Intelligent networked phev energy management method and system based on security posture assessment

Abstract

The application discloses an intelligent network connection PHEV energy management method and system based on a safety situation evaluation, and the method steps comprise the following steps: 1) acquiring vehicle driving condition data; 2) calculating a vehicle safety situation evaluation parameter based on the vehicle driving condition data; 3) determining a current driving state of the vehicle according to the vehicle safety situation evaluation parameter; 4) generating a vehicle energy management scheme based on the current driving state of the vehicle; 5) monitoring real-time driving condition data of a preceding vehicle, if the driving state of the preceding vehicle changes, returning to step 2); if the preceding vehicle maintains the current driving state, returning to step 2) after an interval t. The system comprises a vehicle driving condition data acquisition unit, a vehicle safety situation evaluation parameter calculation unit, a driving state judgment unit and a vehicle energy management scheme generation and execution unit; the application proposes an energy management strategy based on a safety situation evaluation, and provides theoretical and technical support for realizing deep integration of PHEV technology and intelligent network connection technology.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of vehicle control, in particular to an intelligent networked PHEV energy management method and system based on safety situation assessment. BACKGROUND

[0002] The intelligentization and networked of new energy vehicles are inevitable trends of future vehicle development, and a plug-in hybrid electric vehicle (PHEV) is an effective way to achieve energy saving and emission reduction.

[0003] At present, most of the energy management strategies of the plug-in hybrid electric vehicle (PHEV) do not consider the influence of the dynamic time-varying information of the front vehicle motion state, and the vehicle is often sensitive to the running state of the front vehicle during actual driving, and often adjusts the vehicle speed in real time according to the state of the front vehicle, so that the energy management strategy without considering the front vehicle cannot effectively solve the problems of safety and optimal energy management.

[0004] The PHEV control strategy often depends on fixed standard cycle conditions, and there is a certain deviation from the actual road driving conditions, and the influence of the dynamic time-varying information of the front vehicle motion state is ignored, and the energy management strategy cannot be adjusted according to the specific driving state to respond to the change of the driving state of the front vehicle to the change of the driving state of the vehicle, so that the energy loss of the PHEV driving is large. SUMMARY

[0005] The purpose of the application is to provide an intelligent networked PHEV energy management method based on safety situation assessment, which comprises the following steps:

[0006] 1) obtaining vehicle driving condition data;

[0007] 2) calculating a vehicle safety situation evaluation parameter based on the vehicle driving condition data;

[0008] 3) determining the current driving state of the vehicle according to the vehicle safety situation evaluation parameter;

[0009] 4) generating a vehicle energy management scheme based on the current driving state of the vehicle;

[0010] 5) monitoring the driving condition data of the front vehicle in real time, if the driving state of the front vehicle changes, returning to step 2), or returning to step 2) every t time interval.

[0011] Further, the vehicle driving condition data includes acceleration signals and brake pressure signals in the driving process, and front vehicle acceleration signals.

[0012] Further, the driving state includes a cruising state, a following state and a passing state.

[0013] The cruise state refers to that the ego vehicle is in a free driving state and no vehicle exists within a dmax distance in front of the ego vehicle; dmax is a distance threshold;

[0014] The following state refers to that when the ego vehicle is driving, a preceding vehicle exists within a dmax distance in front of the ego vehicle, and the driving speed and acceleration of the preceding vehicle are within a preset stable range.

[0015] The following state refers to that when the ego vehicle is driving, a preceding vehicle exists within a dmax distance in front of the ego vehicle, but the driving speed and / or acceleration of the preceding vehicle is / are outside the preset stable range.

[0016] Further, the safety situation evaluation parameters include an expected acceleration aego of the ego vehicle d , a relative distance D between the ego vehicle and the preceding vehicle, and a time headway T B , and a reciprocal collision time

[0017] Wherein, the expected acceleration aego of the ego vehicle d is as follows:

[0018]

[0019] In the formula, m is the mass of the vehicle; vego is the speed of the ego vehicle; T is the output torque of the drive shaft; T is the braking torque; R is the rolling radius of the wheel; g is the gravity coefficient; θ is the road slope; ρ is the air density coefficient; A is the windward area; C is the air resistance coefficient; C is the rolling resistance coefficient. B a b w d r

[0020] The relative distance D between the ego vehicle and the preceding vehicle, the time headway T B , and the reciprocal collision time are as follows:

[0021] D = Δd (2)

[0022]

[0023]

[0024] In the formula, Δd is the relative distance between the two vehicles, vego is the speed of the ego vehicle, and vrel is the relative speed between the ego vehicle and the preceding vehicle. B r

[0025] Further, in step 3), if the relative distance D between the ego vehicle and the preceding vehicle is greater than dmax, the current driving state of the vehicle is the cruise state.

[0026] ​​​​​​​​If the relative distance D between the ego vehicle and the preceding vehicle is less than or equal to dmax, and the acceleration of the preceding vehicle is within the range of [-(1+β)a f ,(1+β)a f ], the current driving state of the vehicle is a following state; wherein the parameters a i-a is an ideal acceleration of the ego vehicle; a i-f is an ideal acceleration of the preceding vehicle; a f is an actual acceleration of the ego vehicle in the current driving state.

[0027] If the relative distance D between the ego vehicle and the preceding vehicle is less than or equal to dmax, and the acceleration of the preceding vehicle is outside the range of [-(1+β)a f ,(1+β)a f ], the current driving state of the vehicle is a passing state.

[0028] Further, the energy management scheme of the vehicle includes a safety distance between the ego vehicle and the preceding vehicle and a demanded torque of the ego vehicle.

[0029] Further, if the current driving state of the vehicle is the following state, the safety distance d s1 between the ego vehicle and the preceding vehicle is determined as follows:

[0030] d s1 =v r T s +d0(5)

[0031] wherein T s is a default time interval threshold; d0 is a default minimum safety distance; v r is a relative speed between the ego vehicle and the preceding vehicle.

