic perturbation injection mechanism, this application can suppress speed fluctuations caused by sudden changes in road conditions. Simultaneously, by optimizing the action logic of the hydraulic actuator, fuel waste is reduced, achieving dynamic optimization of vehicle energy consumption. This solves the problems in related technologies, such as the fact that the PID-based fuel consumption feedback control method only considers second-order vehicle-to-vehicle interaction and ignores the thermodynamic effect of load as driving force, which easily leads to control instability under steep slopes and heavy loads; and the method of querying preset tables relies on historical statistics, which cannot handle real-time disturbances caused by various driving factors, cannot achieve adaptive optimization of operating conditions, and cannot meet the fuel consumption control needs in actual driving scenarios.

[0190] Figure 10 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0191] When the processor 1002 executes the program, it implements the energy consumption control method based on vehicle load provided in the above embodiments.

[0192] Furthermore, the vehicle also includes: Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0193] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0194] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage.

[0195] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0196] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0197] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0198] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described energy consumption control method based on vehicle load.

[0199] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the energy consumption control method based on vehicle load provided in this application.

[0200] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0201] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0202] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0203] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0204] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0205] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0206] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0207] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for energy consumption control based on vehicle load, characterized in that, Includes the following steps: Acquire various operating data of the vehicle, and calculate the current energy consumption trend of the vehicle based on the various operating data and the pre-constructed high-order coupled dynamic equations; Based on the current energy consumption trend, the current operating condition of the vehicle is detected, and when the current operating condition is in a bistable range, the target operating point of the vehicle is selected based on the estimated energy consumption of the vehicle. The actual fuel consumption of the vehicle is obtained, and a load adjustment command is generated based on the difference between the actual fuel consumption and the target fuel consumption. Based on the target operating point, the vehicle is controlled to execute the load adjustment command to adjust the load and speed of the vehicle until the actual energy consumption of the vehicle reaches the target energy consumption.

2. The method according to claim 1, characterized in that, Before calculating the current energy consumption trend of the vehicle based on the various operational data and the pre-constructed high-order coupled dynamic equations, the method further includes: Historical transportation data of the vehicle is collected, and based on the load value and corresponding energy consumption value in the historical transportation, the energy consumption pattern of the vehicle under different power states is abstracted into a state network, so as to generate a monotonicity model between the load and energy consumption of the vehicle based on the state network. Based on the monotonicity model, a quantitative coupling relationship is constructed between the vehicle's load, road conditions, and dynamics model. Based on the quantitative coupling relationship between the load, the road conditions, and the dynamic model, the pre-constructed high-order coupled dynamic equations for quantifying the energy consumption changes of the vehicle are obtained.

3. The method according to claim 2, characterized in that, The expression for the monotonicity model is: in, Indicates all It is the root and contains edges The sum of the spanning tree weights, Indicates all Root and does not contain The sum of the spanning tree weights of the edges. ,in, All are non-negative real numbers.

4. The method according to claim 2, characterized in that, When the current operating condition is in a bistable range, selecting the target operating point of the vehicle based on the vehicle's estimated energy consumption includes: When the current operating condition is in the bistable range, based on the target potential function, the first attractor of the vehicle under high load and low speed state and its corresponding estimated fuel consumption are calculated, and the second attractor of the vehicle under low load and high speed state and its corresponding estimated fuel consumption are calculated. The vehicle's current road conditions are detected, and if the current road conditions are uphill, the second attractor is selected as the target working point; otherwise, the first attractor is selected as the target working point.

5. The method according to claim 1, characterized in that, Also includes: Based on the various operational data, the phase hysteresis of the vehicle's current driving path is calculated; The critical phase of the vehicle is calculated based on the community coupling decay factor and the speed deviation between the current speed and the target speed of the vehicle. By combining the phase hysteresis and the critical phase, it is determined whether the current operating condition of the vehicle is in the bistable range.

6. The method according to claim 1, characterized in that, The control of the vehicle to execute the load adjustment command to adjust the vehicle's load and speed until the vehicle's actual energy consumption reaches the target energy consumption includes: Based on the objective function, the stability region of the vehicle's control system is analyzed; Based on the stability region, determine whether the current operating condition of the vehicle is chaotic. When the current operating condition is chaotic, a fine-tuning gradient for the load and the rotational speed is generated based on the Lyapunov exponent of the actual energy consumption. The load and rotation speed are adjusted according to the fine-tuning gradient to adjust the current operating condition of the vehicle from a chaotic state to the bistable range until the actual energy consumption of the vehicle reaches the target energy consumption.

7. An energy consumption control device based on vehicle load, characterized in that, include: The acquisition module is used to acquire various operating data of the vehicle, and to calculate the current energy consumption trend of the vehicle based on the various operating data and the pre-constructed high-order coupled dynamic equations. The selection module is used to detect the current operating condition of the vehicle based on the current energy consumption trend, so as to select the target operating point of the vehicle based on the estimated energy consumption of the vehicle when the current operating condition is in a bistable range. The control module is used to acquire the actual fuel consumption of the vehicle, generate a load adjustment command based on the difference between the actual fuel consumption and the target fuel consumption, and control the vehicle to execute the load adjustment command based on the target operating point to adjust the load and speed of the vehicle until the actual energy consumption of the vehicle reaches the target energy consumption.

8. A vehicle, characterized in that, include: The device includes 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 energy consumption control method based on vehicle load as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the energy consumption control method based on vehicle load as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the energy consumption control method based on vehicle load as described in any one of claims 1-6.

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