An Energy Management Method for Hybrid Electric Vehicle ECMS Based on Osprey Optimization Algorithm

Abstract

The invention discloses an ECMS energy management method for hybrid electric vehicles based on the osprey optimization algorithm. The invention relates to the technical field of energy management of hybrid electric vehicles and comprises the following steps: Step 1, collect the vehicle speed and use it as working condition data to judge the current working condition segment to which the vehicle belongs; Step 2, construct an ECMS objective function; Step 3, solve the ECMS objective function through the osprey optimization algorithm to obtain the optimal equivalent factors under all working modes; Step 4, input the optimal equivalent factors into the ECMS objective function to obtain the optimal working mode and the optimal value of its equivalent factor under the current working condition segment, and allocate the engine torque and the battery output torque according to the optimal value of the equivalent factor. The invention has the characteristics of reducing the energy consumption of the whole vehicle, protecting the health of the battery, and improving the economy and practicability.

Description

Technical Field

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

[0002] At present, the research on the optimization of energy management strategies for power-split hybrid vehicles is still relatively limited. The main research problem is to reduce energy consumption and be able to control the battery state of charge at the same time. Existing technologies often use rule-based methods to determine the working mode, and then determine the optimal operating point according to ECMS (equivalent fuel consumption minimization strategy), that is, the working mode determined based on actual experience is selected through the change of the state of charge (SOC) of the battery, and then the lowest fuel consumption point in this working mode is calculated through the equivalent fuel consumption strategy as the optimal operating point. This method considers the selection of the working mode and the determination of the optimal operating point separately, and it is necessary to continuously adjust the mode switching rules to obtain a better operating point. Since the operating point under the selected working mode may not be the optimal operating point, it is difficult to obtain the global optimal value, and the control effect is not good. Moreover, the traditional ECMS constructed based on the exhaustive method needs to continuously modify and verify the mode switching rules through actual vehicle debugging, considering the optimization of the working mode and the operating point separately, which is 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 an ECMS energy management method for hybrid vehicles based on the osprey optimization algorithm. By combining ECMS with the OOA algorithm, while reducing the energy consumption of the whole vehicle, it protects the battery health and improves the economy and practicability.

[0004] The technical solution provided by the present invention is as follows:

[0005] An ECMS energy management method for hybrid vehicles based on the osprey optimization algorithm, comprising the following steps:

[0006] Step 1: Collect the vehicle speed and use it as the operating condition data to judge the current operating condition segment to which the vehicle belongs;

[0007] Step 2: Construct the ECMS objective function:

[0008] ;

[0009] In the formula, is the ECMS objective function, is the instantaneous equivalent fuel consumption, is the current moment, is the end moment;

[0010] The constraint condition is:

[0011] ;

[0012] Wherein, is the minimum torque of the engine, is the engine torque at time is the maximum torque of the engine, is the minimum torque of the motor, is the motor torque, is the maximum torque of the motor, is the minimum speed of the engine, is the engine speed at time is the maximum speed of the engine, is the minimum speed of the motor, is the motor speed at time is the maximum speed of the motor, and are the upper and lower limits of the SOC maintenance range respectively;

[0013] Step three: Solve the ECMS objective function through the osprey optimization algorithm to obtain the optimal equivalent factor under all working modes;

[0014] Step four: Input the optimal equivalent factor into the ECMS objective function to obtain the optimal working mode and its optimal equivalent factor value under the current working condition segment, and allocate the engine torque and the battery output torque according to the optimal equivalent factor value.

[0015] Preferably, the working condition segment includes:

[0016] If , it is a low-speed working condition segment;

[0017] If , it is a medium-speed working condition segment;

[0018] If , it is a high-speed working condition segment;

[0019] If , it is an acceleration working condition segment;

[0020] Wherein, is the vehicle speed.

