A vehicle health status assessment method and system based on Internet of Vehicles big data

Abstract

The present invention relates to the technical field of vehicle health status assessment, and specifically, to a vehicle health status assessment method and system based on vehicle networking big data. It includes the following steps: classify different vehicle types and build a vehicle health standard library; collect the vehicle's power system data, brake system data and suspension system data; use the power data health algorithm to calculate the vehicle's power system health index; use the power data health algorithm to calculate the vehicle's power system health index; use the suspension data health algorithm to calculate the vehicle's suspension system health index; build a weight adaptive model to calculate the comprehensive vehicle health index. The vehicle health status assessment method and system based on vehicle networking big data can dynamically adjust the assessment criteria according to the vehicle type and usage scenario by dynamically updating the vehicle health standard library and the weight adaptive model, and obtain accurate health status assessment results in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle health status assessment, and in particular to a vehicle health status assessment method and system based on Internet of Vehicles big data. Background Art

[0002] A vehicle health status assessment method and system based on Internet of Vehicles big data aims to dynamically update the vehicle health standard library and adaptively adjust the assessment weights. Through the real-time collection of vehicle power system, electrical system, suspension system and other data from the Internet of Vehicles, combined with historical assessment data and vehicle usage scenarios, the accuracy and real-time performance of vehicle health assessment can be controlled, and accurate health status assessment of different types of vehicles in different usage environments can be achieved, providing personalized maintenance suggestions and warnings.

[0003] Existing vehicle health status assessment methods and systems are usually difficult to dynamically adjust according to vehicle types and usage scenarios. Due to the large number of vehicle types and the complex and diverse focus of evaluating vehicles according to different vehicle types, the health assessment results will not match the health status of vehicles of this type, and it will be difficult to meet the needs of complex driving scenarios and differentiated assessments of different types of vehicles. Therefore, a vehicle health status assessment method and system based on Internet of Vehicles big data is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a vehicle health status assessment method and system based on Internet of Vehicles big data, so as to solve the problem raised in the above background technology that due to the large number of vehicle types and the complex and diverse focus of evaluating vehicles according to different vehicle types, the health assessment results will not conform to the health status of this type of vehicle, and it will be difficult to meet the needs of complex driving scenarios and differentiated assessments of different types of vehicles.

[0005] To achieve the above object, the present invention aims to provide a vehicle health status assessment method based on Internet of Vehicles big data, comprising:

[0006] S1. Classify different vehicle types, collect health data of evaluated vehicles through Internet of Vehicles big data, and build a vehicle health standard library;

[0007] S2, collecting vehicle power system data, brake system data and suspension system data;

[0008] S3, using a power data health algorithm and combining the power system data of the vehicle to calculate a power system health index of the vehicle;

[0009] S4, using the power data health algorithm and combining the power system data of the vehicle to calculate the power system health index of the vehicle;

[0010] S5. Calculate the suspension system health index of the vehicle using the suspension data health algorithm and combining the suspension system data of the vehicle;

[0011] S6. Construct a weighted adaptive model. The weighted adaptive model assigns a weighted coefficient to the vehicle's power system, an electric system and a suspension system according to the vehicle type to calculate the vehicle's power system health index, electric system health index and suspension system health index, and calculates a comprehensive vehicle health index.

[0012] As a further improvement of the technical solution, in S1, different vehicle types are classified, and health data of evaluated vehicles are collected through Internet of Vehicles big data to build a vehicle health standard library. The specific method is as follows:

[0013] S1.1. Classify different types of vehicles, including cars, SUVs, trucks, buses and electric vehicles;

[0014] S1.2. Classify and manage the vehicle health data of each vehicle type, including power system health indicators, power system health indicators, and suspension system health indicators;

[0015] S1.3. Collect the evaluated vehicle health data and vehicle standard health data through the Internet of Vehicles big data, and store them in the vehicle health standard library according to different vehicle types and categories;

[0016] S1.4. Design a dynamic update mechanism. The vehicle health standard library utilizes a dynamic update mechanism and automatically updates the vehicle health standard library based on newly evaluated vehicle health data.

