Miga-based isg hybrid system key assembly parameter optimization method

Abstract

The application discloses an ISG hybrid power system key assembly parameter optimization method based on MIGA, which comprises the following steps: 1, establishing an ISG system key assembly model for an ISG hybrid vehicle; 2, selecting a single battery ampere-hour capacity and a gear shifting speed as key parameters of the ISG system key assembly model; 3, constructing a multi-objective optimization function of the ISG system key assembly model based on the selected key parameters; 4, solving the multi-objective optimization function by using a MIGA algorithm to obtain a set of feasible solutions of the ISG hybrid power system; 5, selecting an optimal solution from the set of feasible solutions to form a Pareto optimal solution set, and selecting an optimization scheme with optimal equivalent fuel consumption or an optimization scheme with optimal electric energy consumption from the Pareto optimal solution set; and 6, optimizing the ISG hybrid power system according to the optimization scheme obtained in the step 5.

Description

Technical Field

[0001] This invention belongs to the field of hybrid power system parameter matching technology, specifically involving a method for optimizing key assembly parameters of an ISG hybrid power system based on MIGA. Background Technology

[0002] To fully leverage the advantages of ISG motors and achieve hybrid power functions such as pure electric driving, on-the-go power generation, and combined drive while the vehicle is in motion, thereby improving vehicle performance, existing technologies disclose a method based on a certain type of mobile power generation vehicle. An automatic clutch is added between the engine and the ISG motor, employing this single-axle parallel structure to upgrade the existing mobile power generation vehicle into an ISG hybrid vehicle. During vehicle operation, the automatic clutch can transmit or interrupt engine power to achieve the desired operating mode. The system scheme is as follows: Figure 1 As shown, this hybrid power system mainly consists of a diesel engine, an ISG motor, a clutch, a mechanical automatic transmission, a power battery, a converter, a controller, and high and low voltage loads, among other important components. The controllers are connected to each other via a CAN bus.

[0003] Traditionally, the matching of key parameters of the hybrid power system in the aforementioned ISG hybrid vehicles is completed before the energy management strategy is studied. However, the parameter matching of each component in the powertrain system is affected by the control strategy. Therefore, how to match the key assembly parameters of the system on the basis of perfecting the energy management strategy is a problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for optimizing key assembly parameters of an ISG hybrid power system based on MIGA. The method selects key assembly parameters for the system, uses these selected parameters as optimization variables to establish a system optimization model, and optimizes and matches the key assembly parameters of the ISG hybrid power system with the goal of achieving optimal economic efficiency.

[0005] The technical solution adopted in this invention is as follows:

[0006] The MIGA-based method for optimizing key assembly parameters of ISG hybrid power systems includes the following steps:

[0007] Step 1: Establish key assembly models of the ISG system for ISG hybrid vehicles;

[0008] Step 2: Select the ampere-hour capacity of a single battery cell and the shift speed as the key parameters of the ISG system key assembly model;

[0009] Step 3: Based on the selected key parameters, construct a multi-objective optimization function for the key assembly model of the ISG system;

[0010] Step 4: Use the MIGA algorithm to solve the multi-objective optimization function to obtain the set of feasible solutions for the ISG hybrid power system;

[0011] Step 5: Select the optimal solution from the set of feasible solutions to form the Pareto optimal solution set, and select the optimal solution with the best equivalent fuel consumption or the optimal solution with the best energy consumption from the Pareto optimal solution set.

[0012] Step 6: Match key components of the ISG hybrid power system according to the optimization scheme obtained in Step 5.

[0013] Furthermore, the key assembly model of the ISG system established in step 1 includes the ISG motor model, the power battery model, and the AMT model;

[0014] The engine model was constructed using experimental modeling methods based on bench test data of the engine mounted on the prototype vehicle.

[0015] ISG motor model, including ISG motor output torque and ISG motor efficiency. 、 Characteristic equations for ISG motor power, ISG motor and controller DC output current;

[0016] Power battery models, including electrical characteristic models, thermodynamic characteristic models, and state-of-charge calculation models for lithium-ion power batteries;

[0017] The AMT model includes an optimal fuel economy shift speed model, from which the AMT shift rules are derived.

[0018] Furthermore, the ISG motor model is as follows:

[0019] 1) ISG motor output torque

[0020] ;

[0021] in, T m This provides the output torque for the ISG motor. L m For ISG motor demand load rate, L m >0 indicates electric mode. L m <0 indicates the power generation mode; T m_max This is the maximum torque output of the ISG motor in electric mode;

[0022] and:

[0023] ;

[0024] in, n m This refers to the speed of the ISG motor. f ( n m () is a one-dimensional interpolation function based on the ISG motor speed;

[0025] 2) ISG motor efficiency

[0026] ;

[0027] in, f ( n m , T m () is a two-dimensional interpolation function based on the speed and torque of the ISG motor;

[0028] 3) ISG motor power

[0029] ;

[0030] 4) DC output current of ISG motor and controller

[0031] ;

[0032] in, U m_net This refers to the DC output voltage of the electrical network.

