CN113264032B - Energy management method, device and system for hybrid vehicle - Google Patents

Energy management method, device and system for hybrid vehicle Download PDF

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Publication number
CN113264032B
CN113264032B CN202110415861.6A CN202110415861A CN113264032B CN 113264032 B CN113264032 B CN 113264032B CN 202110415861 A CN202110415861 A CN 202110415861A CN 113264032 B CN113264032 B CN 113264032B
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power
engine
required power
automobile
driving
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CN113264032A (en
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绳有为
刘华东
刘兴波
杨金亮
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China National Heavy Duty Truck Group Jinan Power Co Ltd
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China National Heavy Duty Truck Group Jinan Power Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/10—Accelerator pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/62—Hybrid vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Abstract

The invention provides an energy management method, a device and a system of a hybrid vehicle, wherein the method comprises the steps of establishing a road identification database, a cycle driving identification database and a driving style database; receiving signals of an accelerator pedal and a brake pedal, and obtaining the power demand of the whole vehicle according to the opening degree of the pedals; according to the power demand of the whole automobile, road identification, cycle running, driving style and ideal time length of a prediction power range are used as input of a fuzzy logic algorithm, different running conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the prediction range is output; and distributing the required power of the automobile to the engine and the motor by using the operation planning method according to the required power. Based on the method, the invention also provides an energy management system and a device of the hybrid vehicle, and the invention adds the situation of predicting the future vehicle running power into the original control strategy, and then carries out a real-time dynamic planning algorithm to distribute the required power of the engine and the motor, thereby achieving the long-time optimal power distribution.

Description

Energy management method, device and system for hybrid vehicle
Technical Field
The invention belongs to the technical field of vehicles, and particularly relates to an energy management method, device and system of a hybrid vehicle.
Background
At present, a hybrid automobile composed of an internal combustion engine and an electric motor has become a feasible alternative to a traditional internal combustion engine automobile. The overall performance of a hybrid vehicle, including fuel economy and reduced emissions, depends on the efficiency of the vehicle components and the coordination between the components, i.e., the energy management strategy of the hybrid vehicle plays a very important role in both improving fuel economy and reducing emissions. Although the hybrid tractors vary in construction, the energy management strategy is aimed at maximizing fuel economy, minimizing emissions, and minimizing system costs. Factors that need to be considered with respect to energy management of a hybrid vehicle include: first, the operating point/operating interval of the engine is optimized: the operating point of the engine is set at the optimum operating point on the torque-speed characteristic curve, which is drawn according to the data of the fuel economy and the emission of the engine, while balancing the fuel economy and the emission. Second, minimization of engine power: the operating speed of the engine is controlled in such a way that any rapid fluctuations can be avoided, thereby minimizing the power of the engine. Third, minimization of engine idle time: to improve fuel economy and reduce emissions, the idle time of the engine should be minimized. Fourthly, optimizing the switching time of the engine: the optimization of the on-off time of the engine enables an optimization mode to be carried out from the aspects of the habit of a driver, road conditions, weather, traffic conditions and the like by utilizing the double-power advantages of the hybrid electric vehicle. Fifth, the maximum regenerated energy is obtained: the SOC state of the battery system is optimally set so that the regenerative energy is maximally obtained according to the driver's habit, weather, road conditions and traffic conditions. Sixthly, optimizing the SOC working interval (charge state) of the battery system: and optimizing the SOC working interval and the change speed so as to balance the service life of the battery and the fuel economy of the hybrid electric vehicle. Seventhly, optimizing the working range of the motor: and setting an optimized working interval of the motor on a torque speed characteristic curve, wherein the overall efficiency of the system is in an optimal state at the moment. And eighth, a zero emission policy is followed, namely in certain regions, the hybrid electric vehicle needs to work in an electric-only mode, and the driving mode of the hybrid electric vehicle needs to be met manually or automatically. It is therefore of the greatest importance and challenge to optimally distribute the distribution of the power demanded by the internal combustion engine and the electric motor of the motor vehicle in real time.
