Fuzzy adaptive switching control method for three-point operation mode of range extender

Abstract

The invention is applicable to the technical field of hybrid electric vehicle energy management, and provides a range extender three-point operation mode fuzzy adaptive switching control method, which comprises the following steps of: optimizing an optimal working curve of an APU system offline through a dynamic programming algorithm and a multi-target backbone particle swarm algorithm; the oil-electricity conversion loss rate, the comprehensive emission index and the battery capacity fading rate are used as multi-objective optimization parameters; defining a low power working point, a middle power working point and a high power working point, and combining dual switching logic based on vehicle speed and power; and the fuzzy controller is used for adjusting and switching threshold parameters in real time to dynamically adapt to complex working condition changes. According to the method, the problems that a traditional APU system is unreasonable in generated power point selection, inaccurate in dynamic efficiency characterization and insufficient in control strategy global optimization capability are solved, the fuel economy and the emission performance of the whole vehicle are remarkably improved, the service life of a battery is remarkably prolonged, and the comprehensive performance index of the whole vehicle is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of hybrid electric vehicle energy management, and in particular relates to a fuzzy adaptive switching control method for a three-point operating mode of a range extender. Background Art

[0002] With the global energy crisis and increasingly stringent environmental regulations, hybrid electric vehicles (HEV) and extended-range electric vehicles (R-EEV) have become the core technology path for the transition from traditional fuel vehicles to pure electric vehicles due to their low emissions and high energy efficiency. However, the energy management strategy (EMS) of R-EEV is significantly more complex than that of traditional models. The core challenge is how to coordinate the energy distribution of the engine-driven auxiliary power unit (APU), battery system and drive motor to optimize fuel economy, emission performance and battery life at the same time.

[0003] The existing APU power selection point only considers the oil-to-electricity conversion efficiency, which causes the engine speed to change asynchronously with the vehicle speed, causing the noise and power sense to be mismatched when running at high speed at low speed, and even reduces the NVH performance of the whole vehicle due to resonance. In addition, the energy management strategy based on steady-state efficiency cannot truly reflect the efficiency characteristics of the engine and motor under dynamic conditions, resulting in deviations in the evaluation of energy conversion efficiency and affecting the overall performance of the system. At the same time, fixed switching threshold parameters (such as vehicle speed or power threshold) are difficult to adapt to the frequently changing road conditions, driving habits and ambient temperature in actual driving, resulting in unreasonable energy distribution and reduced cruising range and energy utilization.

[0004] Traditional control logic relies on local state judgment and fails to balance energy consumption, emissions and battery life from a global perspective. For example, although the fixed-point control strategy (CPCS) is simple and easy to implement, the battery is frequently charged and discharged, which accelerates aging. Although the power tracking strategy (PFCS) can optimize fuel consumption, it has high requirements for the dynamic response of the APU and does not consider the impact of battery degradation. Summary of the invention

[0005] The object of the present invention is to provide a fuzzy adaptive switching control method for a three-point operating mode of a range extender, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is implemented as follows: a fuzzy adaptive switching control method for a three-point operation mode of a range extender, characterized in that the method comprises: Determine the corresponding working mode according to the expected power battery SoC trajectory shape, and judge whether APU assistance is required for the corresponding working mode.

[0007] When the APU is assisting, the optimal curve is calculated offline by combining the system oil-electricity conversion loss rate, comprehensive emission index, and battery capacity attenuation rate data.

[0008] Through these parameters, according to Bellman's optimization theory, the APU multi-objective optimal curve is obtained based on the DP algorithm.

[0009] After the action point is determined through the multi-point working strategy, the vehicle speed and power information are collected to determine in real time whether the working point needs to be adjusted. If the working conditions change, the optimal curve needs to be recalculated and the action point needs to be re-determined, which is called the three-point working mode of the APU system.

[0010] The beneficial effects of the present invention are: 1. The APU energy consumption-emission comprehensive evaluation function of this method uses the oil-electric conversion loss rate and the comprehensive exhaust emission index as indicators, and solves the multi-objective optimization problem based on the DP algorithm and the BB-MOPSO algorithm to obtain the optimal curve of the APU system, and considers the problem of battery capacity attenuation rate. Compared with the traditional method, this evaluation method has higher performance in APU work evaluation and can better ensure the safe and reasonable operation of the APU.

