Methanol hybrid power vehicle torque distribution system and distribution method

Abstract

The invention discloses a torque distribution system and method for a methanol hybrid vehicle. The torque distribution system comprises a torque sensor, an acceleration sensor, a vehicle speed sensor, a control unit and an execution mechanism. The acceleration sensor, the vehicle speed sensor, the torque sensor and the execution mechanism are all connected with the control unit. The vehicle state is predicted by constructing the prediction model and real-time data of the sensor, the vehicle driving state and the running states of the methanol engine and the motor are estimated, and noise and errors in the sensor data can be well processed. According to the running state of the hybrid power vehicle, the torque control range of the hybrid power vehicle is obtained, a model prediction control method is sampled, a target function is set, optimal torque distribution is obtained, and the torque of a methanol engine and a motor, the fuel injection amount of the methanol engine, the output power of the motor and the like can be adjusted through a control unit and an execution mechanism. The technical problem that in the prior art, a vehicle torque distribution method is poor in effect is solved.

Description

Technical Field

[0001] The invention belongs to the field of automobiles and relates to a torque distribution method, in particular to a torque distribution system and a distribution method for a methanol hybrid vehicle. Background Art

[0002] Methanol fuel, as a clean, low-carbon alternative energy source, has broad application prospects. Methanol fuel has a wide range of sources. It can be produced from traditional fossil resources such as natural gas and coal, or synthesized from renewable resources such as biomass. This makes methanol fuel have significant advantages in resource diversity and energy security. In addition, compared with gasoline or diesel, the emissions of pollutants such as carbon dioxide, nitrogen oxides and particulate matter in methanol combustion products are lower, which helps to reduce environmental pollution in the transportation sector. Methanol hybrid vehicles combine the advantages of methanol engines and motors to achieve the synergy of traditional fuels and electric drives. Through the reasonable allocation of motors and methanol engines, more efficient fuel utilization can be achieved under different working conditions. For example, when starting and driving at low speeds, the motor can be driven alone to avoid the low efficiency of the methanol engine under low load; under high speed or heavy load conditions, the methanol engine can provide the main power output, and the motor is used for assistance and supplementation, thereby improving the power performance and fuel economy of the whole vehicle.

[0003] However, methanol engines themselves also have some challenges, especially compared with traditional gasoline and diesel engines, the combustion characteristics of methanol fuel and the difficulty of engine control are higher. Methanol has a lower energy density and a higher latent heat of vaporization. In addition, the cold start and low-temperature combustion performance of methanol engines are poor, which can easily cause problems such as unstable idling, which makes the coupling control and torque optimization distribution of methanol engines and motors key. There are related patents that consider the required torque, factors affecting the vehicle's power generation conditions, and the system's output torque capacity to distribute the power system torque, but the specific distribution of vehicle torque depends on the set threshold, and the optimization capability is limited. There are also patents that have complex optimizations for torque distribution, and the execution components have large delays, and it is difficult to balance its dynamic performance and optimization performance.

[0004] Existing torque distribution methods and devices still have limited distribution capacity, complex distribution process, and weak comprehensive processing capabilities. They are unable to bring out the best performance of the vehicle, and cannot achieve the effect of coordinated optimization of the methanol engine and the drive motor, and the overall power system efficiency is limited. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a methanol hybrid vehicle torque distribution system and distribution method to solve the technical problem that the vehicle torque distribution method in the prior art has a poor effect.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions to achieve the above problems:

[0007] A methanol hybrid vehicle torque distribution system comprises a torque sensor, an acceleration sensor, a vehicle speed sensor, a control unit and an actuator; the acceleration sensor, the vehicle speed sensor, the torque sensor and the actuator are all connected to the control unit;

[0008] The torque sensor is used to measure the torque in real time and transmit it to the control unit;

[0009] The acceleration sensor is used to measure the acceleration in real time and transmit it to the control unit;

[0010] The vehicle speed sensor is used to measure the vehicle speed in real time and transmit it to the control unit;

[0011] The control unit is used to determine an optimal torque distribution strategy according to the torque, acceleration and vehicle speed received in real time, and output a control instruction to the actuator according to the determined optimal torque distribution strategy;

[0012] The actuator is used to adjust the torque output of the methanol engine and the motor according to the control command of the control unit.

