Self-adaptive control method for energy management of hybrid electric vehicle

Abstract

The invention discloses a self-adaptive control method for energy management of a hybrid electric vehicle, which comprises the following steps of: S1, performing dimension reduction on an ECMS model based on a universal characteristic curve of an engine to generate a simplified fuel consumption model; s2, calculating the feed-forward power at the moment t according to the dynamic calibration model, and taking the feed-forward power as the initial motor torque according to the following calculation formula; s3, according to the difference value between the current SOC and the target SOC, feedback correction is conducted on the power, a compensation value is calculated, and the calculation formula is shown in the specification; and S4, synthesizing the target electric power, and calculating the target motor torque. According to the method, the calculation amount is reduced by more than 80% through ECMS model dimension reduction, a thousand-level state space required by dynamic planning is compressed into single variable calculation, and real-time processing of an InfineonTC297 single-chip microcomputer becomes possible.

Description

Technical Field

[0001] The present invention belongs to the technical field of hybrid power energy management, and in particular, relates to an adaptive control method for hybrid power vehicle energy management. Background Art

[0002] On-the-go charging is a common energy recovery method for hybrid vehicles. In order to achieve a balance in the state of charge (SOC), power demand control is the primary consideration for on-the-go charging.

[0003] The control methods that are currently widely used are DP dynamic control and fuzzy control based on the equivalent fuel consumption minimum strategy ECMS model.

[0004] However, the essence of existing algorithms is reverse deduction and exhaustive enumeration, which requires huge computational effort and consumes resources that are difficult to meet with the current mainstream configuration of single-chip microcomputers, making them unsuitable for practical application in automotive engineering. Summary of the Invention

[0005] The present invention provides an adaptive control method for hybrid electric vehicle energy management, which solves the problems raised in the above background technology.

[0006] To achieve the above-mentioned object, the present invention adopts a technical solution: an adaptive control method for hybrid electric vehicle energy management, comprising the following steps:

[0007] Step S1: Reduce the dimension of the ECMS model based on the engine universal characteristic curve to generate a simplified fuel consumption model: ;

[0008] Step S2: Dynamically calibrate the model Calculate the feedforward power at time t , and the feed-forward power is used as the initial motor torque , the calculation formula is as follows: ;

[0009] Step S3, according to (The difference between the current SOC and the target SOC) is used to correct the power feedback and calculate the compensation value. The calculation formula is as follows: ;

[0010] Step S4: synthesize target electric power , and calculate the target motor torque .

[0011] Preferably, in step S1, is the instantaneous equivalent fuel consumption of the system at time t, is the instantaneous fuel consumption of the engine at time t, It is the equivalent factor for the conversion of electric energy and fuel consumption, P is the electric power consumption of the whole vehicle, and HL is the lower calorific value of fuel.

[0012] Preferably, in step S2, a and b are empirical values ​​based on calibration correction. The SOC balance point is the target SOC of the vehicle, which is given based on the vehicle speed and the driving mode set by the driver. is the motor power, in kW, Motor speed, unit: r / min.

[0013] Preferably, the PID parameters of step S3 are obtained by actual vehicle calibration, and the calibration process includes collecting climbing, braking and constant speed working condition data optimization at an ambient temperature of -10℃~45℃. .

[0014] The beneficial effects of adopting the above technical solution are:

[0015] 1. ECMS model dimensionality reduction reduces computational complexity by over 80%, compressing the thousands of state spaces required for dynamic programming into a single-variable operation, enabling real-time processing on the Infineon TC297 microcontroller.

[0016] Second, through feedforward-feedback dual closed-loop control, dynamic compensation for operating condition disturbances is achieved, stabilizing the SOC deviation within ±2%, preventing battery overcharge and over-discharge. In the NEDC operating condition test, the SOC fluctuation range is reduced by 60% compared with traditional methods.

[0017] 3. Compared with traditional rule control, the fuel consumption of parallel hybrid vehicles is reduced by 8%-12% per 100 kilometers, and the speed of generating motor torque commands is increased by 3 times through the feedforward power pre-calculation mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall flow chart provided by the present invention. DETAILED DESCRIPTION

[0019] The following is a further detailed description of the specific implementation methods of the present invention through the description of embodiments with reference to the accompanying drawings, with the aim of helping those skilled in the art to have a more complete, accurate and in-depth understanding of the concept and technical solution of the present invention and to facilitate its implementation.

