Hybrid electric vehicle ECMS energy management method based on eagle optimization algorithm

Abstract

The invention discloses a hybrid electric vehicle ECMS energy management method based on an eagle optimization algorithm, and relates to the technical field of hybrid electric vehicle energy management, and the method comprises the following steps: 1, collecting the vehicle speed of a vehicle, and taking the vehicle speed as working condition data to judge the current working condition segment of the vehicle; step 2, constructing an ECMS objective function; step 3, solving the ECMS objective function through an eagle optimization algorithm to obtain optimal equivalent factors in all working modes; and 4, inputting the optimal equivalent factor into an ECMS target function to obtain an optimal working mode and an optimal value of the equivalent factor under the current working condition segment, and distributing the engine torque and the battery output torque according to the optimal value of the equivalent factor. The method has the characteristics that the energy consumption of the whole vehicle is reduced, the health of the battery is protected, and the economical efficiency and the practicability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management of hybrid electric vehicles, and more specifically, to an ECMS energy management method for hybrid electric vehicles based on an Osprey optimization algorithm. Background Art

[0002] At present, research on the optimization of energy management strategies for power-split hybrid vehicles is still relatively limited. The main research issue is to reduce energy consumption and control the battery power at the same time. Existing technologies often use a rule-based method to determine the working mode, and then determine the optimal working point based on ECMS (equivalent fuel consumption minimum strategy), that is, the working mode determined based on actual experience is selected through the change of the battery state of charge (SOC), and then the minimum fuel consumption point in the working mode is calculated as the optimal working point through the equivalent fuel consumption strategy. This method considers the selection of the working mode and the determination of the optimal working point separately, and needs to continuously adjust the mode switching rules to obtain a better working point. Since the working point under the selected working mode may not be the optimal working point, it is difficult to obtain the global optimal value, and the control effect is not good. In addition, the traditional ECMS constructed based on the exhaustive method needs to continuously modify and verify the mode switching rules through actual vehicle debugging, and the working mode and working point optimization are separated for consideration, which has a large workload and cannot guarantee the best economy. Summary of the invention

[0003] The purpose of the present invention is to design and develop a hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm. By combining ECMS with the OOA algorithm, the energy consumption of the entire vehicle is reduced while protecting the health of the battery, thereby improving economy and practicality.

[0004] The technical solution provided by the present invention is: A hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm comprises the following steps: Step 1: Collect the vehicle speed and use it as working condition data to determine the working condition segment to which the vehicle currently belongs; Step 2: Construct ECMS objective function: ; In the formula, is the ECMS objective function, is the instantaneous equivalent fuel consumption, For the current moment, For the end moment; The constraints are: ; In the formula, is the minimum engine torque, for The engine torque at the moment, is the maximum engine torque, is the minimum torque of the motor, is the motor torque, is the maximum torque of the motor, is the minimum engine speed, for Engine speed at the moment, is the maximum engine speed, is the minimum motor speed, for Motor speed at all times, is the maximum motor speed, and They are the upper and lower limits of the SOC maintenance range respectively; Step 3: Solve the ECMS objective function through the Osprey optimization algorithm to obtain the optimal equivalent factor under all working modes; Step 4: Input the optimal equivalent factor into the ECMS objective function to obtain the optimal working mode and the optimal value of the equivalent factor under the current operating condition, and distribute the engine torque and the battery output torque according to the optimal value of the equivalent factor.

[0005] Preferably, the operating condition segment includes: like , it is a low-speed operating condition segment; like , it is a medium-speed operating condition segment; like , it is a high-speed operating condition segment; like , then it is the acceleration condition segment; in, is the speed of the vehicle.

[0006] Preferably, the instantaneous equivalent fuel consumption satisfies: ; In the formula, is the instantaneous fuel consumption rate of the engine, is the battery equivalent fuel consumption.

[0007] Preferably, the battery equivalent fuel consumption satisfies: ; In the formula, is the equivalence factor, is the nominal fuel consumption corresponding to the electrical energy, The fuel has a low calorific value. is the battery power.

[0008] Preferably, the instantaneous fuel consumption rate of the engine satisfies: ; In the formula, is the function symbol.