At present, aiming at a hybrid tractor, an energy distribution strategy of engine and motor torques is only carried out according to factors such as the current working condition of an automobile, for example, the current electric quantity of a battery system, the current speed of the automobile, the required torque of the automobile and the like; FIG. 1 is a schematic diagram of a mechanical topology of a hybrid tractor in the prior art; taking a parallel P2 hybrid power structure as an example, an engine and a motor are coaxial and can both output power to the whole vehicle to drive the whole vehicle; the engine and the motor are connected by a clutch, when a pure electric mode is adopted, the clutch is separated, the output power of the motor drives the whole vehicle to run, and the engine is in idle speed or is stopped at the moment and does not participate in the driving of the whole vehicle; when the engine mode is adopted, the clutch is combined, the output power of the engine drives the whole vehicle to run, and the motor is in an idling state at the moment; when the hybrid vehicle runs, the clutch is combined, and the output power of the engine and the motor drives the whole vehicle to run. Fig. 2 is a CAN topology diagram based on a hybrid tractor in the prior art. The whole vehicle is provided with two CAN control networks, namely a whole vehicle CAN and a power CAN. Fig. 3 is a flowchart of a control strategy of a hybrid vehicle in the prior art. And the HCU receives the conditions of an accelerator pedal, a brake pedal, the speed and the like of the vehicle, calculates the power and the torque required by the vehicle in real time, and then distributes the power and the torque. Under the conditions of low power requirement and low vehicle speed, only the motor is used for driving; the motor and the engine are used together when high power is required; in the state, firstly, the required torque and the power of the engine are calculated in real time according to the high-efficiency area condition of the engine, firstly, the required torque of the engine is matched and met, and then, the residual power and the torque are matched and supplied to the motor. Therefore, torque distribution is carried out by control logic knowledge in the prior art according to the conditions of the current vehicle speed, the current SOC and the like, and the next driving, road budget and driver driving style are not predicted, so that the control strategy can not meet the requirement that the whole vehicle can be in the optimal performance for a long time and the optimal economic performance of an engine can not be met.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy management method, device and system of a hybrid vehicle, the condition of predicting the future vehicle running power is added into an original control strategy, and then a real-time dynamic planning algorithm is carried out to distribute the required power of an engine and a motor, so that the long-time optimal power distribution can be achieved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an energy management method of a hybrid vehicle, comprising the steps of:
establishing a road identification database, a cycle driving identification database and a driving style database;
receiving an accelerator pedal signal and a brake pedal signal, and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output;
and according to the required power of the automobile in the prediction range, distributing the required power of the automobile to the engine and the motor by using an operation planning method.
Further, the road identification is used for identifying the current road condition including the traffic jam condition and the road identified before synchronization according to a GPS, a camera, a radar or a street map in a vehicle controller;
the cycle running identification is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile;
the driving style is used to define the driving habits of the driver.
Further, according to the power demand of the whole vehicle, the ideal time length of road identification, cyclic driving and driving style and the prediction power range is used as the input of a fuzzy logic algorithm, different driving conditions are calibrated as fuzzy rules, and the process of outputting the required power of the vehicle in the prediction range is as follows: the method comprises the steps of obtaining a finished automobile power demand according to the opening degree of an accelerator pedal and the opening degree of a brake pedal, realizing energy distribution of a hybrid system according to the finished automobile power demand, and then dividing an engine operation area and a motor operation area according to the automobile speed and the battery SOC.
Further, the dividing the operation region of the engine and the operation region of the motor according to the vehicle speed and the battery SOC includes:
when the required power is lower than a first threshold and the SOC of the battery is higher than a second threshold, the motor provides all required power;
when the required power is higher than the maximum power which can be provided by the engine at the current engine speed, the motor provides additional power;
when the braking energy is recovered, the motor converts the braking energy into electric energy to charge the battery;
providing power by the electric machine when the engine increases the demanded drive power but is not in the engine efficient zone at the speed of the feed point;
when the SOC of the battery is lower than the third threshold value, the output power of the engine at the moment is higher than the driving power, the engine provides extra power for the motor, and the motor charges the battery.
Further, the fuzzy logic algorithm comprises: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; and further lists the fuzzy control rules.
Further, the process of distributing the required power of the automobile to the engine and the motor by the operation planning method according to the required power of the automobile in the prediction range comprises the following steps:
decomposing a plurality of driving road sections of the driving route according to the mileage or the road mark, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information;
and calculating the required power of the motor in each period according to the total required power of the whole machine in each stroke period and the required power of the engine.
Further, the method for calculating the required power of the motor in each period according to the total required power of the whole machine and the required power of the engine in each stroke period comprises the following steps: and the required power of the motor in each period is equal to the difference value of the total required power of the whole machine in each stroke period and the required power of the engine.
