Tandem type hybrid power vehicle comprehensive energy management method based on particle swarm optimization

Abstract

The invention discloses a particle swarm optimization-based comprehensive energy management method for a tandem hybrid power vehicle. The method comprises the following steps of: calculating traction power and heat dissipation power of a cooling system as a vehicle demand power model; fuel consumption, electric energy consumption and battery loss are adopted as optimization objective functions of an optimization algorithm; according to the characteristics of the tandem hybrid vehicle, the particle swarm optimization is adopted to optimize and adjust the parameters. According to the method, the effectiveness of the optimization algorithm can be enhanced, the vehicle operation energy consumption economy is improved, the optimization result can be quickly obtained, and the development time is saved.

Description

Technical Field

[0001] The present invention relates to the field of hybrid power energy management, and in particular to a comprehensive energy management method for a series hybrid power vehicle based on a particle swarm algorithm. Background Art

[0002] Hybrid vehicles have the characteristics of two power sources. Through coordinated control, they can give full play to the respective advantages of internal combustion engines and batteries. Among them, series hybrid vehicles have the advantages of simple structure, strong low-speed power, and can effectively avoid mileage anxiety. They are currently the most competitive models.

[0003] In the field of hybrid vehicle energy management, the energy management strategy based on particle swarm algorithm has the advantages of simple structure, fast convergence and reaching global optimum. However, the traditional particle swarm algorithm mainly uses traction power as the target power and ignores the battery loss.

[0004] As people have higher requirements for vehicle power and comfort, the power demand of vehicles has also increased. Under high-power conditions, the heat dissipation power demand of vehicles has become greater, and the traditional method of calculating the required power using traction power makes it differ greatly from the actual required power, and thus obtains a control strategy that does not meet the actual needs. Battery aging will affect the performance and economy of hybrid vehicles. High-power charging and discharging, high temperature, etc. will cause greater battery loss. Ignoring the optimization results obtained by battery loss may lead to premature aging of the battery and increase operating costs. Summary of the invention

[0005] The present disclosure provides a comprehensive energy management method for series hybrid vehicles based on a particle swarm algorithm. On the basis of the traditional particle swarm algorithm, the heat dissipation power of the cooling system and the battery loss cost are further considered. By calculating the heat dissipation power of the cooling system and establishing a battery loss model, the effect of the energy management strategy is optimized and the energy consumption economy is improved.

[0006] The present disclosure provides a method for comprehensive energy management of a series hybrid vehicle based on a particle swarm algorithm, comprising the following steps:

[0007] S101: Establishing a calculation model for the comprehensive required power P of a hybrid vehicle;

[0008] S102: Calculate the total cost of fuel consumption, power consumption and battery loss as the optimization objective function of the particle swarm algorithm;

[0009] S103: establishing a hybrid vehicle operation mode and power output control strategy model based on the comprehensive required power P and the battery SOC;

[0010] S104, using a particle swarm algorithm to optimize the working mode or power output switching threshold and the specific output power value included in the control strategy;

[0011] S105, substituting the particle swarm optimization result into the control strategy.

[0012] Furthermore, in step S101, the hybrid vehicle demand power model is:

[0013]

[0014] Among them, m is the mass of the vehicle; g is the acceleration of gravity; f is the rolling resistance; is the slope angle; C d is the drag coefficient; A is the frontal area; a is the acceleration; δ is the rotation mass conversion coefficient; v is the vehicle speed; P f is the fan power; P m is the water pump power; P c is the refrigeration unit power.

[0015] Furthermore, in step S102:

[0016] The total cost of the loss is:

[0017] J=J1+J2+J bat

[0018] Wherein, J is the optimization objective function;

[0019] The fuel consumption cost is:

[0020] J1=c1*m f

[0021] Among them, J1 is the fuel consumption cost, c1 is the fuel price; m f is the fuel consumption rate;

[0022] The cost of electricity consumption is:

[0023]

[0024] Among them, J2 is the electricity consumption cost, c2 is the electricity price; P b Output power to the battery;

[0025] The battery loss cost is:

[0026]

[0027] E a =-31700+370.3c

[0028]

[0029] Among them, J bat is the battery loss cost; N s is the number of series-connected batteries; N p is the number of parallel-connected batteries; M is the price of a single battery; I bat is the battery current; A h is the ampere-hour throughput; B is the pre-factor; E a is the battery activation energy; R is the gas constant; T bat is the temperature of the battery; z is the exponential factor; c is the C-rate of the battery under working conditions; I bat is the current of the battery pack, Q bat is the capacity of the battery pack.

