PMP optimization-based power distribution method for amphibious vehicle

Abstract

The application relates to the technical field of amphibious hybrid power equipment, in particular to an amphibious hybrid power vehicle sailing power distribution method based on PMP optimization, which comprises the following steps: constructing a power source output power relationship of an amphibious hybrid power system; based on a PMP theory, constructing a Hamilton function of reversible motor power; based on the Hamilton function of the reversible motor power, determining a dynamic switching condition of an operation mode, determining optimal working values of a final control variable reversible motor power P m and engine power P eng . The application fully considers key factors that can induce switching condition change caused by power source working change, introduces a Pontryagin minimum principle (PMP) to optimize the switching condition in real time aiming at the problem of poor distribution effect caused by fixed switching condition, and improves overall operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of amphibious hybrid power equipment, and in particular to a navigation power distribution method for an amphibious hybrid power amphibious vehicle based on PMP optimization. Background Art

[0002] Power allocation methods for amphibious hybrid power systems primarily include rule-based, frequency-based, global optimization-based, and instantaneous optimization-based approaches. Rule-based power allocation methods are widely adopted due to their simplicity and ease of implementation. However, the setup process relies heavily on engineering experience, and improper switching conditions can severely impact allocation effectiveness.

[0003] While current power allocation methods can allocate power to power sources by setting switching conditions, they fail to fully consider the key factor that can trigger changes in switching conditions due to changes in power source operation, limiting optimal power allocation. Therefore, to address the issue of poor power allocation due to fixed switching conditions, the Pontryagin Minimum Principle (PMP) was introduced to optimize switching conditions in real time, improving overall operational efficiency. Summary of the Invention

[0004] In view of the above problems, a navigation power distribution method for amphibious hybrid vehicle based on PMP optimization is proposed.

[0005] Therefore, the technical problem addressed by this invention is that rule-based power allocation algorithms have been widely used for hybrid power system power allocation, but the setting of rules relies too much on engineering experience, resulting in poor overall operational effectiveness. This patent utilizes the PMP algorithm to optimize the static switching conditions of the rules, enabling real-time adjustments based on the status of the equipment to obtain dynamic switching conditions, thereby improving the optimization effect of the entire system.

[0006] The implementation of the present invention comprises the following steps:

[0007] 1. Construct the power source output power relationship of the amphibious hybrid power system, including:

[0008] Build required power P ref With engine power P eng , reversible motor power P m The relationship is shown in formula (1).

[0009] P eng =P ref -P m (1)

[0010] Build battery power P bat With reversible motor power P m The relationship is shown in formulas (2) and (3).

[0011]

[0012]

[0013] Where P ref is the required power, P eng is the engine power, P m is the reversible motor power, P bat is the battery power, η m is the working efficiency of the reversible motor, P bat,loss For battery power consumption, Where V oc is the open circuit voltage of the battery, and R is the internal resistance of the battery.

[0014] Build engine power P eng With fuel release power P fuel The relationship between them is shown in formula (4).

[0015]

[0016] Where P fuel Power released for fuel, P eng,loss is the engine friction loss power, and e is the engine thermal efficiency.

[0017] 2. Based on the PMP theory, the Hamiltonian function of the reversible motor power is constructed as shown in formula (5).

[0018]

[0019] Where H(P m ) is the reversible motor power P m Hamiltonian function, δ is the engine working state, and s is the dynamic coordination factor.

[0020] When the amphibious hybrid vehicle is in navigation, it can be divided into hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode, and electric propulsion mode according to the power source participation. Based on the Hamiltonian function of formula (5), the Hamiltonian expression under different working modes can be derived as shown in formula (6).

[0021]

[0022] Where H 1，2,3,4 They represent the Hamiltonian function of the hybrid system when it works in hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode respectively. In order to find the minimum control P of the Hamiltonian function (Equation (6)), m , we need to further analyze the Hamiltonian function for P mThe first and second order derivatives of H1 and H3 are uncertain due to the positive and negative coefficients of the first order terms, which leads to the uncertainty of the calculation of the minimum value of the Hamiltonian function. Therefore, the calculation of H1 and H3 with respect to P is m The first-order derivative and second-order derivative of are shown in formula (7).

[0023]

[0024] From formula (7), we can know that when When , the first-order derivatives of H1 and H3 are both less than 0, showing a downward trend; when When , the first-order derivative of H1 is greater than 0, and the first-order derivative of H3 is less than 0, and the change trends are opposite; when When the first-order derivatives of H1 and H3 are both greater than 0, they show an upward trend and Therefore, the minimum expression of the Hamiltonian function of the reversible motor power is shown in formula (8).

[0025]

[0026] 3. Determine the dynamic switching conditions of the operating mode based on the Hamiltonian function of the reversible motor power and determine the final control variable, the reversible motor power P m And engine power P eng The best working value of:

[0027] According to Hamiltonian function (6), H2 and H4 are related to the reversible motor power P m It is irrelevant, so only the minimum value of H1 and H3 needs to be calculated. H1 and H3 are both quadratic functions of motor power, P m The minimum value can be obtained by taking the first-order derivative of the Hamiltonian function, as shown in formula (9).

