A method and device for adaptively adjusting SOC balance point after identifying working conditions

Abstract

The present invention relates to a method and device for adaptively adjusting the SOC balance point after identifying the working condition. It belongs to the field of auxiliary control technology for energy management of hybrid vehicle models. The device includes an information acquisition and processing layer, a statistical analysis layer, a scene recognition layer, and a final decision layer. The method is to collect external information and internal information of the vehicle, and use a time statistical method to confirm the current vehicle state; combine the signal of the acquisition layer, set a reasonable mapping relationship, and then make a unified correction of the working condition to the mapping result, and divide the final scene score into scene levels; after completing the identification based on the scene classification, a new SOC balance point is determined after compensating the correction value on the basis of the existing system balance point. The present invention calculates a reasonable SOC balance point in real time based on the vehicle operating state and road scene, and the hybrid control main program adjusts the SOC usage window in real time by calling this value.

Description

Technical Field

[0001] The present invention relates to a hybrid vehicle balance point adjustment method and device, and in particular to a method and device for adaptive SOC balance point adjustment after identifying working conditions, belonging to the auxiliary control technical field of hybrid vehicle energy management. Background Art

[0002] Hybrid electric vehicles are divided into plug-in hybrid and non-plug-in hybrid according to whether they have an external charging design. Plug-in hybrid models mostly use large-capacity high-voltage battery systems, so the SOC balance point is mostly fixed, that is, a fixed SOC balance point is selected according to the capacity of the battery pack, the power capacity table, and the requirements of the vehicle attributes. The non-plug-in hybrid vehicles targeted by the present invention have the characteristics of small capacity, fast SOC changes, and power capacity being greatly affected by SOC. The fixed SOC balance point approach is difficult to adapt to the consistency of vehicle performance in multiple scenarios. For example, for the same 50% SOC, the charging and discharging capabilities of battery packs under different temperature conditions are different. If the same SOC balance point is used in summer and winter, it will easily cause the vehicle to be limited in power, insufficient power, and worse fuel consumption in winter, while there will be no obvious impact in summer. In summary, the fixed SOC balance point approach has two disadvantages: first, the performance difference of the whole vehicle cannot be guaranteed after the external environment of the vehicle changes (temperature, altitude, etc.); second, the performance difference of the whole vehicle cannot be guaranteed after the internal state of the vehicle changes (engine capacity is limited, vehicle speed state changes). Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a device for adaptively adjusting the SOC balance point after identifying the working conditions. Based on the information such as driving components, power battery information, vehicle driving information, environmental signals, etc. collected inside the controller, the vehicle's operating status and road scene are summarized and analyzed, and then a reasonable SOC balance point is calculated in real time. The hybrid power control main program calls the balance point value to adjust the SOC usage window in real time to ensure that the performance indicators of the entire vehicle meet the driver's expectations.

[0004] The present invention provides a device for adaptively adjusting the SOC balance point after identifying the working condition, the device comprising:

[0005] Information collection and processing layer: used to collect external and internal information of the vehicle, and calculate and process vehicle signals based on external and internal information;

[0006] Statistical analysis layer: used to confirm the current vehicle status based on the information collected by the processing layer and using time statistics methods;

[0007] Scene recognition layer: used to combine the signal of the acquisition layer, set a reasonable mapping relationship according to the influence weight of the whole vehicle, and then combine the working conditions of the statistical analysis layer to make a unified correction of the working conditions on the mapping results, and divide the final scene scores into scene levels;

[0008] Final decision layer: used to determine a new SOC balance point after completing the identification based on scenario classification and compensating the correction value based on the existing system balance point.

[0009] Furthermore, the external information includes ambient temperature, altitude, rainfall, and GPS; the internal information includes vehicle speed, slope, pedal, air conditioning status, and status information inside the hybrid power controller.

[0010] Furthermore, it is characterized in that: the calculated and processed vehicle signals include the vehicle's speed, acceleration, slope, load, and operating mode.

[0011] Furthermore, the current vehicle state includes mountain road driving, high-speed driving, and plateau driving.