[0033] Furthermore, the power battery model is as follows:

[0034] 1) Electrical characteristic model

[0035] ;

[0036] in, U b_terminal This refers to the terminal voltage of the power battery. k b For the power battery temperature; U b_idle The open-circuit voltage of the power battery depends on its magnitude. k b and SOC ; I b This refers to the current at the power battery terminal. R b The equivalent internal resistance of the power battery depends on its magnitude. k b and SOC ;

[0037] The maximum current in charging mode is:

[0038] ;

[0039] in, U b_max This is the maximum voltage of the power battery;

[0040] The maximum current in discharge mode is:

[0041] ;

[0042] in, U b_min This is the minimum voltage of the power battery;

[0043] 2) Thermodynamic property model

[0044] The power, heat, and temperature generated by the battery are:

[0045] ;

[0046] in, H b_total_power This refers to the total heat generated by the power battery. H b_gen_power This generates heat for the power battery; H b_dissip_power Heat is dissipated from the power battery; H b_Rohm_power This is for the heat generated by the resistance of the power battery; H b_react_power This is the heat generated by the reaction of the power battery; k ( t ), k ( t i These are the power batteries. t time, t i Temperature at any moment; C b The specific heat capacity of the power battery; m b For the quality of power batteries;

[0047] and:

[0048] ;

[0049] ;

[0050] ;

[0051] Where, Δ S This refers to the change in the entropy of the power battery reaction; η bFor power battery energy efficiency; F It is Faraday's constant; k coolant This refers to the coolant temperature. h b The specific heat transfer of the power battery;

[0052] 3) Charge state calculation model

[0053] ;

[0054] in, SOC ( t ), SOC ( t i ) are respectively the power battery at time t, t i The state of charge at any given moment; I b ( t () represents the terminal current of the power battery at time t; η b (•) represents the coulombic efficiency of the power battery; sign[•] is the sign function; Q b The ampere-hour capacity of the power battery.

[0055] Furthermore, the optimal fuel-efficient shift speed model is as follows:

[0056] ;

[0057] in, v gt_eco For optimal fuel economy, shift gears at the appropriate speed. v max_g for g Maximum speed limit; v min_g+1 for g +1 gear minimum speed;

[0058] Furthermore, the multi-objective optimization function established in step 3 is as follows:

[0059] ;

[0060] in, i , j =1,2,3,4; f ( x Let ) be the objective function; C eq Equivalent fuel consumption; C e This refers to the amount of electricity consumed. x= [ Qb_s , v i_i+1_u , v j+1_j_d [This refers to the optimization variables;] g ( Q b_s ), g ( v i_i+1_u ), h ( v i_i+1_u , v j+1_j_d The following are the constraints for the corresponding optimization variables:

[0061] ;

[0062] ;

[0063] ;

[0064] Where, Δ v DEV This is the vehicle speed offset. Q b_s This refers to the ampere-hour capacity range of a single battery cell.

[0065] Compared with the prior art, the beneficial effects of this invention are:

[0066] 1. This invention uses an optimization matching method to optimize the key assembly parameters of the ISG hybrid power system. Considering that the power source of the whole vehicle has been finalized and the motor power is limited, the key parameters of the lithium-ion power battery and AMT are selected for matching optimization.

[0067] 2. This invention selects equivalent fuel consumption and electrical energy consumption as optimization objectives, and uses key parameters of the power battery and AMT as optimization variables to establish a system optimization model. MIGA is used to perform multi-objective optimization of the ISG hybrid system performance, obtaining the Pareto optimal solution set. Optimization results show that the ampere-hour capacity of individual battery cells in the optimized scheme is reduced, which can lower the overall vehicle modification cost and the difficulty of power battery placement; compared with the prototype vehicle, fuel economy is improved, proving the effectiveness of the proposed system parameter optimization and matching method. Attached Figure Description

[0068] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 For ISG hybrid power system solutions;

[0070] Figure 2 (a)-(c) are numerical models of the ISG motor, where Figure 2 (a) is the efficiency model. Figure 2 (b) is the electric external characteristic curve. Figure 2 (c) is the external characteristic curve of power generation.

[0071] Figure 3 This is an equivalent circuit model for a lithium-ion power battery.

[0072] Figure 4 (a)-(d) represent the relationships between SOC and the open-circuit voltage of a single cell under charging conditions, between SOC and the open-circuit internal resistance of a single cell under charging conditions, between SOC and the open-circuit voltage of a single cell under discharging conditions, and between SOC and the open-circuit internal resistance of a single cell under discharging conditions, respectively.

[0073] Figure 5 This is the accelerator pedal load rate adjustment area.

[0074] Figure 6 This describes the shifting pattern of the AMT (Automated Manual Transmission).

[0075] Figure 7 (a)-(b) are the selection curves for upshift speed point and downshift speed point, respectively.

[0076] Figure 8 This is a diagram illustrating the generation principle of MIGA subpopulations / islands.

[0077] Figure 9 This is a flowchart of the MIGA workflow.

[0078] Figure 10 This represents the set of feasible solutions to the multi-objective optimization problem of the ISG hybrid power system.

[0079] Figure 11 Pareto frontier for equivalent fuel consumption and electricity consumption.

[0080] Figure 12 This represents the ampere-hour capacity of a single cell corresponding to the Pareto optimal solution.

[0081] Figure 13(a)-(b) are the shift speed curves for scheme A and scheme B, respectively.

[0082] Figure 14 The results show the comparison of the SOC change curves of the power battery.

[0083] Figure 15 This is a test bench for engine characteristics.

[0084] Figure 16 (a)-(b) are the numerical model of engine fuel consumption and the universal characteristic model of engine, respectively. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0086] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0087] 1. System parameter matching method

[0088] Based on the different matching objectives of the ISG hybrid power system, parameter matching methods are divided into three types: theoretical matching method, operating condition matching method, and optimization matching method.