[0017] As a further improvement of the technical solution, a dynamic update mechanism is designed in S1.4, which is as follows:

[0018] S1.4.1. Collect vehicle health data from Internet of Vehicles big data;

[0019] S1.4.2. Select and set the time interval for the dynamic update mechanism to collect vehicle health data;

[0020] S1.4.3. Whenever the comprehensive vehicle health index of a new vehicle is evaluated, the system adds the comprehensive vehicle health index of the new vehicle to the vehicle health standard library and updates the vehicle health standard library;

[0021] S1.4.4. The dynamic update mechanism will also adjust the weight of the health indicators in the vehicle health standard library according to the vehicle type and vehicle usage scenario;

[0022] S1.4.5. After updating the vehicle health standard library, generate a new version of the vehicle health standard library and retain the old version.

[0023] As a further improvement of the technical solution, in S2, the power system data, brake system data and suspension system data of the vehicle are collected, specifically as follows:

[0024] S2.1. Collect power system data, including engine speed, fuel consumption rate and power output status;

[0025] S2.2, collect power system data, including battery voltage, battery current and number of charge and discharge cycles;

[0026] S2.3. Collect suspension system data, including suspension displacement, body vibration and suspension response time.

[0027] As a further improvement of the technical solution, the power data health algorithm is a technical method for comprehensively calculating the health index of the vehicle power system based on the power system data of the vehicle through a multivariate combination method, a weighted summation method and a nonlinear function method;

[0028] In S3, the power system health index H of the vehicle is calculated by using the power data health algorithm and combining the power system data of the vehicle. power , the specific method is as follows:

[0029] H power =α 1 ·f RPM (t)+α 2 ·g Fuel (t)+α 3 ·h Power (t);

[0030]

[0031] Among them, α 1 , α 2 and α 3 is the weighted coefficient of the power system and satisfies α 1 +α 2 +α 3 =1; f RPM (t) is the health index function based on engine speed data and time t; g Fuel (t) is the health indicator function based on fuel consumption rate data and time t; h Power (t) is the health index function based on the power output state data time t;

[0032] T is the total time length; is the average engine speed within the total time length T; ΔRPM(t) is the deviation of the engine speed at time t; e -0.1t is the exponential decay factor; is the average fuel consumption rate within the total time length T; ΔFuel(t) is the deviation of the fuel consumption rate at time t; is the average power output within the total time length T; ΔPower(t) is the deviation of the power output at time t; ω 1 is the oscillation frequency of the power output.

[0033] As a further improvement of the technical solution, the power data health algorithm is a technical method for comprehensively calculating the power system health index of the vehicle based on the power system data of the vehicle and combining the multivariate regression analysis method;

[0034] In S4, the power data health algorithm is used in combination with the power system data of the vehicle to calculate the power system health index H of the vehicle electric , the specific method is as follows:

[0035] H electric =γ 1 ·f Voltage (t)+γ 2 ·g Current (t)+γ 3 ·k Cycles (t);

[0036]

[0037] Among them, γ 1 , γ 2 and γ 3 is the power system weighting coefficient and satisfies γ 1 +γ 2 +γ 3 =1; f Voltage (t) is the health indicator function based on the battery voltage changing with time t; g Current (t) is the health indicator function based on the battery current changing with time t; k Cycles (t) is a health indicator function based on the number of battery charge and discharge cycles;

[0038] is the average value of the battery voltage within the total time length T; ΔVoltage(t) is the voltage deviation at time t; e -0.05t is the attenuation factor of voltage fluctuation over time; is the average current value in the total time length T; ΔCurrent(t) is the deviation of the current at time t; C(t) is the current number of charge and discharge cycles; C max It is the maximum charge and discharge cycle life of the battery, provided by the manufacturer.

[0039] As a further improvement of the technical solution, the suspension data health algorithm is implemented based on multivariate nonlinear analysis technology, which is used to comprehensively analyze suspension displacement, vehicle body vibration, and suspension reaction time data to evaluate the health of the vehicle suspension system;

[0040] In S5, the suspension system health index H of the vehicle is calculated by using the suspension data health algorithm and combining the suspension system data of the vehicle. suspension , the specific method is as follows:

[0041] H suspension =θ 1 ·f Displacement (t)+θ 2 ·g Vibration (t)+θ 3 ·h Response (t);

[0042]

[0043] Among them, θ 1 ,θ 2 and θ 3 is the weighted coefficient of the suspension system and satisfies θ 1 +θ 2 +θ 3 =1; f Displacement (t) is the health assessment function based on the change of suspension displacement with time t; g Vibration (t) is the health assessment function based on the change of vehicle body vibration with time t; h Response (t) is the health assessment function based on the hanging reaction time;

[0044] is the average value of the suspension displacement in the total time length T; ΔDisplacement(t) is the deviation of the suspension displacement at time t; is the average value of the vehicle body vibration in the total time length T; ΔVibration(t) is the vehicle body vibration deviation at instant t; is the average response time of the suspension system within the total time length T; ΔResponse(t) is the deviation of the suspension response time; ω 2 is the vibration response frequency of the suspension system.

