Work condition information metric method and system for vehicle energy management control

Abstract

This application discloses a method and system for measuring operating condition information for vehicle energy management control. The method includes: determining the type of operating condition information; quantifying the uncertainty of static information using empirical entropy if the information is static; and measuring the information content of static information by reducing the entropy of empirical entropy; and quantifying the uncertainty of dynamic information using information entropy if the information is dynamic; and measuring the information content of dynamic information by reducing the entropy of information entropy. According to this method for measuring operating condition information for vehicle energy management control, by classifying the operating condition information and measuring static and dynamic information using empirical entropy and information entropy respectively, the operating condition information can be measured in a standardized manner.

Description

Technical Field

[0001] This invention belongs to the field of operating condition information measurement technology, and more specifically, this invention relates to a method and system for measuring operating condition information of vehicle energy management and control. Background Technology

[0002] The development of energy-saving and emission-reduction technologies for automobiles has become a top priority. In the field of automotive energy management, energy management technology has stood out due to its outstanding performance.

[0003] With the development of Cellular Vehicle-to-Everything (C-V2X) technology based on public mobile networks, intelligent vehicles, roadside facilities, and platforms are integrating with it to form an intelligent connected vehicle-road-cloud collaborative system. Information from vehicles, roadside facilities, and the cloud is transmitted through the V2X network, enabling information exchange between vehicles, roads, and the cloud, bringing new opportunities for intelligent and energy-saving vehicle control. Some analysts believe that globally optimized energy management methods under information-sharing conditions are the inevitable path for the future development of vehicle energy management.

[0004] Operating condition information is a prerequisite for the completion of energy management strategies. Vehicles acquire multi-source information affecting operating conditions through vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-cloud communication within intelligent connected vehicle-road-cloud collaborative systems. This presents both opportunities and challenges. On the one hand, acquiring multi-source information reduces the uncertainty of operating conditions, and real-time information interaction provides a prerequisite for the real-time application of energy management strategies. On the other hand, information transmission is affected by channel capacity; the amount of information that can be transmitted per unit of time is constrained. Furthermore, redundant information increases the computational load. Therefore, more information is not necessarily better; rather, the transmission of redundant information should be reduced while maximizing fuel-saving potential. Operating condition information is both the foundation for the completion of energy management strategies and influences the degree to which the energy management strategy achieves its energy conservation and emission reduction goals. Therefore, how to measure the amount of operating condition information used for vehicle energy management control is a primary problem that needs to be solved. Summary of the Invention

[0005] The objective of this invention is to at least solve the problem of how to measure the amount of information related to operating conditions used in vehicle energy management control. This objective is achieved through the following technical solution:

[0006] A first aspect of the present invention provides a method for measuring operating condition information for vehicle energy management control, the method comprising:

[0007] Determine the type of operating condition information;

[0008] Since the operating condition information is static, the degree of uncertainty of the static information is quantified using empirical entropy.

[0009] The amount of information in the static information is measured by the entropy reduction of the empirical entropy.

[0010] Since the operating condition information is dynamic, the degree of uncertainty of the dynamic information is quantified using information entropy.

[0011] The amount of information in the dynamic information is measured by the entropy reduction of the information entropy.

[0012] According to the operating condition information measurement method for vehicle energy management control proposed in this application, operating condition information is classified, and static and dynamic information are measured using empirical entropy and information entropy respectively. This allows for standardized measurement of operating condition information, making abstract concepts concrete. This enables the exploration of the mapping relationship between information quantity and energy-saving potential, and the acquisition of an appropriate amount of information to balance the computational load and energy-saving effect of the energy management algorithm. It avoids insufficient information leading to poor energy-saving effects, or excessive information leading to increased computational load and communication costs. This provides a theoretical basis for quantitatively analyzing the impact of information quantity on the energy-saving and emission-reduction targets of energy management, avoiding increased computational load in energy management algorithms, and reducing the data traffic costs of communication logistics network cards used for communication.

[0013] In some embodiments of this application, the step of determining the type of operating condition information includes:

[0014] Get the vehicle's current speed;

[0015] If the current vehicle speed is a fixed value, then the current operating condition of the vehicle is determined to be a fixed operating condition.