[0020] Specifically, if Figure 1 As shown, an adaptive control method for hybrid electric vehicle energy management includes the following steps:

[0021] Step S1: Reduce the dimension of the ECMS model based on the engine universal characteristic curve to generate a simplified fuel consumption model: ;

[0022] Step S2: Dynamically calibrate the model Calculate the feedforward power at time t , and the feed-forward power is used as the initial motor torque , the calculation formula is as follows: ;

[0023] Step S3, according to (The difference between the current SOC and the target SOC) is used to correct the power feedback and calculate the compensation value. The calculation formula is as follows: ;

[0024] Step S4: synthesize target electric power , and calculate the target motor torque .

[0025] It should be noted that the purpose of the ECMS strategy is to obtain the minimum value by controlling variables, integrating engine principles and driving experience, and performing dimensionality reduction processing here. Assuming that the distribution ratio of the engine and motor of the whole vehicle is 100%, that is, driving in pure oil mode, the optimal solution of ECMS, that is, the minimum value, can be approximately considered as the optimal fuel consumption rate given by the engine universal characteristic curve, which can be directly obtained through test bench data.

[0026] In the step S1, is the instantaneous equivalent fuel consumption of the system at time t, is the instantaneous fuel consumption of the engine at time t, It is the equivalent factor for the conversion of electric energy and fuel consumption, P is the electric power consumption of the whole vehicle, and HL is the lower calorific value of fuel.

[0027] In step S2, a and b are empirical values ​​based on calibration correction. The SOC balance point is the target SOC of the vehicle, which is given based on the vehicle speed and the driving mode set by the driver. is the motor power, in kW, Motor speed, unit: r / min.

[0028] The PID parameters of step S3 are obtained through real vehicle calibration. The calibration process includes collecting climbing, braking and constant speed working condition data optimization at an ambient temperature of -10℃~45℃. ;

[0029] The specific working method is described below with specific embodiments: Example 1: Economic Mode Control

[0030] When the driver selects the economy mode and the vehicle speed is less than 60 km / h:

[0031] The target SOC is set to 70%. =0.7;

[0032] The equivalent factor parameters are a=0.12, b=0.75, that is, 0.12*0.7+0.75=0.834;

[0033] The PID parameters are ;

[0034] The actual measured results show that the SOC is maintained at 69.2%~70.8% and the fuel consumption is 4.2L / 100km. Example 2: Sports Mode Control

[0035] When the driver selects the economy mode and the vehicle speed is >80km / h:

[0036] The target SOC is set to 55%. =0.55;

[0037] The equivalent factor parameters are a=0.18, b=0.85, that is, 0.18*0.55+0.85=0.949;

[0038] The PID parameters are ;

[0039] The measured results show that the acceleration response time is shortened by 40% and the SOC fluctuation is ±1.5%.

[0040] The present invention is described above by way of example in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention; or the above-mentioned concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the scope of protection of the present invention.

Claims

1. An adaptive control method for hybrid electric vehicle energy management, characterized in that: The following steps are involved: Step S1: Reduce the dimension of the ECMS model based on the engine universal characteristic curve to generate a simplified fuel consumption model: ; Step S2: Dynamically calibrate the model Calculate the feedforward power at time t , and the feed-forward power is used as the initial motor torque , the calculation formula is as follows: ; Step S3, according to (The difference between the current SOC and the target SOC) is used to correct the power feedback and calculate the compensation value. The calculation formula is as follows: ; Step S4: synthesize target electric power , and calculate the target motor torque .

2. The adaptive control method for hybrid electric vehicle energy management according to claim 1, characterized in that: In the step S1, is the instantaneous equivalent fuel consumption of the system at time t, is the instantaneous fuel consumption of the engine at time t, It is the equivalent factor for the conversion of electric energy and fuel consumption, P is the electric power consumption of the whole vehicle, and HL is the lower calorific value of fuel.

3. The adaptive control method for hybrid electric vehicle energy management according to claim 1, characterized in that: In step S2, a and b are empirical values ​​based on calibration correction. The SOC balance point is the target SOC of the vehicle, which is given based on the vehicle speed and the driving mode set by the driver. is the motor power, in kW, Motor speed, unit: r / min.

4. The adaptive control method for hybrid electric vehicle energy management according to claim 1, characterized in that: The PID parameters of step S3 are obtained through real vehicle calibration. The calibration process includes collecting climbing, braking and constant speed working condition data optimization at an ambient temperature of -10℃~45℃. .

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