[0013] A methanol hybrid vehicle torque distribution method, based on the methanol hybrid vehicle torque distribution system, the method specifically comprises the following steps:

[0014] Step 1: Establish the torque state model of methanol hybrid vehicle The methanol hybrid vehicle torque state model f(x k ,u k ) is defined as The control input is defined as

[0015]

[0016] F drv k R w =T tot k -T loss k

[0017] in:

[0018] m is the total mass of the vehicle, kg;

[0019] is the vehicle throttle acceleration at time k, m / s 2 ;

[0020] is the braking acceleration at time k, m / s 2 ;

[0021] is the total output torque of the vehicle at time k, Nm;

[0022] is the output torque of the methanol engine at time k, Nm;

[0023] is the output torque of the motor at time k, Nm;

[0024] v k is the vehicle speed at time k, km / h;

[0025] F drv k is the wheel driving force on the ground at time k, N;

[0026] R w is the wheel radius, m;

[0027] is the torque loss of the methanol hybrid vehicle at time k, Nm;

[0028] Step 2, obtaining the vehicle status information at the previous moment through the real-time data of the torque sensor, acceleration sensor and vehicle speed sensor;

[0029] The vehicle status information includes torque, acceleration and speed;

[0030] Step 3: Based on the vehicle state information at the previous moment obtained in step 2 and the methanol hybrid vehicle torque state model f(x k ,u k ), using the extended Kalman filter method, the torque, acceleration and speed at the current moment are obtained;

[0031] Step 4: Establish the torque mapping relationship T of the methanol engine eng =f(ω eng ,α thro ) and methanol engine fuel consumption model in:

[0032] ω eng is the speed of the methanol engine, rpm;

[0033] α thro is the throttle opening of the methanol engine, degree;

[0034] Step 5: Based on the torque mapping relationship T of the methanol engine established in step 4 eng =f(ω eng ,αthr ) and methanol engine fuel consumption model Build a systematic predictive model and

[0035] The comprehensive objective function J and constraint conditions B of the prediction model of the system are as follows:

[0036]

[0037] in:

[0038] is the speed of the methanol engine at time k, rpm;

[0039] is the throttle opening of the methanol engine at time k, degrees;

[0040] is the output torque of the methanol engine at time k+i, Nm;

[0041] is the output torque of the motor at time k+i, Nm;

[0042] is the reference torque at time k+i, Nm;

[0043] is the speed of the methanol engine at time k+i, rad / s;

[0044] ω fue and ω tor All are weight coefficients;

[0045] N is the prediction time domain step size;

[0046] T eng,min and T eng,max They are the maximum output torque and minimum output torque of the methanol engine, Nm;

[0047] T mot,min and T mot,max are the maximum output torque and minimum output torque of the motor, Nm;

[0048] ω eng,min and ω eng,max are the maximum speed and minimum speed of the methanol engine, rpm;

[0049] Step 6: Under constraint condition B, calculate multiple objective function values ​​of the comprehensive objective function J, find the minimum value among the multiple objective function values, and calculate the output torque of the methanol engine corresponding to the minimum value. and the motor output torque As the optimal allocation result;

[0050] Step 7: The torque distribution unit adjusts the output torque of the methanol engine and the output torque of the motor according to the optimal distribution result obtained in step 6.

[0051] The present invention also includes the following technical features:

[0052] The specific method of step 3 is:

[0053] Step 3.1, initialization;

[0054] Initialize state variable estimates and the error covariance matrix Set process noise covariance and the measurement noise covariance

[0055] in:

[0056] and They are the methanol engine initial torque estimation accuracy, the motor initial torque estimation accuracy and the speed initial estimation accuracy respectively;

[0057] and They are the methanol engine torque estimation accuracy, motor torque estimation accuracy and speed estimation accuracy during the estimation process;

[0058] and are the measurement errors of the velocity sensor and acceleration sensor respectively;

[0059] Step 3.2, calculate the state transfer matrix according to the following formula Then we get the iterative matrix of the error covariance matrix

[0060]

[0061] Where, j is the number of iterations;

[0062] Step 3.3, calculate the observation matrix H k and the Kalman gain matrix K k ;

[0063]

[0064] in:

[0065] h(x) represents the observation function;

[0066] represents the vehicle acceleration at time k-1;

[0067] Step 3.4, obtain the state variable estimate according to the following formula

[0068]

[0069] in:

[0070] z k are the sampling values ​​of the acceleration sensor, vehicle speed sensor and torque sensor at time k;

[0071] is the j-th state estimate at time k;

[0072] Step 3.5: Determine whether Or j+1≥M. If so, the state variable estimation loop ends and the state variable estimate at time k is output as or If not satisfied, set j=j+1 and return to step 3.2;

[0073]

[0074] in:

[0075] ε is the estimated change threshold of the state variable.

