CN116198521A - Hybrid electric vehicle working condition identification method and system - Google Patents

Abstract

The invention relates to a method and a system for identifying working conditions of a hybrid electric vehicle, which are used for identifying working conditions based on a fuzzy controller, and specifically comprise the following steps: acquiring characteristic parameters of vehicle running; blurring the characteristic parameters; obtaining a fuzzy judgment conclusion through fuzzy reasoning; performing disambiguation on the disambiguation judgment conclusion to obtain a specific working condition type; the design of the fuzzy controller comprises: determining the type of the working condition; extracting characteristic parameters; and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters. Compared with the prior art, the method and the device have the advantages that the running condition type of the hybrid electric vehicle is effectively identified by the designed fuzzy inference rule through analyzing the vehicle motion parameters, the fuzzy controller has higher robustness, less occupied calculation resources, the fuzzy controller does not need an accurate model of a controlled object, and the requirements of the hybrid electric vehicle on the response speed and the stability of a control system can be met.

Description

Hybrid electric vehicle working condition identification method and system

Technical Field

The invention relates to the technical field of hybrid electric vehicles, in particular to a power split type hybrid electric vehicle working condition identification method and system based on a fuzzy controller.

Background

The energy management strategy can achieve optimal distribution among multiple power sources of the hybrid electric vehicle (Hybrid Electric Vehicle, HEV), however, the energy management strategy formulated for a certain known working condition cannot ensure optimal economy in the actual running process of the vehicle. Therefore, if the current driving working condition can be identified according to the real-time state of the vehicle, and then corresponding energy distribution is carried out, the vehicle can run in a more efficient economic area, and therefore, the working condition identification has a certain help to the energy management of the hybrid electric vehicle.

The energy management strategy based on the working condition identification generally classifies driving working conditions into congestion working conditions, urban working conditions, suburban working conditions, high-speed working conditions and the like according to different vehicle driving areas and traffic conditions. Some researchers divide working conditions into pure electric motor, pure engine, engine for charging battery, motor for assisting engine and brake feedback according to the working mode of the hybrid electric vehicle. Or the running information of a period of time in the future is divided into a general working condition, a transitional working condition (long-plug vehicle, long-ascending slope and long-descending slope), a special working condition (long-plug vehicle, long-ascending slope and long-descending slope) and the like.

Common methods for identifying working conditions include neural networks, fuzzy controllers, cluster analysis and the like. Chinese patent CN113859219a discloses a driving condition recognition-based adaptive energy management method for a hybrid electric vehicle, which uses a PCA method to perform dimension reduction processing on characteristic parameters of the working conditions, uses a cluster analysis algorithm to classify the working conditions, considers the influence of the driving conditions on energy management performance, and can optimize equivalent factors in real time by recognizing the driving conditions on line, thereby improving the fuel economy of the vehicle and the working condition adaptability of the energy management strategy. Chinese patent CN113276829a provides a method for optimizing and changing weight of energy saving during vehicle running based on condition prediction, and identifies and predicts the current running condition of the hybrid electric vehicle by BP neural network; the weight coefficients of all items in the objective function are obtained according to the running working conditions and the vehicle states by utilizing fuzzy rules, the energy-saving optimization problem is solved by utilizing the minimum value principle and the dichotomy based on the model prediction control framework, the prediction of the future running working conditions according to the history characteristics of the running working conditions of the vehicle is realized, the self-adaptive coordination capacity of the fuel and electric consumption of the hybrid electric vehicle along with the working conditions is realized, and the fuel economy of the hybrid electric vehicle is improved.

However, the existing working condition identification scheme is large in calculation resource consumption, complex in algorithm and difficult to realize from the engineering viewpoint.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a hybrid electric vehicle working condition identification method and system.

The aim of the invention can be achieved by the following technical scheme:

according to a first aspect of the invention, a method for identifying working conditions of a hybrid vehicle is provided, wherein the method is based on a fuzzy controller for identifying working conditions and comprises the following specific steps:

acquiring characteristic parameters of vehicle running; blurring the characteristic parameters; obtaining a fuzzy judgment conclusion through fuzzy reasoning; performing deblurring on the fuzzy judgment conclusion to obtain a specific working condition type;

the design of the fuzzy controller comprises the following steps:

determining the type of the working condition;

extracting characteristic parameters;

and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

Further, the "extracting feature parameters" includes:

the method comprises the steps of obtaining vehicle motion parameters under different working condition types, including maximum speed, average speed, maximum acceleration, average acceleration, maximum deceleration, average deceleration and idle time proportion, and finding out the vehicle motion parameters which have large differences among different working conditions and have stable values under the same working condition as characteristic parameters.