[0011] 2. A new energy management control strategy is proposed, which combines CD-EV, CD-Blend and CS-Blend strategies to better play the configuration advantages of extended-range vehicles.

[0012] 3. A power allocation method for APU between multiple working points is proposed, including optimizing the number of working points of APU control strategy, the power coverage range at the working point, and the switching logic between working points, which reduces the requirements for the dynamic response of APU.

[0013] 4. The APU multi-point strategies with two different threshold parameter types were analyzed, and a threshold parameter optimized based on the BB-MMOPSO algorithm was proposed, which improved the vehicle's comprehensive performance index by 23.5%. The study compared the performance results of several designed APU working modes horizontally, laying a research foundation for the design and development of subsequent intelligent energy management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a technical solution flow chart of the fuzzy adaptive switching control method of the three-point operation mode of the range extender; Figure 2 It is a schematic diagram of the classification of energy management strategies of R-EEV models in the SoC curve form; Figure 3 It is the universal characteristic diagram of the system engine and ISG motor; Figure 4 It is the engine emission characteristic diagram; Figure 5 Flowchart for offline acquisition of multi-objective optimal curves for APU based on DP algorithm; Figure 6This is the result diagram of multi-objective optimization of the optimal curve of the APU system based on the DP algorithm; Figure 7 The three-point control strategy diagram of the APU system based on vehicle speed switching and power switching; Figure 8 The working point distribution diagram of the three-point control strategy of the APU system based on vehicle speed switching and power switching; Fig. 9 It is a strategy diagram for parameter adaptive adjustment; Fig.10 Membership function and output surface diagram of the fuzzy controller. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] like Figure 1 As shown, a fuzzy adaptive switching control method for a three-point operation mode of a range extender is characterized in that the method includes: The energy consumption and emission data of the APU system are measured through engine performance tests, the universal characteristic diagram of the engine and ISG motor is constructed, and the oil-electric conversion loss rate, comprehensive emission index and battery capacity attenuation rate are calculated to obtain the optimal curve of the APU system offline; According to the expected power battery SoC trajectory shape (such as Figure 2 The corresponding working mode is determined as shown in the figure, and it is judged whether APU assistance is required for the corresponding working mode.

[0017] When the APU is assisting, the optimal curve is calculated offline by combining the system oil-electricity conversion loss rate, comprehensive emission index, and battery capacity attenuation rate data.

[0018] 1. Calculation of oil-to-electricity conversion loss rate of APU system: According to the GB / T18297-2001 automobile engine performance test method, the engine energy consumption curve is measured at 45×12 speed torques. In the test, the engine speed range [1000r / min, 5500r / min] is divided into 45 equal parts, and the torque range [0, 180Nm] is divided into 12 equal parts to obtain the engine operating point. During stable operation, the fuel economy and gas emission data of each operating point are sampled respectively. The energy consumption efficiency characteristic diagram of the APU system components is drawn by piecewise linear fitting, and the universal characteristic diagram of the engine and ISG motor is obtained. The piecewise linear fitting method can avoid the distortion of the energy efficiency MAP diagram. The segmentation points of the engine instantaneous fuel consumption are specified by the original constant speed optimal efficiency point. This constant speed optimal efficiency line, which is the most important for the energy management of the R-EEV model, is fully retained, and the engine fuel consumption characteristics and ISG motor efficiency characteristics are obtained as shown in the figure. Figure 3 shown.

[0019] The APU system engine and generator are mechanically connected. Considering only the engine fuel consumption characteristics cannot truly reflect the vehicle energy consumption. When considering the motor efficiency, the APU system oil-to-electricity conversion loss rate The calculation is as follows: ; In the formula, is the power generation efficiency of the generator, is the efficiency between fuel and engine effective power, is the calorific value of gasoline, The energy transfer loss rate of the APU system is defined as 4.6×107J / kg.

[0020] Defines the energy transfer loss rate of the APU system, The smaller the value, the better the fuel economy of the APU.