[0089] The theoretical matching method is simple to implement, and the matched components can meet the system performance requirements. However, this method only refers to the dynamic performance of the system when initially selecting system parameters. Other performance indicators need to be obtained after integrating the entire system. The final matched dynamic performance may not necessarily meet other performance indicators. Therefore, the theoretical matching method is only a way to provide initial values ​​for the system at the beginning of system design, which can lay the foundation for building an accurate model later.

[0090] The working condition matching method does not consider the vehicle's energy management strategy, transmission shifting rules and other control strategies during the matching calculation process. As a result, the matched and integrated power system may not be able to fully meet the vehicle's performance requirements. The matched power system has not reached the optimal level, but only meets a certain solution in the feasible solution set under the system performance constraints. The system performance still needs to be further optimized.

[0091] The optimization matching method is a technique that uses intelligent optimization algorithms to match parameters of a hybrid power system. It first performs an initial selection of powertrain component parameters, taking into account given vehicle parameters, component characteristic curves, design objectives, and the vehicle's energy management strategy. Based on this, a vehicle performance simulation model is established, using powertrain component parameters such as engine power, motor torque, and battery ampere-hour capacity as optimization variables. Boundary conditions and constraints are determined according to the actual system conditions. The optimization algorithm is then used to optimize the powertrain component parameters, and the matching scheme is evaluated for target performance. While satisfying performance index constraints and operating condition constraints, iterative calculations yield the scheme that achieves optimal performance in terms of power, economy, and emissions.

[0092] Commonly used optimization algorithms in matching methods include gradient algorithms such as quadratic programming and convex optimization. These algorithms require calculating the derivative or gradient of the objective function and are more prone to getting trapped in local optima. In addition, there are non-gradient algorithms such as optimization matching methods based on genetic algorithms, neural networks, particle swarm optimization, and simulated annealing. Non-gradient algorithms do not require the objective function to possess mathematical characteristics such as derivatives or gradients, and therefore have wider applications. This invention, based on research on vehicle energy management strategies, employs non-gradient algorithms from the matching method to optimize key powertrain parameters of the ISG hybrid system to improve overall vehicle performance.

[0093] 2. Key Assembly Model of ISG System

[0094] ISG hybrid vehicles add an ISG motor and a battery pack to the prototype vehicle and re-match the transmission. During vehicle operation, the driving force is provided by both the engine and the ISG motor. Therefore, in addition to the engine, the performance of ISG hybrid vehicles also depends on key system assemblies such as the ISG motor, battery pack, and transmission. First, a key assembly model of the ISG system needs to be established. This model includes: an engine model, an ISG motor model, a battery pack model, and an AMT (Automated Manual Transmission) model. Each model will be described in detail below.

[0095] (1) Engine model

[0096] The methods for establishing engine numerical models are divided into theoretical modeling and experimental modeling. Theoretical modeling uses combustion and heat transfer as its theoretical basis to analyze the actual physical changes within the engine, reflecting the fuel combustion state and heat conduction process. Experimental modeling obtains engine characteristics such as load and speed through bench tests, and then interpolates and fits the experimental data to establish the engine's steady-state output characteristics. Since this invention discusses the vehicle's power and economy from the perspective of overall vehicle control, focusing on the engine model's input and output, this invention adopts experimental modeling. Specifically, it obtains the numerical model based on bench test data of the engine mounted on a prototype vehicle. The engine characteristic test bench is shown below. Figure 15 As shown.

[0097] Based on the engine load characteristics, a cubic spline interpolation fitting method is used to obtain a numerical model of engine fuel consumption, and the relationship between engine output torque and effective fuel consumption rate at different speeds is obtained, such as... Figure 16 As shown in (a); the universal characteristic curve of the engine is further obtained using the engine fuel consumption numerical model, as shown in... Figure 16 As shown in (b), the numerical model of the engine is composed of the relationship curve between output torque and effective fuel consumption rate, as well as the universal characteristic curve of the engine.

[0098] (2) ISG motor model

[0099] ISG motors can achieve functions such as rapid engine starting, electric power assist, and power generation during parking and driving. Therefore, the torque and power output characteristics in electric and power generation modes, as well as the motor efficiency characteristics, are the main performance features of ISG motors, and the characteristic equations for the motor's power, torque, voltage, and current are the basis for its modeling. Thus, the characteristic equations of the ISG motor are:

[0100] (1)

[0101] in, T m This provides the output torque for the ISG motor. L m For ISG motor demand load rate, L m >0 indicates electric mode. L m <0 indicates the power generation mode; T m_max This is the maximum torque output of the ISG motor in electric mode;

[0102] and:

[0103] (2)

[0104] in, n m This refers to the speed of the ISG motor. f ( n m () is a one-dimensional interpolation function based on the ISG motor speed;

[0105] according to T m The efficiency of the ISG motor was obtained. η m for:

[0106] (3)

[0107] in, f ( n m , T m () is a two-dimensional interpolation function based on the speed and torque of the ISG motor;

[0108] based on η m The power of the ISG motor is obtained. P m for:

[0109] (4);

[0110] based on P m The DC output current of the ISG motor and controller is obtained. I m for:

[0111] (5)

[0112] in, U m_net This refers to the DC output voltage of the electrical network.

[0113] The performance indicators of an ISG hybrid power system are independent of the electromagnetic, thermodynamic, and other physical characteristics of the ISG motor; they depend solely on the motor's dynamic characteristics, namely its input and output. Therefore, the ISG motor modeling method, like that of an engine, employs experimental modeling, with the numerical model established based on bench test data and the motor's characteristic equations.