[0032] If the current driving state of the vehicle is the passing state, the safety distance d s2 between the ego vehicle and the preceding vehicle is determined as follows:

[0033] d s2 =v B T s +d0(6)

[0034] wherein v B is a speed of the ego vehicle.

[0035] Further, the step of generating the energy management scheme of the vehicle based on the current driving state of the vehicle includes:

[0036] 1) determining a working phase of the vehicle;

[0037] When the vehicle is working in the power consumption phase, a demanded torque T c of the current driving condition is determined, and if the demanded torque T c of the current driving condition is less than or equal to T M_maxT = T - T m c T = T - T E M_max T is the maximum output torque of the current motor;

[0038] If the demand torque of the current driving condition satisfies T M_max <T c ≤ T M_max + T E_min , the torque T m assigned to the motor is T c = T E_min - T E , and the torque T E_min assigned to the engine is T E_min = T M_max ; T is the minimum output torque of the current engine;

[0039] If the demand torque of the current driving condition satisfies T E_min <T c ≤ T M_max + T E_max , the torque T m assigned to the motor is T c = T E_max - T E , and the torque T E_max assigned to the engine is T E_max = T c ; T is the maximum output torque of the current engine;

[0040] When the vehicle works in the power maintaining phase, the demand torque T c of the current driving condition is judged. If the demand torque T E_min of the current driving condition satisfies T m ≤ T c , the torque T E_min assigned to the motor is T E = T E_min - T E_min ;

[0041] If the demand torque of the current driving condition satisfies T c <T E_max , the torque T m assigned to the motor is 0, and the torque T E assigned to the engine is T c ;

[0042] If the demand torque of the current driving condition satisfies T E_max <T c , the torque T m assigned to the motor is T​​c -T E_max , the torque T E assigned to the engine E_max ;

[0043] 2) determine the current driving state of the vehicle, and correct the torque T m assigned to the motor of the vehicle according to the current driving state of the vehicle.

[0044] Further, if the current driving state of the vehicle is the cruising state, the torque T m does not need to be corrected, and is directly assigned according to the vehicle demand torque T c ; m If the current driving state of the vehicle is the following state, the torque T m is corrected by using formula (7); if the current driving state of the vehicle is the passing state, the torque T m is corrected by using formula (8);

[0045]

[0046] In the formula, T m_max and T m_min are the maximum driving torque and the maximum braking torque of the ego vehicle, respectively; K D1 and K B1 are the correction coefficients of the driving torque and the braking torque in the following mode, respectively;

[0047]

[0048] In the formula, K D2 and K B2 are the correction coefficients of the driving torque and the braking torque in the passing mode, respectively. α is the limit parameter of the torque change rate; is the torque change rate.

[0049] The system applying the intelligent networked PHEV energy management method based on safety situation assessment comprises a vehicle driving condition data acquisition unit, a vehicle safety situation evaluation parameter calculation unit, a driving state judgment unit, and a vehicle energy management scheme generation and execution unit.

[0050] The vehicle driving condition data acquisition unit acquires vehicle driving condition data and transmits the data to the vehicle safety situation evaluation parameter calculation unit.

[0051] The vehicle safety situation evaluation parameter calculation unit processes the vehicle driving condition data, calculates the vehicle safety situation evaluation parameters, and transmits the parameters to the driving state judgment unit.

[0052] The driving state judgment unit determines the current driving state of the vehicle according to the vehicle safety situation evaluation parameters.

[0053] The vehicle energy management scheme generation and execution unit generates a vehicle energy management scheme based on the current driving state of the vehicle and executes the vehicle energy management scheme.

[0054] The technical effect of the present application is self-evident. The present application aims to improve the safety and comprehensive energy consumption of PHEV during driving, studies the coupling modeling method of PHEV traffic environment and front vehicle driving state, constructs a driving safety situation assessment model, clarifies the influence law of safety situation characteristic parameters on PHEV driving performance, and proposes a set of energy management strategy based on safety situation assessment, which provides theoretical and technical support for the deep integration of PHEV technology and intelligent network technology. BRIEF DESCRIPTION OF DRAWINGS

[0055] Fig. 1 is a flowchart of the intelligent networked PHEV energy management method based on safety situation assessment provided by the present application;

[0056] Fig. 2(a)-(b) is a flowchart of obtaining driving condition data of the host vehicle and the front vehicle; Fig. 2(a) is a sensor safety information acquisition process; Fig. 2(b) is a V2V inter-vehicle communication safety information acquisition process;

[0057] Fig. 3 is a flowchart of constructing a safety situation assessment model;

[0058] Fig. 4 is a flowchart of host vehicle working mode switching. DETAILED DESCRIPTION

[0059] The present application will be further described below in conjunction with examples, but should not be understood as limiting the above-mentioned subject matter of the present application to the following examples. Various substitutions and modifications can be made according to ordinary technical knowledge and conventional means in the art without departing from the above-mentioned technical idea of the present application, and all such substitutions and modifications shall be included in the protection scope of the present application.

[0060] Example 1:

[0061] Referring to Figs. 1 to 4 , the intelligent networked PHEV energy management method based on safety situation assessment includes the following steps:

[0062] 1) Obtain vehicle driving condition data;

[0063] 2) Calculate vehicle safety situation evaluation parameters based on the vehicle driving condition data;

[0064] 3) Determine the current driving state of the vehicle according to the vehicle safety situation evaluation parameters;

[0065] 4) Generate a vehicle energy management scheme based on the current driving state of the vehicle;

[0066] 5) Real-time monitoring of the preceding vehicle driving condition data, if the preceding vehicle driving state changes, return to step 2), or every interval t, return to step 2).

[0067] Embodiment 2:

[0068] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is same as embodiment 1, further, the vehicle driving condition data includes acceleration signal and brake pressure signal in the driving process, and the preceding vehicle acceleration signal.

[0069] Embodiment 3:

[0070] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is same as any one of embodiments 1-2, further, the driving state includes cruise state, following state, and passing state.

[0071] The cruise state refers to that the ego vehicle is in a free driving state, and there is no vehicle within a distance dmax in front of the ego vehicle; dmax is a distance threshold.

