A method for optimizing a power distribution strategy model of a hybrid vehicle

Abstract

The application discloses a kind of optimization method of power distribution strategy model of hybrid electric vehicle, in the power distribution strategy basis with demand power, battery SOC as variable, proposed with variable parameter power distribution model, in partial operating mode, the output power of engine and the charge-discharge power of battery are controlled using variable parameter, so as to determine the power distribution of engine and battery under current state.According to the power distribution strategy model, the energy consumption level of the vehicle under certain working condition can be obtained through simulation software; further, by modifying the adjustable parameters of the strategy model, the output power of the engine and the power battery under series mode is changed, thereby changing the power distribution ratio, and then through simulation under different parameters, multiple iterations are performed to obtain the optimal vehicle energy consumption and model parameters.

Description

Technical Field

[0001] This invention relates to an optimization method for a power distribution strategy model of a hybrid electric vehicle. Background Technology

[0002] A hybrid vehicle, broadly speaking, refers to a vehicle whose drive system consists of two or more individual drive systems that can operate simultaneously. The vehicle's power output is provided by a single drive system or by multiple drive systems working together, depending on the actual driving conditions. Due to their energy-saving and low-emission characteristics, hybrid vehicles have become a key focus of automotive research and development and have begun to be commercialized.

[0003] A series hybrid system typically uses an internal combustion engine to directly drive a generator to produce electricity. The generated electrical energy is then transmitted to the battery via a control unit, and from there to the drive motor, where it is converted into kinetic energy. Finally, the vehicle is driven by a transmission. In this configuration, the battery needs to regulate the energy output between the generator and the motor to ensure the vehicle functions properly.

[0004] In the existing series hybrid electric vehicle power distribution strategy, the power distribution between the engine and the power battery in each operating mode is usually determined by a fixed relationship based on the engine's optimal fuel economy curve, NVH performance, and other conditions, which cannot quickly achieve energy consumption optimization under specific operating conditions.

[0005] Therefore, there is an urgent need to develop a method that can quickly achieve energy consumption optimization under specific operating conditions. Summary of the Invention

[0006] The purpose of this invention is to provide an optimization method for a power distribution strategy model of a hybrid electric vehicle, comprising the following steps:

[0007] 1) Obtain the engine OOL curve in series mode of hybrid electric vehicles. The engine OOL curve is the engine optimal operating line (OOL), which is the curve connecting the operating points of the engine at the minimum fuel consumption rate for each same output power.

[0008] Select x local optima on the engine OOL curve, where the engine output power corresponding to each of the x local optima is P1, P2, P3...P... x .

[0009] The allowable output power range of the engine is P. MIN ~P MAX P MIN P MAX To set the lower and upper limits for permissible output power.

[0010] 2) Divide the engine's actual output power into z+1 power ranges, namely P MIN ~P1, P1~P2, P2~P3...P x ~P MAX The corresponding power requirement at the shaft end is B. MIN ~B1, B1~B2, B2~B3...B x ~B MAX B MIN B MAX These represent the lower and upper limits of the required power at the shaft end.

[0011] 3) Allocate power for different power demand ranges and formulate power allocation strategy models.

[0012] When the required power is less than B2, the actual output power P of the engine ENG1 =max{min{P2,P req +P charge +P acp},P MIN}. Among them, P req To convert the required power at the shaft end to the output power at the engine end, P charge P is the battery charging power when the required power is less than B2. acp The power consumption of the accessory. The actual output power P of the battery at this time. BAT1 =P ENG1 -P req -P acp .

[0013] When the power demand is in the range of B4 to B5, the engine uses power following mode to output power, and the actual output power P of the engine is... ENG2 =P req Among them, P req This converts the required power at the shaft end to the power output at the engine end. At this point, the actual output power P of the battery... BAT2 =P req +P acp -P ENG2 P acp The power consumption of the accessory.

[0014] When the required power is greater than B5, the engine output power P ENG3 =P MAX At this time, the actual output power P of the battery BAT3 =P req +P acp -P ENG3 , where P req To convert the required power at the shaft end to the output power at the engine end, P acpP represents the power consumption of the accessory. ENG3 This is the engine's maximum output power.

[0015] When the power demand is in the range of B2 to B3, proceed to step 4).

