CN110422161B - Energy supply control method and system and logistics device applying same - Google Patents
Energy supply control method and system and logistics device applying same Download PDFInfo
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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
- 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
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- 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
- B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
- B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
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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
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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
- 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
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
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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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/24—Energy storage means
- B60W2710/242—Energy storage means for electrical energy
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
An energy supply control method, system and applied logistics device thereof comprise: acquiring mixed electric quantity power data; acquiring quantized transform data; discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data; iteratively processing the fuzzy characteristic data to obtain energy supply control data; and controlling the power battery and the engine by the energy supply control data. The invention solves the technical problems of poor energy-saving effect and short service life of the logistics transportation equipment in the prior art.
Description
Technical Field
The invention relates to a hybrid power control technology, in particular to an energy supply control method and system and a logistics device applied by the energy supply control method and system.
Background
Hybrid transportation devices have at least two power sources, typically including an engine, an auxiliary power unit, and a battery as the source. The energy required for the operation of the hybrid transportation equipment, a part of which comes from the internal combustion engine, converts chemical energy in the fuel into mechanical energy; the other part is from a power battery, and the electric energy is converted into mechanical energy through a motor. The distribution mode of the whole vehicle required energy among power sources is an energy management strategy of logistics transportation equipment. Because the energy conversion paths and the efficiencies of different power sources are different, the comprehensive energy consumption of the logistics transportation equipment is different due to the adoption of different energy management strategies. To improve the economics of logistics transportation facilities, a variety of energy management strategies have emerged. The energy management strategy and the online optimization method in the prior art cannot obtain a global optimal solution, cannot fully exert the energy-saving potential of logistics transportation equipment, cannot obtain the optimal overall economy, do not consider the influence of the power loss of a motor, and cannot ensure the lowest comprehensive energy consumption of a power system at the selected working point of the engine.
In summary, the technical problems of poor energy saving effect and short battery life of the logistics transportation equipment exist in the prior art.
Disclosure of Invention
In view of the technical problems of poor energy saving effect and short battery life of the logistics transportation equipment in the prior art, the invention aims to provide an energy supply control method, an energy supply control system and a logistics device applied by the energy supply control method, which solve the technical problems of poor energy saving effect and short battery life of the logistics transportation equipment in the prior art, and the energy supply control method comprises the following steps: acquiring mixed electric quantity power data; acquiring quantized transform data; discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data; iteratively processing the fuzzy characteristic data to obtain energy supply control data; so as to supply power to control the data control power battery and the engine.
In an embodiment of the present invention, the step of converting the fuzzy feature data includes: extracting the characteristics of the engine battery in the hybrid electric quantity power data; acquiring a preset simulation prediction model; and processing the characteristics of the engine battery by using a preset simulation prediction model to obtain fuzzy characteristic data.
In an embodiment of the present invention, the step of calculating the fuzzy feature data includes: acquiring fuzzy division data in a simulation prediction model; dividing the characteristics of the engine battery into fuzzy subsets according to the fuzzy division data; the fuzzy subsets are processed with a simulated predictive model to obtain fuzzy feature data.
In an embodiment of the present invention, the step of iteratively obtaining the energization control data includes: acquiring a preset iteration proportion parameter and an iteration termination condition; iterating the fuzzy characteristic data according to the iteration proportion parameter until an iteration termination condition is met; and saving the current fuzzy characteristic data as energy supply control data.
In one embodiment of the present invention, an energy supply control system includes: the induction acquisition device is used for acquiring and acquiring mixed electric quantity power data; a data converter for obtaining quantized converted data; the data processor is used for discretely processing the hybrid electric quantity power data according to the quantitative conversion data to obtain fuzzy characteristic data, and is connected with the induction acquisition device and the data converter; the energy supply data processing device is used for processing the fuzzy characteristic data in an iterative mode to obtain energy supply control data and is connected with the data processor; and the energy controller is used for controlling the power battery and the engine by the energy supply control data and is connected with the energy supply data processing device.
In one embodiment of the present invention, a data processor includes: the power and electric quantity unit is used for extracting an engine power characteristic and a battery electric quantity characteristic in the hybrid electric quantity and power data; the processing unit is used for acquiring a preset simulation prediction model and is connected with the power and electric quantity unit; and the characteristic data unit is used for processing the power characteristic of the engine and the electric quantity characteristic of the battery by using the preset simulation prediction model to obtain the fuzzy characteristic data, and is connected with the processing unit.
In one embodiment of the present invention, the feature data unit includes: the dividing component is used for acquiring fuzzy dividing data in the simulation pre-model; the engine power component is used for dividing the engine power characteristics into engine fuzzy subsets according to the fuzzy division data and is connected with the division component; the battery electric quantity component is used for dividing the battery electric quantity characteristics into electric quantity fuzzy subsets according to the fuzzy division data and is connected with the division component; and the characteristic processing component is used for processing the engine fuzzy subset and the electric quantity fuzzy subset by using the simulation prediction model to obtain fuzzy characteristic data, and is connected with the battery electric quantity component.
In one embodiment of the present invention, a powered data processing apparatus includes: the strategy receiving unit is used for acquiring preset genetic strategy data; the extraction unit is used for extracting iteration proportion parameters, proportion parameters and iteration termination conditions in the preset genetic strategy data and is connected with the strategy receiving unit; the iteration unit is used for iterating the fuzzy characteristic data according to iteration parameters and proportion parameters until the fuzzy characteristic data meets the iteration termination condition, and the iteration unit is connected with the extraction unit; and the control storage unit is used for storing the current fuzzy characteristic data as energy supply control data and is connected with the iteration unit.