An energy management device of a hybrid vehicle comprises a vehicle control unit, an engine controller, an engine, a motor controller and a motor;
the input end of the whole vehicle controller receives a door pedal signal and a brake pedal signal; the output end is respectively connected with an engine controller and a motor controller;
the vehicle control unit receives a door pedal signal and a brake pedal signal and respectively acquires a road identification database, a cycle driving identification database and a driving style database; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; according to the required power of the automobile in the prediction range, the operation planning method distributes the required power of the automobile to the engine and the motor; sending the required power of the engine to an engine controller, and sending the required power of the motor to a motor controller; the engine controller controls the electric wheels of the engine to rotate according to the power required by the engine; and the motor controller controls the motor to drive the wheels to rotate according to the required power of the motor.
An energy management system for a hybrid vehicle includes a database building module, an energy prediction module, and an allocation module
The database establishing module is used for establishing a road identification database, a cycle travel identification database and a driving style database;
the energy prediction module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output;
and the distribution module is used for distributing the required power of the automobile to the engine and the motor by using an operation planning method according to the required power of the automobile in the prediction range.
Further, the energy prediction module comprises a calculation module and a prediction module;
the calculation module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal;
the prediction module is used for calibrating different driving conditions as fuzzy rules according to the input of a fuzzy logic algorithm by taking road identification, cyclic driving and driving styles and the ideal time length of a prediction power range as the input of the whole vehicle power demand, and outputting the required power of the vehicle in the prediction range.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention provides an energy management method, a device and a system of a hybrid vehicle, wherein the method comprises the steps of establishing a road identification database, a cycle driving identification database and a driving style database; receiving an accelerator pedal signal and a brake pedal signal, and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; and distributing the required power of the automobile to the engine and the motor by using the operation planning method according to the required power of the automobile in the prediction range. The invention discloses an energy management method based on a hybrid vehicle and also provides an energy management system and a device of the hybrid vehicle.
The hybrid electric vehicle optimizes the matching strategy of the motor power and the engine power, optimizes the strategy of the hybrid electric vehicle, ensures that the hybrid electric vehicle has better economy, and ensures that the hybrid electric vehicle can ensure the optimal performance in a longer working period.
Drawings
FIG. 1 is a schematic diagram of a mechanical topology of a hybrid tractor in the prior art;
FIG. 2 is a CAN topological diagram based on a hybrid power tractor in the prior art;
FIG. 3 is a flowchart of a control strategy for a hybrid vehicle according to the prior art;
FIG. 4 is a flowchart of an optimal energy strategy in combination with a cyclic pattern recognition algorithm according to embodiment 1 of the present invention;
FIG. 5 is a flowchart of a control strategy for a hybrid vehicle in embodiment 1 of the present invention;
fig. 6 is a schematic diagram of a hybrid tractor driving pattern recognition algorithm in embodiment 1 of the present invention;
fig. 7 is a schematic diagram of fuzzy logic power distribution in embodiment 1 of the present invention.
FIG. 8 is a schematic diagram of fuzzy logic input and output in embodiment 1 of the present invention;
fig. 9 is a schematic diagram of fuzzy control rules in embodiment 1 of the present invention.
Fig. 10 is a schematic diagram of determining the optimal power distribution of the total power by the dynamic planning method in embodiment 1 of the present invention, fig. 11 is a schematic diagram of determining the optimal power distribution of the engine by the dynamic planning method in embodiment 1 of the present invention, and fig. 12 is a schematic diagram of determining the optimal power distribution of the motor by the dynamic planning method in embodiment 1 of the present invention;
fig. 13 is a schematic view of an energy management device of a hybrid vehicle according to embodiment 2 of the present invention;
fig. 14 is a schematic diagram of an energy management system of a hybrid vehicle according to embodiment 3 of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Example 1
The embodiment 1 of the invention provides an energy management method, device and system of a hybrid vehicle. FIG. 4 is a flowchart of an optimal energy strategy combined with a cyclic pattern recognition algorithm according to embodiment 1 of the present invention;
the method comprises the steps of establishing a road identification database, a cycle driving identification database and a driving style database;
receiving an accelerator pedal signal and a brake pedal signal, and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output;
and distributing the required power of the automobile to the engine and the motor by using the operation planning method according to the required power of the automobile in the prediction range.