[0045] As a further improvement of the present technical solution, in S6, a weighted adaptive model is constructed. The weighted adaptive model assigns a weighted coefficient of the vehicle power system, a weighted coefficient of the electric system, and a weighted coefficient of the suspension system according to the vehicle type to calculate the power system health index, the electric system health index, and the suspension system health index of the vehicle, and calculates the comprehensive vehicle health index, as follows:

[0046] S6.1. The weight adaptation model consists of four main parts:

[0047] Power system weighting coefficient: α 1 , α 2 and α 3 ;

[0048] Power system weighting coefficient: γ 1 , γ 2 and γ 3 ;

[0049] Suspension system weighting factor: θ 1 ,θ 2 and θ 3 ;

[0050] Comprehensive vehicle health index weighting coefficient: λ 1 , 2 and λ 3 ;

[0051] S6.2. Initialize the weighted coefficient matrix W according to the vehicle type:

[0052]

[0053] S6.3. Dynamically adjust the above weighting coefficients according to vehicle type and vehicle usage scenario;

[0054] S6.4. Calculate the power system health index H based on the adjusted power system weighting coefficient, electric power system weighting coefficient and suspension system weighting coefficient. power , Power system health index H electric and suspension system health index H suspension ;

[0055] S6.5, based on the adjusted comprehensive vehicle health index weighting coefficient λ 1 , 2 and λ 3 , calculate the comprehensive vehicle health index.

[0056] As a further improvement of the technical solution, in S6.5, according to the adjusted comprehensive vehicle health index weighting coefficient λ 1 , 2 and λ 3 , calculate the comprehensive vehicle health index H vehicle , the specific method is as follows:

[0057] H vehicle =λ 1 ·H power +λ 2 ·H electric +λ 3 ·H suspension;

[0058] Among them, λ 1 is the weighted coefficient of the power system health index; 2 is the weighting coefficient of the power system health index; 3 is the weighting coefficient of the suspension system health index.

[0059] By using the comprehensive weighting factor λ 1 ,λ 2 ,λ 3 , combined with the health indicators of the vehicle's power system, electrical system and suspension system, the vehicle's comprehensive health index H is finally calculated vehicle This comprehensive health index can fully reflect the overall operating status of the vehicle and provide data support for vehicle maintenance and management.

[0060] On the other hand, the present invention provides a vehicle health status assessment system based on Internet of Vehicles big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned vehicle health status assessment method based on Internet of Vehicles big data.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. In the vehicle health status assessment method and system based on Internet of Vehicles big data, based on the dynamically updated vehicle health standard library and real-time collected vehicle data, the health assessment weight can be dynamically adjusted according to different vehicle types and usage scenarios to ensure the accuracy and timeliness of the assessment results.

[0063] 2. In the vehicle health status assessment method and system based on the big data of the Internet of Vehicles, a weighted adaptive model is used to combine different vehicle types, and weighted calculations are performed according to the health indicators of the vehicle's power system, electrical system and suspension system to achieve accurate assessment of the comprehensive health index. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Embodiment 1:

[0067] See also Figure 1 As shown, this embodiment provides a vehicle health status assessment method based on Internet of Vehicles big data, including the following steps:

[0068] S1. Classify different vehicle types, collect health data of evaluated vehicles through Internet of Vehicles big data, and build a vehicle health standard library;

[0069] In this embodiment S1, different vehicle types are classified, and health data of evaluated vehicles are collected through the big data of the Internet of Vehicles to build a vehicle health standard library. The specific method is as follows:

[0070] S1.1. Classify different types of vehicles, including cars, SUVs, trucks, buses and electric vehicles;

[0071] S1.2. Classify and manage the vehicle health data of each vehicle type, including power system health indicators, power system health indicators, and suspension system health indicators;

[0072] S1.3. Collect the evaluated vehicle health data and vehicle standard health data through the Internet of Vehicles big data, and store them in the vehicle health standard library according to different vehicle types and categories;

[0073] S1.4. Design a dynamic update mechanism. The vehicle health standard library utilizes a dynamic update mechanism and automatically updates the vehicle health standard library based on newly evaluated vehicle health data.