[0012] The present invention also provides a method for adaptively adjusting the SOC balance point after identifying the working conditions, characterized in that: the method specifically comprises: collecting external information and internal information of the vehicle, and calculating and processing vehicle signals in combination with the external information and the internal information; confirming the current vehicle state by using a time statistics method based on the information of the collection and processing layer; setting a reasonable mapping relationship based on the influence weight of the whole vehicle in combination with the signal of the collection layer, and then making a unified correction of the working conditions to the mapping results in combination with the working conditions of the statistical analysis layer, and dividing the final scene scores into scene levels; after completing the identification based on the scene classification, determining a new SOC balance point after compensating the correction value on the basis of the existing system balance point.

[0013] Furthermore, in the method, the external information includes ambient temperature, altitude, rainfall, and GPS; the internal information includes vehicle speed, slope, pedal, air-conditioning status, and status information inside the hybrid power controller; and the calculated and processed vehicle signals include vehicle speed, acceleration, slope, load, and operating mode.

[0014] Furthermore, in the method, scene classification is specifically as follows: the influencing factors used for scene classification definition include: signals from the information collection layer, including driving mode, air-conditioning status, load mass, rainfall status, and mountain road driving condition signs, plateau driving condition signs, high-speed driving condition signs, and idle power generation condition signs after processing by the statistical analysis layer. Based on the vehicle impact assessment of each influencing factor, a weight coefficient is set, and finally the current scene score of the vehicle is obtained in a numerical statistical way, and the scene score is divided into five scene gradient levels.

[0015] Furthermore, in the method, the calculated and processed vehicle signals include the vehicle's speed, acceleration, slope, load, and operating mode.

[0016] The method and device for adaptively adjusting the SOC balance point after identifying the working condition of the present invention have the following advantages compared with the solutions of the prior art:

[0017] 1. Dynamically adjust the vehicle's SOC balance point to ensure that the vehicle's power and economy meet the driver's expectations;

[0018] 2. Intelligent decision-making based on the current SOC status and the reference balance point after scenario recognition improves the actual customer experience. For example, the decision-making basis for idle power generation conditions is based on the offset correction of the current SOC point rather than the reference balance point.

[0019] 3. The present invention calculates a reasonable SOC balance point in real time based on the vehicle running status and road scene. The hybrid power control main program adjusts the SOC usage window in real time by calling this value. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of a device for adaptively adjusting the SOC balance point after identifying the working condition according to the present invention.

[0021] Figure 2 This is a schematic diagram of the method for adaptively adjusting the SOC balance point after identifying the operating condition of the present invention.

[0022] Figure 3 This is a schematic diagram of rainfall signal calibration.

[0023] Figure 4 This is a schematic diagram of GPS signal calibration.

[0024] Figure 5 This is the principle diagram for determining mountain road driving conditions.

[0025] Figure 6 This is a schematic diagram for judging high-speed driving conditions.

[0026] Figure 7 Schematic diagram for determining plateau driving conditions.

[0027] Figure 8 Schematic diagram of scene division.

[0028] Fig. 9 Schematic diagram of the adaptive SOC balance point determination process. DETAILED DESCRIPTION

[0029] like Figure 1 , Figure 2As shown, the algorithm involved in the present invention includes information collection and processing layer, statistical analysis layer, scene recognition layer, and final decision layer. By deconstructing the vehicle's operating status and environment, the vehicle's SOC balance point is adjusted in real time. The functions and implementations of the specific modules are as follows;

[0030] 1. Information collection and processing layer: collects external and internal information of the vehicle, including ambient temperature, altitude, rainfall, GPS and other information; internal information of the vehicle includes vehicle speed, slope, pedal, air conditioning status and status information inside the hybrid power controller (such as operating mode status, driving force status, fault diagnosis, etc.). Combined with relevant information, calculates and processes vehicle speed, acceleration, slope, load, operating mode and other signals;

[0031] Vehicle speed, acceleration, pedal change rate, operating mode, air conditioning status, slope, load, ambient temperature, altitude and other signals are all necessary signals for the vehicle control system, and are independently processed in the signal processing module of the control system software. Among them, the slope and load information signals depend on whether the vehicle is equipped with ESP. If not equipped with ESP, the signal processing module of the vehicle control system cannot provide the signal. The above signals are not further processed in the information collection and processing layer of the present invention, and are only used as signal references;