[0028]

[0029] Substituting formula (9) into formula (6) and combining it with formula (8) we can obtain the dynamic switching condition as follows:

[0030] when hour, H4 takes the minimum value, otherwise H1 takes the minimum value.

[0031] when hour, H4 takes the minimum value, otherwise H2 takes the minimum value.

[0032] when hour, H4 takes the minimum value, otherwise H3 takes the minimum value.

[0033] The reversible motor efficiency η under the above switching conditions m, engine thermal efficiency e, coordination factor s, and engine friction loss power P eng,loss It is not a constant, so the switching conditions formulated by the Hatch density function will change with the change of the operating state of the hybrid system, overcoming the defect of the traditional rule-based method of artificially determining the boundary conditions.

[0034] In summary, combining equations (6)-(8) and the above dynamic switching conditions, the final control variable reversible motor power P can be obtained m And engine power P eng The best working value is:

[0035]

[0036] P eng =P ref -P m (11)

[0037] The beneficial effect of the present application is that: the present application fully considers the key factor that the change of the power source operation may induce the change of the switching condition. In order to solve the problem of poor distribution effect caused by fixed switching conditions, the Pontryagin Minimum Principle (PMP) is introduced to optimize the switching conditions in real time, thereby improving the overall operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation or the description of the prior art.

[0039] Figure 1 A block diagram of an amphibious vehicle provided for one embodiment of the present invention;

[0040] Figure 2 A flow chart of a method for allocating navigation power for a hybrid amphibious vehicle based on PMP optimization provided in one embodiment of the present invention;

[0041] Figure 3 These are the four working modes of the present invention;

[0042] Figure 4 For the present invention The changing trend of Hamiltonian function curve;

[0043] Figure 5 For the present invention The changing trend of Hamiltonian function curve;

[0044] Figure 6 For the present invention The changing trend of Hamiltonian function curve;

[0045] Figure 7This is a torque (power) distribution diagram in an embodiment of the present invention;

[0046] Figure 8 This is a working mode distribution diagram in a real-time case of the present invention.

[0047] Marking Description:

[0048] 101- drive motor; 102- controller; 103- natural gas engine; 104- transmission;

[0049] 105- reversible motor; 106- battery pack; 107- propeller. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the technical solution of the present application, the present invention is further described in detail below with reference to the accompanying drawings and the best embodiment.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0052] Reference Figure 1-5 , as one embodiment of the present invention, provides a PMP optimized method for allocating navigation power for a hybrid amphibious vehicle, comprising:

[0053] S101: Construct the power source output power relationship of the amphibious hybrid power system;

[0054] Build required power P ref With engine power P eng , reversible motor power P m The relationship is shown in formula (1).

[0055] P eng =P ref -P m (1)

[0056] Build battery power P bat With reversible motor power P m The relationship is shown in formulas (2) and (3).

[0057]

[0058] Where P ref is the required power, P eng is the engine power, P m is the reversible motor power, P bat is the battery power, η m is the working efficiency of the reversible motor, P bat,loss For battery power consumption, Where V oc is the open circuit voltage of the battery, and R is the internal resistance of the battery.

[0059] Build engine power P eng With fuel release power P fuel The relationship between them is shown in formula (4).

[0060]

[0061] Where P fuel Power released for fuel, P eng,loss is the engine friction loss power, and e is the engine thermal efficiency.

[0062] S102: Based on the PMP theory, construct the Hamiltonian function of the reversible motor power; as shown in formula (5).

[0063]

[0064] Where H(P m ) is the reversible motor power P m Hamiltonian function, δ is the engine working state, and s is the dynamic coordination factor.

[0065] When the amphibious hybrid vehicle is in navigation state, it can be divided into hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode according to the power source participation. Figure 3 shown.

[0066] According to the Hamiltonian function of formula (5), the Hamiltonian expressions under different working modes can be derived as shown in formula (6).

[0067]

[0068] Where H 1，2,3,4 They represent the Hamiltonian function of the hybrid system when it is working in hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode. In order to find the minimum control P of the Hamiltonian function (Equation (6)), m , we need to further analyze the Hamiltonian function for P m The first and second order derivatives of . Among them, H1 and H3 have uncertainty because of the positive and negative coefficients of the first order terms, which leads to uncertainty in the calculation of the minimum value of the Hamiltonian function. Therefore, H1 and H3 are calculated with respect to P m The first-order derivative and second-order derivative of are shown in formula (7).

[0069]

[0070] From formula (7), we can know that when When , the first-order derivatives of H1 and H3 are both less than 0, showing a downward trend; when When , the first-order derivative of H1 is greater than 0, and the first-order derivative of H3 is less than 0, and the change trends are opposite; when When the first-order derivatives of H1 and H3 are both greater than 0, they show an upward trend and In order to analyze more vividly where the minimum value of the Hamiltonian function is obtained, the as well as When , the general trend of the Hamiltonian function curve is as follows: Figure 4-6 shown.

[0071] Therefore, the minimum expression of the Hamiltonian function of the reversible motor power is shown in formula (8).