FIG. 5 is a flowchart of a control strategy for a hybrid vehicle in embodiment 1 of the present invention; and (3) adding cycle driving identification, driver style identification and road identification on data collected by the vehicle control unit. And respectively establishing a road identification database, a cycle driving identification database and a driving style database. And storing the road identification database, the cycle driving identification database and the driving style database in the vehicle control unit.
The road identification refers to the condition of the current road condition, and is distinguished from the cyclic driving identification, the road identification is the condition of the current road condition, but not the condition in the whole cyclic process, the road identification condition is mainly based on data collected by a GPS, a radar or a camera which are installed on a vehicle, and then the analysis of the road identification, such as the GPS, the radar or the camera identifies the better road condition of the suburb road or the muddy road condition in the operation, even the road congestion condition is identified.
The cycle driving recognition is the condition of a working cycle, and the current driving mode of the automobile is determined according to the speed, the acceleration and the cycle storage data of the automobile.
The style of drivers mainly aims at the driving operation habits of drivers, some drivers drive slowly, and some drivers drive with larger amplitude, and the amplitude of the opening change rate of an accelerator pedal and the opening change rate of a brake pedal of an aggressive driver is larger than a first maximum set threshold value F1, and the speed is larger than a second maximum set threshold value V1;
the amplitude of the accelerator pedal change rate and the brake pedal change rate of the robust driver is in a range of F2 or more and F1 or less, and the speed is also in a range of V2 or more and V1 or less;
the magnitude of the rate of change of the accelerator pedal and the rate of change of the brake pedal of the prudent driver is less than a first minimum set threshold F2 and the speed is less than a second minimum set threshold V2.
Quantifying the driving style of a driver, wherein F1 is 50%, and V1 is 70 km/h; f2 was 30%, and V2 was 40 km/h.
After the vehicle control unit receives the information of the three databases, the vehicle control unit redistributes the required power of the vehicle to the engine and the motor; (note that there is no mechanical brake here, but only the driving state) the power distribution can be performed to the engine ECU and the motor MCU according to the three input quantity conditions, and the motor will perform power distribution and adjustment on the power distribution according to the three input quantity conditions at any time.
A braking mode portion:
when a brake pedal is stepped on, the vehicle control unit gives an instruction to the motor MCU to enable the motor MCU to generate negative torque, and the motor MCU controls the motor to generate electric braking, so that the vehicle can stop as soon as possible and generate electricity to feed back to the battery for charging.
When the driving mode is the same, only the previous accelerator pedal is changed into the current brake pedal, and when the vehicle controller receives a brake pedal signal and receives the cycle driving recognition, the driving style of a driver and the road recognition, the vehicle controller recognizes the torque regenerative braking power of the control motor, so that the vehicle can brake better.
The mechanical braking refers to the traditional pneumatic braking on the vehicle, when a brake pedal is stepped on, a braking gas circuit system on the vehicle can generate braking force, so that the vehicle stops, and at the moment, the vehicle controller only determines the stroke amount and the switching value when the brake pedal is stepped on, and does not control the mechanical braking.
The mechanical braking does not involve our new control logic and does not involve our patent content.
In summary, the following steps: when the vehicle is driven, the vehicle control unit distributes engine power and motor power;
when the vehicle is braked, the vehicle control unit only controls the braking power of the motor and does not control mechanical braking.
Fig. 6 is a schematic diagram of a hybrid tractor driving pattern recognition algorithm in embodiment 1 of the present invention. The vehicle controller receives an accelerator pedal signal and a brake pedal signal, and obtains a vehicle power demand according to the accelerator pedal opening and the brake pedal opening; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output;
the fuzzy logic rule determines the energy distribution of the hybrid system by the vehicle control unit according to the required power, and then divides the running area of the engine and the running area of the motor according to the vehicle speed V and the battery SOC, and the fuzzy logic rule specifically comprises the following steps:
when the required power is lower than a first threshold and the SOC of the battery is higher than a second threshold, the motor provides all required power; wherein the first threshold is 20% of the maximum power of the engine and the second threshold is 80% of the total charge of the battery.