[0114] The motor was tested using a dynamometer at different output torques under specific speed modes. The ISG motor's output speed, torque, motor and controller efficiency, and power supply voltage and current were measured at speeds of 0, 400 r / min to 2800 r / min (in 400 r / min intervals). The test data from both motoring and generator modes were interpolated using mathematical methods to obtain a numerical efficiency model for the ISG motor across its entire operating range. Figure 2 As shown in (a), the external characteristic curves of torque and power are obtained by fitting the test data of each speed and the corresponding maximum torque of the ISG motor, as shown in Figure 1. Figure 2 (b)- Figure 2 As shown in (c).

[0115] (3) Power battery model

[0116] As one of the most important subsystems in hybrid vehicles, the power battery's main function is to provide energy for the ISG motor to operate in electric mode, converting chemical energy into electrical energy. It also absorbs electrical energy from the motor during parking, driving, or regenerative braking, converting electrical energy back into chemical energy through electrochemical reactions. This inevitably leads to frequent charging and discharging of the power battery. Therefore, the primary advantage of a power battery used in hybrid vehicles must be high energy density. Furthermore, considering temperature rise and lifespan, it must have low internal resistance, long cycle life, and be safe and reliable, while also being cost-effective and lightweight. In recent years, battery technology has made significant progress, resulting in many high-performance batteries that are widely used in hybrid vehicles. Currently, typical battery technologies used in hybrid vehicles include lead-acid batteries, nickel-cadmium batteries (NiCd), nickel-metal hydride batteries (NiMH), and lithium-ion batteries (Li-ion). Their basic technical requirements are compared in Table 1.

[0117]

[0118] Lead-acid batteries, the earliest type of power battery, have matured technologically and offer advantages such as low cost and easy component recycling. However, their lower energy density and charge / discharge efficiency, shorter cycle life, higher self-discharge rate, and the dangers posed by repeated charging and discharging limit their application in hybrid vehicles. While nickel-cadmium batteries overcome some of the shortcomings of lead-acid batteries and offer superior overall performance, their "memory effect" leads to performance degradation, and the heavy metals they contain cause serious environmental pollution, thus limiting their application prospects. Nickel-metal hydride batteries improve upon the "memory effect" of nickel-cadmium batteries while effectively reducing environmental pollution; however, their high self-discharge efficiency prevents them from functioning properly at high temperatures. Lithium-ion batteries, with their superior technical requirements compared to the other three types, have seen rapid development in hybrid systems in recent years. Furthermore, with significant reductions in manufacturing costs, lithium-ion batteries are poised to become the optimal choice for hybrid vehicles. Therefore, this invention selects lithium-ion batteries as the power battery for the ISG hybrid system.

[0119] The foundation and core of establishing a power battery model lies in determining the battery's internal resistance and the characteristic functions of the open-circuit power supply with respect to temperature and state of charge (SOC). This type of power battery model is called an internal resistance model. Compared to more complex electrochemical models, it can simply and accurately simulate the chemical reactions inside the battery. Therefore, the modeling method for lithium-ion power batteries adopts the internal resistance model, and its equivalent circuit model is as follows: Figure 3 As shown.

[0120] ① Electrical characteristic model

[0121] According to such Figure 3 Based on the equivalent circuit model shown, establish the electrical equations of the power battery:

[0122] (6)

[0123] in, U b_terminal This refers to the terminal voltage of the power battery. k b For the power battery temperature; U b_idle The open-circuit voltage of the power battery depends on its magnitude. k b and SOC ; I b This refers to the current at the power battery terminal. R b The equivalent internal resistance of the power battery depends on its magnitude. k b and SOC.

[0124] The maximum current in charging mode is:

[0125] (7)

[0126] in, U b_max This is the maximum voltage of the power battery.

[0127] The maximum current in discharge mode is:

[0128] (8)

[0129] in, U b_min This is the minimum voltage of the power battery.

[0130] ②Thermodynamic property model

[0131] Based on the equivalent circuit model and the thermodynamic characteristics of the power battery, the power, heat, and temperature generated by the battery are defined as follows:

[0132] (9)

[0133] in, H b_total_power This refers to the total heat generated by the power battery. H b_gen_power This generates heat for the power battery; H b_dissip_power Heat is dissipated from the power battery; H b_Rohm_power This is for the heat generated by the resistance of the power battery; H b_react_power This is the heat generated by the reaction of the power battery; k ( t ), k ( t i These are the power batteries. t time, t i Temperature at any moment; C b The specific heat capacity of the power battery; m b For the quality of power batteries.

[0134] The heat generated by the resistance of the power battery is produced by the battery's internal resistance, that is:

[0135] (10)

[0136] The heat generated by the reaction of a power battery is produced by an internal chemical reaction, namely:

[0137] (11)

[0138] In the formula, Δ S This refers to the change in the entropy of the power battery reaction; η b For power battery energy efficiency; F F is the Faraday constant, F = 96487 C / mol.

[0139] The heat transferred from the power battery to the surrounding environment is called heat loss, i.e.:

[0140] (12)

[0141] in, k coolant This refers to the coolant temperature. h b This refers to the specific heat transfer of the power battery.