[0072] The following state refers to that when the ego vehicle is driving, there is a preceding vehicle within a distance dmax in front of the ego vehicle, and the driving speed and acceleration of the preceding vehicle are within a preset stable range.

[0073] The following state refers to that when the ego vehicle is driving, there is a preceding vehicle within a distance dmax in front of the ego vehicle, but the driving speed and / or acceleration of the preceding vehicle is / are outside the preset stable range.

[0074] Embodiment 4:

[0075] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is same as any one of embodiments 1-3, further, the safety situation evaluation parameter includes the expected acceleration a d of the ego vehicle, the relative distance D between the ego vehicle and the preceding vehicle, and the time headway T B of the ego vehicle and the preceding vehicle.

[0076] Embodiment 5:

[0077] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is same as any one of embodiments 1-4, further, the expected acceleration a d of the ego vehicle is as follows:

[0078]

[0079] In the formula, m is the mass of the vehicle; v B is the speed of the ego vehicle; T a is the output torque of the drive shaft; T b is the brake torque; and Rw is the rolling radius of the wheel; g is the gravity coefficient; θ is the road slope; p is the air density coefficient; A is the windward area; C d is the air resistance coefficient; C r is the rolling resistance coefficient;

[0080] Example 6:

[0081] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is the same as any one of examples 1-5, further, the relative distance D between the ego vehicle and the front vehicle, the headway T B , and the collision time reciprocal are respectively as follows:

[0082] D = Ad (2)

[0083]

[0084]

[0085] In the formula, Ad is the relative distance between the two vehicles, v B is the speed of the ego vehicle, v r is the relative speed between the ego vehicle and the front vehicle.

[0086] Example 7:

[0087] The intelligent networked PHEV energy management method based on safety situation assessment, the technical content is the same as any one of examples 1-6, further, in step 3), if the relative distance D between the ego vehicle and the front vehicle is greater than dmax, the current driving state of the vehicle is cruising state;

[0088] If the relative distance D between the ego vehicle and the front vehicle is less than or equal to dmax, and the acceleration of the front vehicle is within the range of [- (1+β)a f , (1+β)a f ], the current driving state of the vehicle is following state; wherein, the parameters a i-a is the ideal acceleration of the ego vehicle; a i-f is the ideal acceleration of the front vehicle; a f is the actual acceleration of the ego vehicle in the actual driving state;

[0089] If the relative distance D between the ego vehicle and the front vehicle is less than or equal to dmax, and the acceleration of the front vehicle is outside the range of [- (1+β)a f , (1+β)a f ], the current driving state of the vehicle is passing state.

[0090] Example 8:

[0091] The intelligent networked PHEV energy management method based on the safety situation assessment, the technical content is the same as any one of embodiments 1-7, further, the vehicle energy management scheme includes the safety distance of the ego vehicle and the front vehicle and the demand torque of the ego vehicle.

[0092] Embodiment 9:

[0093] The intelligent networked PHEV energy management method based on the safety situation assessment, the technical content is the same as any one of embodiments 1-8, further, if the current driving state of the vehicle is the following state, the safety distance d s1 is as follows:

[0094] d s1 = v r T s + d0(5)

[0095] In the formula, T s is a default time interval threshold; d0 is a default minimum safety distance; v r is the relative speed of the ego vehicle and the front vehicle;

[0096] If the current driving state of the vehicle is the passing state, the safety distance d s2 of the ego vehicle and the front vehicle is as follows:

[0097] d s2 = v B T s + d0(6)

[0098] In the formula, v B is the ego vehicle speed.

[0099] Embodiment 10:

[0100] The intelligent networked PHEV energy management method based on the safety situation assessment, the technical content is the same as any one of embodiments 1-9, further, based on the current driving state of the vehicle, the step of generating the vehicle energy management scheme includes:

[0101] 1) judging the working phase of the vehicle;

[0102] When the vehicle works in the power consumption phase, the demand torque T c of the current driving condition is judged, if the demand torque T c of the current driving condition satisfies T M_nax , the torque T m assigned to the motor is T c , the torque T E assigned to the engine is 0; T M_max is the maximum output torque of the current motor;

[0103] If the demand torque of the current driving condition satisfies TM_max <T c ≤T M_max +T E_min , the torque T m assigned to the motor is T c -T E_min , the torque T E assigned to the engine is T E_min ; T E_min is the minimum output torque of the current engine;

[0104] If the demand torque T M_max +T E_min <T c ≤T M_max +T E_max of the current driving condition is satisfied, the torque T m assigned to the motor is T c -T E_max , the torque T E assigned to the engine is T E_max ; T E_max is the maximum output torque of the current engine;

[0105] When the vehicle works in the power maintaining phase, the demand torque T c of the current driving condition is judged, if the demand torque T c ≤T E_min of the current driving condition is satisfied, the torque T m assigned to the motor is T c -T E_min , the torque T E assigned to the engine is T E_min ;

[0106] If the demand torque T E_min <T c ≤T E_max of the current driving condition is satisfied, the torque T m assigned to the motor is 0, the torque T E assigned to the engine is T c ;

[0107] If the demand torque T E_max <T c of the current driving condition is satisfied, the torque T m assigned to the motor is T c -T E_max , the torque T E assigned to the engine is T E_max ;

[0108] 2) judging the current driving state of the vehicle, and distributing the torque Tm is modified.

[0109] Embodiment 11:

[0110] The intelligent networked PHEV energy management method based on the safety situation assessment, the technical content is the same as any one of embodiments 1-10, further, if the current driving state of the vehicle is a cruising state, T m is directly modified according to the vehicle demand torque T c is distributed T m , if the current driving state of the vehicle is a following state, T m is modified by formula (7), if the current driving state of the vehicle is a passing state, T m is modified by formula (8);

[0111]

[0112] In the formula, T m_max and T m_min are the maximum drive torque and the maximum brake torque of the ego vehicle respectively; K D1 and K B1 are the correction coefficients of the drive torque and the brake torque in the following mode respectively;

[0113]

[0114] In the formula, K D2 and K B2 are the correction coefficients of the drive torque and the brake torque in the passing mode respectively. Alpha is the limit parameter of the torque change rate; is the torque change rate.