[0016] Based on the fact that the current working condition is a defined working condition, the working condition information is determined to be static information;

[0017] Since the current vehicle speed is a dynamically changing value, the current operating condition of the vehicle is determined to be an uncertain operating condition.

[0018] Based on the fact that the current operating condition is an uncertain operating condition, the operating condition information is determined to be dynamic information.

[0019] In some embodiments of this application, in the step of quantifying the uncertainty of the static information using empirical entropy based on the fact that the operating condition information is static information, the formula for calculating the empirical entropy is as follows:

[0020]

[0021] In the formula, D is the dataset, |D| represents the sample size, i.e., the number of samples, and there are K classes C. k k = 1, 2, ..., K, |C k | Belongs to class C k The number of samples, i.e., the frequency.

[0022] In some embodiments of this application, the calculation steps for the empirical entropy are as follows:

[0023] Obtain a deterministic operating condition curve with a total length of N seconds;

[0024] Round the vehicle speed at time t to the nearest whole number, v(t) = round(v(t)), t = 0, 1, 2, ... N;

[0025] Count the frequency of each vehicle speed category | C k |;

[0026] Calculate the frequency under each vehicle speed category

[0027] Calculate the empirical entropy of deterministic operating condition curves

[0028] In some embodiments of this application, the step of quantifying the uncertainty of the static information using empirical entropy based on the fact that the operating condition information is static information includes:

[0029] Compare the empirical entropy of the static information;

[0030] The degree of uncertainty of the static information is determined based on the magnitude of the empirical entropy, wherein the empirical entropy is positively correlated with the degree of uncertainty of the static information;

[0031] And / or, the step of measuring the amount of information in the static information based on the entropy reduction of the empirical entropy includes:

[0032] The maximum empirical entropy obtained;

[0033] The maximum empirical entropy is used as the minuend and the other empirical entropies are subtracted to obtain the entropy reduction of the empirical entropy;

[0034] The information content of the static information is measured by the entropy reduction of the empirical entropy, wherein the entropy reduction of the empirical entropy is positively correlated with the information content of the static information.

[0035] In some embodiments of this application, in the step of quantifying the uncertainty of dynamic information using information entropy based on the fact that the operating condition information is dynamic information, the basic formula for calculating the information entropy is as follows:

[0036] H(X)=-∫f(x)log f(x)dx

[0037] Where X is a random variable, f(x) is a probability density function, and x is the vehicle speed v.

[0038] In some embodiments of this application, the step of quantifying the uncertainty of the dynamic information using information entropy based on the fact that the operating condition information is dynamic information includes:

[0039] Based on the fact that the typical shape of the feasible region for vehicle speed in the dynamic information is a rectangular speed range, the formula for calculating the information entropy is:

[0040]

[0041] In the formula, v is the vehicle speed, and t is the vehicle speed. s For the operating time, v cm v is the maximum speed limit for the vehicle. cn As the lower limit of vehicle speed, Δv c =v cm -v cn ;

[0042] And / or, based on the fact that the typical shape of the feasible region of vehicle speed in the dynamic information is a triangular vehicle speed range, the formula for calculating the information entropy is:

[0043]

[0044] In the formula, v is the vehicle speed, a am For the upper limit of acceleration, t am For the car to accelerate from 0 to v m The shortest time, where Δt is the sampling time interval.

[0045] And / or, based on the fact that the typical shape of the feasible region of vehicle speed in the dynamic information is an irregular vehicle speed range, the formula for calculating the information entropy is:

[0046] H V3 =Δt·log(Δv) f +a m Δt)+Δt·log(Δv f +2a m Δt)+...+Δt·log(Δv f +n1a m Δt)+Δt·log(Δv1+(n1+1)a1Δt)+Δt·log(Δv1+(n1+2)a1Δt)+...+Δt·log(Δv1+n2a1Δt)+(t1-n2Δt)logΔv c

[0047] Among them, the vehicle speed range is from v cn ~v cm Change to v fn ~v fm Considering the vehicle's acceleration and deceleration capabilities, t afn The vehicle speed is from v cnAccelerate to V fn The shortest time, v fn -v cn =t afn ·a am , t dfm The vehicle speed is from v cm Decelerate to V fm The shortest time, v cm -v fm =t dfm ·a dm a m =a am +a dm Δv f =v fm -v fn Δv c =v cm -v cn , t1 = max(t afm ,t dfn If t afm >t dfn Then a1 = a am Δv1=v cm -v fn Conversely, if t afm ≤t dfn Then a1 = a dm Δv1=v fm -v cn .