[0076] M is the maximum number of iterations.

[0077] Compared with the prior art, the present invention has the following beneficial technical effects:

[0078] The present invention predicts the vehicle state by constructing a prediction model and real-time data of sensors, estimates the vehicle driving state, the running state of the methanol engine and the motor, and can well handle the noise and error in the sensor data. According to the running state of the hybrid vehicle, the torque control range of the hybrid vehicle is obtained, the sampling model prediction control method is used, the objective function is set, and the optimal torque distribution is obtained. The methanol engine and motor torque, the fuel injection amount of the methanol engine, the output power of the motor, etc. that can be adjusted by the control unit and the actuator are solved. The technical problem of poor effect of the vehicle torque distribution method in the prior art is solved.

[0079] The specific contents of the present invention are further explained in detail below in conjunction with embodiments. DETAILED DESCRIPTION

[0080] It should be noted that, unless otherwise specified, all components in the present invention are components known in the art.

[0081] Specific embodiments of the present invention are given below. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solution of this application fall within the protection scope of the present invention.

[0082] The present invention provides a methanol hybrid vehicle torque distribution system, comprising a torque sensor, an acceleration sensor, a vehicle speed sensor, a control unit and an actuator; the acceleration sensor, the vehicle speed sensor, the torque sensor and the actuator are all connected to the control unit;

[0083] The torque sensor is used to measure the torque in real time and transmit it to the control unit;

[0084] The acceleration sensor is used to measure the acceleration in real time and transmit it to the control unit;

[0085] The vehicle speed sensor is used to measure the vehicle speed in real time and transmit it to the control unit;

[0086] The control unit is used to determine an optimal torque distribution strategy according to the torque, acceleration and vehicle speed received in real time, and output a control instruction to the actuator according to the determined optimal torque distribution strategy;

[0087] The actuator is used to adjust the torque output of the methanol engine and the motor according to the control instructions of the control unit.

[0088] Preferably, the torque sensors are HBM-T40B and HBM-T40FM, the control unit is Renesas RH850, and the actuators are throttle and motor controller.

[0089] A methanol hybrid vehicle torque distribution method is also provided. Based on the methanol hybrid vehicle torque distribution system, the method specifically includes the following steps:

[0090] Step 1: Establish the torque state model of methanol hybrid vehicle Torque state model f(x k ,u k ) is defined as The control input is defined as

[0091]

[0092] F drv k R w =T tot k -T loss k

[0093] in:

[0094] m is the total mass of the vehicle, kg;

[0095] is the vehicle throttle acceleration at time k, m / s 2 ;

[0096] is the braking acceleration at time k, m / s 2 ;

[0097] is the total output torque of the vehicle at time k, Nm;

[0098] is the output torque of the methanol engine at time k, Nm;

[0099] is the output torque of the motor at time k, Nm;

[0100] v k is the vehicle speed at time k, km / h;

[0101] F drv k is the wheel driving force on the ground at time k, N;

[0102] R w is the wheel radius, m;

[0103] is the torque loss of the methanol hybrid vehicle at time k, Nm;

[0104] Step 2, obtaining the vehicle status information at the previous moment through the real-time data of the torque sensor, acceleration sensor and vehicle speed sensor;

[0105] Vehicle status information includes torque, acceleration, and speed;

[0106] Step 3: Based on the vehicle state information at the previous moment obtained in step 2 and the methanol hybrid vehicle torque state model f(x k ,u k ), using the extended Kalman filter method, the torque, acceleration and speed at the current moment are obtained;

[0107] Step 4: Establish the torque mapping relationship T of the methanol engine eng =f(ω eng ,α thro ) and methanol engine fuel consumption model in:

[0108] ω eng is the speed of the methanol engine, rpm;

[0109] αthro is the throttle opening of the methanol engine, degree;

[0110] Step 5: Based on the torque mapping relationship T of the methanol engine established in step 4 eng =f(ω eng ,α thr ) and methanol engine fuel consumption model Build a systematic predictive model and

[0111] The comprehensive objective function J and constraint conditions B of the system's prediction model are as follows:

[0112]

[0113] in:

[0114] is the speed of the methanol engine at time k, rpm;

[0115] is the throttle opening of the methanol engine at time k, degrees;

[0116] is the output torque of the methanol engine at time k+i, Nm;

[0117] is the output torque of the motor at time k+i, Nm;

[0118] is the reference torque at time k+i, Nm;

[0119] is the speed of the methanol engine at time k+i, rad / s;