[0021] 2. Calculation of comprehensive emission index: The engine comprehensive emission (CO, HC and NOx) MAP is established from test measurements. According to the GB / T18297-2001 automobile engine performance test method, the engine CO emission, CH emission and NOx emission are measured at 45×12 speed torques. The engine emission characteristic results are obtained by interpolation method. The emission characteristic functions of CO, CH and NOx gases are defined as follows: , and The calculation is as follows: ; In the formula, , and It is the result of CO, CH, and NOx gas emissions. and are the maximum and minimum values ​​of CO gas emissions, and are the maximum and minimum values ​​of CH gas emission, and are the maximum and minimum values ​​of NOx gas emissions respectively; APU comprehensive exhaust emission characteristic function Considering the conventional exhaust gas CO, CH and NOx, the calculation is as follows: ; In the formula, , , They are , and The weight coefficient of .

[0022] [ ξ CO ,ξ CH ,ξ NOx ] T =[0.4, 0.3, 0.3] T . Defines the comprehensive emission value of the APU system, The smaller the value, the better the exhaust quality. Figure 4 shown.

[0023] 3. Calculation of battery capacity attenuation rate The battery capacity loss model is based on the semi-empirical life model. This model uses the battery current I(t), charge / discharge rate and number of cycles. and As the main evaluation factor, the battery capacity attenuation rate The model is as follows: ; In the formula, Indicates the battery capacity decay rate, is the depth of discharge, =0.7, is the cumulative capacity of the battery, is the battery current, is the total cycle time, is the number of cycles, =1700, and are the fitting parameters.

[0024] In summary, after obtaining the correlation coefficient, the optimal curve of the APU system can be obtained offline based on the DP algorithm, and the target value of the APU working point is calculated as follows: ; ; ; in, Indicates the real-time power value of the APU system. Indicates the real-time speed value of the APU system. Indicates the real-time torque value of the APU system. Indicates the actual upper power limit at the operating point, Indicates the actual lower power limit at the operating point, represents the vehicle's required power, Indicates the lower limit of the switching threshold between adjacent working points.

[0025] Based on the dynamic programming algorithm and the multi-objective backbone particle swarm algorithm, the optimal working curve of the APU system is optimized offline. The optimal working curve uses the oil-electric conversion loss rate, the comprehensive emission index and the battery capacity attenuation rate as multi-objective optimization parameters; According to Bellman's optimization theory, the principle of offline acquisition of APU multi-objective optimal curve based on DP algorithm is as follows: Figure 5 In order to analyze and compare the optimization results under different objectives and explore the optimization space of energy management strategy, the cost function of DP algorithm is set C oil_ele , E com and I com_APU Although only one APU optimal working curve is needed to be applied in engineering practice for real-time control, the three optimization curves obtained for different optimization objectives can be used as a basis for evaluating and comparing the effectiveness of real-time control strategies.

[0026] The state equation of the APU system model is expressed as follows: ; In the formula, represents the state vector of the system, represents the control variables, i.e., the speed increment and the torque increment; parameter It can be calculated by the following formula: ; To reduce the dimensionality of the optimization problem, the APU is selected ∆P 0 The operating point of the interval and the required power of the APU is PAPU_min Increase to P APU_max , the control variables are simplified to speed increment and torque increment ( ∆n APU , ∆T APU ), once the output power of the APU is determined, the output power of the battery can be determined according to the required power. The detailed state equation of the power battery and APU system is described as follows: ; In order to minimize the performance functional, an optimization algorithm must be used to find the optimal control variables at each moment, that is, { u (0), u (1), …, u ( N -1)}. Then the state variable { x (0), x (1), …, x ( N -1)}. The optimization goal is to locate the optimal control variable ( n APU , T APU ) to minimize the cost function as follows: ; In the formula, Indicates the cumulative number of power increases.

[0027] In order to ensure the safe and reasonable operation of the APU during the optimization process, the speed and torque of the APU are usually limited by the mechanical characteristics of the engine and generator and the power command issued by the vehicle control strategy to the APU controller. The following inequality constraints need to be met: ; The multi-objective offline optimization results of the optimal working curve of the APU system based on the DP algorithm are as follows: Figure 6 shown.