[0048] In some embodiments of this application, in the step of measuring the information content of the dynamic information based on the entropy reduction of the information entropy,

[0049] Without considering constraints, the amount of information is minimal at this point, with the vehicle speed variable in the range [0, v]. m The information entropy H follows a uniform distribution and is used as the minuend when calculating entropy reduction. M =t s logv m , where v m The maximum achievable vehicle speed under operating conditions, t s Operating time;

[0050] The amount of information input ΔH considering the road speed limit as a road constraint R =H M -H V1 ;

[0051] The information ΔH resulting from considering the vehicle's acceleration and deceleration capabilities, a constraint on vehicle performance, is considered. V =2(t)am logv m -H V2 );

[0052] Considering the information ΔH brought about by the joint constraints of traffic flow, vehicles, and roads. VR =t f1 logΔv c -t f2 logΔv f -2H V3 , where t f1 The vehicle's departure speed range is v cn ~v cm The total time of traffic flow, t f2 For vehicles with a speed range of v fn ~v fm The total time of traffic flow.

[0053] In some embodiments of this application, the step of quantifying the uncertainty of the dynamic information using information entropy based on the fact that the operating condition information is dynamic information includes:

[0054] Compare the information entropy of the dynamic information;

[0055] The degree of uncertainty of the dynamic information is determined based on the magnitude of the information entropy, wherein the information entropy is positively correlated with the degree of uncertainty of the dynamic information;

[0056] And / or, the step of measuring the amount of information in the dynamic information based on the entropy reduction of the information entropy includes:

[0057] Obtain the information entropy without considering constraints;

[0058] The information entropy without considering constraints is used as the minuend and the information entropy under other constraints is subtracted to obtain the entropy reduction of the information entropy.

[0059] The information content of the dynamic information is measured by the entropy reduction of the information entropy, wherein the entropy reduction of the information entropy is positively correlated with the information content of the dynamic information.

[0060] A second aspect of the present invention provides a condition information measurement system for vehicle energy management control, the condition information measurement system comprising: a processor and a memory;

[0061] The memory is used to store one or more program instructions;

[0062] The processor is configured to run one or more program instructions to execute the operating condition information measurement method for vehicle energy management control as described above.

[0063] The operating condition information measurement method for vehicle energy management control provided by this invention can standardize and measure operating condition information, making abstract concepts concrete. This allows for the exploration of the mapping relationship between information quantity and energy-saving potential, and the acquisition of an appropriate amount of information to balance the computational load of energy management algorithms with energy-saving effects. It avoids insufficient information leading to poor energy-saving effects, or excessive information leading to increased computational load and communication costs. This provides a theoretical basis for quantitatively analyzing the impact of information quantity on energy management's energy-saving and emission-reduction targets, avoiding increased computational load in energy management algorithms, and reducing the data traffic costs of communication logistics network cards used for communication. Attached Figure Description

[0064] Figure 1 A flowchart illustrating a method for measuring operating condition information for vehicle energy management control according to an embodiment of this application is shown schematically.

[0065] Figure 2 A structural relationship diagram of a method for measuring operating condition information for vehicle energy management control according to an embodiment of this application is shown schematically.

[0066] Figure 3 This is a static information statistical method.

[0067] Figure 4 The graphs are for four deterministic operating conditions.

[0068] Figure 5 This is a dynamic information statistics method.

[0069] Figure 6 This is a schematic diagram of a rectangular vehicle speed range.

[0070] Figure 7 This is a schematic diagram of the triangular vehicle speed range.

[0071] Figure 8 This is a schematic diagram showing the speed range of irregularly shaped vehicles.

[0072] Figure 9 This is a graphical representation of the input information under road constraints.