[0142] ③ Charge state calculation model

[0143] To facilitate obtaining the state of charge (SOC) of the power battery during the operation of ISG hybrid vehicles, the ampere-hour method is used to calculate the SOC value, i.e.:

[0144] (13)

[0145] in, SOC ( t ), SOC ( t i ) are respectively the power battery at time t, t i The state of charge at any given moment; I b ( t () represents the terminal current of the power battery at time t; η b (•) represents the coulombic efficiency of the power battery; sign[•] is the sign function; Q b The ampere-hour capacity of the power battery.

[0146] Based on experimental data on the hybrid pulse power characteristics of power batteries provided by the battery supplier, the battery characteristic parameters were calibrated offline, and the characteristics of individual cells at different temperatures were calculated, namely, the relationship between SOC and open-circuit voltage and internal resistance of individual cells under different temperatures and states. Figure 4 As shown. From Figure 4 As can be seen, lithium-ion batteries have open-circuit voltage and internal resistance characteristics that change slowly with temperature and SOC. This advantage makes it easier to control the power flow of hybrid vehicles.

[0147] The parameters of the lithium-ion power battery in the ISG hybrid system are shown in Table 2.

[0148]

[0149] (4) AMT model

[0150] Compared to the prototype vehicle, the ISG hybrid vehicle uses both the engine and the ISG motor to meet the vehicle's torque requirements. The ISG hybrid system needs to rationally allocate the load rates of the two power sources based on the vehicle's torque demands. If the prototype vehicle's mechanical manual transmission (MT) were used, the performance advantages of both the vehicle and the motor could not be maximized. Therefore, the ISG hybrid vehicle of this invention uses an automated manual transmission (AMT). The AMT combines electronic control technology with the manual transmission, resulting in higher transmission efficiency and reliability, easier handling, and a more compact structure. This allows the ISG hybrid vehicle to achieve excellent driving performance and optimal energy distribution throughout the system.

[0151] Unlike manual transmission (MT) shifting, automatic transmission (AMT) shifting is primarily achieved automatically based on gear control parameters. Therefore, the AMT model needs to be matched with shifting patterns based on the MT model to ensure accurate shifting timing for gear changes. Currently, commonly used AMT shifting patterns include single-parameter, dual-parameter, and multi-parameter shifting patterns. Compared to single-parameter shifting patterns, dual-parameter shifting patterns can achieve better control. Therefore, this invention selects a dual-parameter shifting pattern using accelerator pedal load rate and vehicle speed as gear control parameters, dividing the accelerator pedal load rate into three segments (high, medium, and low) for segmented gear control, such as... Figure 5 As shown. When the accelerator pedal is under low load, a single-parameter shifting pattern that controls the gear solely based on vehicle speed is used to ensure stable and comfortable driving. When the accelerator pedal is under medium load, an economical shifting pattern is used to keep the engine operating in its economical operating range. At this time, the load change rate is small, ensuring the vehicle primarily consumes the least amount of fuel and saves fuel. When the accelerator pedal is under high load, a power-oriented shifting pattern is used to meet the vehicle's acceleration, hill-climbing, and other power performance requirements. At this time, the load change rate is large, ensuring the vehicle obtains optimal power performance.

[0152] The shift pattern refers to the relationship between shift timing and gear control parameters. Developing a reasonable AMT shift pattern ensures good fuel economy while meeting power requirements. Therefore, the upshift and downshift curves that change with accelerator pedal load and vehicle speed are crucial for establishing an AMT model. Figure 16 (a) and Figure 16 (b) The functional relationship between engine output power, engine speed, effective fuel consumption rate, and accelerator pedal load rate can be obtained, namely:

[0153] (14)

[0154] in, be The engine's effective fuel consumption rate; n e Engine speed; P e This refers to the engine's output power. L a To increase the accelerator pedal load rate.

[0155] The relationship between engine speed and vehicle speed is:

[0156] (15)

[0157] in, i 0 The speed ratio of the main reducer; i w This refers to the speed ratio of the wheel-side reducer.

[0158] Based on equations (14) and (15), the relationship between effective fuel consumption rate and vehicle speed and accelerator pedal load rate is obtained, namely:

[0159] (16)

[0160] Ignoring the effects of slope resistance, the following equations are derived from the vehicle's traction balance equations: g The total drive power of the vehicle in each gear:

[0161] (17)

[0162] On the engine universal characteristic curve, with the accelerator pedal load rate remaining constant, the vehicle speed corresponding to the intersection of the fuel consumption curves of two adjacent gears is the optimal economical shift speed for the vehicle under that driving condition, meaning that the overall fuel consumption rate of the two adjacent gears is equal.

[0163] (18)

[0164] in, B e_g , B e_g+1 They are respectively g block, g +1 gear engine overall fuel consumption rate.

[0165] Substituting equations (16) and (17) into equation (18), we get:

[0166] (19)

[0167] in, b e_g , b e_g+1 They are respectively g block,g+ The effective fuel consumption rate of the engine in first gear;

[0168] Substituting equation (17) into equation (19), we get:

[0169]

[0170] Solve the nonlinear differential equation (20) to obtain the velocity solution set. v ={ v1,v2,…,vk,…},in k To determine the number of solutions, v The optimal fuel-efficient shift speed is determined by comparing the maximum and minimum vehicle speeds corresponding to each gear. v gt_eco ,Right now:

[0171] (twenty one)

[0172] In the formula, v max_g for g Maximum speed limit; v min_g+1 for g +1 gear minimum speed.

[0173] According to equation (21), the upshift and downshift speeds corresponding to the load rates of each accelerator pedal are obtained, i.e., the AMT shifting rules, such as... Figure 6 As shown, upshift and downshift curves are plotted based on the AMT shifting pattern. To ensure smooth downshifting, the AMT downshift curve exhibits a downshift delay.