[0115] Embodiment 12:

[0116] The system applying the intelligent networked PHEV energy management method based on the safety situation assessment according to any one of embodiments 1-11, comprising a vehicle driving condition data acquisition unit, a vehicle safety situation evaluation parameter calculation unit, a driving state judgment unit, a vehicle energy management scheme generation and execution unit;

[0117] The vehicle driving condition data acquisition unit acquires vehicle driving condition data and transmits it to the vehicle safety situation evaluation parameter calculation unit;

[0118] The vehicle safety situation evaluation parameter calculation unit processes the vehicle driving condition data, calculates the vehicle safety situation evaluation parameter, and transmits it to the driving state judgment unit;

[0119] The driving state judgment unit determines the current driving state of the vehicle according to the vehicle safety situation evaluation parameter;

[0120] The vehicle energy management scheme generation and execution unit generates a vehicle energy management scheme based on the current driving state of the vehicle and executes the vehicle energy management scheme.

[0121] The system works, and the steps of the method described in any one of embodiments 1-11 are performed.

[0122] Embodiment 13:

[0123] A smart connected PHEV energy management method based on security posture assessment, comprising the following steps:

[0124] Obtain the driving condition data of each security posture of the vehicle;

[0125] Obtain the driving condition data of the ego vehicle and the preceding vehicle through acceleration sensors, pedal opening sensors, vehicle-to-vehicle communication protocols (V2V) and other traffic protocols.

[0126] Wherein, the specific way to define each driving state of the vehicle is:

[0127] Obtain the driving flow data of the city vehicle, define three different driving states according to the driving state: cruising state, following state and passing state; According to the special vehicle spacing information of the following state, the driving data of the preceding vehicle and the ego vehicle are captured, and the driving information of the preceding vehicle and the ego vehicle is processed to obtain the acceleration range of the preceding vehicle that the ego vehicle can enter the following state in the following state. The traffic flow information is divided into different states by whether there is a preceding vehicle and whether the acceleration of the preceding vehicle is in a certain range.

[0128] Wherein, the safety posture evaluation parameters are selected by extracting the driving state data of the vehicle;

[0129] The longitudinal behavior of the ego vehicle in the driving condition is represented by different degrees of acceleration, and the behavior parameters of the relative motion between the ego vehicle and the preceding vehicle are represented by the relative distance D, the headway T B , and the inverse of the collision time Therefore, the expected acceleration a d , the relative distance D, the headway T B , and the inverse of the collision time are used as safety posture evaluation parameters for driving conditions.

[0130] Wherein, the fuzzy clustering membership degree determines the membership degree of the vehicle safety posture variable.

[0131] By quantifying the driving condition under different safety postures and reading the driving data under this condition, the driving data is divided into several sets, and the center of the set is used to represent the entire set. The scores of each safety posture are calculated; the acceleration a dFor example, the fuzzy clustering is determined, a similar matrix R of n dimensions is established by establishing the similar relation of samples by the Euclidean distance method, the fuzzy equivalent matrix R of R is calculated e , the equivalent matrix R of R is obtained by threshold value e λ eλ , according to R eλ Classify the driving condition data; approximate estimate the membership function by using a triangular membership function, divide the safety situation parameters into K classes, namely C1, C2, …, CK K , wherein the hth fuzzy set C h Contains N h Elements, the present application needs to determine three vertices (a h , 0), (b h , 0), (c h , 0) to determine the triangle, and the membership function of the fuzzy set C h Can be obtained from the three vertices.

[0132] Construct a vehicle driving safety situation evaluation model;

[0133] By fuzzy processing the driving condition data of each safety situation of the vehicle, extracting the safety situation elements, determining the safety situation evaluation parameters, determining the fuzzy clustering membership degree of the vehicle variable membership degree number, and constructing the safety situation evaluation model.

[0134] Among them, the determination of the different working modes of the vehicle;

[0135] Based on the obtained driving condition safety situation quantitative value, the fuzzy clustering obtained by dividing the driving state and the approximate estimate membership function, the safety situation quantitative value is divided into different intervals, and the interval intersection point is taken as the critical point, and the critical point is The safety situation quantitative value is divided into three different safety situation intervals, corresponding to three different working modes, namely cruise mode, following mode and passing mode.

[0136] Among them, the vehicle energy management strategy based on the logic threshold rule;

[0137] The present application takes a parallel plug-in hybrid electric vehicle as an optimization object, and the plug-in hybrid electric vehicle is divided into an electric quantity consumption stage (CD) and an electric quantity maintenance stage (CS) according to the battery pack electric quantity consumption degree, and driving state torque rules of the electric quantity consumption stage and the electric quantity maintenance stage are respectively given.

[0138] Among them, the vehicle power source is distributed and optimized for different working modes;

[0139] Cruise mode is free to travel, no front car influence on the travel conditions of the car, no need to consider the front car on the safety situation of the car, no need to optimize the output torque of the motor, that is, directly according to the vehicle demand torque T c The distribution motor torque T m ; wherein T c is the demand torque under the current driving condition; T m is the torque distributed to the vehicle motor;

[0140] Follow mode is in stable state driving, the front car driving state is relatively stable, can be in keeping a certain following safety distance driving, in order to ensure the safety situation under the current driving state, the maximum drive torque of the car needs to be limited, at the same time, the brake pedal opening can be increased to increase the brake torque of the car, so as to obtain more energy recovery, therefore, the torque correction coefficient K D1 and K B1 are introduced respectively, which are the correction coefficients of the drive torque and the brake torque of the follow mode, used to reflect the different degrees of drive torque limitation under different safety situations;

[0141] Among them, the passing mode is in unstable state driving, the front car driving state is unstable, the acceleration fluctuation range exceeds [- (1+β)a f , (1+β)a f ]. Need and front car keep passing mode safety distance driving, in order to ensure the safety situation under the current driving state, not only to limit the drive torque of the car, but also to limit the change rate of the drive torque of the car. Therefore, K D2 and K B2 the correction coefficients of the drive torque and the brake torque of the passing mode are introduced to ensure the safety of the car in the passing mode; and the limitation parameter of the torque change rate α ensures that the torque of the car can be smoothly transitioned when entering the passing mode to improve the driving comfort.