[0073] Figure 10 A graphical representation of the input information under vehicle constraints.

[0074] Figure 11 A graphical representation of the input information under road-vehicle joint constraints. Detailed Implementation

[0075] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0076] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0077] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0078] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature, as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "below," "above," "over," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure. For example, if the device in the figure is flipped, an element described as "below other elements or features" or "below other elements or features" would subsequently be oriented as "above other elements or features" or "above other elements or features." Therefore, the example term "below" can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or in other directions), and the spatial relative descriptors used in the text will be interpreted accordingly.

[0079] During vehicle operation, a variety of operating condition information is provided. Among them, vehicle speed is an operating condition information that is closely related to vehicle energy management. Therefore, in the process of vehicle energy management, it is necessary to measure vehicle speed as an operating condition information. The following is a detailed explanation of using vehicle speed as an operating condition information.

[0080] like Figures 1 to 11 As shown, this application proposes a method for measuring operating condition information for vehicle energy management control, which includes:

[0081] Using vehicle speed as operating condition information

[0082] S10: Determine the type of operating condition information.

[0083] Specifically, when a vehicle is traveling at a constant speed, its speed is constant. However, when the vehicle is affected by factors such as road conditions, its speed will change. Therefore, it is necessary to determine the type of operating condition information and adopt different measurement methods based on different determination results.

[0084] The process of judging the operating condition information includes the following steps: S11: Obtain the current vehicle speed; S12: If the current vehicle speed is a fixed value, then the current operating condition of the vehicle is determined to be a fixed operating condition; S13: If the current operating condition is a fixed operating condition, then the operating condition information is determined to be static information; S14: If the current vehicle speed is a dynamically changing value, then the current operating condition of the vehicle is determined to be an uncertain operating condition; S15: If the current operating condition is an uncertain operating condition, then the operating condition information is determined to be dynamic information.

[0085] This application measures information from both static and dynamic perspectives. Under deterministic operating conditions, where vehicle speeds at various times are fixed (e.g., the WLTC standard test cycle), the uncertainty of static information is measured using the parameter of empirical entropy, and entropy reduction is used to compare the amount of information in static information. Under indeterminate operating conditions, where vehicle speeds at various times are dynamically variable (e.g., predicting the speed of the vehicle ahead), the uncertainty of dynamic information is measured using the parameter of information entropy, and entropy reduction is used to quantify the amount of dynamic information resulting from constraints.

[0086] It should be understood that information is a very broad concept. To measure the degree of uncertainty of random variables, Shannon introduced the concept of information entropy in information theory. The concept of entropy originates from thermophysics and is used to represent the degree of disorder in a system. Extending to the information level, the greater the information entropy, the greater the uncertainty of the information variable. The basic function of information is to reduce the uncertainty of things. In other words, the amount of information is related to the uncertainty of the system that is eliminated. That is, when measuring the amount of information, the parameter of entropy reduction is used.

[0087] S20: Based on the fact that the operating condition information is static information, the degree of uncertainty of the static information is quantified by using empirical entropy.

[0088] Specifically, the empirical entropy parameter is used for quantification to characterize the degree of uncertainty in vehicle speed classification under deterministic operating conditions. The larger the empirical entropy, the greater the uncertainty in vehicle speed classification, and vice versa. First, the basic formula for calculating static empirical entropy is clarified.

[0089] Let the dataset be D, where |D| represents its sample size, i.e., the number of samples. Suppose there are K classes C. k , k = 1, 2, ..., K, |C k | Belongs to class C k The number of samples is the frequency. Then the empirical entropy H(D) of dataset D is

[0090]

[0091] For static information on vehicle speed and operating conditions, the dataset consists of vehicle speeds at various times, ranging from 0 to 120 km / h. Figure 3 The two different speed curves shown in the right figure are discretized at 1 km / h time intervals, resulting in 121 categories: k = 0 km / h, 1 km / h, 2 km / h, ..., 120 km / h. These categories are projected onto the y-axis, and the frequency of each category is counted. The frequency histogram is shown below. Figure 3 As shown in the left figure, the method for calculating empirical entropy is as follows:

[0092] ① Obtain a deterministic operating condition curve with a total length of N seconds, and round the vehicle speed at time t, v(t)=round(v(t)), t=0,1,2,…N;

[0093] ② Count the frequency of each vehicle speed category | C k |;

[0094] ③ Calculate the frequency under each vehicle speed category

[0095] ④ Calculate the empirical entropy of the deterministic operating condition curve

[0096] The greater the empirical entropy of static information, the greater the uncertainty in vehicle speed classification; conversely, the smaller the empirical entropy of static information, the smaller the uncertainty in vehicle speed classification.