[0174] 3. Selection of key assembly parameters for the ISG system

[0175] The ISG hybrid system is an upgrade from a mobile power supply system. Its key components include an engine, ISG motor, lithium-ion battery, and AMT (Automated Manual Transmission). While the engine and ISG motor are already finalized, a lithium-ion battery has been added, and the transmission has been re-matched. Given the limited power of the motor, the parameters of the battery and AMT significantly impact overall vehicle performance. Initial parameter selection aims to maximize system requirements but may lead to unnecessary waste, increasing upgrade costs, and not necessarily achieving optimal system performance. Therefore, it is necessary to re-optimize and match the key parameters of the battery and AMT to improve overall vehicle performance and reduce upgrade costs.

[0176] (1) Selection of key parameters for lithium-ion power batteries

[0177] In the matching process of ISG hybrid systems, the models of power components and transmission components determine the overall vehicle cost, with the cost of the power battery accounting for the majority. Therefore, the matching of key power battery parameters not only affects the power and economy of hybrid vehicles but also directly determines the overall vehicle cost. Power battery parameters include voltage level, power output, single-cell ampere-hour capacity, and number of cells. Among these, the ampere-hour capacity of single cells has a significant impact on the vehicle's power and economy, SOC fluctuations, and overall vehicle cost. Selecting a reasonable ampere-hour capacity for the power battery can reduce the overall vehicle cost to a certain extent while ensuring vehicle power and economy, which is of great significance for ISG hybrid vehicles. Therefore, this invention selects the ampere-hour capacity of single cells as a key parameter for lithium-ion power batteries.

[0178] Traditional methods for determining battery ampere-hour capacity involve calculations based on requirements such as pure electric driving range, maximum speed, and acceleration performance. However, the ISG hybrid vehicle of this invention aims to provide mobile power, and has no specific range requirements in pure electric mode. Furthermore, the parameters of key powertrain components such as the engine and ISG motor are already largely determined, thus defining the vehicle's performance indicators. Using traditional methods to match battery ampere-hour capacity would result in some waste of capacity, and increasing capacity would lead to increased battery size, thereby increasing the difficulty of hybrid vehicle deployment and upgrade costs. Therefore, this invention determines the range of battery ampere-hour capacity based on the principle of meeting the power requirements of the ISG motor.

[0179] As the primary energy storage component in hybrid vehicles, the power output of the battery during discharge... P b The peak operating conditions of the ISG motor should be met at least, that is:

[0180] (twenty two)

[0181] in, P b This refers to the output power of the power battery. P m_max This refers to the peak power of the ISG motor.

[0182] from Figure 2 As can be seen from (b), the peak power of the ISG motor in equation (22) is... P m_max =30kW, calculate P b get:

[0183] (twenty three)

[0184] To extend the lifespan of power batteries, their maximum discharge power and maximum discharge current need to be limited to protect the batteries, i.e., satisfying the following:

[0185] (twenty four)

[0186] in, P b_dis_max This refers to the maximum discharge power of the power battery. I b_dis_max This is the maximum discharge current of the power battery; U b This refers to the rated voltage of the power battery. k max The maximum discharge rate of a lithium-ion power battery is generally taken as... k max =10h -1 .

[0187] The range of ampere-hour capacity of the power battery can be calculated using equation (24):

[0188] (25)

[0189] Since the number of parallel battery packs is 4, the calculated range of ampere-hour capacity for a single battery is as follows:

[0190] (26)

[0191] in, Q b_s This refers to the ampere-hour capacity of a single battery cell.

[0192] In hybrid vehicle applications, both excessively large and excessively small lithium-ion battery capacities cause inconvenience in production operations and loading / unloading. Therefore, considering the power battery manufacturing process and economic efficiency, the upper limit of the ampere-hour capacity of a single lithium battery cell should not exceed 40 Ah, resulting in the following range of ampere-hour capacities for single batteries:

[0193] (27)

[0194] (2) Key parameters of AMT

[0195] Since AMT (Automated Manual Transmission) is based on MT (Manual Manual Transmission) and matches shift patterns, it achieves gear changes according to the gear control parameters set in the shift patterns. Shift patterns are a crucial factor affecting the power and fuel economy of hybrid vehicles; therefore, selecting appropriate gear control parameters is essential for obtaining a reasonable shift pattern. In the dual-parameter shift pattern selected in this invention, shift speed has a significant impact on overall vehicle performance. Therefore, this invention selects shift speed as a key control variable for AMT.

[0196] like Figure 7As shown, this invention takes selecting 6 shift speed points on each gear line as an example, and uses these 6 shift speed points as AMT control parameters, that is:

[0197] (28)

[0198] in, v i_i+1_u for i gear shift i +1 gear speed; v j+1_j_d for j +1 gear down j Stop the car's speed.

[0199] 4. Optimization methods for key assembly parameters of ISG system

[0200] (1) Global Exploration Optimization Algorithm

[0201] For ISG hybrid power systems, the objective function, design variables, and constraints of the optimization problem may be nonlinear, discontinuous, or nondifferentiable. Therefore, it is impossible to use information such as derivatives and gradients to find the optimal solution, and traditional numerical optimization and direct search methods cannot avoid getting trapped in local optima. Therefore, this invention uses a Multi-Island Genetic Algorithm (MIGA) to optimize the key powertrain parameters of the ISG hybrid power system.