[0142] Among them, the driving condition safety situation changes the mode switching model of the car;

[0143] In the driving process, when the car sensor or V2V senses the change of the front car driving condition, it will trigger the reevaluation of the safety situation, and quantify the current safety situation value Whether the current driving scene working mode of the car changes or not.

[0144] The application is an intelligent networked PHEV energy management method based on security situation assessment; through the use of various vehicle-mounted sensors and V2V communication protocols, driving safety data is obtained, and security situation assessment parameters are determined. The following state acceleration range is obtained by a specific vehicle spacing, the driving state of different vehicles is divided, the security situation quantization value of each driving state is obtained, the driving data is fuzzed, the security situation membership degree number is determined by using fuzzy clustering membership, the security situation membership degree function is obtained, and the security situation assessment model is constructed. The security situation assessment of different driving conditions obtains the security situation quantization value of each condition and divides the quantization value into different intervals, corresponding to different working modes. Based on the logical threshold rule, the vehicle power source is distributed to achieve the purpose of reducing the energy consumption of the vehicle, and the driving state of the preceding vehicle is identified through the vehicle-mounted sensor and V2V communication protocol. After the driving state of the preceding vehicle is changed, the driving state of the preceding vehicle is changed, and the driving state of the preceding vehicle is changed.

[0145] Embodiment 14:

[0146] An intelligent networked PHEV energy management method based on security situation assessment, comprising the following steps:

[0147] S1 Obtain the safety driving condition data of the vehicle

[0148] The specific mode is:

[0149] 1) Based on the vehicle-mounted acceleration sensor and the vehicle-mounted brake pedal sensor, the acceleration signal and the brake pressure signal of the vehicle during driving are obtained, and the expected acceleration of the vehicle in the current driving state is obtained.

[0150] 2) Based on the vehicle-to-vehicle communication protocol (V2V), the dedicated short-range communication technology (DSRC), the radio frequency identification technology (RFID), the global positioning system (GPS), the sensor technology, the radar camera, the image processing and the intelligent transportation system (ITS), the driving data exchange between the vehicle and the preceding vehicle is realized, the relative distance and the relative speed between the two vehicles are obtained, and the sudden situation collision time is estimated.

[0151] S2 Construct a vehicle driving security situation assessment model

[0152] The specific mode is:

[0153] 1) Define the driving state of the vehicle in the urban vehicle driving information flow

[0154] Obtain the driving flow information of a certain city vehicle, define the vehicle driving flow information, and define the driving flow information as three driving states according to the driving state of the vehicle: cruising state, following state and passing state.

[0155] The cruise state is that the ego vehicle is in a free driving state, there is no front vehicle, and the influence of the driving condition change of the front vehicle on the driving condition of the ego vehicle does not need to be considered.

[0156] The following state is that the driving speed and acceleration of the front vehicle are stable in a certain range in a road section, and the ego vehicle follows the front vehicle to reduce the energy consumption of the ego vehicle under the condition of ensuring a safe distance.

[0157] The passing state is that the driving speed and acceleration of the front vehicle are unstable in a certain range in a road section, and the ego vehicle adjusts the output torque of the ego vehicle according to the speed and acceleration change range of the front vehicle in a period of time to achieve a safe passing state.

[0158] 2) Extraction of vehicle following state data in urban vehicle traffic flow information

[0159] For the vehicle information extraction in the following state in the urban vehicle traffic flow, according to the relevant literature, the most intuitive parameter reflecting the following state in the driving state is the relative distance range d r between the two vehicles.

[0160] Therefore, the extraction condition of the vehicle following state is determined as follows: through simulation experiment analysis, it is found that using a 7s time window to analyze the driving state of the vehicle can well and objectively reflect the state of the vehicle in the driving process. The typical driving state analyzed by a time window higher than 7s has similarity, and cannot effectively reflect the acceleration relationship between the front vehicle and the ego vehicle in the following state. However, a time window lower than 7s cannot reflect the typical following state, and thus cannot provide a basis for subsequent driving state division. Therefore, a 7s time window is selected to analyze the relative distance between the two vehicles, and machine learning is used to determine a plurality of vehicle following state feature segments with a 7s time length and a relative distance d r between the two vehicles in the range. f The front vehicle acceleration a f , the front vehicle acceleration change rate a′ a , the ego vehicle acceleration a a , and the ego vehicle acceleration change rate a′ a in each segment are respectively composed into a driving information set about the following state of the front vehicle and the ego vehicle.

[0161] 3) Determination of ideal points of vehicle following state driving data

[0162] The extracted front vehicle and rear vehicle driving data sets are respectively constructed into two-dimensional space domains. Euclidean distance method is used to find ideal points.

[0163] Because the data sets are small and concentrated in the above given following state, Euclidean distance method is used:

[0164]

[0165] We can obtain an ideal coordinate system with the shortest distance to all points in the entire dataset, representing the driving information dataset under vehicle following conditions. We then obtain the ideal preceding vehicle acceleration 'a' under following conditions. i-f And ideal vehicle acceleration a i-a The acceleration rate of change is too small to be ignored.

[0166] 4) Classification of vehicle driving status in urban vehicle driving information flow

[0167] The ideal acceleration 'a' of the preceding vehicle under a given following condition can be obtained using the method described above. i-f And ideal vehicle acceleration a i-f The ideal acceleration values ​​of the preceding and following vehicles can reflect the range of accelerations required for both vehicles to enter a following state.

[0168]

[0169] Therefore, the acceleration range of the vehicle that can enter the following state is:

[0170] [-(1+β)a f ,(1+β)a f (3)

[0171] Therefore, by using the presence of the vehicle in front and the acceleration range of the vehicle in front, the traffic flow information of a city can be divided into cruising state, following state, and passing state.