[0021] Preferably, the instantaneous equivalent fuel consumption satisfies:

[0022] ;

[0023] Wherein, is the instantaneous fuel consumption rate of the engine, is the equivalent fuel consumption of the battery.

[0024] Preferably, the equivalent fuel consumption of the battery satisfies:

[0025] ;

[0026] In the formula, is the equivalent factor, is the nominal fuel consumption corresponding to electric energy, is the lower calorific value of fuel, is the battery power.

[0027] Preferably, the instantaneous fuel consumption rate of the engine satisfies:

[0028] ;

[0029] In the formula, is the function symbol.

[0030] Preferably, the osprey optimization algorithm specifically includes the following steps:

[0031] Step a, create an osprey population and initialize the osprey population:

[0032] ;

[0033] ;

[0034] In the formula, is the overall matrix of osprey positions, is the position of the th osprey, is the th osprey's th dimension value, , 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 random number in the interval is the lower bound of the th problem variable, is the th problem variable's upper bound,

[0035] The osprey fitness value satisfies:

[0036] ;

[0037] In the formula, is the overall matrix of fitness, is the The fitness function obtained by an osprey;

[0038] Step b, update the fitness function:

[0039] ;

[0040] In the formula, is the fish position set of the th osprey, is the position of the th osprey, is the best candidate solution, is the fitness value corresponding to the th osprey;

[0041] Step c, after determining the position of the fish, the osprey will fish, and at this time, the fitness value and the osprey position in stage 1 are iteratively updated:

[0042] ;

[0043] ;

[0044] In the formula, is the value of the th dimension of the th osprey after fishing, is the th dimension of the fish position randomly selected by the th osprey from its fish school , is a random number from the set , is the value of the th osprey in the th dimension after iterative update;

[0045] The osprey position is updated to:

[0046] ;

[0047] In the formula, is the position of the th osprey after stage 1 update, is the position of the th osprey after fishing, is the fitness value of the th osprey after fishing;

[0048] Step d, after an osprey hunts a fish, it will take it to a suitable position to eat, and at this time, the fitness value and the osprey position in stage 2 are iteratively updated:

[0049] ;

[0050] ;

[0051] In the formula, is the value of the th dimension after the th osprey finds a fish-eating position, is the number of iterations, is the maximum number of iterations, is the value of the th dimension of the th osprey after the second-stage iterative update;

[0052] Therefore, the osprey position is updated as:

[0053]

[0054] In the formula, is the position of the th osprey after the stage 2 update, is the fitness value of the th osprey after eating fish, is the th dimension value of the fish-eating position found by the th osprey;

[0055] Step e, when the end condition is reached, obtain the global optimal value of the osprey position, that is, the optimal equivalent factor under each working mode.

[0056] Preferably, the fitness function obtained by the th osprey satisfies:

[0057] ;

[0058] In the formula, is the time battery value, is the SOC target value.

[0059] Preferably, the battery SOC value at the time satisfies:

[0060] ;

[0061] In the formula, is the battery current, is the maximum battery capacity, is the initial time, is the battery SOC value at the initial time, represents the integral with respect to time.

[0062] Preferably, the battery current satisfies:

[0063]

[0064] wherein is the open-circuit voltage, is the battery resistance, is the power required at the battery output terminal.

[0065] Preferably, it further includes:

[0066] Step Five: Plot the optimal operating mode and the optimal value of its equivalent factor corresponding to each operating condition segment into a two-dimensional table, and then perform power distribution by offline calling the data in the two-dimensional table.

[0067] The beneficial effects of the present invention are as follows:

[0068] (1) A hybrid vehicle ECMS energy management method based on the osprey optimization algorithm designed and developed by the present invention combines the selection of operating modes and the optimization of operating points, integrates the application of the equivalent fuel consumption minimization strategy (ECMS) in reducing the vehicle's energy consumption and the excellent performance of the osprey optimization algorithm (OOA) in iteratively optimizing the control target, realizes the improvement of both vehicle energy consumption and battery SOC protection, has better economy, and can control the SOC at the end of the operating condition within the allowable deviation range, and realizes the reasonable distribution of the vehicle drive power between the engine and the battery while reducing energy consumption.