[0074] In this embodiment S1.4, a dynamic update mechanism is designed, which is as follows:

[0075] S1.4.1. Collect vehicle health data from Internet of Vehicles big data;

[0076] S1.4.2. Select and set the time interval for the dynamic update mechanism to collect vehicle health data;

[0077] S1.4.3. Whenever the comprehensive vehicle health index of a new vehicle is evaluated, the system adds the comprehensive vehicle health index of the new vehicle to the vehicle health standard library and updates the vehicle health standard library;

[0078] S1.4.4. The dynamic update mechanism will also adjust the weight of the health indicators in the vehicle health standard library according to the vehicle type and vehicle usage scenario;

[0079] S1.4.5. After updating the vehicle health standard library, generate a new version of the vehicle health standard library and retain the old version.

[0080] In this embodiment, the health indicators include: a power system health indicator, an electric power system health indicator, and a suspension system health indicator;

[0081] The health indicators in the vehicle health standard library have different importance for different vehicle types and usage scenarios. The dynamic update mechanism needs to adjust the weights of various health indicators according to these differences. The dynamic update mechanism will dynamically adjust the weights of health indicators in the standard library according to the vehicle type, as follows:

[0082] 1. Adjust the weights of sedans and electric vehicles in the vehicle health standard library according to vehicle type:

[0083] Sedan: The health of the powertrain of a traditional fuel vehicle is the most important, and the health of its engine and transmission directly affects the performance of the vehicle. Therefore, in the health assessment of a sedan, the weight of the powertrain health indicator will be higher.

[0084] Health weight distribution: power system health 50%, suspension system health 30%, power system health 20%;

[0085] Electric vehicles: The core of electric vehicles is the power system, including battery status, charging efficiency, and endurance;

[0086] Health weight distribution: power system health 55%, suspension system health 20%, power system health 25%;

[0087] 2. Adjust the weights of urban driving and long-distance transportation in the vehicle health standard library according to vehicle usage scenarios:

[0088] Urban driving scenario: In urban driving, vehicles frequently start and stop, accelerate and decelerate. The health of the braking system is very important. The dynamic update mechanism will increase the weight of the braking system accordingly to ensure that the braking system remains in the best condition during frequent operations.

[0089] Health weight distribution: power system health 50%, electrical system health 25%, suspension system health 25%.

[0090] Long-distance transportation scenarios: Long-distance transportation requires vehicles to maintain efficient operation for a long time, and the continuous stability of the power system and power system is particularly critical;

[0091] Health weight distribution of fuel trucks: 60% for power system health, 15% for suspension system health, and 15% for electrical system health;

[0092] Electric vehicle health weight distribution: power system health 70%, power system health 20%, suspension system health 10%.

[0093] S2, collecting vehicle power system data, brake system data and suspension system data;

[0094] In this embodiment S2, the vehicle's power system data, brake system data, and suspension system data are collected, specifically as follows:

[0095] S2.1. Collect power system data, including engine speed, fuel consumption rate and power output status;

[0096] S2.2, collect power system data, including battery voltage, battery current and number of charge and discharge cycles;

[0097] S2.3. Collect suspension system data, including suspension displacement, body vibration and suspension response time.