[0172] 5) Extract security situation assessment parameters

[0173] In this invention, the longitudinal safety behavior of the vehicle during driving is manifested as the desired acceleration a. d The difference is that, therefore, the desired acceleration a d As a parameter for evaluating the safety status of a vehicle under driving conditions, its expression is:

[0174]

[0175] In the formula, m is the total vehicle mass; v B For vehicle speed; T a T is the output torque of the drive shaft. b R is the braking torque; w Where is the wheel rolling radius; g is the gravity coefficient; θ is the road slope; ρ is the air density coefficient; A is the frontal area; C d C is the air drag coefficient. r This is the rolling resistance coefficient.

[0176] The longitudinal safety behavior performance between the ego vehicle and the front vehicle is D and T B 、 The expression is:

[0177]

[0178] Wherein, Δd is the relative distance between the two vehicles, v B is the ego vehicle speed, v r is the relative speed between the ego vehicle and the front vehicle. However, when the ego vehicle and the front vehicle have similar or same speed, T r will be infinite, which loses the reference meaning. In order to improve the situation that the single T B value cannot accurately describe the safety situation between the vehicles, the present application uses T to describe the relative motion time between the two vehicles, and T B and T are used to describe the motion state between the vehicles.

[0179] 6) Determine the membership degree number of the vehicle driving safety situation variable

[0180] Through the above driving state division method, the urban driving flow information under different safety situations is quantified, and the driving data information under each driving condition is read. According to the driving state division, the driving data information is divided into several sets, the center of the set is used to represent the whole set, and the scores of each safety situation are calculated.

[0181] Taking acceleration a d as an example, a similar matrix R of n dimensions is established by using the Euclidean distance method to establish the similarity relationship of the sample, a fuzzy equivalent matrix R e of R is calculated, a λ cut matrix R e of the equivalent matrix R eλ is obtained through a threshold λ, and the driving condition data is classified according to R eλ ; the membership degree function is approximately estimated by using a triangular membership function, the safety situation parameters are divided into K classes, that is, C1, C2, …, C K , wherein the hth fuzzy set C h contains N h elements, and the present application needs to determine three vertices (a h , 0), (b h , 0), and (c h , 0) to determine the triangle, and the calculation formula is as follows:

[0182] a h = min (a d,1 , a d,2 , …, a d,Nh )

[0183]

[0184] c h = max(a d,1 ,a d,2 ,…,a d, N h )

[0185] After obtaining the 3 vertices of the triangular membership function by formula 6, the membership function of the fuzzy set C h is determined as follows:

[0186]

[0187] 7) Constructing a vehicle driving safety situation assessment model

[0188] By fuzzy processing the driving condition data under different safety situations of the vehicle, extracting the safety situation elements, determining the safety situation assessment parameters, determining the fuzzy clustering membership, and determining the vehicle variable membership, a safety situation assessment model is constructed.

[0189] S3. Dividing the vehicle into different working modes using the safety situation model

[0190] The specific method is as follows:

[0191] 1) Obtaining the safety situation quantitative value under different driving conditions

[0192] The safety situation quantitative value is obtained by inputting the self-driving safety related information into the safety situation assessment model

[0193] 2) Dividing the safety situation value interval corresponding to different working modes

[0194] Based on the safety situation quantification of different driving conditions, and the fuzzy clustering and approximate estimation of the membership function obtained by the scoring and evaluation system, the safety situation quantitative value is divided into different intervals, and the interval critical value is The safety situation quantitative value is divided into three different safety situation intervals, corresponding to three different working modes, namely, the cruising mode, the following mode, and the passing mode.

[0195] The cruising mode is that the ego vehicle is in a free driving state, there is no preceding vehicle, and the influence of the driving condition change of the preceding vehicle on the driving condition of the ego vehicle does not need to be considered.

[0196] The following mode is that the preceding vehicle has a stable driving speed and acceleration within a certain range for a certain distance, and the acceleration of the preceding vehicle is considered to be in a stable state within the range of ]-(1+β)a f ,(1+β)a f ] of the ego vehicle, the ego vehicle follows the preceding vehicle to reduce the energy consumption of the ego vehicle.

[0197] Wherein the traffic mode is that the front vehicle is unstable in a certain range of driving speed and acceleration in a certain distance, and the acceleration fluctuation range of the front vehicle exceeds [- (1+β)a f ,(1+β)a f Approximately consider driving in an unstable state, the ego vehicle determines the safety distance according to the speed and acceleration change range of the front vehicle in a period of time, adjusts the output torque of the ego vehicle, and achieves the purpose of reducing the energy consumption of the ego vehicle;

[0198] Wherein the following mode safety distance d s1 And the traffic mode safety distance d s2 Is determined by using a general default time interval threshold T s And a default minimum safety distance d0, and the calculation formula is:

[0199] d s1 =v r T s +d0 (8)

[0200] d s2 =v B T s +d0 (9)

[0201] S4 distributes and optimizes the vehicle power source based on the logic threshold rule:

[0202] The specific mode is:

[0203] Vehicle energy management strategy based on logic threshold rule:

[0204] The present application takes P2 configuration plug-in hybrid electric vehicle as the optimization object, and the P2 configuration plug-in hybrid electric vehicle is divided into power consumption stage (CD) and power maintenance stage (CS) according to the degree of battery pack power consumption. The switching of the power consumption stage and the power maintenance stage is determined by S CD / CS ;

[0205] 1) In the power consumption stage (CD), the torque demand can be divided into pure electric drive mode and parallel hybrid drive mode, and the logic state is as follows:

[0206] When in pure electric drive mode, when the driving working state of the vehicle is T c ≤T M_max ; The torque distribution of the vehicle is T m =T c .

[0207] When in parallel hybrid drive mode, when the driving working state of the vehicle is T M_max <T c ≤T M_max+T E_min When the driving working state of the vehicle is in T m = T c -T E_min , T E = T E_min ; when the driving working state of the vehicle is in T M_max +T E_min < T c ≤ T M_max +T E_max , the torque distribution of the vehicle is T m = T c -T E_max , T E = T E_max .