[0032] The processing of the rain signal comprehensively considers the feedback status of the wiper, the window, and the sunroof. The basic processing logic is as follows: Figure 3 As shown:

[0033] Calibration principle: The recommended time for timing is 10 to 30 seconds;

[0034] GPS signal processing diagram Figure 4 As shown, based on the latitude and longitude information and system date information, combined with the database, the approximate altitude and temperature information of the vehicle's environment is determined:

[0035] Calibration principle: The database is the GPS boundary of the region and the historical monthly weather of each region;

[0036] 2. Statistical analysis layer: Based on the information from the collection and processing layer, the time statistical method is used to confirm the current vehicle status, such as mountain road driving, high-speed driving, plateau driving, etc.;

[0037] The judgment and processing of mountain road driving conditions mainly includes the judgment of three situations, and the three situations are in an or relationship. First, within a certain period of time, the number of times the slope and vehicle speed exceed the set value exceeds a certain value; second, within a certain period of time, the slope value is very large and the vehicle speed meets the requirements; finally, within a certain period of time, the change in altitude exceeds a certain value. The specific processing logic diagram is as follows Figure 5 As shown:

[0038] Calibration principles: The recommended calibration time for counting and timing is 200-300s, the calibration value for counting and timing is 100-150, and the calibration value for slope is 2-5%; the calibration value for vehicle speed is 30-50kph; the calibration value for large slope is 10-15%, the vehicle speed is 20-40kph, and the recommended setting time for timing is 50-100s; the recommended delay time for altitude is 200-300s, and the calibration value for altitude change is 300-600s;

[0039] The judgment and processing of high-speed driving conditions refers to the vehicle speed exceeding a certain value for a certain period of time, in which the vehicle speed will take into account the correction of the slope. The processing diagram is as follows Figure 6 As shown:

[0040] Calibration principle: The calibration curve principle of slope and speed is that the greater the slope value, the lower the speed. The recommended time setting is 200 to 300 seconds.

[0041] The judgment and processing of plateau driving conditions refer to the average altitude information obtained by GPS and the altitude within a certain period of time. The processing diagram is as follows Figure 7 As shown:

[0042] Calibration principles: The recommended high altitude calibration value is 2500-3000m, and the recommended high altitude timing setting time is 100-200s. The recommended average altitude calibration value is 1500-2000m, and the average altitude timing setting time is 100-200s.

[0043] 3. Scene recognition layer: such as Figure 8 As shown, combined with some signals of the acquisition layer, according to the influence weight of the whole vehicle, a reasonable mapping relationship is set, and then combined with the working conditions of the statistical analysis layer, the mapping results are uniformly corrected according to the working conditions, and the final scene scores are divided into scene levels. The premise of the hierarchical definition of the scene is that the key input influencing quantities of the scene classification need to be determined. In the present invention, the influencing factors used for the scene classification definition include: signals of the information acquisition layer such as driving mode, air conditioning status, load mass, rainfall status, and mountain road driving condition signs, plateau driving condition signs, high-speed driving condition signs, and idle power generation condition signs after processing by the statistical analysis layer. Based on the vehicle impact assessment of each influencing factor, the weight coefficient is set, and finally the current scene score of the vehicle is obtained in a numerical statistical manner, and the scene score is divided into five scene gradient levels.

[0044] Calibration principle: The driving modes are Eco / Nor / Pwr from low to high in terms of intensity. The principle of SOC correction for driving mode mapping is that the more intense the driving mode, the greater the SOC correction value; the greater the air conditioning and load power, the greater the SOC correction value; the greater the load, the greater the SOC correction value; the greater the rainfall indicated by the rainfall status, the greater the SOC correction value; the principle of working condition correction is: the correction coefficient for mountain road driving conditions is recommended to be 1.3~1.5, the correction coefficient for plateau driving conditions is recommended to be 1.1~1.3, and the correction coefficient for high-speed driving conditions is recommended to be 1.1~1.2; the principle of scene classification is recommended to upgrade one gear every 3 points;