[0202] The principle of MIGA's subpopulation / island generation is as follows: Figure 8 As shown. Unlike the single-population evolution of traditional GA, MIGA divides a large population into several subpopulations, figuratively called "islands." On each island, traditional GA is used for subpopulation evolution. Every certain number of generations, individuals from each island are selected according to a certain proportion to complete the migration process between islands, increasing the diversity of individuals within each island. Therefore, MIGA has better global solution capabilities and computational efficiency than traditional GA, and is more adaptable, requiring no calculation of the gradient value of the function, only evaluating the design point.

[0203] like Figure 9 As shown, the MIGA algorithm includes the following steps:

[0204] ① Initialize the population composed of individuals, and decompose a large population into several "islands";

[0205] ② At regular migration intervals, individuals from each island are selected according to a certain inter-island migration rate and transferred to other islands to complete the exchange of individuals between populations;

[0206] ③ Calculate the fitness value of each individual in the population, and use the fitness ratio method to select individuals with higher fitness as the first generation population;

[0207] ④ Perform crossover on individuals in the first generation population, using two individuals as parents to crossover their gene chains, thereby producing two new individuals as their offspring, with a crossover probability of Pc.

[0208] ⑤ Perform mutation operations on the new individuals to obtain new individuals that are different from other individuals by generating mutations at certain gene positions on the chromosome, with a mutation probability of Pm;

[0209] ⑥ When the termination criterion is met, the iteration ends, and the individual with the highest fitness value in the population is output as the optimal solution to the optimization problem. Otherwise, repeat steps ②-⑤ until the termination condition is met.

[0210] This invention selects key parameters (single battery ampere-hour capacity, shift speed) as individuals, and uses the MIGA algorithm to execute steps 1-6 to solve the multi-objective optimization model established below, thereby obtaining a set of feasible solutions.

[0211] (2) Multi-objective optimization model

[0212] In order to improve the fuel economy of the vehicle, and considering the impact of the ampere-hour capacity of the power battery on the SOC fluctuation, this invention selects the equivalent fuel consumption and the electrical energy consumption as optimization targets, and uses the ampere-hour capacity of the power battery cell and the 48 shift speeds as optimization variables to establish an optimization model as shown in equation (29), so as to ensure that the equivalent fuel consumption is minimized while further reducing the redundant conversion of electrical energy, thereby making the SOC change stable.

[0213] (29)

[0214] in, i , j =1,2,3,4; f ( x Let ) be the objective function; C eq Equivalent fuel consumption; C e This refers to the amount of electricity consumed. x= [ Q b_s , v i_i+1_u , v j+1_j_d [This refers to the optimization variables;] g ( Q b_s ), g ( v i_i+1_u ), h ( v i_i+1_u ,v j+1_j_d ) are the constraints of the corresponding optimization variables.

[0215] In the optimization model shown in equation (29), the constraints are... g ( Q b_s The equation is (27), and the constraint conditions are... g ( v i_i+1_u The upshift speed between gears must not exceed the maximum upshift speed between gears, that is:

[0216] (30)

[0217] To ensure smooth downshifting and avoid cyclic shifting, there is a speed deviation between downshifting and upshifting speeds, i.e., a constraint. h ( v i_i+1_u , v j+1_j_d )for:

[0218] (31)

[0219] Where, Δ v DEV This represents the vehicle speed offset.

[0220] The MIGA algorithm is used to solve equation (29), and the set of feasible solutions to the multi-objective optimization problem of the ISG hybrid power system considering economy under simulated off-road cyclic conditions is obtained, as shown in Figure 10.

[0221] The optimal solution is selected from the set of feasible solutions to form the Pareto optimal solution set, and the Pareto fronts of the equivalent fuel consumption Ceq and the electricity consumption Ce are obtained, such as... Figure 11 As shown. From Figure 11 The trends in the values ​​of the two objective functions, equivalent fuel consumption and electrical energy consumption, show that the two objective functions are contradictory, indicating that it is difficult for both objective functions to be optimal simultaneously. Furthermore, the ampere-hour capacity of each individual battery cell corresponds to a different optimal solution. Figure 12 As shown. Therefore, it is necessary to select the required optimization scheme from the perspective of the importance of optimization and the overall vehicle matching cost.

[0222] By weighing the relationship between the two objective variables and the ampere-hour capacity of a single battery cell, two sets of optimization schemes are selected from the Pareto optimal solution set (e.g., ...). Figure 11 and Figure 12As shown in Table 3, this invention discusses two different optimization schemes, A and B, and the optimization results are shown in Table 3. As can be seen from the results in Table 3, different optimization objectives lead to different optimization strategies, thus meeting different engineering needs. Scheme A focuses on optimizing equivalent fuel consumption, while Scheme B focuses on optimizing electrical energy consumption. The ampere-hour capacity of individual batteries in both schemes is reduced, which can lower the overall vehicle modification cost and the difficulty of power battery placement. Compared with the prototype vehicle, the fuel economy of both schemes is improved, with the highest fuel saving rate being 14.26% (Scheme A). The fuel saving effect is further improved compared to the initial scheme, indicating that the proposed system parameter optimization matching approach is effective. The final shift speed curves for the two optimization schemes are shown in Table 3. Figure 13 As shown.

[0223]

[0224] Example

[0225] To further verify the accuracy of the system parameter optimization matching approach proposed in this paper, the shifting rules and individual battery ampere-hour capacities of schemes A and B were applied respectively to conduct vehicle economy simulation verification under the C-WTVC cycle conditions specified by the national standard. Table 4 shows the comparison of vehicle economy. Figure 14 To compare the SOC change curves of the power battery, the volatility comparison of the SOC change curves is calculated as shown in Table 5.