[0208] 2) In the electric quantity maintaining stage (CS), the driving mode can be divided into the on-board charging mode, the engine driving mode and the hybrid driving mode by the demand of the torque, and the logic state is as follows:

[0209] When the driving working state of the vehicle is in T c ≤ T E_min , the vehicle is in the on-board charging mode, and the torque distribution of the vehicle is T m = T c -T E_min , T E = T E_min ; when the driving working state of the vehicle is in T E_min < T c ≤ T E_max , the vehicle is in the engine driving mode, and the torque distribution of the vehicle is T E = T c ; when the driving working state of the vehicle is in T E_max < T c , the vehicle is in the hybrid driving mode, and the torque distribution of the vehicle is T m = T c -T E_max , T E = T E_max .

[0210] Wherein, T c is the demand torque of the current driving working condition, T M_max is the maximum output torque of the current motor, T E_max and T E_min are the maximum and minimum output torques of the current engine respectively, T m is the torque distributed to the motor, and T E is the torque distributed to the engine.

[0211] The specific mode for the distribution optimization of the power sources of the vehicle in different working modes is as follows:

[0212] 1) Cruise mode is in free state driving, there is no front vehicle to affect the driving conditions of the vehicle, the front vehicle does not need to consider the influence of the safety situation of the vehicle, and the output torque of the motor does not need to be optimized, that is, directly according to the vehicle demand torque T c Distributed motor torque T m ; wherein T c is the demand torque under the current driving condition; T m is the torque distributed to the vehicle motor;

[0213] 2) Follow mode is in stable state driving, the front vehicle driving state is relatively stable, and the vehicle can be driven under the condition of keeping a certain following safety distance. In order to ensure the safety situation under the current driving condition, the maximum driving torque of the vehicle is limited, and the brake pedal opening degree can be increased to increase the vehicle braking torque to obtain more energy recovery, and the expression is:

[0214]

[0215] In the formula, T m_max and T m_min are the maximum driving torque and the maximum braking torque of the vehicle respectively; K D1 and K B1 are the correction coefficients of the driving torque and the braking torque of the follow mode respectively.

[0216] Wherein K D1 and K B1 come from the current safety situation value corresponding to the safety situation membership function combined with the current expectation mapping, K D1 decreases with the increase of the safety situation, which is used to reflect the different degrees of driving torque limitation under different safety situations; K B1 increases with the increase of the safety situation to improve the braking energy recovery;

[0217] 3) Passing mode is in unstable state driving, the front vehicle driving state is unstable, and the acceleration fluctuation range exceeds [- (1 + β) a f , (1 + β) a f ]. It needs to keep the passing mode safety distance with the front vehicle driving, in order to ensure the safety situation under the current driving condition and avoid the occurrence of large acceleration, and also to avoid the occurrence of sudden acceleration, so the driving torque of the vehicle needs to be limited, and the change rate of the driving torque of the vehicle also needs to be limited, and the brake pedal opening degree can be increased to increase the vehicle braking torque to obtain more energy recovery, and the expression is:

[0218]

[0219] In the formula, KD2 and K B2 are the correction coefficients of driving torque and braking torque in the passing mode, respectively, K D2 and K B2 are defined and derived as K D1 and K B1 are similar, but K D2 is slightly smaller than K D1 is slightly larger than K B2 is slightly larger than K B1 the purpose is to increase the degree of driving torque limitation, to ensure the safety of the ego vehicle in the passing mode; α is the limit parameter of the torque change rate, to ensure that the ego vehicle torque can be smoothly transitioned when entering the passing mode, to improve the driving comfort. is the torque change rate.

[0220] S5 constructs the driving condition safety situation change ego vehicle mode switching model:

[0221] The specific way is:

[0222] During driving, when the ego vehicle sensor or vehicle-to-vehicle communication protocol (V2V) senses the change of the front vehicle driving condition, it will trigger a re-safety situation assessment, to quantify the current safety situation value whether it crosses the original safety situation interval, to determine whether the ego vehicle current driving scene working mode changes;

[0223] When the current vehicle driving mode changes, but the safety situation quantitative value stays in the same safety situation interval, the ego vehicle working mode does not change, the sensor and V2V inter-vehicle communication are used to determine the current driving state of the front vehicle, to change the safety distance in the current mode, and to optimize the output torque based on the S4 calculation method, to reduce the energy consumption of the ego vehicle;

[0224] When the current vehicle driving mode changes and the safety situation quantitative value also changes the safety situation interval, the ego vehicle switches to the working mode suitable for the current driving condition through the safety situation value in the current driving condition;

[0225] When the safety situation value the ego vehicle driving mode is switched to the cruise mode, when the safety situation value the ego vehicle driving mode is switched to the following mode, when the safety situation value the ego vehicle driving mode is switched to the passing mode;

[0226] The sensor and V2V inter-vehicle communication are used to determine the current driving state of the front vehicle, to change the safety distance in the current mode, and to optimize the output torque based on the S4 calculation method, to reduce the energy consumption of the ego vehicle.

Claims

1. An intelligent connected PHEV energy management method based on security posture assessment, characterized in that, The method comprises the following steps: 1) obtaining vehicle driving condition data; 2) calculating vehicle safety situation evaluation parameters based on the vehicle driving condition data; 3) determining the current driving state of the vehicle according to the vehicle safety situation evaluation parameters; 4) generating a vehicle energy management scheme based on the current driving state of the vehicle; 5) monitoring the driving condition data of the preceding vehicle in real time, and if the driving state of the preceding vehicle changes, returning to step 2), or returning to step 2) every t time interval; The driving state comprises a cruising state, a following state and a passing state; The cruising state refers to that the ego vehicle is in a free driving state and there is no vehicle within a distance dmax in front of the ego vehicle; dmax is a distance threshold; The following state refers to that when the ego vehicle is driving, there is a preceding vehicle within a distance dmax in front of the ego vehicle, and the driving speed and acceleration of the preceding vehicle are within a preset stable range; The passing state refers to that when the ego vehicle is driving, there is a preceding vehicle within a distance dmax in front of the ego vehicle, but the driving speed and / or acceleration of the preceding vehicle is / are outside the preset stable range; In step 3), if the relative distance D between the ego vehicle and the preceding vehicle is greater than dmax, the current driving state of the vehicle is the cruising state; If the relative distance D between the ego vehicle and the preceding vehicle is less than or equal to dmax, and the acceleration of the preceding vehicle is within the range of [- (1 + β) a f , (1 + β) a f ], the current driving state of the vehicle is a following state; wherein the parameters a i-a is the ideal acceleration of the ego vehicle; a i-f is the ideal acceleration of the preceding vehicle; and a f is the actual acceleration of the ego vehicle. If the relative distance D between the ego vehicle and the front vehicle is less than or equal to dmax, and the acceleration of the front vehicle is outside the range of [-(1+β)a f ,(1+β)a f ], the current driving state of the vehicle is the passing state.