[0009] Preferably, the Osprey optimization algorithm specifically comprises the following steps: Step a: Create and initialize a population of ospreys: ; ; In the formula, is the overall matrix of Osprey positions, It is The location of the Osprey. For the Osprey The value of the dimension, , is the number of ospreys, i.e. the number of equivalent factors, , is the number of problem variables, i.e. the number of working modes, Is the interval The random numbers in It is The lower bound of the problem variable, It is The upper bound of the problem variable, The Osprey's fitness value satisfies: ; In the formula, is the overall fitness matrix, For the The fitness function obtained by each osprey; Step b: Update the fitness function: ; In the formula, It is Osprey fish position collection, For the The location of the Osprey. is the best candidate solution, For the The fitness value corresponding to each osprey; Step c: After determining the location of the fish, the osprey will start fishing. At this time, the fitness value and the osprey position of stage 1 are iteratively updated: ; ; In the formula, For the The first osprey after catching fish The value of the dimension, For the Osprey from its school of fish The first randomly selected fish position in dimension, For collections from A random number, After iterative update Osprey in The value of the dimension; Osprey position updated to: ; In the formula, Updated for Phase 1 Osprey positions, For the The position of an osprey after catching a fish. For the The fitness value of an osprey after catching fish; Step d: After the osprey kills a fish, it will take it to a suitable location to eat it. At this time, the fitness value and the osprey position of stage 2 are iteratively updated: ; ; In the formula, For the The first time an osprey finds a place to eat fish The value of the dimension, is the number of iterations, is the maximum number of iterations, The second phase is the first phase after the iteration update. Osprey in The value of the dimension; Therefore, the Osprey position is updated to:

[0010] In the formula, Updated for Phase 2 Osprey positions, For the The fitness value of an osprey after eating a fish, For the The first location of the fish eaten by the osprey The value of the dimension; Step e: when the end condition is reached, the global optimal value of the osprey position is obtained, that is, the optimal equivalent factor under each working mode.

[0011] Preferably, the The fitness function obtained by each osprey satisfies: ; In the formula, for Battery value at all times, is the SOC target value.

[0012] Preferably, the The battery SOC value at this moment satisfies: ; In the formula, is the battery current, is the maximum capacity of the battery, is the initial moment, is the battery SOC value at the initial moment, Represents the integral over time.

[0013] Preferably, the battery current satisfies:

[0014] In the formula, is the open circuit voltage, is the battery resistance, The power required at the battery output.

[0015] Preferably, it also includes: Step 5: Draw the optimal working mode and the optimal value of the equivalent factor corresponding to each working condition segment into a two-dimensional table, and then call the data in the two-dimensional table offline to distribute the power.

[0016] The beneficial effects of the present invention are: (1) The present invention designs and develops a hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm, which combines the selection of the working mode with the optimization of the working point, and comprehensively applies the application of the equivalent fuel consumption minimization strategy (ECMS) in reducing the energy consumption of the whole vehicle and the excellent performance of the Osprey optimization algorithm (OOA) in iterative optimization of the control target, thereby improving both the vehicle energy consumption and the battery SOC protection, having better economy, and being able to control the SOC at the end of the working condition within the allowable deviation range, thereby reducing the energy consumption and realizing the reasonable distribution of the vehicle driving power between the engine and the battery.

[0017] (2) The hybrid vehicle ECMS energy management method based on the Osprey optimization algorithm designed and developed by the present invention combines the OOA algorithm with the ECMS, uses the OOA algorithm to offline simulate and optimize the equivalent factor to find the value that meets the battery SOC control requirement under a certain working mode, forms a two-dimensional table, and then uses the ECMS strategy to select the corresponding optimal working point and the corresponding working mode online according to the actual working conditions, thereby reducing the workload of online calculations or actual vehicle tests, and at the same time can select the optimal working mode in real time according to different working conditions without formulating complex switching rules.

[0018] (3) The present invention designs and develops a hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm. For hybrid buses, the driving cycle can be divided into segments according to the actual location of the bus stop. The equivalent fuel consumption factor of each segment is optimized offline by the Osprey optimization algorithm. Then, the OOA optimization result is converted into a two-dimensional lookup table, which can be used to adjust the online control strategy in real time to obtain the best instantaneous energy distribution in the hybrid powertrain. The online part uses ECMS to select the working mode corresponding to the lowest equivalent fuel consumption point of the optimization result as the optimal working mode for the segment to improve adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm described in the present invention.

[0020] Figure 2 Schematic diagram of the ECMS strategy of the present invention.

[0021] Figure 3 It is a flow chart of the OOA algorithm described in the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below so that those skilled in the art can implement it according to the description.