[0030] Furthermore, the method for obtaining the fuel consumption rate m f in step S102 includes:

[0031] According to the map diagrams of the engine and the generator, the map diagram of the auxiliary power unit is obtained by combination, and the optimal fuel consumption points of the auxiliary power unit at each power point are calculated, and the optimal fuel curve is fitted. Then, according to the generated power allocated by the control strategy, the fuel consumption rate is obtained.

[0032] Furthermore, the policy model in step S103 specifically includes:

[0033] (1) When SOC > 0.8, the pure electric drive mode of the energy storage unit is adopted:

[0034] If P > S2 max , then the output power: S1 = P – S2 max , S2 = S2 max ;

[0035] Otherwise, S1 = 0; S2 = P;

[0036] Among them, S1 is the output power of the auxiliary power unit, i.e., the engine-generator set, and S2 is the output power of the energy storage module;

[0037] (2) When 0.3 ≤ SOC ≤ 0.8, the hybrid drive mode of the auxiliary power unit and the energy storage unit is adopted;

[0038] If 0 < P < the first threshold P1, then the output power is: S1 = 0, S2 = P;

[0039] If P1 < P < the second threshold P2, then S1 = P, S2 = 0;

[0040] If P > P2, then S1 = the specific output power value P3, S2 = P - P3;

[0041] If P≤0, then S1=0, S2=P.

[0042] Furthermore, the step S104 specifically includes the following steps:

[0043] The particle swarm is initialized with the first threshold value P1, the second threshold value P2, and the specific output power value P3 as optimization parameters, including: setting the particle swarm size M=60, the particle boundary conditions P1: [0 50], P2: [40 90], P3: [80 130], the neighborhood range λ=20, the particle position is randomly distributed, the example speed=0, and the maximum number of iterations N=100;

[0044] The fitness function is set as the optimization objective function, the initial position of the particle is recorded as the historical best position, and the fitness function is calculated for each particle;

[0045] Based on the initial conditions and the first iteration, starting from the second iteration, at each iteration step, the position and velocity of each particle are calculated and established as the optimal position of the single particle and the optimal position of the group;

[0046] When the number of iterations reaches the set maximum number of iterations, the optimization process ends.

[0047] Compared with the prior art, the beneficial effects of the present invention are: (1) by introducing the cooling system heat dissipation power and battery loss model, the effectiveness of formulating energy management strategies is improved; (2) based on the particle swarm algorithm to optimize the mode switching threshold, the optimization results can be obtained quickly, saving development time; (3) the energy economy of vehicle operation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0049] Figure 1 The flowchart of the integrated energy management method of a series hybrid vehicle based on a particle swarm algorithm is shown in Figure 1. Figure 2 ;

[0050] Figure 2 is an exemplary control strategy flow chart;

[0051] Figure 3 Optimize the flow chart for the particle swarm algorithm. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] The present disclosure provides a comprehensive energy management method for a series hybrid vehicle based on a particle swarm algorithm. In order to solve the problem of large difference between the actual traction power and the short battery life, a cooling system heat dissipation power and battery loss model are introduced, and the mode switching threshold is optimized based on the particle swarm algorithm to improve the energy consumption economy of the vehicle operation.

[0054] In an exemplary embodiment, as shown in the attached Figure 1 As shown, the series hybrid vehicle with comprehensive energy management based on particle swarm algorithm includes:

[0055] S1: Auxiliary power unit, including an engine and a generator. The engine crankshaft is connected to the generator rotor, and the engine can drive the generator rotor to rotate and generate electricity.

[0056] S2: Energy storage module, which can provide electrical energy for the load and store the energy output by the auxiliary power unit. When the power generated by the auxiliary power unit is greater than the total required power, the excess power is used to charge the energy storage module; when the power generated by the auxiliary power unit is less than the total required power, the lower power is output by the energy storage module.