[0069] (2) The hybrid vehicle ECMS energy management method based on the osprey optimization algorithm designed and developed by the present invention, through the combination of the OOA algorithm and ECMS, uses the OOA algorithm to perform offline simulation to optimize the equivalent factor to find the value that meets the battery SOC control requirements under a certain operating mode, forms a two-dimensional table, and then uses the ECMS strategy to online select the corresponding optimal operating point and the corresponding operating mode according to the actual operating condition requirements, which reduces the online calculation or the workload of vehicle tests, and at the same time can select the optimal operating mode in real time according to different operating conditions without formulating complex switching rules.

[0070] (3) A hybrid vehicle ECMS energy management method based on the osprey optimization algorithm designed and developed by the present invention. For hybrid buses, the driving cycle can be divided into segments according to the actual location of bus stops. The equivalent fuel consumption factors of each segment are optimized offline by the osprey optimization algorithm, and then the optimization results of OOA are 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 operating mode corresponding to the lowest point of the equivalent fuel consumption of the optimization result as the optimal operating mode for this segment to improve adaptability. Description of the Drawings

[0071] Figure 1 It is a schematic flow chart of the ECMS energy management method for hybrid electric vehicles based on the osprey optimization algorithm of the present invention.

[0072] Figure 2 It is a schematic flow chart of the ECMS strategy of the present invention.

[0073] Figure 3 It is a schematic flow chart of the OOA algorithm of the present invention. Detailed implementation manners

[0074] The following further detailed description of the present invention is provided to enable those skilled in the art to implement it with reference to the text of the specification.

[0075] As Figure 1 shown, an ECMS energy management method for hybrid electric vehicles based on the osprey optimization algorithm provided by the present invention is an energy management strategy that combines the osprey optimization algorithm (OOA) with the comprehensive equivalent fuel consumption minimization strategy (ECMS), abbreviated as OOA+ECMS, and includes the following steps:

[0076] Step 1: Collect the vehicle speed , and use it as the operating condition data to determine the current operating condition segment to which the vehicle belongs;

[0077] Among them, the operating condition segment specifically includes:

[0078] In a WLTC standard cycle operating condition, it is divided into a low-speed operating condition segment ( ), a medium-speed stage ( ), a high-speed segment ( ), and an acceleration segment ( );

[0079] In this embodiment, the vehicle speed can be obtained through a vehicle speed sensor;

[0080] Step 2: As Figure 2 shown, construct an ECMS objective function, which specifically includes the following steps:

[0081] Step 1: Use Aemsim to build a vehicle physical model and build a control model in Matlab / Simulink, which specifically includes:

[0082] 1). Engine model:

[0083] ;

[0084] In the formula, is the engine torque at time is a function symbol, representing the function related to and ; is the engine speed at a certain moment, is the engine's instantaneous fuel consumption rate, which can be obtained by looking up the quasi-static engine universal characteristic MAP diagram through speed and torque;

[0085] 2) Battery model:

[0086] ;

[0087] ;

[0088] In the formula, is the battery current (A), is the open-circuit voltage (V), is the battery resistance (Ω), is the required power at the battery output terminal (W), is the battery SOC value at a certain moment, is the maximum battery capacity ( ), is the initial moment, is the battery SOC value at the initial moment, represents the integral with respect to time;

[0089] Step 2: Construct the ECMS objective function according to the equivalent fuel consumption:

[0090] ;

[0091] ;

[0092] ;