[0016] 4) Divide the battery SOC into five SOC ranges: forced power generation zone 0%–35%, power generation priority zone 35%–45%, power balance zone 45%–65%, power consumption priority zone 65%–85%, and charging prohibited zone 85%–100%.

[0017] 5) Power allocation is performed for different SOC ranges.

[0018] The actual output power P of the engine in the power generation priority area ENG4 =min{P2, L1}, power reference line L1 = k1*P N P N As the baseline for the power reference line, P here N =P req P req To convert the required power at the shaft end to the power output at the engine end, k1 is a reserved adjustable parameter. At this point, the actual power P of the battery... BAT4 =P req +P acp -P ENG4 .

[0019] The actual output power P of the engine in the electricity priority zone BAT5 =min{P1, L2}, power reference line L2 = k2 * P N k2 is a reserved adjustable parameter. At this time, the actual power of the battery is P. BAT5 =P req +P acp -P ENG5 .

[0020] The actual output power P of the forced generation engine ENG6 =P2. At this time, the actual power of the battery is P. BAT6 =P req +P acp -P ENG6 .

[0021] The actual output power P of the engine in the no-charging zone ENG7 =P1. The actual power of the battery at this time is P. BAT7 =P req +P acp -P ENG7 .

[0022] In the battery balance zone, the engine outputs power in a following mode, and the actual output power of the engine in the battery balance zone is P.ENG8 =P req At this time, the actual power of the battery is P. BAT8 =P req +P acp -P ENG8 .

[0023] 6) Obtain the energy consumption values ​​of the vehicle under fixed test conditions using simulation software. By modifying parameters k1 and k2, and then inputting them into the model again for energy consumption simulation, several energy consumption results can be obtained.

[0024] 7) By modifying parameters and performing simulation iterations, the power distribution ratio between the engine and the power battery is adjusted, and the optimal energy consumption simulation results are obtained.

[0025] Furthermore, the simulation software mentioned in step 6) is a co-simulation of Simulink and GT.

[0026] Furthermore, the energy consumption values ​​of the vehicle under fixed test conditions include the driving range (km), the state of charge (SOC, %) of the power battery, the engine fuel consumption (g), the engine output power (kW), the battery power (kW), the drive motor power (kW), and the wheel-end power demand (kW).

[0027] Furthermore, the steps for obtaining the engine OOL curve in series mode of a hybrid electric vehicle include:

[0028] 1.1) Obtain engine universal data and generator efficiency.

[0029] 1.2) Based on the engine's universal data and generator efficiency, establish the engine's OOL curve. Further, the steps to obtain the vehicle's energy consumption values ​​under fixed test conditions include:

[0030] 6.1) Build a vehicle powertrain model.

[0031] 6.2) Configure initial values ​​and variables.

[0032] 6.3) Import vehicle driving conditions.

[0033] 6.4) Simulate and solve the energy consumption under the working conditions. Obtain the energy consumption value of the vehicle under fixed test conditions from the corresponding module model of the software, and record the analysis results.

[0034] 6.5) Adjust parameters and perform simulation iterations.

[0035] Furthermore, in step 7), the step of adjusting the power distribution ratio between the engine and the power battery by modifying parameters and performing simulation iterations includes:

[0036] 7.1) Set up the test matrix.

[0037] 7.2) An exhaustive method is used to simulate and iterate all combinations of power distribution ratios between engines and power batteries.

[0038] 7.3) Select the top n power allocation ratio combinations with optimal energy consumption.

[0039] 7.4) The power distribution ratio combinations of the first n groups of engines and power batteries are refined to obtain m groups of power distribution ratio combinations of engines and power batteries.

[0040] 7.5) Repeat the simulation iteration until the difference between the two energy consumption results is less than the preset threshold.

[0041] Furthermore, the fixed test condition described in step 6) is the WLTC test condition.