Further, the "fuzzy rule and fuzzy inference rule of design feature parameters" includes:

designing a fuzzy language { VH, H, M, L, VL }, which corresponds to high, medium, low and low respectively;

design type coefficients 1, 2 and 3 respectively correspond to urban working conditions, suburban working conditions and high-speed working conditions;

presetting a fuzzy language demarcation value and a fuzzy reasoning rule of each characteristic parameter;

and verifying the working condition recognition result through simulation, if the recognition result meets the precision requirement, completing the design, otherwise, adjusting the fuzzy language demarcation value of each characteristic parameter, correspondingly adjusting the fuzzy reasoning rule, and repeating the step.

Further, presetting a fuzzy language demarcation value and a fuzzy reasoning rule of each characteristic parameter according to experience and data analysis, wherein the data analysis specifically comprises the following steps: and acquiring the value range of the characteristic parameters under different working condition types, and determining the fuzzy language demarcation value and the fuzzy inference rule according to the value range.

Further, each fuzzy language demarcation value of each characteristic parameter is respectively adjusted through a control variable method, the validity of the fuzzy inference rule is verified along with the adjustment of the fuzzy language demarcation value of the characteristic parameter, and if failure exists, the fuzzy inference rule is adjusted.

Further, the operating mode types include urban operating mode, suburban operating mode and high-speed operating mode.

Further, the characteristic parameters include an average vehicle speed and an idle time ratio.

Further, the fuzzy language { VH, H, M, L, VL } is designed to be very high, medium, low and very low respectively;

the average speed of the vehicle is 20km/h, 40km/h, 80km/h and 120km/h which are five fuzzy language demarcation values, and the idling time proportion is 0.025, 0.1, 0.2 and 0.5 which are five fuzzy language demarcation values of the idling time proportion.

Further, the design type coefficients 1, 2 and 3 respectively correspond to urban working conditions, suburban working conditions and high-speed working conditions;

the fuzzy inference rule is as follows:

if "average vehicle speed=vl", or "average vehicle speed=l", the fuzzy judgment conclusion is 1;

if "average vehicle speed=m", or "average vehicle speed=h and idle time ratio=h", or "average vehicle speed=h and idle time ratio=vh", or "average vehicle speed=vh and idle time ratio=h", or "average vehicle speed=vh and idle time ratio=vh", the fuzzy judgment result is 2;

the fuzzy judgment conclusion is 3 if "average vehicle speed=h and idle time ratio=vl", or "average vehicle speed=h and idle time ratio=l", or "average vehicle speed=h and idle time ratio=m", or "average vehicle speed=vh and idle time ratio=vl", or "average vehicle speed=vh and idle time ratio=l", or "average vehicle speed=vh and idle time ratio=m".

According to a second aspect of the present invention, there is provided a hybrid vehicle condition recognition system for performing condition recognition based on a fuzzy controller, including:

the data extraction module is used for obtaining characteristic parameters of vehicle running;

the fuzzy input module is used for fuzzifying the characteristic parameters;

the fuzzy reasoning module is used for obtaining a fuzzy judgment conclusion through fuzzy reasoning;

the output module is used for performing defuzzification on the fuzzy judgment conclusion to obtain a specific working condition type;

the design of the fuzzy controller comprises the following steps:

determining the type of the working condition;

extracting characteristic parameters;

and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

Compared with the prior art, the invention has the following beneficial effects:

by analyzing the vehicle motion parameters, the running condition type of the hybrid electric vehicle is effectively identified by a designed fuzzy inference rule, the fuzzy controller has higher robustness and occupies less calculation resources, the fuzzy controller does not need an accurate model of a controlled object, the requirements of the hybrid electric vehicle on the response speed and stability of a control system can be met, the self-adaptive adjustment of a follow-up real vehicle control strategy can be used as a theoretical premise, and ideas and methods are provided for improving the fuel economy of the vehicle.