[0098] S3, using a power data health algorithm and combining the power system data of the vehicle to calculate a power system health index of the vehicle;

[0099] In this embodiment, the power data health algorithm is a technical method for comprehensively calculating the health index of the vehicle power system based on the vehicle power system data through a multivariate combination method, a weighted summation method, and a nonlinear function method;

[0100] In this embodiment S3, the power data health algorithm is used in combination with the power system data of the vehicle to calculate the power system health index H of the vehicle. power , the specific method is as follows:

[0101] H power =α 1 ·f RPM (t)+α 2 ·g Fuel (t)+α 3 ·h Power (t);

[0102]

[0103] In this embodiment, α 1 , α 2 and α 3 is the weighted coefficient of the power system and satisfies α 1 +α 2 +α 3 =1; f RPM (t) is the health index function based on engine speed data and time t; g Fuel (t) is the health indicator function based on fuel consumption rate data and time t; h Power (t) is the health index function based on the power output state data time t;

[0104] T is the total time length; is the average engine speed within the total time length T; ΔRPM(t) is the deviation of the engine speed at time t; e -0.1t is the exponential decay factor; is the average fuel consumption rate within the total time length T; ΔFuel(t) is the deviation of the fuel consumption rate at time t; is the average power output within the total time length T; ΔPower(t) is the deviation of the power output at time t; ω 1 is the oscillation frequency of the power output.

[0105] In this embodiment, α 1 , α 2 and α 3 is the weighted coefficient of the power system and satisfies α 1 +α 2 +α 3 =1;α 1 is the weight of the engine speed health index; α 2 is the weight of the fuel consumption rate health index; α 3 is the weight of the power output status health index; the three power system weighting coefficients are assigned specific weighting coefficient values ​​according to the vehicle type through the weight adaptive model.

[0106] S4, using the power data health algorithm and combining the power system data of the vehicle to calculate the power system health index of the vehicle;

[0107] In this embodiment, the power data health algorithm is a technical method for comprehensively calculating the power system health index of the vehicle based on the power system data of the vehicle and combining the multivariate regression analysis method;

[0108] In this embodiment S4, the power data health algorithm is used in combination with the power system data of the vehicle to calculate the power system health index H of the vehicle. electric , the specific method is as follows:

[0109] H electric =γ 1 ·f Voltage (t)+γ 2 ·g Current (t)+γ 3 ·k Cycles (t);

[0110]

[0111] Among them, γ 1 , γ 2 and γ 3 is the power system weighting coefficient and satisfies γ 1 +γ 2 +γ 3 =1; f Voltage (t) is the health indicator function based on the battery voltage changing with time t; g Current(t) is the health indicator function based on the battery current changing with time t; k Cycles (t) is a health indicator function based on the number of battery charge and discharge cycles;

[0112] is the average value of the battery voltage within the total time length T; ΔVoltage(t) is the voltage deviation at time t; e -0.05t is the attenuation factor of voltage fluctuation over time; is the average current value in the total time length T; ΔCurrent(t) is the deviation of the current at time t; C(t) is the current number of charge and discharge cycles; C max It is the maximum charge and discharge cycle life of the battery, provided by the manufacturer.

[0113] In this embodiment, γ 1 , γ 2 and γ 3 is the power system weighting coefficient and satisfies γ 1 +γ 2 +γ 3 =1;γ 1 is the weight of battery voltage in power system health assessment; γ 2 is the weight of battery current in power system health assessment; γ 3 is the weight of the number of charge and discharge cycles in the health assessment of the power system; the three power system weighting coefficients are assigned specific weighting coefficient values ​​according to the vehicle type through the weight adaptive model.

[0114] S5. Calculate the suspension system health index of the vehicle using the suspension data health algorithm and combining the suspension system data of the vehicle;

[0115] The suspension data health algorithm is based on multivariate nonlinear analysis technology and is used to comprehensively analyze suspension displacement, body vibration, and suspension response time data to evaluate the health of the vehicle suspension system.

[0116] Multivariate nonlinear analysis technology is a data analysis method used to deal with complex relationships between multiple variables;

[0117] In this embodiment S5, the suspension data health algorithm is used in combination with the suspension system data of the vehicle to calculate the vehicle suspension system health index H suspension , the specific method is as follows:

[0118] H suspension =θ 1 ·f Displacement (t)+θ 2 ·g Vibration (t)+θ 3 ·h Response (t);

[0119]

[0120] Among them, θ 1 ,θ 2 and θ 3 is the weighted coefficient of the suspension system and satisfies θ 1 +θ 2 +θ 3 =1; f Displacement (t) is the health assessment function based on the change of suspension displacement with time t; g Vibration (t) is the health assessment function based on the change of vehicle body vibration with time t; h Response (t) is the health assessment function based on the hanging reaction time;

[0121] is the average value of the suspension displacement in the total time length T; ΔDisplacement(t) is the deviation of the suspension displacement at time t; is the average value of the vehicle body vibration in the total time length T; ΔVibration(t) is the vehicle body vibration deviation at instant t; is the average response time of the suspension system within the total time length T; ΔResponse(t) is the deviation of the suspension response time; ω 2 is the vibration response frequency of the suspension system.