[0093] In the formula, is the ECMS objective function, is the instantaneous equivalent fuel consumption (L / h), the engine's instantaneous fuel consumption rate (L / h), is the battery equivalent fuel consumption (L / h), is the equivalent fuel consumption factor, abbreviated as the equivalent factor, is the nominal fuel consumption (L / h) corresponding to electric energy, is the low calorific value of fuel (kJ / kg), is the battery power (kW), is the end moment;

[0094] The corresponding constraint condition is:

[0095] ;

[0096] Wherein, is the minimum torque of the engine, is the engine torque at time is the maximum torque of the engine, is the minimum torque of the motor, is the motor torque at time is the maximum torque of the motor, is the minimum speed of the engine, is the engine speed at time is the maximum speed of the engine, is the minimum speed of the motor, is the motor speed at time is the maximum speed of the motor, and are the upper and lower limits of the SOC maintenance range respectively.

[0097] The equivalent factor is a key parameter of the ECMS strategy. Its magnitude determines the distribution relationship of the vehicle drive power between the engine and the battery. For the ECMS strategy, the equivalent factor determines the optimal working mode and the optimal operating point. Therefore, ECMS is used to calculate the optimal working mode, power distribution and operating point corresponding to a specific equivalent factor.

[0098] Step 3: As shown in Figure 3 , solve the ECMS objective function through the osprey optimization algorithm to obtain the optimal equivalent factor under all working modes;

[0099] In the ECMS energy management method for hybrid electric vehicles 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 section of working conditions ends, the overall vehicle energy consumption and the battery SOC obtained are also different. In order to maintain the healthy state of the battery, it is necessary to control the SOC at the end of the working conditions near a target value. Therefore, it is necessary to optimize the equivalent factor to find the value that meets the battery SOC control requirements. In order to achieve the goals of reducing the overall vehicle energy consumption and controlling the 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 specific steps of the OOA are as follows:

[0100] Step a: Create an osprey population and initialize the osprey population:

[0101] ;

[0102] ;

[0103] In the formula, is the overall matrix of the osprey position, is the position of the th osprey, is the th value (problem variable) of the th dimension of the th osprey, 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], is the lower bound of the th problem variable, is the upper bound of the th problem variable. Thus, the fitness value of this problem satisfies:

[0104] ;

[0105] In the formula, is the overall matrix of fitness, is the fitness function obtained by the th osprey;

[0106] The fitness function obtained by the th osprey satisfies:

[0107] ;

[0108] In the formula, is the SOC target value;

[0109] Step b: Update the fitness function:

[0110] ;

[0111] In the formula, is the fish position set of the th osprey, is the position of the th osprey, is the best candidate solution, is the fitness value corresponding to the th osprey;

[0112] Step c: After determining the position of the fish, the osprey will fish. During this process, the position of the osprey changes. Therefore, perform the iteration update of the osprey position in Phase 1:

[0113] ;

[0114] ;

[0115] Wherein, is the value of the th dimension of the osprey after fishing, ; is the th dimension of the fish position randomly selected by the th osprey from its fish group ( ), is a random number from the set ; is the value of the th dimension of the th osprey after iterative update;

[0116] Therefore, the osprey position is updated as:

[0117] ;

[0118] Wherein, is the position of the th osprey after the update in Phase 1, is the position of the th osprey after fishing, is the fitness value of the th osprey after fishing;

[0119] That is, after fishing, 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 the fish group;

[0120] Step d: After an osprey hunts a fish, it will take it to a suitable position to eat. Therefore, perform iterative updates on the fitness value and the osprey position in Phase 2, that is, calculate a new random position as the "suitable position for eating fish":

[0121] ;

[0122] ;

[0123] Wherein, is the value of the th dimension of the th osprey after finding the position for eating fish, is the number of iterations, is the maximum number of iterations, is the value of the th dimension of the th osprey after iterative update in the second phase;

[0124] Therefore, the osprey position is updated to:

[0125]

[0126] In the formula, is the th osprey position after the update in stage 2, is the fitness value of the th osprey after eating fish, is the value of the th dimension of the fish-eating position found by the th osprey;

[0127] Step e: When the end condition is reached, obtain the global optimal value of the osprey position, that is, the optimal equivalent factor under each working mode;

[0128] 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 this working condition segment is within the allowable range (less than 1%).