[0120] ω fue and ω tor All are weight coefficients;

[0121] N is the prediction time domain step size;

[0122] T eng,min and T eng,max They are the maximum output torque and minimum output torque of the methanol engine, Nm;

[0123] T mot,min and T mot,max are the maximum output torque and minimum output torque of the motor, Nm;

[0124] ω eng,min and ω eng,max are the maximum speed and minimum speed of the methanol engine, rpm;

[0125] Step 6: Under constraint condition B, calculate multiple objective function values ​​of the comprehensive objective function J, find the minimum value among the multiple objective function values, and calculate the output torque of the methanol engine corresponding to the minimum value. and the motor output torque As the optimal allocation result;

[0126] Step 7: The torque distribution unit adjusts the output torque of the methanol engine and the output torque of the motor according to the optimal distribution result obtained in step 6.

[0127] In the above technical solution, the vehicle state is predicted by constructing a prediction model and real-time data of sensors, and the vehicle driving state, methanol engine and motor operating state are estimated, which can well handle the noise and error in the sensor data. According to the operating state of the hybrid vehicle, the torque control range of the hybrid vehicle is obtained, the sampling model prediction control method is used, the objective function is set, and the optimal torque distribution is obtained. The methanol engine and motor torque, the fuel injection amount of the methanol engine, the output power of the motor, etc. can be adjusted by the control unit and the actuator, so as to solve the technical problem that the vehicle torque distribution method in the prior art is not effective.

[0128] The specific contents of the present invention are further explained in detail below in conjunction with embodiments.

[0129] The specific method of step 3 is:

[0130] Step 3.1, initialization;

[0131] Initialize state variable estimates and the error covariance matrix Set process noise covariance and the measurement noise covariance

[0132] in:

[0133] and They are the methanol engine initial torque estimation accuracy, the motor initial torque estimation accuracy and the speed initial estimation accuracy respectively;

[0134] and They are the methanol engine torque estimation accuracy, motor torque estimation accuracy and speed estimation accuracy during the estimation process;

[0135] and are the measurement errors of the velocity sensor and acceleration sensor respectively;

[0136] Step 3.2, calculate the state transfer matrix according to the following formula Then we get the iterative matrix of the error covariance matrix

[0137]

[0138] Where, j is the number of iterations;

[0139] Step 3.3, calculate the observation matrix H k and the Kalman gain matrix K k ;

[0140]

[0141] in:

[0142] h(x) represents the observation function;

[0143] represents the vehicle acceleration at time k-1;

[0144] Step 3.4, obtain the state variable estimate according to the following formula

[0145]

[0146] in:

[0147] z k are the sampling values ​​of the acceleration sensor, vehicle speed sensor and torque sensor at time k;

[0148] is the j-th state estimate at time k;

[0149] Step 3.5: Determine whether Or j+1≥M. If so, the state variable estimation loop ends and the state variable estimate at time k is output as or If not satisfied, set j=j+1 and return to step 3.2;

[0150]

[0151] in:

[0152] ε is the estimated change threshold of the state variable.

[0153] M is the maximum number of iterations.

[0154] In the above technical solution, state variable estimation is one of the important means to achieve optimized management and safe operation of methanol hybrid vehicles. It is necessary to use a variety of sensors to obtain vehicle operation data, as well as control variables and vehicle model information for calculation and analysis to obtain real-time vehicle state data information. When estimating state variables, a large amount of vehicle operation state data is required, including complex state parameters such as vehicle speed, methanol engine and motor speed torque, motor current, battery power, and operating temperature of each system. Control variables refer to control instructions generated by driver and passenger operations. These instructions are transmitted to the controller by the control panel and input into the vehicle model together with the state parameters for calculation to obtain the vehicle state variable estimation results.

[0155] Common state variable estimation algorithms include extended Kalman filtering and recursive least squares method. Through the organic combination of multiple estimation algorithms, more accurate state variable estimation results can be obtained. The sampling extended Kalman filter algorithm of the present invention uses mathematical models and sensor data to predict vehicle states, estimate vehicle driving states, methanol engine and motor operating states, and can well handle noise and errors in sensor data.