[0122] In this embodiment, θ 1 ,θ 2 and θ 3 is the weighted coefficient of the suspension system and satisfies θ 1 +θ 2 +θ 3 =1;θ 1 is the weighting coefficient of suspension displacement; θ 2 is the weighting coefficient of vehicle body vibration; θ 3 is the weighting coefficient of suspension reaction time; the three suspension system weighting coefficients are assigned specific weighting coefficient values ​​according to the vehicle type through the weight adaptive model.

[0123] S6. Construct a weighted adaptive model. The weighted adaptive model assigns a weighted coefficient of the vehicle power system, a weighted coefficient of the electric system, and a weighted coefficient of the suspension system according to the vehicle type to calculate the power system health index, the electric system health index, and the suspension system health index of the vehicle, and calculates a comprehensive vehicle health index;

[0124] In this embodiment S6, a weighted adaptive model is constructed. The weighted adaptive model assigns a weighted coefficient of the vehicle power system, a weighted coefficient of the electric system, and a weighted coefficient of the suspension system according to the vehicle type to calculate the power system health index, the electric system health index, and the suspension system health index of the vehicle, and calculates the comprehensive vehicle health index, as follows:

[0125] S6.1. The weight adaptation model consists of four main parts:

[0126] Power system weighting coefficient: α 1 , α 2 and α 3 ;

[0127] Power system weighting coefficient: γ 1 , γ 2 and γ 3 ;

[0128] Suspension system weighting factor: θ 1 ,θ 2 and θ 3 ;

[0129] Comprehensive vehicle health index weighting coefficient: λ 1 , 2 and λ 3 ;

[0130] S6.2. Initialize the weighted coefficient matrix W according to the vehicle type:

[0131]

[0132] In this embodiment:

[0133]

[0134]

[0135] S6.3. Dynamically adjust the above weighting coefficients according to vehicle type and vehicle usage scenario;

[0136] In this embodiment:

[0137] The weighting coefficient is adjusted according to the vehicle type. The specific adjustment method is as follows:

[0138] Passenger cars: For passenger cars, engine speed and fuel efficiency play a leading role in the power system. In the power system, the stability of voltage and current is more important. The suspension system focuses on comfort, with body vibration and suspension displacement having a greater weight.

[0139] Off-road vehicle: The power output of an off-road vehicle is particularly important under complex road conditions, and the performance of the suspension system also requires a higher weight. The weight of the power system is relatively small.

[0140] Trucks: Fuel efficiency and power output are particularly important for trucks in long-distance transportation. The stability of voltage in the power system has a great impact on the long-term operation of trucks. The suspension system mainly focuses on the stability of displacement to cope with heavy load requirements.

[0141] Electric vehicles: In electric vehicles, the power system is the core, so the battery voltage and current have a greater weight. In the power system, the stability of power output is also very important. The suspension system focuses on comfort and stability.

[0142] Adjust the weighting coefficient according to the vehicle usage scenario. The specific adjustment method is as follows:

[0143] City driving:

[0144] Powertrain: In urban driving, engine speed and stability are more important, so α 1 The weight of fuel efficiency is relatively small.

[0145] Power system: In the case of frequent start-stop, the current fluctuation is greater, so γ 2 The weight should be higher.

[0146] Suspension system: Since urban roads are usually flat, the displacement and vibration stability of the suspension system play an important role in comfort; therefore 1 and θ 2 The weight of should be larger;

[0147] High speed driving:

[0148] Power system: When driving at high speed, the stability of power output and fuel efficiency are crucial, so α 2 and α 3 The weight of needs to be increased;

[0149] Power system: The stability of battery voltage is extremely important during high-speed driving, so γ 1 The weight should be increased.

[0150] Suspension system: When driving at high speed, the reaction time of the suspension system is crucial, so 3 The weight should be increased.

[0151] Off-road driving:

[0152] Power system: In off-road environments, power output is particularly important, especially when facing complex terrain. 3 The weight should be increased significantly.