[0097] S30: The amount of information in static information is measured by the entropy reduction of empirical entropy.

[0098] Specifically, entropy reduction is used to compare the amount of information in static information. When the operating conditions have traversed all vehicle speed categories and tend towards a uniform distribution, the maximum empirical entropy of the static information is obtained. This maximum entropy is used as the minuend when calculating entropy reduction by comparing the amount of static information. Operating conditions with a larger entropy reduction have more vehicle speed information and a smaller speed range. If the speed ranges are similar, operating conditions with a larger entropy reduction are more stable throughout the entire process. When the vehicle speed remains constant, the minimum empirical entropy of the static information is obtained, the entropy reduction reaches its maximum, and the vehicle's operating conditions are most stable.

[0099] Examples of methods for comparing static information, such as Figure 4 The diagram shows four different operating conditions. Condition ① covers all speed categories. Condition ② shows a more volatile speed compared to Condition ③. Condition ③ shows a more stable speed compared to Condition ②. Condition ④ shows a constant speed. Calculate the empirical entropy for each condition using the formula above:

[0100] H1=6.92bit H2=5.17bit H3=3.31bit H4=0bit

[0101] Because the greater the uncertainty in vehicle speed classification, that is, the more chaotic the vehicle speed frequency distribution, the greater the empirical entropy, the maximum empirical entropy of static information (such as...) can be obtained when the operating conditions have traversed all vehicle speed categories and tend towards a uniform distribution. Figure 4 In condition ①, the uncertainty reaches its maximum. This condition is used as the minuend when calculating the entropy reduction by comparing static information. Therefore, the entropy reductions for conditions ②, ③, and ④ are respectively:

[0102] ΔH2=1.75bit ΔH3=3.61bit ΔH4=6.92bit

[0103] When the frequency distribution of vehicle speeds becomes more concentrated, the empirical entropy decreases (e.g.) Figure 4 The conditions shown are ② and ③. Condition ② is relatively more turbulent, with a slightly larger empirical entropy and a slightly smaller entropy reduction. In other words, the speed range of the condition with a larger entropy reduction is smaller. If the speed range is similar, the condition with a larger entropy reduction is more stable throughout the entire process.

[0104] When the vehicle speed remains constant under operating conditions (e.g.) Figure 4 Under condition ④), the minimum empirical entropy for obtaining static information is reached, the entropy reduction reaches its maximum, and the vehicle's operating condition is most stable.

[0105] Similarly, Figure 3 According to the above rules, the empirical entropy of the two working conditions shown is smaller because the distribution of working condition one is more concentrated.

[0106] S40: Based on the fact that the operating condition information is dynamic, the uncertainty of the dynamic information is quantified by information entropy.

[0107] Specifically, information entropy is used as a parameter for quantification to characterize the uncertainty of vehicle speed. The higher the information entropy, the greater the uncertainty of vehicle speed, and vice versa. When the information entropy is 0, the vehicle speed at each moment is fixed, transforming dynamic information into static information. First, the basic formula for calculating dynamic information entropy is clarified.

[0108] The entropy H(X) of a discrete random variable X is defined as:

[0109]

[0110] In this context, the alphabet (i.e., the value space) of the random variable X is χ, the probability density function is p(x)=Pr(X=x), x∈χ, the base of the logarithm log is 2, and the unit of entropy is expressed in bits.

[0111] For a continuous random variable X, its entropy H(X) is defined as:

[0112] H(X)=-∫f(x)log f(x)dx

[0113] Wherein, the cumulative distribution function of the random variable X is F(x)=Pr(X≤x), f(x) is the probability density function of X, f(x)=F'(x), ∫f(x)=1, and x is the vehicle speed v.