[0042] The technical effects of this invention are undeniable. By modifying parameters to perform energy consumption simulation, this invention enables rapid iteration and optimization of the model and energy consumption simulation results, thereby reducing vehicle fuel consumption and improving vehicle economy. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the power system of the hybrid electric vehicle in series mode according to the present invention;

[0044] Figure 2 This is a schematic diagram of a power allocation strategy model;

[0045] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0046] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0047] Example 1:

[0048] An optimization method for a power distribution strategy model of a hybrid electric vehicle includes the following steps:

[0049] 1) Obtain the engine OOL curve in series mode of hybrid electric vehicles. The engine OOL curve is the engine optimal operating line (OOL), which is the curve connecting the operating points of the engine at the minimum fuel consumption rate for each same output power.

[0050] Select x local optima on the engine OOL curve, where the engine output power corresponding to each of the x local optima is P1, P2, P3...P... x .

[0051] The allowable output power range of the engine is P. MIN ~P MAX P MIN P MAX To set the lower and upper limits for permissible output power.

[0052] 2) Divide the engine's actual output power into z+1 power ranges, namely P MIN ~P1, P1~P2, P2~P3...P x ~P MAX The corresponding power requirement at the shaft end is B. MIN ~B1, B1~B2, B2~B3...B x ~B MAX B MIN B MAX These represent the lower and upper limits of the required power at the shaft end.

[0053] 3) Allocate power for different power demand ranges and formulate power allocation strategy models.

[0054] When the required power is less than B2, the actual output power P of the engine ENG1 =max{min{P2,P req +P charge +P acp},P MIN}. Among them, P req To convert the required power at the shaft end to the output power at the engine end, P charge P is the battery charging power when the required power is less than B2. acp The power consumption of the accessory. The actual output power P of the battery at this time. BAT1 =P ENG1 -P req -P acp

[0055] When the power demand is in the range of B4 to B5, the engine uses power following mode to output power, and the actual output power P of the engine is... ENG2 =P req Among them, P req This converts the required power at the shaft end to the power output at the engine end. At this point, the actual output power P of the battery... BAT2 =P req +P acp -P ENG2 P acp The power consumption of the accessory.

[0056] When the required power is greater than B5, the engine output power P ENG3 =P MAX At this time, the actual output power P of the battery BAT3 =P req +P acp -P ENG3 , where P req To convert the required power at the shaft end to the output power at the engine end, P acp P represents the power consumption of the accessory. ENG3 This is the engine's maximum output power.

[0057] When the power demand is in the range of B2 to B3, proceed to step 4).

[0058] 4) Divide the battery SOC into five SOC ranges: forced power generation zone 0%–35%, power generation priority zone 35%–45%, power balance zone 45%–65%, power consumption priority zone 65%–85%, and charging prohibited zone 85%–100%.

[0059] 5) Power allocation is performed for different SOC ranges.

[0060] See Figure 2 The actual output power P of the engine in the power generation priority area ENG4 =min{P2, L1}, power reference line L1 = k1*P N P N As the baseline for the power reference line, P here N =P req P req To convert the required power at the shaft end to the power output at the engine end, k1 is a reserved adjustable parameter. At this point, the actual power P of the battery... BAT4 =P req +P acp -P ENG4 .

[0061] The actual output power P of the engine in the electricity priority zone BAT5 =min{P1, L2}, power reference line L2 = k2 * P N k2 is a reserved adjustable parameter. At this time, the actual power of the battery is P. BAT5 =P req +P acp -P ENG5 .

[0062] The actual output power P of the forced generation engine ENG6 =P2. At this time, the actual power of the battery is P. BAT6 =P req +P acp -P ENG6 .

[0063] The actual output power P of the engine in the no-charging zone ENG7 =P1. The actual power of the battery at this time is P. BAT7 =P req +P acp -P ENG7 .

[0064] In the battery balance zone, the engine outputs power in a following mode, and the actual output power of the engine in the battery balance zone is P. ENG8 =P req At this time, the actual power of the battery is P. BAT8 =P req +P acp -P ENG8 .

[0065] 6) Obtain the energy consumption values ​​of the vehicle under fixed test conditions using simulation software. By modifying parameters k1 and k2, and then inputting them into the model again for energy consumption simulation, several energy consumption results can be obtained.

[0066] 7) By modifying parameters and performing simulation iterations, the power distribution ratio between the engine and the power battery is adjusted to obtain the optimal energy consumption simulation result. The power distribution ratio between the engine and the power battery corresponding to the optimal energy consumption simulation result is the final power distribution strategy of the generated hybrid vehicle.