Claims

1. A methanol hybrid vehicle torque distribution system, characterized in that: It includes a torque sensor, an acceleration sensor, a vehicle speed sensor, a control unit and an actuator; the acceleration sensor, the vehicle speed sensor, the torque sensor and the actuator are all connected to the control unit; The torque sensor is used to measure the torque in real time and transmit it to the control unit; The acceleration sensor is used to measure the acceleration in real time and transmit it to the control unit; The vehicle speed sensor is used to measure the vehicle speed in real time and transmit it to the control unit; The control unit is used to determine an optimal torque distribution strategy according to the torque, acceleration and vehicle speed received in real time, and output a control instruction to the actuator according to the determined optimal torque distribution strategy; The actuator is used to adjust the torque output of the methanol engine and the motor according to the control command of the control unit.

2. A torque distribution method for a methanol hybrid vehicle, characterized in that: Based on the methanol hybrid vehicle torque distribution system according to claim 1, the method specifically comprises the following steps: Step 1: Establish the torque state model of methanol hybrid vehicle The methanol hybrid vehicle torque state model f(x k ,u k ) is defined as The control input is defined as in: m is the total mass of the vehicle, kg; is the vehicle throttle acceleration at time k, m / s 2 ; is the braking acceleration at time k, m / s 2 ; is the total output torque of the vehicle at time k, Nm; is the output torque of the methanol engine at time k, Nm; is the output torque of the motor at time k, Nm; v k is the vehicle speed at time k, km / h; F drv k is the wheel driving force on the ground at time k, N; R w is the wheel radius, m; is the torque loss of the methanol hybrid vehicle at time k, Nm; Step 2, obtaining the vehicle status information at the previous moment through the real-time data of the torque sensor, acceleration sensor and vehicle speed sensor; The vehicle status information includes torque, acceleration and speed; Step 3: Based on the vehicle state information at the previous moment obtained in step 2 and the methanol hybrid vehicle torque state model f(x k ,u k ), using the extended Kalman filter method, the torque, acceleration and speed at the current moment are obtained; Step 4: Establish the torque mapping relationship T of the methanol engine eng =f(ω eng ,α thro ) and methanol engine fuel consumption model in: ω eng is the speed of the methanol engine, rpm; α thro is the throttle opening of the methanol engine, degree; Step 5: Based on the torque mapping relationship T of the methanol engine established in step 4 eng =f(ω eng ,α thr ) and methanol engine fuel consumption model Build a systematic predictive model and The comprehensive objective function J and constraint conditions B of the prediction model of the system are as follows: in: is the speed of the methanol engine at time k, rpm; is the throttle opening of the methanol engine at time k, degrees; is the output torque of the methanol engine at time k+i, Nm; is the output torque of the motor at time k+i, Nm; is the reference torque at time k+i, Nm; is the speed of the methanol engine at time k+i, rad / s; ω fue and ω tor All are weight coefficients; N is the prediction time domain step size; T eng,min and T eng,max are the maximum output torque and minimum output torque of the methanol engine, Nm; T mot,min and T mot,max are the maximum output torque and minimum output torque of the motor, Nm; ω eng,min and ω eng,max are the maximum and minimum speeds of the methanol engine, rpm; Step 6: Under constraint condition B, calculate multiple objective function values ​​of the comprehensive objective function J, find the minimum value among the multiple objective function values, and calculate the output torque of the methanol engine corresponding to the minimum value. and the motor output torque As the optimal allocation result; Step 7: The torque distribution unit adjusts the output torque of the methanol engine and the output torque of the motor according to the optimal distribution result obtained in step 6.

3. The torque distribution method for a methanol hybrid vehicle as claimed in claim 1, characterized in that: The specific method of step 3 is: Step 3.1, initialization; Initialize state variable estimates and the error covariance matrix Set process noise covariance and the measurement noise covariance in: and They are the methanol engine initial torque estimation accuracy, the motor initial torque estimation accuracy and the speed initial estimation accuracy respectively; and They are the methanol engine torque estimation accuracy, motor torque estimation accuracy and speed estimation accuracy during the estimation process; and are the measurement errors of the velocity sensor and acceleration sensor respectively; Step 3.2, calculate the state transfer matrix according to the following formula Then we get the iterative matrix of the error covariance matrix Where, j is the number of iterations; Step 3.3, calculate the observation matrix H k and the Kalman gain matrix K k ; in: h(x) represents the observation function; represents the vehicle acceleration at time k-1; Step 3.4, obtain the state variable estimate according to the following formula in: z k are the sampling values ​​of the acceleration sensor, vehicle speed sensor and torque sensor at time k; is the j-th state estimate at time k; Step 3.5: Determine whether Or j+1≥M. If so, the state variable estimation loop ends and the state variable estimate at time k is output as or If not satisfied, set j=j+1 and return to step 3.2; in: ε is the estimated change threshold of the state variable. M is the maximum number of iterations.

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