[0114] exist Figure 5The right figure illustrates the speed range of a combined scenario (acceleration from start-up, a period of minimum and maximum speed limits, and finally stopping). The feasible speed range at each moment is projected onto the y-axis. The probability density at several moments is then illustrated (left figure). Other moments can be calculated using the same method. For the complete scenario, the probability density f(x) at each moment needs to be calculated. The typical shape of the feasible speed domain, the scenario, and the corresponding formula for calculating information entropy are then introduced.

[0115] ① Rectangular speed range: If within time t s The maximum speed limit for vehicles inside is v cm The lower limit of vehicle speed is v cn The case where the vehicle speed distribution is uniform and there are no other constraints, such as Figure 6 As shown, the information entropy H of the shaded area V1 for:

[0116]

[0117] ② Triangular vehicle speed range: If the scenario is as follows Figure 7 The figure shows the vehicle starting condition. Considering the vehicle's acceleration capability, its upper limit of acceleration is a. am , t am It is the car speed accelerating from 0 to V. m The shortest time, v m =t am ·a am The vehicle speed distribution satisfies a uniform distribution and has no other constraints. The information entropy H of the shaded region is... V2 for:

[0118]

[0119] In the formula, Δt is the sampling time interval.

[0120] ③Speed ​​range of irregularly shaped vehicles: If the scene is as follows Figure 8 The image shows a vehicle transitioning from free flow to non-free flow, with speeds ranging from v... cn ~v cm Change to v fn ~v fm Considering the vehicle's acceleration and deceleration capabilities, t afn The vehicle speed is from v cn Accelerate to V fn The shortest time, v fn -v cn =t afn ·a am , t dfm The vehicle speed is from v cm Decelerate to V fm The shortest time, v cm -v fm=t dfm ·a dm The vehicle speed distribution satisfies a uniform distribution and has no other constraints. The information entropy H of the shaded region is... V3 for:

[0121] H V3 =Δt·log(Δv) f +a m Δt)+Δt·log(Δv f +2a m Δt)+...+Δt·log(Δv f +n1a m Δt)+Δt·log(Δv1+(n1+1)a1Δt)+Δt·log(Δv1+(n1+2)a1Δt)+...+Δt·log(Δv1+n2a1Δt)+(t1-n2Δt)logΔv c

[0122] Among them, a m =a am +a dm Δv f =v fm -v fn Δv c =v cm -v cn , t1 = max(t afm ,t dfn If t afm >t dfn Then a1 = a am Δv1=v cm -v fn Conversely, if t afm ≤t dfn Then a1 = a dm Δv1=v fm -v cn .

[0123] S50: The amount of information in dynamic information is measured by the entropy reduction of information entropy.

[0124] Specifically, the greater the entropy reduction, the greater the amount of information and the smaller the uncertainty of the vehicle speed; conversely, the smaller the entropy reduction, the smaller the amount of information and the greater the uncertainty of the vehicle speed.

[0125] Having defined the information entropy of the operating conditions under the above scenario, the next step is to measure the amount of information derived from the constraints. Information amount represents the degree to which the uncertainty of vehicle speed at each moment is reduced. Within the same time period, the less information obtained, the larger the feasible range of vehicle speed, and the greater the uncertainty. Without considering constraints, the amount of information is minimal at this point, which is reflected in the vehicle speed feasible region as the maximum range of vehicle speed at each moment, and the vehicle speed variable being within [0, v]. m The information entropy H follows a uniform distribution. M for:

[0126] H M =t s logv m

[0127] At this point, the information entropy of the vehicle speed information is at its maximum, and the uncertainty is also at its maximum. This information is used as the minuend when calculating the entropy reduction.

[0128] Conversely, the more information acquired within the same time period, the smaller the feasible range of vehicle speed and the lower the uncertainty. Many factors influence operating condition information; the more constraints considered, the greater the amount of information. We will use vehicle and road constraints as an example to illustrate the amount of information resulting from considering different constraints.