[0057] S4: Cooling system, including cooling fan, radiator, cooling water pump and refrigeration unit. Used to dissipate heat for energy storage module, auxiliary power unit and drive module.

[0058] S5: The drive module drives the vehicle with the drive motor. According to the vehicle speed information, combined with the current working point and efficiency of the drive module, the required traction power of the drive module is calculated.

[0059] S3: The control module allocates power to the auxiliary power unit and the energy storage module according to the comprehensive power requirements of the energy storage module SOC, the drive module and the cooling system and the control strategy based on the particle swarm algorithm to meet the power requirements of the cooling system and the drive module.

[0060] The hybrid vehicle integrated energy management method applied to the above structure comprises the following steps:

[0061] Step 1: Build a hybrid vehicle power demand model

[0062] Calculate the comprehensive power demand according to the hybrid vehicle power demand model:

[0063]

[0064] Among them, m is the mass of the vehicle; g is the acceleration of gravity; f is the rolling resistance; is the slope angle; C d is the drag coefficient; A is the frontal area; a is the acceleration; δ is the rotation mass conversion coefficient; v is the vehicle speed; P f is the fan power; P m is the water pump power; P c is the refrigeration unit power.

[0065] Step 2: Determine the optimization objective function of the particle swarm algorithm

[0066] The optimization objective function is established by calculating the fuel consumption cost, electricity consumption cost and battery loss cost.

[0067] The formula for calculating the battery loss cost is:

[0068]

[0069] E a =-31700+370.3c

[0070]

[0071] Among them, A h is the ampere-hour throughput; B is the pre-factor; E a is the battery activation energy; R is the gas constant; T bat is the temperature of the battery; z is the exponential factor; c is the C rate of the battery under working conditions; I bat is the current of the battery pack, Q bat is the capacity of the battery pack; J bat is the battery loss cost; N s N is the number of batteries connected in series; p is the number of batteries in parallel; M is the price of a single battery; I bat is the battery current.

[0072] J1=c1*m f

[0073]

[0074] J=J1+J2+J bat

[0075] Among them, c1 is the fuel price; m f is the fuel consumption rate; c2 is the electricity price; P bis the battery output power; J1 is the fuel consumption cost; J2 is the electricity consumption cost; and J is the optimization objective function. The fuel consumption rate is obtained by combining the map of the engine and the generator to obtain the map of the auxiliary power unit, and then calculating the optimal fuel consumption point of the auxiliary power unit at each power point, fitting the optimal fuel curve, and then obtaining the fuel consumption rate according to the power generation power allocated by the control strategy.

[0076] Step 3: Establish a hybrid vehicle operation mode switching and power output control strategy model based on the comprehensive required power P and battery SOC; as shown in the attached Figure 2 As shown, it mainly includes:

[0077]

[0078] Among them, S1 max is the maximum power that the auxiliary power unit can output, S2 max It is the maximum power that the energy storage unit can output.

[0079] Step 4: Based on the vehicle's working mode and power output switching thresholds P1 and P2, as well as the specific output power value P3, the particle swarm algorithm is used for optimization and adjustment, as shown in the attached figure. Figure 3 As shown:

[0080] (1) Particle swarm initialization. Five initial conditions are set: the particle swarm size M = 60, the particle boundary conditions P1 =

[050] , P2 = [40 90], P3 = [80 130], the neighborhood range λ = 20, the particle positions are randomly distributed, the example speed = 0, and the maximum number of iterations N = 100.

[0081] (2) The fitness function is set as the optimization objective function, the initial position of the particle is recorded as the historical best position, and the fitness function is calculated for each particle.

[0082] (3) Based on the initial conditions and the first iteration, starting from the second iteration, at each iteration step, the position and velocity of each particle are calculated and established as the optimal position of the single particle and the optimal position of the group.

[0083] (4) When the number of iterations reaches the set maximum number of iterations, the optimization process ends.

[0084] Step 5: Substitute the particle swarm optimization results into the control strategy.