When the required power is higher than the maximum power which can be provided by the engine at the current engine speed, the motor provides additional power;
when the braking energy is recovered, the motor converts the braking energy into electric energy to charge the battery;
providing power by the electric machine when the engine increases the required drive power but is not in a high efficiency region of the engine at the given point speed;
when the SOC of the battery is lower than a third threshold value, the output power of the engine at the moment is higher than the driving power, the engine provides extra power for the motor, and the motor charges the battery, and the third threshold value is 30% of the total amount of the battery.
Therefore, variables, namely the required power P of the whole vehicle and the SOC of the battery, are input into the fuzzy logic algorithm. Because the required power P of the whole vehicle is distributed between the engine and the motor, the requirement that the engine works in a high-efficiency region is met firstly, so that oil can be saved more, and the motor PHair-like deviceAnd because the response is fast, the motor is controlled by adopting a following strategy. Thus the fuzzy control output variables are the current output power and the motor output power PElectric power. Fig. 7 shows a schematic diagram of fuzzy logic power distribution in embodiment 1 of the present invention.
FIG. 8 is a schematic diagram of fuzzy logic input and output in embodiment 1 of the present invention; the areas of the input and output variables are all {0, 1 }. For PHair-like deviceAnd PElectric powerAnd 0 represents that the power is 0, 0.5 represents the optimal power value of the engine, the power value is determined according to the rotating speed of the engine and the optimal power efficiency, 1 represents the maximum power value of the engine, and the rest values correspond to the power by adopting a linear interpolation method.
The region of the battery SOC corresponds to the maximum minimum value allowed for the battery pack, where 0 represents SOC of 0 and 1 represents SOC of 100%.
Similarly, the whole vehicle demand P situation is similar, and the three functional relations are the overlapped symmetrical triangle and trapezoid shown in FIG. 7.
The total vehicle power demand P defines 5 fuzzy subsets: s1, S2, S3, S4, S5; SOC defines three: s1, S3, S5; pHair-like deviceIs S1, S2, S3, S4, S5; after determining each input and output variable fuzzy subset and membership function, fuzzy control rules can be listed, as shown in fig. 9, which is a schematic diagram of fuzzy control rules in embodiment 1 of the present invention.
And distributing the required power of the automobile to the engine and the motor by using the operation planning method according to the required power of the automobile in the prediction range. And determining the accurate control power of the engine and the motor at each time point in the prediction range according to the whole automobile power profile.
Firstly, decomposing a driving route into a plurality of driving road sections according to mileage or road marks, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information;
in a general driving cycle, a fixed driving road section is divided into a plurality of driving road sections according to the distribution of mileage or traffic lights, and the driving time of each driving road section is calculated. For example, the driving time of two traffic lights is taken as a period or two crossroads are taken as a period, the period is not fixed, but adjust the situation such as in the urban area according to the road condition change moment, we take the traffic light as the carrier to decompose, so when the GPS or the camera detects the traffic light, then calculating the running time of the automobile adjacent to the traffic light, comparing with the prediction logic stored in the system, adjusting the output power parameter to the prediction control logic, according to the input conditions of an accelerator pedal, a brake pedal, a battery SOC, a gradient sensor, camera radar acquisition, GPS acquisition, driver style acquisition and the like, in contrast to the parameter tables stored internally and specified previously in our HCU system, if the input parameters are within the error range of each parameter case stored in the input parameters, the output power of the response is obtained. For example, the original power output to the engine is P1, if the power is detected in advance through a GPS and a camera, the power output of the engine in the current road section can be predicted to be P1+, and the power output of P1+ is the optimal fuel saving point, so the vehicle controller sends a power demand of P1+ to the engine ECU, and the engine responds to the power of P1+, so that the power of the motor P is P-P1 +.