2. The intelligent connected PHEV energy management method based on security posture assessment according to claim 1, wherein, The vehicle driving condition data comprises acceleration signals and brake pressure signals during driving, and preceding vehicle acceleration signals.

3. The SSPA-based intelligent connected PHEV energy management method of claim 1, wherein, The safety situation evaluation parameter includes a self-vehicle expected acceleration a d , a relative vehicle distance D of the self-vehicle and the preceding vehicle, a headway T B , a collision time reciprocal T r -1 ; where the ego vehicle desired acceleration a d As follows: where m is the mass of the vehicle; v B is the vehicle speed; T a is the drive shaft output torque; T b is the braking torque; R w is the wheel rolling radius; g is the gravitational coefficient; θ is the road slope; p is the air density coefficient; A is the windward area; C d is the air resistance coefficient; C r is the rolling resistance coefficient; Relative vehicle distance D and vehicle head time distance T between the ego vehicle and the preceding vehicle B , collision time reciprocal are respectively as follows: D = Δd (2) where Δd is the relative distance between the two vehicles, v r is the relative speed between the ego vehicle and the preceding vehicle.

4. The SSP-based intelligent connected PHEV energy management method of claim 1, wherein, The vehicle energy management scheme comprises a safety distance between the ego vehicle and the preceding vehicle and a required torque of the ego vehicle.

5. The SSP-based intelligent connected PHEV energy management method of claim 4, wherein, If the current driving state of the vehicle is the following state, the safety distance d between the ego vehicle and the preceding vehicle s1 As shown below: d s1 = v r T s + d0(5) In the formula, T s is a default time distance threshold; d0 is a default minimum safety distance; v r is the relative speed of the ego vehicle and the preceding vehicle; If the current driving state of the vehicle is the passing state, the safety distance d between the ego vehicle and the front vehicle s2 As shown below: d s2 = v B T s + d0(6) In the formula, v B is the ego vehicle speed.

6. The SSP-based intelligent connected PHEV energy management method of claim 4, wherein, The step of generating a vehicle energy management scheme based on the current driving state of the vehicle comprises: 1) judging the working phase of the vehicle; When the vehicle is operating in the power consumption phase, the demand torque T c is determined, and if the demand torque T c of the current driving condition is less than the maximum output torque T M_max of the current motor, the torque T m assigned to the motor is T c , and the torque T E assigned to the engine is 0; T M_max is the maximum output torque of the current motor. If the demand torque of the current driving condition meets T M_max <T c ≤T M_max +T E_min , the torque T m assigned to the motor is T c -T E_min , and the torque T E assigned to the engine is T E_min ; T E_min is the minimum output torque of the current engine. If the demand torque of the current driving condition meets T M_max +T E_min <T c ≤T M_max +T E_max , the torque T m assigned to the motor = T c -T E_max , and the torque T E assigned to the engine = T E_max ; T E_max is the maximum output torque of the current engine; When the vehicle is operated in the electric power maintenance phase, the demand torque T c is judged, and if the demand torque T c of the current driving condition is less than or equal to T E_min , the torque T m assigned to the motor is T c -T E_min , and the torque T E assigned to the engine is T E_min ; If the demand torque of the current driving condition satisfies T E_min <T c ≤T E_max , the torque T m assigned to the motor = 0, and the torque T E assigned to the engine = T c ; If the torque required under the current driving conditions meets T E_max <T c Then the torque T distributed to the motor m =T c -T E_max The torque T allocated to the engine E =T E_max ; 2) determining the current driving state of the vehicle and distributing the torque T m is corrected.

7. The SSP-based intelligent connected PHEV energy management method of claim 6, wherein, If the current running state of the vehicle is cruising, then T m Without correction, T c is directly converted according to the vehicle demand torque T m If the current running state of the vehicle is following, then t m is corrected by formula (7), and if the current running state of the vehicle is passing, then T m is corrected by formula (8); In the formula, T m_max and T m_min are the maximum drive torque and the maximum braking torque of the own vehicle, respectively; K D1 and K B1 are correction coefficients of the drive torque and the braking torque of the following mode, respectively. In the formula, K D2 and K B2 are correction coefficients of the drive torque and the brake torque of the traveling mode, respectively; and α is a limit parameter of the torque change rate; is the torque change rate.

8. The system for applying the intelligent connected PHEV energy management method based on security posture assessment according to any one of claims 1-7, characterized in that: The vehicle driving condition data acquisition unit, the vehicle safety situation evaluation parameter calculation unit, the driving state judgment unit and the vehicle energy management scheme generation and execution unit are provided. The vehicle driving condition data acquisition unit acquires vehicle driving condition data and transmits the data to the vehicle safety situation evaluation parameter calculation unit; The vehicle safety situation evaluation parameter calculation unit processes the vehicle driving condition data to calculate vehicle safety situation evaluation parameters and transmits the parameters to the driving state judgment unit; The driving state judgment unit determines the current driving state of the vehicle according to the vehicle safety situation evaluation parameters; The vehicle energy management scheme generation and execution unit generates a vehicle energy management scheme based on the current driving state of the vehicle and executes the vehicle energy management scheme.

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