[0226]

[0227] The results show that scheme A improves the overall vehicle fuel economy by 1.21% compared to the operating condition identification, but there is redundant energy conversion; scheme B reduces redundant energy conversion by 7.28%, further reduces the SOC volatility and makes the change more stable, further verifying the accuracy and effectiveness of the system parameter optimization matching method based on the MIGA algorithm.

[0228] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Any simple modifications, alterations, and equivalent changes made to the above embodiments based on the inventive essence shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing key assembly parameters of an ISG hybrid power system based on MIGA, comprising the following steps: Step 1: Establish key assembly models of the ISG system for ISG hybrid vehicles; Step 2: Select the ampere-hour capacity of a single battery cell and the shift speed as the key parameters of the ISG system key assembly model; Step 3: Based on the selected key parameters, construct a multi-objective optimization function for the key assembly model of the ISG system; Step 4: Use the MIGA algorithm to solve the multi-objective optimization function to obtain the set of feasible solutions for the ISG hybrid power system; Step 5: Select the optimal solution from the set of feasible solutions to form the Pareto optimal solution set, and select the optimal solution with the best equivalent fuel consumption or the optimal solution with the best energy consumption from the Pareto optimal solution set. Step 6: Match key components of the ISG hybrid power system according to the optimization scheme obtained in Step 5; Among them, the key assembly model of the ISG system established in step 1 includes the engine model, ISG motor model, power battery model and AMT model; The engine model was constructed using experimental modeling methods based on bench test data of the engine mounted on the prototype vehicle. ISG motor model, including ISG motor output torque and ISG motor efficiency. 、 Characteristic equations for ISG motor power, ISG motor and controller DC output current; Power battery models, including electrical characteristic models, thermodynamic characteristic models, and state-of-charge calculation models for lithium-ion power batteries; The AMT model includes the optimal fuel economy shift speed model, and the AMT shift rules are obtained based on the optimal fuel economy shift speed model. The optimal fuel-efficient shift speed model is as follows: ； in, v gt_eco For optimal fuel economy, shift gears at the appropriate speed. v max_g for g Maximum speed limit; v min_g+1 for g +1 gear minimum speed; The multi-objective optimization function established in step 3 is: ； in, i , j =1,2,3,4; f ( x Let ) be the objective function; C eq Equivalent fuel consumption; C e This refers to the amount of electricity consumed. x= [ Q b_s , v i_i+1_u , v j+1_j_d [This refers to the optimization variables;] g ( Q b_s ), g ( v i_i+1_u ), h ( v i_i+1_u , v j+1_j_d The following are the constraints for the corresponding optimization variables: ； ； ； Where, Δ v DEV This is the vehicle speed offset. Q b_s This refers to the ampere-hour capacity range of a single battery cell.

2. The method for optimizing key assembly parameters of an ISG hybrid power system based on MIGA according to claim 1, characterized in that, The ISG motor model is as follows: 1) ISG motor output torque ； in, T m This provides the output torque for the ISG motor. L m For ISG motor demand load rate, L m >0 indicates electric mode. L m <0 indicates the power generation mode; T m_max This is the maximum torque output of the ISG motor in electric mode; and: ； in, n m This refers to the speed of the ISG motor. f ( n m () is a one-dimensional interpolation function based on the ISG motor speed; 2) ISG motor efficiency ； in, f ( n m , T m () is a two-dimensional interpolation function based on the speed and torque of the ISG motor; 3) ISG motor power ； 4) DC output current of ISG motor and controller ； in, U m_net This refers to the DC output voltage of the electrical network.

3. The method for optimizing key assembly parameters of an ISG hybrid power system based on MIGA according to claim 1, characterized in that, The power battery model is as follows: 1) Electrical characteristic model ； in, U b_terminal This refers to the terminal voltage of the power battery. k b For the power battery temperature; U b_idle The open-circuit voltage of the power battery depends on its magnitude. k b and SOC ; I b This refers to the current at the power battery terminal. R b The equivalent internal resistance of the power battery depends on its magnitude. k b and SOC ; The maximum current in charging mode is: ； in, U b_max This is the maximum voltage of the power battery; The maximum current in discharge mode is: ； in, U b_min This is the minimum voltage of the power battery; 2) Thermodynamic property model The power, heat, and temperature generated by the battery are: ； in, H b_total_power This refers to the total heat generated by the power battery. H b_gen_power This generates heat for the power battery; H b_dissip_power Heat is dissipated from the power battery; H b_Rohm_power This is for the heat generated by the resistance of the power battery; H b_react_power This is the heat generated by the reaction of the power battery; k ( t ), k ( t i These are the power batteries. t time, t i Temperature at any moment; C b The specific heat capacity of the power battery; m b For the quality of power batteries; and: ； ； ； Where, Δ S This refers to the change in the entropy of the power battery reaction; η b For power battery energy efficiency; F It is Faraday's constant; k coolant This refers to the coolant temperature. h b The specific heat transfer of the power battery; 3) Charge state calculation model ； in, SOC ( t ), SOC ( t i ) are respectively the power battery at time t, t i The state of charge at any given moment; I b ( t () represents the terminal current of the power battery at time t; η b (•) represents the coulombic efficiency of the power battery; sign[•] is the sign function; Q b The ampere-hour capacity of the power battery.

Images