[0129] Road constraints include infrastructure such as traffic lights and traffic rules such as speed limits, and are determined by... Figure 9 It can be seen that road speed limits reduce the feasible range of vehicle speed, reduce uncertainty, and decrease entropy by ΔH. R This refers to the amount of information brought about by the speed limit on that road. In other words, the speed information entropy of the shaded area is the amount of information input considering road constraints.

[0130] ΔH R =H M -H V1

[0131] Vehicle constraints include inherent performance parameters such as acceleration and braking capabilities, such as... Figure 10 As shown, the vehicle's acceleration and deceleration capabilities limit the range of speeds it can reach after starting and before stopping, and its entropy decrease ΔH V This refers to the amount of information that is affected by vehicle performance constraints.

[0132] ΔH V =2(t) am logv m -H V2 )

[0133] The combined constraints of roads and vehicles on vehicle speed are mainly reflected in traffic flow, such as... Figure 11As shown, when vehicles move from free flow to non-free flow, to ensure traffic safety and reduce congestion, traffic management departments often use the 85th and 15th percentile speeds as speed limits. This changes the speed limit from a road-wide limit to a traffic flow limit, reducing the permissible speed range and decreasing entropy by ΔH. VR This refers to the amount of information that is derived from the combined constraints of vehicles and roads.

[0134] ΔH VR =t f1 logΔv c -t f2 logΔv f -2H V3

[0135] The operating condition information measurement method for vehicle energy management control provided by this invention can standardize and measure operating condition information, making abstract concepts concrete. This allows for the exploration of the mapping relationship between information quantity and energy-saving potential, and the acquisition of an appropriate amount of information to balance the computational load of energy management algorithms with energy-saving effects. It avoids insufficient information leading to poor energy-saving effects, or excessive information leading to increased computational load and communication costs. This provides a theoretical basis for quantitatively analyzing the impact of information quantity on energy management's energy-saving and emission-reduction targets, avoiding increased computational load in energy management algorithms, and reducing the data traffic costs of communication logistics network cards used for communication.

[0136] This application also proposes a condition information measurement system for vehicle energy management control, the condition information measurement system including: a processor and a memory;

[0137] The memory is used to store one or more program instructions;

[0138] The processor is configured to run one or more program instructions to execute the operating condition information measurement method for vehicle energy management control as described above.

[0139] The present invention also provides a computer storage medium containing one or more program instructions, the one or more program instructions being used by a microwave heating system of a microwave heating device based on the thermoelectric effect to execute the microwave heating method described above.

[0140] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0141] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0142] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0143] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0144] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0145] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for measuring operating condition information for vehicle energy management control, characterized in that, The method for measuring operating condition information includes: Determine the type of operating condition information; Since the operating condition information is static, the degree of uncertainty of the static information is quantified using empirical entropy. The formula for calculating the empirical entropy is as follows: In the formula, D is the dataset. This represents the sample size, i.e., the number of samples. Let there be K classes. k=1, 2, ...K Belongs to class The number of samples, i.e., the frequency. ; The steps for calculating the empirical entropy are as follows: Obtain a deterministic operating condition curve with a total length of N seconds; Round the vehicle speed at time t to the nearest whole number, v(t) = round(v(t)), t = 0, 1, 2, ... N; Count the frequency of each vehicle speed category ; Calculate the frequency under each vehicle speed category ; Calculate the empirical entropy of deterministic operating condition curves ; The amount of information in the static information is measured by the entropy reduction of the empirical entropy. Since the operating condition information is dynamic, the degree of uncertainty of the dynamic information is quantified using information entropy. The amount of information in the dynamic information is measured by the entropy reduction of the information entropy.

2. The method for measuring operating condition information for vehicle energy management control according to claim 1, characterized in that, The steps for determining the type of operating condition information include: Get the vehicle's current speed; If the current vehicle speed is a fixed value, then the current operating condition of the vehicle is determined to be a fixed operating condition. Based on the fact that the current working condition is a defined working condition, the working condition information is determined to be static information; Since the current vehicle speed is a dynamically changing value, the current operating condition of the vehicle is determined to be an uncertain operating condition. Based on the fact that the current operating condition is an uncertain operating condition, the operating condition information is determined to be dynamic information.