[0067] Example 2:

[0068] The main steps in this embodiment are the same as in embodiment 1, except that the step of obtaining the engine OOL curve in the series mode of a hybrid electric vehicle includes:

[0069] 1.1) Obtain engine universal data and generator efficiency.

[0070] 1.2) Establish the engine OOL curve based on the engine universal data and generator efficiency. The OOL curve is selected based on the minimum value of the engine universal data and generator efficiency.

[0071] Example 3:

[0072] The main steps in this embodiment are the same as in Embodiment 1, except that the simulation software mentioned in step 6) is a co-simulation of Simulink and GT. In this embodiment, the fixed test condition is the WLTC test condition.

[0073] The energy consumption values ​​of the vehicle under fixed test conditions include the driving range (km), the state of charge (SOC, %) of the power battery, the engine fuel consumption (g), the engine output power (kW), the battery power (kW), the drive motor power (kW), and the wheel-end power demand (kW).

[0074] The steps to obtain the energy consumption values ​​of a vehicle under fixed test conditions include:

[0075] 6.1) Build a vehicle powertrain model.

[0076] 6.2) Configure initial values ​​and variables.

[0077] 6.3) Import vehicle driving conditions.

[0078] 6.4) Simulate and solve the energy consumption under the working conditions. Obtain the energy consumption value of the vehicle under fixed test conditions from the corresponding module model of the software, and record the analysis results.

[0079] 6.5) Adjust parameters and perform simulation iterations.

[0080] Example 3:

[0081] The main steps in this embodiment are the same as in embodiment 1. However, step 7), which involves adjusting the power distribution ratio between the engine and the power battery by modifying parameters and performing simulation iterations, includes:

[0082] 7.1) Set the experimental matrix (e.g., parameter k) 1＝ 1.0, 1.05, 1.10, 1.15, 1.20. k 2＝ 0.8, 0.85, 0.9, 0.95, 1.0).

[0083] 7.2) An exhaustive method is used to simulate and iterate all combinations of power distribution ratios between engines and power batteries.

[0084] 7.3) Select the top n power allocation ratio combinations with optimal energy consumption.

[0085] 7.4) The power distribution ratio combinations of the first n groups of engines and power batteries are refined to obtain m groups of power distribution ratio combinations of engines and power batteries.

[0086] 7.5) Repeat the simulation iteration until the difference between the two energy consumption results is less than the preset threshold.

[0087] Example 4:

[0088] This embodiment provides an optimization method for a power distribution strategy model of a hybrid electric vehicle. This method is applied to a series-mode hybrid electric vehicle. See [link to relevant documentation]. Figure 1 The powertrain of this type of hybrid vehicle includes an engine, motor I, an inverter, motor II, a power battery, and a fuel tank. Motor I is a generator. Figure 1 Solid lines in the middle represent series and parallel topological relationships, but do not represent specific mechanical structures.

[0089] When the clutch is engaged, motor II drives motor I in parallel. When the clutch is disengaged, motor I and motor II drive motor II in series.

[0090] See Figure 3 The method includes the following steps:

[0091] 1) Select x local optima on the engine OOL curve of the series hybrid electric vehicle, where the engine output power corresponding to each of the x local optima is P1, P2, P3...P x The engine's permissible output power range is P. MIN ~P MAX .

[0092] 2) Divide the actual output power of the engine into z+1 power intervals, namely P MIN ~P1, P1~P2, P2~P3...P x ~P MAX The corresponding power requirement at the shaft end is B. MIN ~B1, B1~B2, B2~B3...B x ~B MAX .

[0093] 3) Allocate power according to different demand power ranges and formulate power allocation strategy models, such as... Figure 2 As shown.

[0094] When the required power is less than B2, the actual output power P of the engine ENG1 =max{min{P2,P req +P charge +P acp},P MIN}. Among them, P req To convert the required power at the shaft end to the output power at the engine end, P charge P is the battery charging power when the required power is less than B2. acp The power consumption of the accessory. The actual output power P of the battery at this time. BAT1 =P ENG1 -P req -P acp

[0095] When the power demand is in the range of B4 to B5, the engine uses power following mode to output power, and the actual output power P of the engine is... ENG2 =P req Among them, P req This converts the required power at the shaft end to the power output at the engine end. At this point, the actual output power P of the battery... BAT2 =P req +P acp -P ENG2P acp The power consumption of the accessory.