[0129] Step four: As Figure 2 shown, calculate the equivalent fuel consumption of each working point according to the initialized osprey position (equivalent factor). For a working condition point, there are multiple working modes to choose from, and each working mode has multiple working points that can meet the control requirements. Using the ECMS strategy, calculate the equivalent fuel consumption of each working point under all working modes, and select the working mode and working point corresponding to the lowest equivalent fuel consumption as the optimization result. During the calculation process, the equivalent factor is used as the position of the osprey and is input by the OOA algorithm;

[0130] In the figure, is the number of optional working modes, is the current working mode.

[0131] Among them, for the power-split hybrid vehicle defined in the present invention, it 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 regenerative braking energy recovery mode (high gear and low gear). Among them, the two HEV modes and the regenerative braking energy recovery mode are all hybrid modes, so the distribution of the output torque needs to be carried out.

[0132] Step five: Plot the optimal working mode corresponding to each working condition segment and its optimal value of the equivalent factor into a two-dimensional table, and then the data in the two-dimensional table can be called offline for power distribution.

[0133] In this embodiment, the osprey population , the maximum number of iterations .

[0134] In this embodiment, the SOC target value is 60%.

[0135] A hybrid vehicle ECMS energy management method based on the osprey optimization algorithm designed and developed by the present invention combines the selection of working modes and the optimization of working points. In the OOA algorithm, the osprey positions represent the equivalent factors and form a matrix. After initializing the matrix, ECMS is used to calculate the equivalent fuel consumption for each position, obtaining the equivalent fuel consumption values of each working point under each working mode. Then, the equivalent factors are optimized through the OOA algorithm combined with the objective function. The working point corresponding to the optimal equivalent factor obtained is the optimal working point under this mode. Then, ECMS selects the working mode corresponding to the minimum equivalent fuel consumption among these optimal working points as the optimal working mode. This strategy realizes the reasonable distribution of the vehicle driving power between the engine and the battery by using the OOA algorithm to optimize the ECMS equivalent factors and traversing and optimizing all working modes under the selected working conditions, reducing the workload of online calculation or real vehicle tests. At the same time, it can also select the optimal working mode in real time according to different working conditions without formulating complex switching rules. The OOA algorithm can adaptively adjust its search strategy according to environmental changes, improving the ability to handle problems in a dynamic environment. The obtained optimal value is closer to the objective function, achieving the reduction of the vehicle's overall energy consumption while protecting the battery health.

[0136] Although the embodiments of the present invention have been disclosed as above, it is not limited to only the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the embodiments shown and described here.

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; The instantaneous equivalent fuel consumption satisfies: ； In the formula, is the instantaneous fuel consumption rate of the engine, is the battery equivalent fuel consumption; The battery equivalent fuel consumption meets the following requirements: ； In the formula, is the equivalent factor, is the nominal fuel consumption corresponding to the electrical energy, The fuel has a low calorific value. is the battery power; Step 3: Solve the ECMS objective function through the Osprey optimization algorithm to obtain the optimal equivalent factor under all working modes; 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; Among them, the The fitness function obtained by each osprey satisfies: ； In the formula, for Battery SOC value at the moment, is the SOC target value; Step b: Update the fitness function: ； In the formula, It is Osprey fish location 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 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; 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 working condition, and distribute the engine torque and the battery output torque according to the optimal value of the equivalent factor; 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.

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 vehicle's speed.

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 fuel consumption rate of the engine satisfies: ； In the formula, is the function symbol.

4. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 3, 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.

5. The hybrid electric vehicle ECMS energy management method based on the Osprey optimization algorithm as claimed in claim 4, 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.

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