The number of the traffic lights, the crossroads or two sides divides the traffic lights into a plurality of small road sections, and the corresponding running time of each small road section is the period, so the time length of the first time period is not always consistent with that of the second time period. And comparing the input conditions such as an accelerator pedal, a brake pedal, a battery SOC, a gradient sensor, camera radar acquisition, GPS acquisition, driver style acquisition and the like with a parameter table which is stored in the HCU system and is determined in the prior table, and if the input parameters are in the error range of each parameter condition stored in the HCU system, obtaining the output power Pc of the response. Fig. 10 is a schematic diagram of determining the total power optimal power allocation by the dynamic planning method. According to the first time period, the total power optimization demand power in the first prediction step is obtained as P ═ Pb; in the second prediction step, the first time period P is Pc, and the second time period P is Pj; in the nth prediction step, the total required power in the first time period is P ═ Pg, and the second time period P ═ Ph … …, the nth time period P ═ Pw;
for example, when the vehicle runs in urban power, the speed of a driver is 40km/h in a first period (for example, the running time of the vehicle with two traffic lights), and the conditions that the acceleration is 2ms, the gradient sensor is 0, the SOC of a battery is 80% and the like are detected; the time length of the next period can be judged according to GPS positioning, the distance between the traffic light of the next week can be obtained according to GPS, then the time from the traffic light to the next traffic light of the automobile is roughly calculated according to the automobile speed, the acceleration and the gradient condition, so that the time period is obtained, and then the required power P of the whole automobile in the period is obtained according to the automobile speed, the accelerator pedal, the brake pedal and the driving habit of a driver according to fuzzy logic prediction; and then, the optimal power Peng of the engine in the period is obtained according to the SOC, the high-efficiency curve of the engine, the habit of a driver, the road condition and the like, the power P required by the vehicle is pre-predicted by the HCU before the vehicle runs in the period, and the power requirement for sending the Peng is started by the engine ECU in advance. Therefore, the power of the vehicle is adjusted, and the condition of saving oil and electricity is achieved. Fig. 11 is a schematic diagram of the dynamic planning method for determining the optimal power distribution of the engine in embodiment 1 of the present invention. The first time period Peng-Pb 1 in the first prediction step; in the second prediction step, the first time period Peng is Pc1, the second time period Peng is Pj1, and so on, in the nth prediction step, the engine required power in the first time period Peng 1, the engine required power in the second time period Peng is Ph1 …, and the nth time period Peng is Pw 1;
and the required power of each period of each prediction step of the motor is determined according to the total required power-the required power of the engine, and fig. 12 is a schematic diagram of determining the optimal power distribution of the motor by the real-time embodiment 1 dynamic planning method of the invention. The first time period Pe in the first prediction step is Pb-Pb1, the first time period Pe in the second prediction step is Pc-Pc1, the second time period Pe is Pj-Pj1, and so on, the first time period required power in the Nth prediction step is Pe-Pg 1, and the second time period required power is Pe in the Ph-Ph1 … …, the Nth time period Pe is Pw-Pw1.
When the above is combined to explain the prediction range of the nth step, the total power requirement is as follows: according to different time periods, the method comprises the following steps: a first time period P (1) ═ Pg, a second time period P (2) ═ Ph … …, nth time period P (N) ═ Pw;
the engine power demand: according to different time periods, the method comprises the following steps: a first time period Peng (1) ═ Pg1, a second time period Peng (2) ═ Ph1 … …, nth time period p (N) eng ═ Pw 1;
the power requirement of the motor is as follows: according to different time periods, the method comprises the following steps: the first time period Pe (1) ═ Pg-Pg1, and the second time period Pe (2) ═ Ph-Ph1 … … nth time period p (N) e ═ Pw-Pw1. Thus, the accurate control power of the engine and the motor at each predicted time point is obtained
Example 2
Based on the energy management method for the hybrid vehicle provided in embodiment 1 of the present invention, embodiment 2 of the present invention also provides an energy management device for a hybrid vehicle. Fig. 13 is a schematic diagram of an energy management device of a hybrid vehicle according to embodiment 2 of the present invention. The device comprises a vehicle controller, an engine, a motor controller and a motor;
the input end of the whole vehicle controller receives a door pedal signal and a brake pedal signal; the output end is respectively connected with an engine controller and a motor controller;
the vehicle control unit receives a door pedal signal and a brake pedal signal and respectively acquires a road identification database, a cycle driving identification database and a driving style database; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; according to the required power of the automobile in the prediction range, the operation planning method distributes the required power of the automobile to the engine and the motor; sending the required power of the engine to an engine controller, and sending the required power of the motor to a motor controller;
the engine controller controls the electric wheels of the engine to rotate according to the power required by the engine; the motor controller controls the motor to drive the wheels to rotate according to the power required by the motor.
Example 3
Based on the energy management method for the hybrid vehicle provided in embodiment 1 of the present invention, embodiment 3 of the present invention also provides an energy management system for the hybrid vehicle, and fig. 14 shows a schematic diagram of the energy management system for the hybrid vehicle in embodiment 3 of the present invention. The system comprises a database establishing module, an energy prediction module and an allocation module;
the system comprises a database building module, a road identification database, a cycle driving identification database and a driving style database, wherein the database building module is used for building the road identification database, the cycle driving identification database and the driving style database;
the energy prediction module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output;
and the distribution module is used for distributing the required power of the automobile to the engine and the motor by using the operation planning method according to the required power of the automobile in the prediction range.