[0023] like Figure 1 As shown, the present invention provides a hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm, which is an energy management strategy that combines the Osprey optimization algorithm (OOA) with the comprehensive equivalent fuel consumption minimum strategy (ECMS), referred to as OOA+ECMS, and includes the following steps: Step 1: Collect vehicle speed and use it as working condition data to determine the working condition segment to which the vehicle currently belongs; The operating condition fragments specifically include: In a WLTC standard cycle, it is divided into low-speed operating segments ( )、Medium speed stage( )、High-speed clips( ) and accelerated segments ( ); In this embodiment, the vehicle speed may be obtained by a vehicle speed sensor; Step 2: Figure 2 As shown, constructing the ECMS objective function includes the following steps: Step 1: Use Aemsim to build a vehicle physical model and build a control model in Matlab / Simulink, including: 1) Engine model: ; In the formula, for The engine torque at the moment, is a function symbol, which means and Related functions, for Engine speed at the moment, is the instantaneous fuel consumption rate of the engine, which can be obtained by looking up the quasi-static engine universal characteristic MAP diagram through the speed and torque; 2) Battery model: ; ; In the formula, is the battery current (A), is the open circuit voltage (V), is the battery resistance (Ω), is the power required at the battery output (W), for Battery SOC value at the moment, is the maximum capacity of the battery ( ), is the initial moment, is the battery SOC value at the initial moment, represents the integral over time; Step 2: Construct the ECMS objective function based on equivalent fuel consumption: ; ; ; In the formula, is the ECMS objective function, is the instantaneous equivalent fuel consumption (L / h), Engine instantaneous fuel consumption rate (L / h), is the battery equivalent fuel consumption (L / h), is the equivalent fuel consumption factor, referred to as the equivalent factor. is the nominal fuel consumption corresponding to the electric energy (L / h), is the lower calorific value of fuel oil (kJ / kg), is the battery power (kW), For the end moment; The corresponding constraints are: ; In the formula, is the minimum engine torque, for The engine torque at the moment, is the maximum engine torque, is the minimum torque of the motor, for The motor torque at the time, is the maximum torque of the motor, is the minimum engine speed, for The engine speed at the moment, is the maximum engine speed, is the minimum motor speed, for The motor speed at the moment, is the maximum motor speed, and They are the upper and lower limits of the SOC maintenance range respectively.

[0024] The equivalence factor is a key parameter of the ECMS strategy. Its size determines the distribution relationship of the vehicle's driving power between the engine and the battery. For the ECMS strategy, the equivalence factor determines the optimal operating mode and the optimal operating point. Therefore, ECMS is used to calculate the optimal operating mode, power distribution and operating point corresponding to a specific equivalence factor.

[0025] Step 3: Figure 3 As shown, the ECMS objective function is solved by the Osprey optimization algorithm to obtain the optimal equivalent factor under all working modes; In the hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm described in the present invention, the equivalent factor is a key parameter of the ECMS strategy. Different equivalent factors correspond to different control strategies. When a period of operation is completed, the obtained vehicle energy consumption and battery SOC are also different. In order to maintain the health of the battery, the SOC at the end of the operation needs to be controlled near a target value. Therefore, it is necessary to optimize the equivalent factor to find a value that meets the battery SOC control requirements. In order to achieve the goal of reducing vehicle energy consumption and controlling SOC, the Osprey optimization algorithm (OOA) is combined with ECMS. The task of OOA is to find the optimal equivalent factor. Therefore, the position of the Osprey represents the value of the equivalent factor. The OOA specifically includes the following steps: Step a: Create and initialize a population of ospreys: ; ; In the formula, is the overall matrix of Osprey positions, It is The location of the Osprey. For the Osprey The value of the dimension (problem variable), , is the number of ospreys, i.e. the number of equivalent factors, , is the number of problem variables, i.e. the number of working modes, is a random number in the interval [0,1], It is The lower bound of the problem variable, It is The upper bound of the problem variable, so the fitness value of the problem satisfies: ; In the formula, is the overall fitness matrix, For the The fitness function obtained by each osprey; The said The fitness function obtained by each osprey satisfies: ; In the formula, is the SOC target value; Step b: Update the fitness function: ; In the formula, It is Osprey fish position collection, For the The location of the Osprey. is the best candidate solution, For the The fitness value corresponding to each osprey; Step c: After determining the location of the fish, the osprey will start fishing. During this process, the osprey's position changes, so the osprey's position is iteratively updated in phase 1: ; ; In the formula, For the The first osprey after catching fish The value of the dimension, For the An osprey from its school of fish ) is the first randomly selected fish position in dimension, For collections from A random number, After iterative update Osprey in The value of the dimension; Therefore, the Osprey position is updated to: ; In the formula, Updated for Phase 1 Osprey positions, For the The position of an osprey after catching a fish. For the The fitness value of an osprey after catching fish; That is, after fishing is finished, the fitness values ​​are compared. If the fitness value of the new position is less than the fitness value of the original osprey position, the new position is selected. If it is greater than the fitness function of the original position, the original position is still used, and the remaining positions are regarded as fish schools; Step d: After the osprey kills a fish, it will take it to a suitable location to eat it. Therefore, the fitness value and the osprey position are iteratively updated in stage 2, that is, a new random position is calculated as a "position suitable for eating fish": ; ; In the formula, For the The first time an osprey finds a place to eat fish The value of the dimension, is the number of iterations, is the maximum number of iterations, After the second phase of iterative update Osprey in The value of the dimension; Therefore, the Osprey position is updated to:

[0026] In the formula, Updated for Phase 2 Osprey positions, For the The fitness value of an osprey after eating a fish, For the The first location of the fish eaten by the osprey The value of the dimension; Step e: when the end condition is reached, the global optimal value of the osprey position is obtained, that is, the optimal equivalent factor under each working mode; The end condition is that the maximum number of iterations T is reached or the deviation between the SOC value and the target value at the end of the operating condition segment is within the allowable range (less than 1%).

[0027] Step 4: Figure 2 As shown, the equivalent fuel consumption of each working point is calculated according to the initialized osprey position (equivalent factor). For one working point, there are multiple working modes to choose from, and there are multiple working points in each working mode that can meet the control requirements. The ECMS strategy is adopted to calculate the equivalent fuel consumption of each working point in all working modes, and the working mode and working point corresponding to the lowest equivalent fuel consumption are selected as the optimization result. In the calculation process, the equivalent factor is used as the position of the osprey and is input by the OOA algorithm. In the figure is the number of optional working modes, The current working mode.

[0028] Among them, the power-split hybrid electric vehicle defined in the present invention includes two EV modes (pure electric low gear and pure electric high gear), two HEV modes (hybrid low gear and hybrid high gear) and a braking energy recovery mode (high gear and low gear). The two HEV modes and the braking energy recovery mode are hybrid modes, so the output torque needs to be distributed.

[0029] Step 5: Draw the optimal working mode and the optimal value of the equivalent factor corresponding to each working condition segment into a two-dimensional table, and then call the data in the two-dimensional table offline to distribute the power.

[0030] In this embodiment, the osprey population , maximum number of iterations .

[0031] In this embodiment, the SOC target value is 60%.

[0032] The invention designs and develops a hybrid electric vehicle ECMS energy management method based on an osprey optimization algorithm, which combines the selection of working modes with the optimization of working points. In the OOA algorithm, the osprey position represents an equivalent factor and forms a matrix. After the matrix is ​​initialized, the ECMS is used to calculate the equivalent fuel consumption of each position to obtain the equivalent fuel consumption value of each working point under each working mode. Then, the equivalent factor is optimized by the OOA algorithm in combination with an objective function. The working point corresponding to the obtained optimal equivalent factor is the optimal working point under the mode. Then, the ECMS selects the working mode corresponding to the minimum equivalent fuel consumption among these optimal working points as the optimal working mode. The strategy realizes the reasonable distribution of vehicle driving power between the engine and the battery by optimizing the ECMS equivalent factor using the OOA algorithm and traversing and optimizing all working modes under the selected working conditions, thereby reducing the workload of online calculation or actual vehicle test, and at the same time, the optimal working mode can be selected in real time according to different working conditions without formulating complex switching rules. The OOA algorithm can adaptively adjust its search strategy according to changes in the environment, thereby improving the ability to handle problems in a dynamic environment, and the obtained optimal value is closer to the objective function, thereby realizing the protection of battery health while reducing the energy consumption of the whole vehicle.