Drawings

FIG. 1 is a flow chart of a fuzzy controller-based condition recognition algorithm;

FIG. 2 is a schematic diagram of a city operating mode vehicle speed curve;

FIG. 3 is a schematic diagram of a suburban operating mode vehicle speed curve;

FIG. 4 is a graph of a high speed operating mode vehicle speed curve;

FIG. 5 is a graph of membership function for average vehicle speed;

FIG. 6 is a graph of membership function for idle time scale;

FIG. 7 is a graphical illustration of membership functions for a condition type coefficient;

FIG. 8 is a diagram of NEDC condition recognition effect;

FIG. 9 is a schematic diagram of WLTC condition identifying effect;

FIG. 10 is a schematic diagram of the CombDC condition recognition effect.

Detailed Description

The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, and obviously, the described embodiment is only a part of the embodiment of the present invention, but not all the embodiments, and the protection scope of the present invention is not limited to the following embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

Reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic may be included in at least one implementation of the invention. In the description of the present invention, it should be understood that the terms "first," "second," and "third," etc. in the description and claims of the invention and in the above figures are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

The present specification provides method operational steps as an example or flow diagram, but may include more or fewer operational steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. In actual system or server product execution, the steps may be performed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) or in an order that is not timing-constrained, as per the methods shown in the embodiments or figures.

The invention provides a working condition identification method of a power split type hybrid electric vehicle, which is based on a fuzzy controller for carrying out working condition identification and specifically comprises the following steps:

acquiring characteristic parameters of vehicle running; blurring the characteristic parameters; obtaining a fuzzy judgment conclusion through fuzzy reasoning; performing disambiguation on the disambiguation judgment conclusion to obtain a specific working condition type;

the working condition identification flow is shown in figure 1 and mainly comprises the steps of characteristic parameter extraction, input quantity fuzzification, fuzzy reasoning, fuzzy solution and the like. Firstly, through parameter analysis on standard working conditions, characteristic parameters which can represent the types of the working conditions are determined and used as input of a fuzzy controller. The input quantity fuzzification determines the membership degree of the input quantity according to the membership degree function, and the fuzzy language is used for replacing the numerical value of each input characteristic parameter. The fuzzy inference rule infers a fuzzy judgment conclusion based on fuzzy logic according to the fuzzy input quantity, and in order to obtain clear output, the fuzzy judgment conclusion needs to be defuzzified, namely the fuzzy judgment conclusion is converted into a specific working condition type.

The design of the fuzzy controller comprises the following steps:

determining the type of the working condition; extracting characteristic parameters; and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

(1) The "extracting feature parameters" includes:

the method comprises the steps of obtaining vehicle motion parameters under different working condition types, including maximum speed, average speed, maximum acceleration, average acceleration, maximum deceleration, average deceleration and idle time proportion, and finding out the vehicle motion parameters which have large differences among different working conditions and have stable values under the same working condition as characteristic parameters.

(2) The "fuzzy rule and fuzzy inference rule of design characteristic parameters" includes:

designing a fuzzy language { VH, H, M, L, VL }, which corresponds to high, medium, low and low respectively;

design type coefficients 1, 2 and 3 respectively correspond to urban working conditions, suburban working conditions and high-speed working conditions;

presetting a fuzzy language demarcation value and a fuzzy reasoning rule of each characteristic parameter;

and verifying the working condition recognition result through simulation, if the recognition result meets the precision requirement, completing the design, otherwise, adjusting the fuzzy language demarcation value of each characteristic parameter, correspondingly adjusting the fuzzy reasoning rule, and repeating the step.

Specifically, the fuzzy language demarcation value and the fuzzy inference rule of each characteristic parameter are preset according to experience and data analysis, and the data analysis specifically comprises: and acquiring the value range of the characteristic parameters under different working condition types, and determining the fuzzy language demarcation value and the fuzzy inference rule according to the value range. And respectively adjusting each fuzzy language demarcation value of each characteristic parameter by a control variable method, and adjusting the fuzzy inference rule along with the adjustment of the fuzzy language demarcation value of the characteristic parameter to verify the validity of the fuzzy inference rule, and if failure exists, adjusting the fuzzy inference rule.