[0096] When the required power is greater than B5, the engine output power P ENG3 =P MAX At this time, the actual output power P of the battery BAT3 =P req +P acp -P ENG3 , where P req To convert the required power at the shaft end to the output power at the engine end, P acp P represents the power consumption of the accessory. ENG3 This is the engine's maximum output power.

[0097] When the power demand is in the range of B2 to B3, proceed to step 4).

[0098] 4) Divide the battery SOC into five SOC ranges: forced power generation zone 0%–35%, power generation priority zone 35%–45%, power balance zone 45%–65%, power consumption priority zone 65%–85%, and charging prohibited zone 85%–100%.

[0099] 5) Power allocation is performed for different SOC ranges.

[0100] The actual output power P of the engine in the power generation priority area ENG4 =min{P2, L1}, power reference line L1 = k1*P N k1 is a reserved adjustable parameter. At this time, the actual power P of the battery... BAT4 =P ENG4 -P req -P acp .

[0101] The actual output power P of the engine in the electricity priority zone BAT5 =min{P1, L2}, power reference line L2 = k2 * P N k2 is a reserved adjustable parameter. At this time, the actual power of the battery is P. BAT5 =P ENG5 -P req -P acp .

[0102] The actual output power P of the forced generation engine ENG6 =P2. At this time, the actual power of the battery is P. BAT6 =P ENG6 -P req -P acp .

[0103] The actual output power P of the engine in the no-charging zone ENG7 =P1. The actual power of the battery at this time is P.BAT7 =P ENG7 -P req -P acp .

[0104] In the battery balance zone, the engine outputs power in a following mode, and the actual output power of the engine in the battery balance zone is P. ENG8 =P req At this time, the actual power of the battery is P. BAT8 =,,,

[0105] 6) Obtain the energy consumption values ​​of the vehicle under fixed test conditions using simulation software. By modifying parameters k1 and k2 (e.g., k1 = 1.1, k2 = 0.8, k1 = 1.1, k2 = 0.9, k1 = 1.05, k2 = 0.8, k1 = 1.05, k2 = 0.9, etc.), and then substituting them into the model again for energy consumption simulation, several energy consumption results can be obtained.

[0106] 7) By modifying parameters and performing simulation iterations, the power distribution ratio between the engine and the power battery is adjusted, and the optimal energy consumption simulation results are obtained.

[0107] In this embodiment, when optimizing energy consumption for specific operating conditions (such as WLTC conditions), the traditional multi-point following method was used, resulting in a fuel consumption of 5.6L / 100km for the vehicle. By changing to the series mode power distribution strategy optimization model of this invention, and performing multiple iterations, the lowest fuel consumption of 5.4L / 100km was obtained, representing a 3.6% improvement in energy consumption.