[0033] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm, characterized in that: The steps include: Step 1: Collect the vehicle speed and use it as working condition data to determine the working condition segment to which the vehicle currently belongs; Step 2: Construct ECMS objective function: ； In the formula, is the ECMS objective function, is the instantaneous equivalent fuel consumption, For the current moment, For the end moment; The constraints are: ； In the formula, is the minimum engine torque, for The engine torque at the moment, is the maximum engine torque, is the minimum torque of the motor, is the motor torque, is the maximum torque of the motor, is the minimum engine speed, for Engine speed at the moment, is the maximum engine speed, is the minimum motor speed, for Motor speed at all times, is the maximum motor speed, and They are the upper and lower limits of the SOC maintenance range respectively; Step 3: Solve the ECMS objective function through the Osprey optimization algorithm to obtain the optimal equivalent factor under all working modes; Step 4: Input the optimal equivalent factor into the ECMS objective function to obtain the optimal working mode and the optimal value of the equivalent factor under the current operating condition, and distribute the engine torque and the battery output torque according to the optimal value of the equivalent factor.

2. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 1, characterized in that: The operating condition fragments include: like , it is a low-speed operating condition segment; like , it is a medium-speed operating condition segment; like , it is a high-speed operating condition segment; like , then it is the acceleration condition segment; in, is the speed of the vehicle.

3. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 2, characterized in that: The instantaneous equivalent fuel consumption satisfies: ； In the formula, is the instantaneous fuel consumption rate of the engine, is the battery equivalent fuel consumption.

4. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 3, characterized in that: The battery equivalent fuel consumption meets the following requirements: ； In the formula, is the equivalence factor, is the nominal fuel consumption corresponding to the electrical energy, The fuel has a low calorific value. is the battery power.

5. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 4, characterized in that: The instantaneous fuel consumption rate of the engine satisfies: ； In the formula, is the function symbol.

6. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 5, characterized in that: The Osprey optimization algorithm specifically includes the following steps: Step a: Create and initialize a population of ospreys: ； ； In the formula, is the overall matrix of Osprey positions, It is The location of the Osprey. For the Osprey The value of the dimension, , is the number of ospreys, i.e. the number of equivalent factors, , is the number of problem variables, i.e. the number of working modes, Is the interval The random numbers in It is The lower bound of the problem variable, It is The upper bound of the problem variable, The Osprey's fitness value satisfies: ； In the formula, is the overall fitness matrix, For the The fitness function obtained by each osprey; Step b: Update the fitness function: ； In the formula, It is Osprey fish position collection, For the The location of the Osprey. is the best candidate solution, For the The fitness value corresponding to each osprey; Step c: After determining the location of the fish, the osprey will start fishing. At this time, the fitness value and the osprey position of stage 1 are iteratively updated: ； ； In the formula, For the The first osprey after catching fish The value of the dimension, For the Osprey from its school of fish The first randomly selected fish position in dimension, For collections from A random number, After iterative update Osprey in The value of the dimension; Osprey position updated to: ； In the formula, Updated for Phase 1 Osprey positions, For the The position of an osprey after catching a fish. For the The fitness value of an osprey after catching fish; Step d: After the osprey kills a fish, it will take it to a suitable location to eat it. At this time, the fitness value and the osprey position of stage 2 are iteratively updated: ； ； In the formula, For the The first time an osprey finds a place to eat fish The value of the dimension, is the number of iterations, is the maximum number of iterations, After the second phase of iterative update Osprey in The value of the dimension; Therefore, the Osprey position is updated to: ； In the formula, Updated for Phase 2 Osprey positions, For the The fitness value of an osprey after eating a fish, For the The first location of the fish eaten by the osprey The value of the dimension; Step e: when the end condition is reached, the global optimal value of the osprey position is obtained, that is, the optimal equivalent factor under each working mode.

7. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 6, characterized in that: The said The fitness function obtained by each osprey satisfies: ； In the formula, for Battery SOC value at the moment, is the SOC target value.

8. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 7, characterized in that: Said The battery SOC value at this moment satisfies: ； In the formula, is the battery current, is the maximum capacity of the battery, is the initial moment, is the battery SOC value at the initial moment, Represents the integral over time.

9. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 8, characterized in that: The battery current satisfies: ； In the formula, is the open circuit voltage, is the battery resistance, The power required at the battery output.

10. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 9, characterized in that: Also includes: Step 5: Draw the optimal working mode and the optimal value of the equivalent factor corresponding to each working condition segment into a two-dimensional table, and then call the data in the two-dimensional table offline to distribute the power.

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