3. The method for measuring operating condition information for vehicle energy management control according to claim 1 or 2, characterized in that, The step of quantifying the uncertainty of the static information using empirical entropy, based on the assumption that the operating condition information is static information, includes: Compare the empirical entropy of the static information; The degree of uncertainty of the static information is determined based on the magnitude of the empirical entropy, wherein the empirical entropy is positively correlated with the degree of uncertainty of the static information; And / or, the step of measuring the amount of information in the static information based on the entropy reduction of the empirical entropy includes: The maximum empirical entropy obtained; The maximum empirical entropy is used as the minuend and the other empirical entropies are subtracted to obtain the entropy reduction of the empirical entropy; The information content of the static information is measured by the entropy reduction of the empirical entropy, wherein the entropy reduction of the empirical entropy is positively correlated with the information content of the static information.

4. The method for measuring operating condition information for vehicle energy management control according to claim 1 or 2, characterized in that, In the step of quantifying the uncertainty of dynamic information using information entropy based on the dynamic nature of the operating condition information, the basic formula for calculating the information entropy is as follows: Where X is a random variable. Let x be the probability density function and v be the vehicle speed.

5. The method for measuring operating condition information for vehicle energy management control according to claim 4, characterized in that, The step of quantifying the uncertainty of dynamic information using information entropy, based on the dynamic nature of the operating condition information, includes: Based on the fact that the typical shape of the feasible region for vehicle speed in the dynamic information is a rectangular speed range, the formula for calculating the information entropy is: In the formula, v is the vehicle speed. For operating time, For vehicle speed limit, This is the lower limit of vehicle speed. ; And / or, based on the fact that the typical shape of the feasible region of vehicle speed in the dynamic information is a triangular vehicle speed range, the formula for calculating the information entropy is: In the formula, The upper limit of acceleration, To accelerate the car from 0 to The shortest time, The sampling time interval, ; And / or, based on the fact that the typical shape of the feasible region of vehicle speed in the dynamic information is an irregular vehicle speed range, the formula for calculating the information entropy is: Among them, the vehicle speed range is from Become Considering the vehicle's acceleration and deceleration capabilities, Is the vehicle speed from Accelerate to The shortest time, , Is the vehicle speed from slow down to The shortest time, , , , , , ,like ,but , Conversely, if ,but , .

6. The method for measuring operating condition information for vehicle energy management control according to claim 5, characterized in that, In the step of measuring the amount of information in the dynamic information based on the entropy reduction of the information entropy... Without considering constraints, the amount of information at this point is minimal, and the vehicle speed variable is... It follows a uniform distribution and is used as the minuend when calculating entropy reduction; at this point, the information entropy... ,in, The maximum achievable speed under operating conditions. Operating time; The amount of information input considering road constraints such as speed limits ; The amount of information derived from considering the vehicle's acceleration and deceleration capabilities, a constraint on vehicle performance. ; Considering the information content brought about by traffic flow as a joint constraint of vehicles and roads ,in, The vehicle's departure speed range is The total time of traffic flow For vehicles within the speed range of The total time of traffic flow.

7. The method for measuring operating condition information for vehicle energy management control according to claim 4, characterized in that, The step of quantifying the uncertainty of dynamic information using information entropy, based on the dynamic nature of the operating condition information, includes: Compare the information entropy of the dynamic information; The degree of uncertainty of the dynamic information is determined based on the magnitude of the information entropy, wherein the information entropy is positively correlated with the degree of uncertainty of the dynamic information; And / or, the step of measuring the amount of information in the dynamic information based on the entropy reduction of the information entropy includes: Obtain the information entropy without considering constraints; The information entropy without considering constraints is used as the minuend and the information entropy under other constraints is subtracted to obtain the entropy reduction of the information entropy. The information content of the dynamic information is measured by the entropy reduction of the information entropy, wherein the entropy reduction of the information entropy is positively correlated with the information content of the dynamic information.

8. A system for measuring operating condition information for vehicle energy management control, characterized in that, The operating condition information measurement system includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to execute the operating condition information measurement method for vehicle energy management control as described in any one of claims 1-7.

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