[0085] The above technical scheme is only an exemplary embodiment of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the application methods and principles disclosed in the present invention, and it is not limited to the method described in the above specific embodiment of the present invention. Therefore, the method described above is only preferred and does not have a restrictive meaning.

Claims

1. An integrated energy management method for a series hybrid vehicle based on a particle swarm algorithm, comprising the following steps: S101: Establish a calculation model for the integrated demand power P of the hybrid vehicle; S102: Calculate the total cost of fuel consumption, electricity consumption, and battery loss as the optimization objective function of the particle swarm algorithm; S103: Establish a working mode and power output control strategy model for the hybrid vehicle based on the integrated demand power P and the battery SOC; S104: Use the particle swarm algorithm to optimize the working mode or power output switching threshold and specific output power value included in the control strategy; S105: Substitute the particle swarm optimization result into the control strategy.

2. The method according to claim 1, characterized in that In the step S101, the demand power model of the hybrid vehicle is: Among them, m is the mass of the vehicle; g is the acceleration of gravity; f is the rolling resistance; is the slope angle; C d is the drag coefficient; A is the frontal area; a is the acceleration; δ is the rotation mass conversion coefficient; v is the vehicle speed; P f is the fan power; P m is the water pump power; P c is the refrigeration unit power.

3. The method according to claim 1 or 2, characterized in that: In the step S102: The total cost of loss is: J=J1+J2+J bat Where J is the optimization objective function; Where the fuel consumption cost is: J1=c1*m f Among them, J1 is the fuel consumption cost, c1 is the fuel price; m f is the fuel consumption rate; The electricity consumption cost is: Among them, J2 is the electricity consumption cost, c2 is the electricity price; P b Output power to the battery; The battery loss cost is: It is a =-31700+370.3c Among them, J bat is the battery loss cost; N s N is the number of batteries connected in series; p is the number of batteries in parallel; M is the price of a single battery; I bat is the battery current; A h is the ampere-hour throughput; B is the pre-factor; E a is the battery activation energy; R is the gas constant; T bat is the temperature of the battery; z is the exponential factor; c is the C rate of the battery under working conditions; I bat is the current of the battery pack, Q bat is the capacity of the battery pack.

4. The method according to claim 3, characterized in that The fuel consumption rate m in step S102 f The methods for obtaining include: According to the map of the engine and the generator, the map of the auxiliary power unit is obtained by combination, and the best fuel consumption point of the auxiliary power unit at each power point is calculated, and the best fuel curve is fitted. Then, according to the generated power allocated by the control strategy, the fuel consumption rate is obtained.

5. The method according to claim 1, characterized in that The strategy model in the step S103 specifically includes: (1) When SOC > 0.8, the pure electric drive mode of the energy storage unit is adopted: If P>S2 max , then the output power: S1 = P – S2 max , S2=S2 max ; Otherwise, S1 = 0; S2 = P; Where S1 is the output power of the auxiliary power unit, i.e., the engine - generator set, and S2 is the output power of the energy storage module; (2) When 0.3 ≤ SOC ≤ 0.8, the hybrid drive mode of the auxiliary power unit and the energy storage unit is adopted; If 0 < P < the first threshold P1, the output power is: S1 = 0, S2 = P; If P1 < P < the second threshold P2, then S1 = P, S2 = 0; If P > P2, then S1 = the specific output power value P3, S2 = P - P3; If P ≤ 0, then S1 = 0, S2 = P.

6. The method according to claim 5, characterized in that The step S104 specifically includes the following steps: Taking the first threshold P1, the second threshold P2, and the specific output power value P3 as optimization parameters, perform particle swarm initialization, including: setting the particle swarm size M = 60, the boundary conditions of the particles P1: [0 50], P2: [40 90], P3: [80 130], the neighborhood range λ = 20, the particle positions are randomly distributed, the particle velocity = 0, and setting the maximum number of iterations N = 100; Set the fitness function as the optimization objective function, record the initial position of the particle as the historical best position, and calculate the fitness function for each particle; Based on the initial conditions and the first iteration, starting from the second iteration, in each iteration step, calculate the position and velocity of each particle and determine them as the single - particle optimal position and the group - optimal position; When the number of iterations reaches the set maximum number of iterations, the optimization process ends.

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