(1) In this embodiment, the condition types including urban conditions, suburban conditions, and high-speed conditions are first determined. In the prior art, the working condition types are divided into three working conditions of high speed, medium speed and low speed, or a general working condition, a transition working condition (long-plug vehicle, long-ascending slope and long-descending slope), a special working condition (long-plug vehicle, long-ascending slope and long-descending slope) and the like, and the quick response and the simplicity of the system are considered, so the working condition types are urban working conditions, suburban working conditions and high-speed working conditions, the quick judgment of the vehicle on the current running working condition is facilitated, and the hybrid system further responds quickly.

In order to determine the characteristic parameters for the condition identification, 12 sets of standard conditions and segments are selected for analysis, including 4 sets of urban conditions, 4 sets of suburban conditions and 4 sets of high-speed conditions, as shown in fig. 2-4. The standard working condition and segment parameter analysis is shown in table 1, wherein V_max and V_mean respectively represent the highest vehicle speed and the average vehicle speed, A_max and A_mean respectively represent the maximum acceleration and the average acceleration, D_max and D_mean respectively represent the maximum deceleration and the average deceleration, R_idle respectively represent the idle time proportion, and types 1, 2 and 3 respectively represent urban working conditions, suburban working conditions and high-speed working conditions. As can be seen by comparison, the ratio of the maximum speed, the average speed and the idle time has larger difference for different working condition types, while the difference of the maximum acceleration, the average acceleration, the maximum deceleration and the average deceleration among different working conditions is not obvious. In addition, certain conditions may exhibit a higher maximum vehicle speed and a lower average vehicle speed, such as suburban conditions Artemis Road, US06 (segment 1) where the maximum vehicle speed is high but the average vehicle speed is low compared to the high speed condition Highway. Therefore, compared with the highest vehicle speed, the average vehicle speed can better reflect the working condition characteristics. Therefore, the ratio of the average vehicle speed to the idle time is selected as the characteristic parameter for the condition recognition.

TABLE 1 Standard operating Condition and fragment parameter analysis

(2) The fuzzy languages of high (VH), high (H), medium (M), low (L) and low (VL) are designed to replace specific values of average speed and idle time proportion, so that fuzzification of input variables is completed, and the working condition type coefficients of 1 (urban working condition), 2 (suburban working condition) and 3 (high-speed working condition) are used as output variables.

Firstly, according to experience and data analysis, fuzzy language demarcation values and fuzzy inference rules of each characteristic parameter can be set, for example, 12 groups of standard working conditions and the value ranges of average speed and idle time proportion in urban working conditions, suburban working conditions and high-speed working conditions in segments are observed, and 4 demarcation values are set to divide the characteristic parameters into five fuzzy languages. Of course, the upper limit and the lower limit of the characteristic parameter may be determined, and the 4 demarcation values are obtained by dividing the upper limit range and the lower limit range into five equal parts, or may be set by a person skilled in the art in other manners, which are not described in detail herein.

After the fuzzy language demarcation value is initially set, the fuzzy language demarcation corresponding to the input variable is obtained, and then the fuzzy inference rule is reversely deduced according to the output variable. In order to ensure the effectiveness of the fuzzy language demarcation value and the fuzzy inference rule, the working condition recognition result needs to be verified through simulation, if the recognition result meets the precision requirement, the design is completed, otherwise, the fuzzy language demarcation value of each characteristic parameter is adjusted, and the fuzzy inference rule is correspondingly adjusted. When the fuzzy language demarcation is adjusted, each fuzzy language demarcation value of each characteristic parameter is respectively adjusted through a control variable method, for example, when the first fuzzy language demarcation value of the average speed is adjusted, the rest fuzzy language demarcation values of the average speed and the fuzzy language demarcation values of the idle time proportion are fixed, so that the proper speed demarcation value is found through continuous adjustment, and then the rest fuzzy language demarcation values of the average speed and the fuzzy language demarcation values of the idle time proportion are adjusted. And along with the adjustment of the fuzzy language demarcation value of the characteristic parameter, the validity of the fuzzy inference rule needs to be verified, and if the fuzzy inference rule is invalid, the fuzzy inference rule is adjusted.