[0045] 4. Final decision-making level: Fig. 9 As shown in the figure, after the scene classification is completed, a new SOC balance point is determined after compensating the correction value based on the existing system balance point. It should be noted that the SOC correction value at this time expresses the numerical result of the weight influence under the working conditions. This numerical result is universal and needs to be adapted in combination with the battery pack capacity of the vehicle. Therefore, it is necessary to add a layer of capacity-based correction on the basis of the correction value. The sum of the final result after correction and the SOC balance point of the system is the adaptive SOC balance point after scene recognition;

[0046] Calibration principle: The scene classification refers to the definition of the recognition layer; the principle of the battery pack capacity correction curve is that the larger the capacity, the smaller the correction coefficient;

[0047] Example: By collecting vehicle speed status and making time statistics, it is confirmed that the vehicle is traveling at high speed and the speed fluctuation is not large. At the same time, the energy consumption status of the air conditioner and DCDC is collected, and this high-speed driving with air conditioning on is defined as an application scenario with moderate SOC demand. A reference SOC balance point is provided based on the moderate demand application scenario; combined with the current system SOC balance point, considering that the vehicle is in a high-speed driving state, the driver's sensitivity to starting and stopping is greatly reduced, and there is no need to shut down the engine under high-speed conditions, the system finally decides to use the reference SOC balance point as the current system balance point target.

[0048] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A device for adaptively adjusting the SOC balance point after identifying the working condition, characterized in that: The device comprises: Information collection and processing layer: used to collect external and internal information of the vehicle, and calculate and process vehicle signals based on external and internal information; Statistical analysis layer: used to confirm the current vehicle status, including mountain road driving, high-speed driving, and plateau driving, based on the information collected by the processing layer and using time statistics methods; Scene recognition layer: used to combine the signals from the information collection and processing layer, set a reasonable mapping relationship according to the influence weight of the whole vehicle, and then make a unified correction to the working conditions of the mapping results in combination with the working conditions of the statistical analysis layer, and divide the final scene scores into scene levels; Final decision layer: used to determine a new SOC balance point after compensating the correction value based on the existing system balance point after completing the identification based on the scenario classification; The external information includes ambient temperature, altitude, rainfall, and GPS; the internal information includes vehicle speed, slope, pedal, air conditioning status, and status information inside the hybrid power controller.

2. The device for adaptively adjusting the SOC balance point after identifying the working condition according to claim 1, characterized in that: The calculated and processed vehicle signals include the vehicle's speed, acceleration, slope, load, and operating mode.

3. The device for adaptively adjusting the SOC balance point after identifying the working condition according to claim 1, characterized in that: The current vehicle status includes mountain road driving, high-speed driving, and plateau driving.

4. A method for adaptively adjusting the SOC balance point after identifying the working condition, characterized in that: The method specifically comprises: collecting external information and internal information of the vehicle, and calculating and processing vehicle signals in combination with the external information and the internal information; Based on the information collected from the information collection and processing layer, the current vehicle status is confirmed using time statistics methods; combined with the signals from the information collection and processing layer, a reasonable mapping relationship is set according to the influence weight of the entire vehicle, and then combined with the working conditions of the statistical analysis layer, the mapping results are uniformly corrected for the working conditions, and the final scene scores are divided into scene levels; After the identification is completed based on the scenario classification, a new SOC balance point is determined after compensating the correction value based on the existing system balance point; In the method, the external information includes ambient temperature, altitude, rainfall, and GPS; the internal information includes vehicle speed, slope, pedal, air conditioning status, and status information inside the hybrid power controller; the calculated and processed vehicle signals include vehicle speed, acceleration, slope, load, and operating mode.

5. The method for adaptively adjusting the SOC balance point after identifying the working condition according to claim 4, characterized in that: In the method, scene classification is specifically as follows: the influencing factors used for scene classification definition include: signals from the information collection and processing layer, including driving mode, air-conditioning status, load mass, rainfall status, and mountain road driving condition signs, plateau driving condition signs, high-speed driving condition signs, and idle power generation condition signs after processing by the statistical analysis layer. Based on the vehicle impact assessment of each influencing factor, a weight coefficient is set, and finally the current scene score of the vehicle is obtained in a numerical statistical way, and the scene score is divided into five scene gradient levels.

6. The method for adaptively adjusting the SOC balance point after identifying the working condition according to claim 4, characterized in that: In the method, the calculated and processed vehicle signals include the vehicle's speed, acceleration, slope, load, and operating mode.

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