Claims

1. An optimization method for a power distribution strategy model of a hybrid electric vehicle, characterized in that, Includes the following steps: 1) Obtain the engine OOL curve in series mode of the hybrid electric vehicle; select x local optima on the engine OOL curve; where the x local optima correspond to engine output power P1, P2, P3, ..., P x The engine's permissible output power range is P. MIN ~P MAX ;P MIN The lower limit of permissible output power; P MAX Maximum allowable output power; 2) Divide the engine's actual output power into x+1 power ranges, respectively P MIN ~P1, P1~P2, P2~P3,…,P x ~P MAX The corresponding power requirement at the shaft end is B. MIN ~ B1, B1~B2, B2~B3,…,B x ~B MAX B MIN B is the lower limit of the shaft end power requirement; MAX This represents the upper limit of the power required at the shaft end. 3) Allocate power according to different power demand ranges and formulate a power allocation strategy model: When the required power is less than B2, the actual output power P of the engine ENG1 =max{min{P2,P req +P charge +P acp },P MIN }; where P req To convert the required power at the shaft end to the output power at the engine end, P charge P is the battery charging power when the required power is less than B2. acp The power consumption of the accessory; the actual output power P of the battery at this time. BAT1 = P ENG1 -P req -P acp ; When the power demand is in the range of B4 to B5, the engine uses power following mode to output power, and the actual output power P of the engine is... ENG2 =P req Among them, P req The required power at the shaft end is converted to the power output at the engine end; at this time, the actual output power P of the battery is... BAT2 =P req +P acp - P ENG2 P acp Power consumption of the accessories; When the required power is greater than B5, the engine output power P ENG3 =P MAX At this time, the actual output power P of the battery BAT3 =P req +P acp -P ENG3 , where P req To convert the required power at the shaft end to the output power at the engine end, P acp P represents the power consumption of the accessory. ENG3 This is the engine's maximum output power; When the required power is in the range of B2 to B3, proceed to step 4); 4) Divide the battery SOC into five SOC ranges: forced power generation zone 0%–35%, power generation priority zone 35%–45%, power balance zone 45%–65%, power consumption priority zone 65%–85%, and charging prohibited zone 85%–100%; 5) Power allocation for different SOC ranges: The actual output power P of the engine in the power generation priority area ENG4 =min{P2, L1}, power reference line L1=k1*P N P N As the baseline for the power reference line, P here N =P req P req To convert the required power at the shaft end to the power output at the engine end, k1 is a reserved adjustable parameter; at this time, the actual power P of the battery... BAT4 = P req +P acp -P ENG4 ; The actual output power P of the engine in the electricity priority zone ENG5 =min{P1, L2}, power reference line L2=k2*P N k2 is a reserved adjustable parameter; at this time, the actual power of the battery is P. BAT5 = P req +P acp -P ENG5 ; The actual output power P of the forced generation engine ENG6 =P2; At this time, the actual power of the battery is P. BAT6 = P req +P acp -P ENG6 ; The actual output power P of the engine in the no-charging zone ENG7 =P1; At this time, the actual power of the battery is P. BAT7 = P req +P acp -P ENG7 ; In the battery balance zone, the engine outputs power in a following mode, and the actual output power of the engine in the battery balance zone is P. ENG8 =P req At this time, the actual power of the battery is P. BAT8 =P req +P acp -P ENG8 ; 6) Obtain the energy consumption value of the vehicle under fixed test conditions through simulation software; by modifying parameters k1 and k2, and then inputting them into the model again to perform energy consumption simulation, several energy consumption results can be obtained. 7) By modifying parameters and performing simulation iterations, the power distribution ratio between the engine and the power battery is adjusted, and the optimal energy consumption simulation results are obtained.

2. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 1, characterized in that: The simulation software mentioned in step 6) is a co-simulation of Simulink and GT.

3. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 2, characterized in that: The energy consumption values ​​of the vehicle under fixed test conditions include the driving range under the test conditions, the state of charge (SOC) of the power battery, engine fuel consumption, engine output power, battery power, drive motor power, and wheel-end power requirements.

4. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 2, characterized in that: The steps to obtain the energy consumption values ​​of a vehicle under fixed test conditions include: 1) Build a model of the vehicle's powertrain system; 2) Configure initial values ​​and variables; 3) Import vehicle driving conditions; 4) Simulate and solve the energy consumption under operating conditions. Obtain the energy consumption values ​​of the vehicle under fixed test conditions from the corresponding module model in the software, and record the analysis results. 5) Adjust the parameters and perform simulation iterations.

5. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 1, characterized in that: Step 7), which involves adjusting the power distribution ratio between the engine and the power battery by modifying parameters and performing simulation iterations, includes the following steps: 1) Set up the experiment matrix; 2) An exhaustive method is used to simulate and iterate all combinations of power distribution ratios between engines and power batteries; 3) Select the top n power allocation ratio combinations with optimal energy consumption; 4) Refine the power distribution ratio combinations of the first n groups of engines and power batteries to obtain m groups of power distribution ratio combinations of engines and power batteries. 5) Repeat the simulation iteration until the difference between the two energy consumption results is less than the preset threshold.

6. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 1, characterized in that: The fixed test condition mentioned in step 6) is the WLTC condition.

7. The optimization method for a power distribution strategy model of a hybrid electric vehicle according to claim 1, characterized in that: The steps to obtain the engine OOL curve in series mode of a hybrid electric vehicle include: 1) Obtain engine universal data and generator efficiency; 2) Establish the engine OOL curve based on the engine universal data and generator efficiency.

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