[0028] Define three operating points of the APU system, including low power, medium power and high power, and divide the power coverage intervals respectively. At the same time, design a switching strategy based on vehicle speed and a switching strategy based on power respectively. After the action point is determined through this multi-point working strategy, the vehicle speed and power information are collected to determine in real time whether the working point needs to be adjusted. If the working conditions change, the optimal curve needs to be recalculated and the action point needs to be re-determined, which is called the three-point working mode of the APU system, as follows: Under the three-point strategy of the APU system, the APU is in low power (Low power, P L), Medium power, P M ) and High power, P H ) to distribute power and dynamically switch between them. Three-point control strategy based on speed ( MCS v_b ) and power-based three-point control strategy ( MCS p_b ) are switched according to vehicle speed and required power, such as Figure 7 shown.

[0029] Select a speed sequence data in the CCDC cycle condition and analyze MCS v_b and MCS p_b The APU operating point distribution under the strategy is as follows Figure 8 As shown: It can be seen that MCS v_b and MCS p_b There are obvious differences in the APU working point allocation results. This difference is mainly reflected in MCS v_b The switching frequency of the APU working point is small and the switching process is "smooth". This is because the vehicle speed does not increase or decrease suddenly, and the APU working points are scattered P L , P M and P H Between three working points. MCS p_b In the strategy, the frequency of the change in the vehicle's required power is significantly greater than the vehicle speed. The APU's operating point is mainly concentrated at the low power operating point. When accelerating at low speed and decelerating at high speed, MCS p_b The battery participation rate is significantly higher than MCS v_b .

[0030] Fixed switching threshold parameters cannot adapt well to the complex changes of actual driving conditions, and thus cannot achieve ideal comprehensive performance. For this reason, a parameter optimization module based on fuzzy algorithm is designed. MCS v_b and MCS p_b Strategy, abbreviated as S vb_pf and S pb_pf; With parameter adjustable (Parameteradjustable) adaptive parameter tuning module MCS v_b and MCS p_b The strategy is defined as AMCS v_b and AMCS p_b Strategy, abbreviated as S vb_pa and S pb_pa , the principle is as follows Fig. 9 shown.

[0031] Design a fuzzy logic controller that takes battery SoC deviation and bus current as input, outputs a threshold adjustment factor, and dynamically updates the threshold parameters based on real-time operating conditions; Threshold Adjustment Factor of Fuzzy Logic Controller ξ , update the threshold parameter value in real time according to the following formula: ; ; ; ; In the formula, and yes AMCS vb The threshold switching value of the working point in the middle, and is the initial value, and yes AMCS pb The threshold switching value of the working point in the middle, and is the initial value. The 60 (km / h), 90 (km / h), 0.4 and 0.6 in the formula are empirical values ​​based on simulation test results.

[0032] The fuzzy controller is used to realize the real-time adjustment of the control parameters, and the Mamdani method is also used for fuzzy logic calculation. The triangle is selected as the membership function of the input and output, and five fuzzy sets are selected. Fig.10 As shown, ΔSoC and battery bus current I bat For input, ΔSoC ∈[-0.05, 0.05], I bat ∈[-60A, 60A], the output is the threshold adjustment factor ξ∈[-1, 1]. When the SoC is high, the threshold parameter value is increased to increase battery discharge, and when the SoC is low, the threshold parameter value is reduced to increase battery charging. The fuzzy rules of the two control strategies are the same, and their design principles are as follows: 1) When SoC act Smaller or SoC pre When the deviation is large, in order to eliminate the target deviation and avoid over-discharge of the battery, and the danger of over-discharge of the battery, ξ should be negative; 2) when SoC act near SoC pre When the battery is charging, ξ Should be positive. The adjustment rules are shown in Table 1.

[0033] Table 1. Fuzzy control rules for adaptive strategy ; The membership function and output surface of the fuzzy controller are as follows: Fig.10 shown.