The energy prediction module comprises a calculation module and a prediction module;
the calculation module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal;
the prediction module is used for calibrating different driving conditions as fuzzy rules according to the input of a fuzzy logic algorithm by taking road identification, cyclic driving and driving styles and the ideal time length of a prediction power range as the input of the whole vehicle power demand, and outputting the required power of the vehicle in the prediction range.
The road identification is used for identifying the current road condition including traffic jam condition and the road identified before synchronization according to GPS, a camera, a radar or a street map stored in a vehicle controller;
the cycle running identification is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile;
the driving style is used to define the driving habits of the driver.
According to the invention, the power demand of the whole vehicle is obtained according to the opening degree of an accelerator pedal and the opening degree of a brake pedal, the energy distribution of the hybrid system is realized according to the power demand of the whole vehicle, and then the running area of an engine and the running area of a motor are divided according to the vehicle speed and the SOC of a battery.
Dividing the operation region of the engine and the operation region of the motor according to the vehicle speed and the battery SOC includes:
when the required power is lower than a first threshold and the SOC of the battery is higher than a second threshold, the motor provides all required power;
when the required power is higher than the maximum power which can be provided by the engine at the current engine speed, the motor provides additional power;
when the braking energy is recovered, the motor converts the braking energy into electric energy to charge the battery;
providing power by the electric machine when the engine increases the required drive power but is not in a high efficiency region of the engine at the given point speed;
when the SOC of the battery is lower than the third threshold value, the output power of the engine at the moment is higher than the driving power, the engine provides extra power for the motor, and the motor charges the battery.
The fuzzy logic algorithm comprises the following steps: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; and further lists the fuzzy control rules.
According to the required power of the automobile in the prediction range, the process of distributing the required power of the automobile to the engine and the motor by the operation planning method comprises the following steps: decomposing a plurality of driving road sections of the driving route according to the mileage or the road mark, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information;
and calculating the required power of the motor in each period according to the total required power of the whole machine and the required power of the engine in each stroke period, wherein the required power of the motor in each period is equal to the difference value of the total required power of the whole machine and the required power of the engine in each stroke period.
According to the method, the situation of predicting the future automobile running power is added into the original control strategy, and then the real-time dynamic planning algorithm is carried out to distribute the required power of the engine and the motor, so that the optimal power distribution for a long time can be achieved, the matching strategy of the motor power and the engine power is optimized, the hybrid tractor strategy is optimized, the hybrid vehicle is better in economy, and the hybrid tractor can guarantee the optimal performance in a longer working period.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.

Claims (8)

1. A method of energy management for a hybrid vehicle, comprising the steps of:
establishing a road identification database, a cycle driving identification database and a driving style database;
receiving an accelerator pedal signal and a brake pedal signal, and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; the cycle running is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile; the fuzzy logic algorithm comprises: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; further listing fuzzy control rules;
according to the required power of the automobile in the prediction range, the operation planning method distributes the required power of the automobile to the engine and the motor; the specific process is as follows: decomposing a plurality of driving road sections of the driving route according to the mileage or the road mark, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information; and calculating the required power of the motor in each period according to the total required power of the whole machine in each stroke period and the required power of the engine.
2. The energy management method of a hybrid vehicle according to claim 1,
the road identification is used for identifying the current road condition including traffic jam condition and the road identified before synchronization according to a GPS, a camera, a radar or a street map in a vehicle controller;
the driving style is used to define the driving habits of the driver.
3. The energy management method of a hybrid vehicle according to claim 1, wherein the process of calibrating different driving conditions as fuzzy rules according to the power demand of the entire vehicle with road recognition, cyclic driving and driving style and ideal time length of the predicted power range as input of the fuzzy logic algorithm, and outputting the required power of the vehicle within the predicted range is as follows: the method comprises the steps of obtaining a finished automobile power demand according to the opening degree of an accelerator pedal and the opening degree of a brake pedal, realizing energy distribution of a hybrid system according to the finished automobile power demand, and then dividing an engine operation area and a motor operation area according to the automobile speed and the battery SOC.