[0072]

[0073] S103: Determine the dynamic switching condition of the operating mode based on the Hamiltonian function of the reversible motor power, and determine the final control variable reversible motor power P m And engine power P eng The best working value of

[0074] According to Hamiltonian function (6), H2 and H4 are related to the reversible motor power P m It is irrelevant, so only the minimum value of H1 and H3 needs to be calculated. H1 and H3 are both quadratic functions of motor power, P m The minimum value can be obtained by taking the first-order derivative of the Hamiltonian function, as shown in formula (9).

[0075]

[0076] Substituting formula (9) into formula (6) and combining it with formula (8) we can obtain the dynamic switching condition as follows:

[0077] when hour, H4 takes the minimum value, otherwise H1 takes the minimum value.

[0078] when hour, H4 takes the minimum value, otherwise H2 takes the minimum value.

[0079] when hour, H4 takes the minimum value, otherwise H3 takes the minimum value.

[0080] The reversible motor efficiency η under the above switching conditions m , engine thermal efficiency e, coordination factor s, and engine friction loss power P eng,lossIt is not a constant, so the switching conditions formulated by the Hatch density function will change with the change of the operating state of the hybrid system, overcoming the defect of the traditional rule-based method of artificially determining the boundary conditions.

[0081] In summary, combining equations (6)-(8) and the above dynamic switching conditions, the final control variable reversible motor power P can be obtained m And engine power P eng The best working value is:

[0082]

[0083] P eng =P ref -P m (11)

[0084] S104: Output reversible motor power and engine power

[0085] The power distribution method of the amphibious hybrid vehicle optimized by PMP is used for simulation analysis. The fluctuation of power (torque) and working mode is shown in the following figure. Figure 7-8 shown.

[0086] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications derived therefrom remain within the scope of protection of the present invention.

Claims

1. The navigation power distribution method of the amphibious hybrid vehicle based on PMP optimization is characterized by: include: (1) Construct the power source output power relationship of the amphibious hybrid system; (2) Based on the PMP theory, construct the Hamiltonian function of the reversible motor power; (3) Determine the dynamic switching conditions of the operating mode based on the Hamiltonian function of the reversible motor power and determine the final control variable reversible motor power P m And engine power P eng The best working value of Among them, in step (2), based on the PMP theory, the Hamiltonian function of the reversible motor power is constructed, which specifically includes: The Hamiltonian function of the reversible motor power is constructed according to the PMP theory. The Hamiltonian function of the reversible motor power is as follows: Where H(P m ) is the reversible motor power P m Hamiltonian function, P ref is the required power, P m is the reversible motor power, η m is the working efficiency of the reversible motor, δ is the working state of the engine, s is the dynamic coordination factor, P eng,loss is the engine friction loss power, e is the engine thermal efficiency, Where V oc is the open circuit voltage of the battery, and R is the internal resistance of the battery; Based on the Hamiltonian function of the reversible motor power, the Hamiltonian expressions of the hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode are derived: Where H 1，2,3,4 They represent the Hamiltonian functions of the hybrid power system when it operates in hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode, and electric propulsion mode respectively; The minimum expression of the Hamiltonian function of the reversible motor power is shown as follows: In step (3), the dynamic switching condition of the operating mode is determined based on the Hamiltonian function of the reversible motor power, and the final control variable reversible motor power P is determined. m And engine power P eng The best working values ​​include: Reversible motor power P m The minimum value is obtained by taking the first-order derivative of the Hamiltonian function, as shown below: Substituting the above formula into the Hamiltonian expression calculation, and combining it with the Hamiltonian function minimum expression of the reversible motor power, the dynamic switching condition can be obtained as follows: when hour, H4 takes the minimum value, otherwise H1 takes the minimum value; when hour, H4 takes the minimum value, otherwise H2 takes the minimum value; when hour, H4 takes the minimum value, otherwise H3 takes the minimum value; Combining the Hamiltonian expressions and dynamic switching conditions of the hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode, the final control variable reversible motor power P is obtained. m And engine power P eng The best working value is: P eng =P ref -P m 。 2. The method for distributing navigation power of a hybrid amphibious vehicle based on PMP optimization according to claim 1 is characterized in that: The step (1) of constructing the output power relationship of the power source of the amphibious hybrid power system specifically includes: Build required power P ref With engine power P eng , reversible motor power P m The relationship is shown in the following formula: P eng =P ref -P m Build battery power P bat and reversible motor power P m The relationship is shown in the following formula: Where P eng is the engine power, P bat is the battery power, P bat,loss Consumes power for the battery; Build engine power P eng With fuel release power P fuel The relationship that exists is shown in the following formula: Where P fuel Free up power for fuel.

3. The method for allocating navigation power of a hybrid amphibious vehicle based on PMP optimization according to claim 1 is characterized in that: When the amphibious hybrid power amphibious vehicle is in navigational state, the operation modes are divided into hybrid propulsion mode, mechanical propulsion mode, mechanical charging propulsion mode and electric propulsion mode according to the participation of the power source of the amphibious hybrid power system.

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