[0034] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0035] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0036] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A fuzzy adaptive switching control method for three-point operation mode of a range extender, characterized in that: The method comprises: The energy consumption and emission data of the APU system are measured through engine performance tests, the universal characteristic diagram of the engine and ISG motor is constructed, and the oil-electric conversion loss rate, comprehensive emission index and battery capacity attenuation rate are calculated to obtain the optimal curve of the APU system offline; Based on the dynamic programming algorithm and the multi-objective backbone particle swarm algorithm, the optimal working curve of the APU system is optimized offline. The optimal working curve uses the oil-electric conversion loss rate, the comprehensive emission index and the battery capacity attenuation rate as multi-objective optimization parameters; Define three operating points of the APU system, including low power, medium power and high power, and divide the power coverage intervals respectively. At the same time, design a switching strategy based on vehicle speed and a switching strategy based on power respectively. Design a fuzzy logic controller that takes battery SoC deviation and bus current as input, outputs a threshold adjustment factor, and dynamically updates the threshold parameters based on real-time operating conditions; Adaptively switch the APU operating point according to real-time operating conditions.

2. The method according to claim 1, characterized in that The oil-to-electricity conversion loss rate The calculation is as follows: ； In the formula, is the power generation efficiency of the generator, is the efficiency between fuel and engine effective power, is the calorific value of gasoline, Defines the energy transfer loss rate of the APU system.

3. The method according to claim 2, characterized in that The calculation of the comprehensive emission index, wherein the emission data includes CO emission, CH emission and NOx emission, defines the emission characteristic functions of CO, CH and NOx gases as follows: , and The calculation is as follows: ； In the formula, , and It is the result of CO, CH, and NOx gas emissions. and are the maximum and minimum values ​​of CO gas emissions, and are the maximum and minimum values ​​of CH gas emission, and are the maximum and minimum values ​​of NOx gas emissions respectively; APU comprehensive exhaust emission characteristic function Considering the conventional exhaust gas CO, CH and NOx, the calculation is as follows: ； In the formula, , , They are , and The weight coefficient of .

4. The method according to claim 3, characterized in that The calculation of the battery capacity attenuation rate is specifically as follows: ； In the formula, Indicates the battery capacity decay rate, is the depth of discharge, is the cumulative capacity of the battery, is the battery current, is the total cycle time, is the number of cycles, and are the fitting parameters.

5. The method according to claim 4, characterized in that The optimal curve of the APU system is obtained offline, and the target value of the APU working point is calculated as follows: ； ； ； in, Indicates the real-time power value of the APU system. Indicates the real-time speed value of the APU system. Indicates the real-time torque value of the APU system. Indicates the actual upper power limit at the operating point, Indicates the actual lower power limit at the operating point, represents the vehicle's required power, Indicates the lower limit of the switching threshold between adjacent working points.

6. The method according to claim 1, characterized in that The optimal working curve of the optimized APU system is specifically: Define the APU system model, and the state equation is expressed as: ； In the formula, Indicates The state vector of the system at time instant, Indicates The control variables at the moment are the speed increment and torque increment; Define the state vector With the optimization objective, the state vector includes the speed and torque ,Right now: ； A section of APU output power variation is selected as ∆P 0 The interval is taken as the research interval, and APU is selected ∆P 0 The operating point of the interval and the APU power requirement from P APU_min Increase to P APU_max , in speed increments ∆n APU and torque increment ∆T APU As a control variable, the output power of the battery is determined according to the required power; in, P APU_min Increase to P APU_max They represent the minimum value of APU output power and the maximum value of APU output power in the study interval respectively; The detailed state equations of the power battery and APU system are described as follows: ； in, Indicates The speed of the APU at the moment, Indicates The output torque of the APU at time Indicates The increment of APU speed at the moment, Indicates The increment of APU output torque at the moment; To minimize the comprehensive energy consumption-emission evaluation function The goal is to include oil-electric conversion loss rate and comprehensive emission index: ； In the formula, and They represent the weight coefficients of the system oil-to-electricity conversion loss rate and the emission comprehensive index, represents the instantaneous optimization objective, Indicates the cumulative number of power increases.

7. The method according to claim 6, characterized in that The updating formula of the threshold adjustment factor is: ； ； ； ； In the formula, and yes AMCS v_b The threshold switching value of the working point in the middle, and is the initial value, and yes AMCS p_b The threshold switching value of the working point in the middle, and is the initial value.

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