In the embodiment, according to experience and data analysis, the values are respectively 20km/h, 40km/h, 80km/h and 120km/h and are five fuzzy language demarcation values of average vehicle speed. The values of the five fuzzy language demarcations are respectively 0.025, 0.1, 0.2 and 0.5 which are the idle time proportion. The trapezoidal function is used as the membership function of the input variable and the output variable, the function is simple to calculate, and the requirements of real vehicle control on operation speed and control precision can be met. Finally, the membership functions of the input variables shown in fig. 5-6 are obtained.

The membership function of the output variable is shown in FIG. 7 as the output of the fuzzy controller. According to the average speed and idle time, the controller outputs a value between 0 and 1, and according to the output value, the corresponding working condition type coefficient, namely the corresponding working condition type is determined. Of course, it is understood that the membership function of the output variable is also obtained by constant adjustment. In this embodiment, the designed fuzzy inference rule is shown in table 2, and the value of the output variable can be obtained by inference by using fuzzy logic according to the membership function value of the input variable.

Table 2 fuzzy inference rules

The fuzzy inference rules are as follows:

if "average vehicle speed=vl", or "average vehicle speed=l", the fuzzy judgment conclusion is 1;

if "average vehicle speed=m", or "average vehicle speed=h and idle time ratio=h", or "average vehicle speed=h and idle time ratio=vh", or "average vehicle speed=vh and idle time ratio=h", or "average vehicle speed=vh and idle time ratio=vh", the fuzzy judgment result is 2;

the fuzzy judgment conclusion is 3 if "average vehicle speed=h and idle time ratio=vl", or "average vehicle speed=h and idle time ratio=l", or "average vehicle speed=h and idle time ratio=m", or "average vehicle speed=vh and idle time ratio=vl", or "average vehicle speed=vh and idle time ratio=l", or "average vehicle speed=vh and idle time ratio=m".

In order to verify the effect of the working condition identification, three working conditions are adopted for verification: the standard circulation working condition NEDC for representing the stable operation of the vehicle, the standard circulation working condition WLTC for representing the operation condition of the vehicle under the working condition of a complex road and the working condition combDC formed by combining the test data of the real vehicle. Fig. 8-10 show simulation results for three conditions, respectively. The NEDC working condition identification result is better in accordance with the actual working condition type; the urban and high-speed working conditions of WLTC and CombDC are better in identification effect, and the urban working conditions can appear in the identification result because part of periods of suburban working conditions have the characteristics of urban working conditions.

According to the invention, through analyzing the vehicle motion parameters, the running condition type of the hybrid electric vehicle is effectively identified by the designed fuzzy inference rule, the fuzzy controller has higher robustness, less occupied calculation resources, the fuzzy controller does not need an accurate model of a controlled object, the requirements of the hybrid electric vehicle on the response speed and stability of a control system can be met, the self-adaptive adjustment of a follow-up real vehicle control strategy can be used as a theoretical premise, and the thought and method are provided for improving the fuel economy of the vehicle. The system is simple, low in required computing resource, easy to use in engineering practice and good in robustness.

The invention also provides a working condition identification system of the power split type hybrid electric vehicle, which is used for identifying working conditions based on the fuzzy controller and comprises the following steps:

the data extraction module is used for obtaining characteristic parameters of vehicle running;

the fuzzy input module is used for fuzzifying the characteristic parameters;

the fuzzy reasoning module is used for obtaining a fuzzy judgment conclusion through fuzzy reasoning;

the output module is used for resolving the fuzzy judgment conclusion to obtain a specific working condition type;

the design of the fuzzy controller comprises:

determining the type of the working condition;

extracting characteristic parameters;

and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.

The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

Claims (10)

1. The method for identifying the working condition of the hybrid electric vehicle is characterized by carrying out working condition identification based on a fuzzy controller, and specifically comprises the following steps:

acquiring characteristic parameters of vehicle running; blurring the characteristic parameters; obtaining a fuzzy judgment conclusion through fuzzy reasoning; performing deblurring on the fuzzy judgment conclusion to obtain a specific working condition type;

the design of the fuzzy controller comprises the following steps:

determining the type of the working condition;

extracting characteristic parameters;

and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

2. The method for identifying the working condition of the hybrid vehicle according to claim 1, wherein the step of extracting the characteristic parameters comprises the steps of:

the method comprises the steps of obtaining vehicle motion parameters under different working condition types, including maximum speed, average speed, maximum acceleration, average acceleration, maximum deceleration, average deceleration and idle time proportion, and finding out the vehicle motion parameters which have large differences among different working conditions and have stable values under the same working condition as characteristic parameters.