4. The energy management method of a hybrid vehicle according to claim 3, wherein said dividing the operation region of the engine and the operation region of the motor according to the vehicle speed and the battery SOC comprises:
when the required power is lower than a first threshold and the SOC of the battery is higher than a second threshold, the motor provides all required power;
when the required power is higher than the maximum power which can be provided by the engine at the current engine speed, the motor provides additional power;
when the braking energy is recovered, the motor converts the braking energy into electric energy to charge the battery;
providing power by the electric machine when the engine increases the required drive power but is not in a high efficiency region of the engine at the given point speed;
when the battery SOC is lower than the third threshold value, the output power of the engine at the moment is higher than the driving power, the engine provides additional power for the motor, and the motor charges the battery.
5. The energy management method of a hybrid vehicle according to claim 1, wherein the method of calculating the motor required power per cycle from the total required power of the whole machine and the required power of the engine per stroke cycle comprises: and the required power of the motor in each period is equal to the difference value of the total required power of the whole machine in each stroke period and the required power of the engine.
6. An energy management device of a hybrid vehicle is characterized by comprising a vehicle control unit, an engine controller, an engine, a motor controller and a motor;
the input end of the whole vehicle controller receives an accelerator pedal signal and a brake pedal signal; the output end is respectively connected with an engine controller and a motor controller;
the vehicle control unit receives an accelerator pedal signal and a brake pedal signal and respectively acquires a road identification database, a cycle driving identification database and a driving style database; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; according to the required power of the automobile in the prediction range, the operation planning method distributes the required power of the automobile to the engine and the motor; sending the required power of the engine to an engine controller, and sending the required power of the motor to a motor controller; the cycle running is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile; the fuzzy logic algorithm comprises: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; further listing fuzzy control rules; the specific process of distributing the required power of the automobile to the engine and the motor by the operation planning method according to the required power of the automobile in the prediction range comprises the following steps: decomposing a plurality of driving road sections of the driving route according to the mileage or the road mark, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information; calculating the required power of the motor in each period according to the total required power of the whole machine and the required power of the engine in each stroke period;
the engine controller controls the engine to drive wheels to rotate according to the power required by the engine;
and the motor controller controls the motor to drive the wheels to rotate according to the required power of the motor.
7. The energy management system of the hybrid vehicle is characterized by comprising a database building module, an energy prediction module and an allocation module;
the database establishing module is used for establishing a road identification database, a cycle travel identification database and a driving style database;
the energy prediction module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal; according to the power demand of the whole automobile, road identification, cyclic driving and driving styles and the ideal time length of a predicted power range are used as the input of a fuzzy logic algorithm, different driving working conditions are calibrated to be used as fuzzy rules, and the required power of the automobile in the predicted range is output; the cycle running is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile; the fuzzy logic algorithm comprises: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; further listing fuzzy control rules;
the distribution module is used for distributing the required power of the automobile to the engine and the motor by using an operation planning method according to the required power of the automobile in the prediction range; the specific process is as follows: decomposing a plurality of driving road sections of the driving route according to the mileage or the road mark, and recording the driving time of each driving road section as a travel cycle; predicting total required power of the whole engine and required power of an engine in each travel period by adopting a real-time dynamic planning algorithm according to battery information, an operating environment and automobile system information; and calculating the required power of the motor in each period according to the total required power of the whole machine in each stroke period and the required power of the engine.
8. The energy management system of a hybrid vehicle according to claim 7, wherein the energy prediction module comprises a calculation module and a prediction module;
the calculation module is used for receiving an accelerator pedal signal and a brake pedal signal and obtaining the power demand of the whole vehicle according to the opening degree of the accelerator pedal and the opening degree of the brake pedal;
the prediction module is used for calibrating different driving conditions as fuzzy rules and outputting the required power of the automobile in a prediction range according to the power requirement of the whole automobile by taking road identification, cyclic driving and driving styles and the ideal time length of the prediction power range as the input of a fuzzy logic algorithm; the cycle running is used for determining the current running mode of the automobile according to the speed, the acceleration and the cycle storage data of the automobile; the fuzzy logic algorithm comprises: determining a subset of inputs; the input subsets comprise a first fuzzy subset of the required power of the whole vehicle and a second fuzzy subset of the SOC of the battery; determining fuzzy subsets and membership functions of engine output variables; and further lists the fuzzy control rules.
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