[0153] Power system: Since the power system pressure in off-road environment is less, the overall weight of the power system can be reduced.

[0154] Suspension system: Off-road driving places high demands on the suspension system, especially body vibration and suspension response time. 2 and θ 3 The weight should be significantly increased.

[0155] S6.4. Calculate the power system health index H based on the adjusted power system weighting coefficient, electric power system weighting coefficient and suspension system weighting coefficient. power , Power system health index H electric and suspension system health index H suspension ;

[0156] S6.5, based on the adjusted comprehensive vehicle health index weighting coefficient λ 1 , 2 and λ 3 , calculate the comprehensive vehicle health index.

[0157] In this embodiment S6.5, according to the adjusted comprehensive vehicle health index weighting coefficient λ 1 , 2 and λ 3 , calculate the comprehensive vehicle health index H vehicle , the specific method is as follows:

[0158] H vehicle =λ 1 ·H power +λ 2 ·H electric +λ 3 ·H suspension ;

[0159] Among them, λ 1 is the weighted coefficient of the power system health index; 2 is the weighting coefficient of the power system health index; 3 is the weighting coefficient of the suspension system health index.

[0160] By using the comprehensive weighting factor λ 1 ,λ 2 ,λ 3 , combined with the health indicators of the vehicle's power system, electrical system and suspension system, the vehicle's comprehensive health index H is finally calculated vehicle This comprehensive health index can fully reflect the overall operating status of the vehicle and provide data support for vehicle maintenance and management.

[0161] Embodiment 2:

[0162] This embodiment provides a vehicle health status assessment system based on Internet of Vehicles big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned vehicle health status assessment method based on Internet of Vehicles big data.

[0163] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A vehicle health status assessment method based on Internet of Vehicles big data, characterized in that: The following steps are involved: S1. Classify different vehicle types, collect health data of evaluated vehicles through Internet of Vehicles big data, and build a vehicle health standard library; S2, collecting vehicle power system data, electrical system data and suspension system data; S3, using a power data health algorithm and combining the power system data of the vehicle to calculate a power system health index of the vehicle; The power data health algorithm is a technical method for comprehensively calculating the health index of the vehicle power system based on the vehicle power system data through a multivariate combination method, a weighted summation method and a nonlinear function method; In S3, the power system health index of the vehicle is calculated by using the power data health algorithm and combining the power system data of the vehicle. , the specific method is as follows: ； ； ; ; in, , and is the power system weighting coefficient, and satisfies + + ; Based on engine speed data and time Health indicator function; Based on fuel consumption data and time Health indicator function; Based on the power output status data time Health indicator function; is the total time length; For the total time length Average engine speed within For in time Deviation of engine speed; is the exponential decay factor; For the total time length Average fuel consumption rate within For in time Deviations in fuel consumption rate; For the total time length Average power output within For in time Deviation of power output; is the oscillation frequency of the power output; S4, using the power data health algorithm and combining the power system data of the vehicle to calculate the power system health index of the vehicle; The power data health algorithm is a technical method for comprehensively calculating the power system health index of the vehicle based on the power system data of the vehicle and combining the multivariate regression analysis method; In S4, the power data health algorithm is used in combination with the power system data of the vehicle to calculate the power system health index of the vehicle , the specific method is as follows: ; ; ; ; in, , and is the power system weighting coefficient and satisfies + + ; Based on the battery voltage over time Function of changing health indicators; Based on the battery current over time Function of changing health indicators; is a health indicator function based on the number of battery charge and discharge cycles; For the total time length The average battery voltage within For in time Voltage deviation; is the attenuation factor of voltage fluctuation over time; For the total time length The average current in For in time Deviation of current; is the current number of charge and discharge cycles; The maximum charge and discharge cycle life of the battery, provided by the manufacturer; S5. Calculate the suspension system health index of the vehicle using the suspension data health algorithm and combining the suspension system data of the vehicle; The suspension data health algorithm is implemented based on multivariate nonlinear analysis technology and is used to comprehensively analyze suspension displacement, body vibration, and suspension reaction time data to evaluate the health of the vehicle suspension system; S6. Construct a weighted adaptive model. The weighted adaptive model assigns a weighted coefficient to the vehicle's power system, an electric system and a suspension system according to the vehicle type to calculate the vehicle's power system health index, electric system health index and suspension system health index, and calculates a comprehensive vehicle health index.

2. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 1 is characterized by: In S1, different vehicle types are classified, and the health data of evaluated vehicles are collected through the big data of the Internet of Vehicles to build a vehicle health standard library. The specific method is as follows: S1.

1. Classify different types of vehicles, including cars, SUVs, trucks, buses and electric vehicles; S1.

2. Classify and manage the vehicle health data of each vehicle type, including power system health indicators, power system health indicators, and suspension system health indicators; S1.

3. Collect the evaluated vehicle health data and vehicle standard health data through the Internet of Vehicles big data, and store them in the vehicle health standard library according to different vehicle types and categories; S1.

4. Design a dynamic update mechanism. The vehicle health standard library utilizes a dynamic update mechanism and automatically updates the vehicle health standard library based on newly evaluated vehicle health data.

3. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 2 is characterized by: The dynamic update mechanism is designed in S1.4, as follows: S1.4.

1. Collect vehicle health data from Internet of Vehicles big data; S1.4.

2. Select and set the time interval for the dynamic update mechanism to collect vehicle health data; S1.4.

3. Whenever the comprehensive vehicle health index of a new vehicle is evaluated, the system adds the comprehensive vehicle health index of the new vehicle to the vehicle health standard library and updates the vehicle health standard library; S1.4.

4. The dynamic update mechanism will also adjust the weight of the health indicators in the vehicle health standard library according to the vehicle type and vehicle usage scenario; S1.4.

5. After updating the vehicle health standard library, generate a new version of the vehicle health standard library and retain the old version.

4. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 1 is characterized by: In S2, the vehicle's power system data, electrical system data, and suspension system data are collected, as follows: S2.

1. Collect power system data, including engine speed, fuel consumption rate and power output status; S2.2, collect power system data, including battery voltage, battery current and number of charge and discharge cycles; S2.

3. Collect suspension system data, including suspension displacement, body vibration and suspension response time.

5. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 1 is characterized by: In S5, the suspension system health index of the vehicle is calculated by using the suspension data health algorithm and combining the suspension system data of the vehicle. , the specific method is as follows: ; ; ; ; in, , and is the weighted coefficient of the suspension system and satisfies + + ; Based on the suspension displacement over time Changing health assessment functions; Based on the vehicle body vibration over time Changing health assessment functions; is a health assessment function based on hanging reaction time; For the total time length The average value of the internal suspension displacement; For in time Deviation of suspension displacement; For the total time length Average value of internal body vibration; For the moment Body vibration deviation; For the total time length Average reaction time of the internal suspension system; is the deviation of hanging reaction time; is the vibration response frequency of the suspension system.

6. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 1 is characterized by: In S6, a weighted adaptive model is constructed. The weighted adaptive model assigns a weighted coefficient of the vehicle power system, a weighted coefficient of the electric system, and a weighted coefficient of the suspension system according to the vehicle type to calculate the power system health index, the electric system health index, and the suspension system health index of the vehicle, and calculates the comprehensive vehicle health index, as follows: S6.

1. The weight adaptation model consists of four main parts: Power system weighting coefficient: , and ; Power system weighting factor: , and ; Suspension system weighting factor: , and ; Comprehensive vehicle health index weighting factor: , and ; S6.

2. Initialize the weighting coefficient matrix according to the vehicle type : ； S6.

3. Dynamically adjust the above weighting coefficients according to vehicle type and vehicle usage scenario; S6.

4. Calculate the power system health index based on the adjusted power system weighting coefficient, electrical system weighting coefficient and suspension system weighting coefficient , Power System Health Indicators and suspension health indicators ; S6.

5. Based on the adjusted comprehensive vehicle health index weighting coefficient , and , calculate the comprehensive vehicle health index.

7. The vehicle health status assessment method based on Internet of Vehicles big data according to claim 6 is characterized by: In S6.5, according to the adjusted comprehensive vehicle health index weighting coefficient , and , calculate the comprehensive vehicle health index , the specific method is as follows: ； in, is the weighting coefficient of the power system health index; is the weighting coefficient of the power system health index; is the weighting coefficient of the suspension system health index.

8. A vehicle health status assessment system based on Internet of Vehicles big data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the vehicle health status assessment method based on Internet of Vehicles big data as described in any one of claims 1 to 7.

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