3. The method for identifying the working condition of the hybrid vehicle according to claim 2, wherein the "fuzzy rule and fuzzy inference rule of the design characteristic parameter" includes:

designing a fuzzy language { VH, H, M, L, VL }, which corresponds to high, medium, low and low respectively;

design type coefficients 1, 2 and 3 respectively correspond to urban working conditions, suburban working conditions and high-speed working conditions;

presetting a fuzzy language demarcation value and a fuzzy reasoning rule of each characteristic parameter;

and verifying the working condition recognition result through simulation, if the recognition result meets the precision requirement, completing the design, otherwise, adjusting the fuzzy language demarcation value of each characteristic parameter, correspondingly adjusting the fuzzy reasoning rule, and repeating the step.

4. The method for identifying the working condition of the hybrid vehicle according to claim 3, wherein the fuzzy language demarcation value and the fuzzy inference rule of each characteristic parameter are preset according to experience and data analysis, and the data analysis specifically comprises: and acquiring the value range of the characteristic parameters under different working condition types, and determining the fuzzy language demarcation value and the fuzzy inference rule according to the value range.

5. The method for identifying the working condition of the hybrid electric vehicle according to claim 3, wherein each fuzzy language demarcation value of each characteristic parameter is respectively adjusted by a control variable method, the validity of the fuzzy inference rule is verified along with the adjustment of the fuzzy language demarcation value of the characteristic parameter, and if the fuzzy inference rule is invalid, the fuzzy inference rule is adjusted.

6. The hybrid vehicle condition identification method of claim 1, wherein the condition types include urban conditions, suburban conditions and high speed conditions.

7. The method for identifying the working condition of the hybrid vehicle according to claim 6, wherein the characteristic parameter comprises an average vehicle speed and an idling time ratio.

8. The method for identifying the working condition of the hybrid vehicle according to claim 7, wherein the fuzzy language { VH, H, M, L, VL } is designed to be very high, medium, low and very low respectively;

the average speed of the vehicle is 20km/h, 40km/h, 80km/h and 120km/h which are five fuzzy language demarcation values, and the idling time proportion is 0.025, 0.1, 0.2 and 0.5 which are five fuzzy language demarcation values of the idling time proportion.

9. The hybrid vehicle condition identification method of claim 8, wherein the design type coefficients 1, 2, 3 correspond to city conditions, suburban conditions and high-speed conditions, respectively;

the fuzzy inference rule is as follows:

if "average vehicle speed=vl", or "average vehicle speed=l", the fuzzy judgment conclusion is 1;

if "average vehicle speed=m", or "average vehicle speed=h and idle time ratio=h", or "average vehicle speed=h and idle time ratio=vh", or "average vehicle speed=vh and idle time ratio=h", or "average vehicle speed=vh and idle time ratio=vh", the fuzzy judgment result is 2;

the fuzzy judgment conclusion is 3 if "average vehicle speed=h and idle time ratio=vl", or "average vehicle speed=h and idle time ratio=l", or "average vehicle speed=h and idle time ratio=m", or "average vehicle speed=vh and idle time ratio=vl", or "average vehicle speed=vh and idle time ratio=l", or "average vehicle speed=vh and idle time ratio=m".

10. The utility model provides a hybrid vehicle operating mode identification system which characterized in that carries out operating mode identification based on fuzzy controller, includes:

the data extraction module is used for obtaining characteristic parameters of vehicle running;

the fuzzy input module is used for fuzzifying the characteristic parameters;

the fuzzy reasoning module is used for obtaining a fuzzy judgment conclusion through fuzzy reasoning;

the output module is used for performing defuzzification on the fuzzy judgment conclusion to obtain a specific working condition type;

the design of the fuzzy controller comprises the following steps:

determining the type of the working condition;

extracting characteristic parameters;

and designing a fuzzy rule and a fuzzy reasoning rule of the characteristic parameters.

Images