In one embodiment of the present invention, a logistics apparatus includes: an equipment housing; the energy supply device is arranged inside the equipment shell; the energy supply control system of the logistics device is installed in the energy supply device and comprises: the induction acquisition device is used for acquiring and acquiring mixed electric quantity power data; a data converter for obtaining quantized converted data; a data processor for discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data; the energy supply data processing device is used for iteratively processing the fuzzy characteristic data to obtain energy supply control data; and the energy controller is used for controlling the power battery and the engine by the energy supply control data.
As described above, the present invention provides an energy supply control method, an energy supply control system, and a logistics device using the same, which overcome the disadvantages of the prior art, and provide a method for determining an engine operating point, which considers the influence of the engine operating point on the motor loss, and in a dynamic programming method, for a certain transfer path of a State of Charge (SOC) in two adjacent stages, since the variation of the SOC is determined, a corresponding target battery power can be obtained.
In summary, the invention provides an energy supply control method, an energy supply control system and a logistics device using the energy supply control system, and solves the technical problems of poor energy saving effect and short battery life of logistics transportation equipment in the prior art.
Drawings
Fig. 1 is a schematic diagram illustrating the steps of an energy supply control method according to the present invention.
Fig. 2 is a detailed flow chart of step S3 in fig. 1 in an embodiment.
FIG. 3 is a schematic diagram of fuzzy inference model data processing according to the present invention.
Fig. 4 is a schematic flowchart illustrating a specific implementation of step S33 in fig. 2.
FIG. 5 is a schematic diagram of a membership function of the required power of the motor according to the present invention.
FIG. 6 is a schematic diagram of membership function of the power battery pack of the present invention.
FIG. 7 is a schematic diagram of a power membership function of the auxiliary power unit of the present invention.
FIG. 8 is a schematic diagram of a fuzzy inference MAP for power distribution of an extended range electric logistics vehicle according to the present invention.
Fig. 9 is a flowchart illustrating a specific process of step S4 in fig. 1 in an embodiment.
Fig. 10 is a schematic diagram illustrating a simulation path processing data flow of the extended range electric logistics vehicle.
Fig. 11 shows a schematic diagram of the connection of the energy supply control system device of the invention.
Fig. 12 is a schematic diagram showing the connection of specific units of the data processor 3 in fig. 11 in an embodiment.
FIG. 13 is a diagram illustrating the connection of specific components of the feature data unit 33 of FIG. 12 in one embodiment
Fig. 14 is a schematic diagram showing the connection of specific units of the powered data processing device 4 in fig. 11 in one embodiment.
Fig. 15 is a schematic view showing the connection of the logistics apparatus used in the present invention.
Description of the element reference numerals
1 Induction acquisition device
2 data converter
3 data processor
4 energy supply data processing device
5 energy controller
31 power electric quantity unit
32 processing unit
33 characteristic data unit
41 policy receiving unit
42 extraction unit
43 iteration unit
44 control memory cell
331 dividing assembly
332 engine power assembly
333 battery power component
334 feature processing assembly
10 device housing
20 energy supply device
30 energy supply control system
Description of step designations
Method steps S1-S5
Method steps S31-S33
Method steps S331-S333
Method steps S41-S43
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Referring to fig. 1 to 11, it should be understood that the structures shown in the drawings are only for the purpose of understanding and reading the present disclosure, and are not intended to limit the conditions and conditions of the present invention, so that the present disclosure is not limited to the details of the technology, and any modifications of the structures, changes of the proportion and adjustments of the size, which are within the scope of the present disclosure, should not affect the function and the achievement of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Referring to fig. 1, a schematic diagram of a power supply control method according to the present invention is shown, as shown in fig. 1, a power supply control method includes:
s1, acquiring and obtaining the Power data of the hybrid electric quantity, in an embodiment, the extended range electric logistics vehicle includes two energy sources, namely, an APU (Auxiliary Power Unit) and a Power battery, and the entire vehicle operation mode of the extended range electric logistics vehicle can be operated in a pure electric mode, an extended range mode or a hybrid electric mode (HEV) as required. When the electric vehicle works in the range extending mode, the fuel saving rate is infinitely close to that of the pure electric vehicle along with the increase of the capacity of the battery pack, and the electric vehicle is a stable transition vehicle type of the pure electric vehicle;
s2, obtaining the quantized transformation data, in one embodiment, by quantizing the coefficient thetaSOC、
Fuzzification operation is carried out on the required power of the driving motor and the SOC of the ternary lithium power battery from actual continuous values to obtain discrete values;
s3, discretely processing the hybrid electric quantity and power data according to the quantized conversion data to obtain fuzzy characteristic data, wherein in one embodiment, precise values Of required power Of a driving motor, SOC (State Of Charge), APU (auxiliary Power Unit) required power and the like are converted into fuzzy language values by defining quantization coefficients through a fuzzy control strategy;
s4, processing the fuzzy characteristic data iteratively to obtain energy supply control data, in one embodiment, through a scaling coefficient
The discrete solution of the output variable APU required power is defuzzified to obtain an actual solution, and in the embodiment, a genetic algorithm can be adopted to optimize fuzzy control energy management strategy parameters of the extended range electric logistics vehicle;
and S5, controlling the power battery and the engine by the energy supply control data, wherein in one embodiment, when the electric quantity of the battery is consumed to a certain degree, the engine is started, and the engine supplies energy to the battery to charge the power battery. When the electric quantity of the battery is sufficient, the engine stops working, the battery drives the motor to provide the whole vehicle drive, the fuel cell vehicle FCV, the hybrid electric vehicle HEV and the pure electric vehicle EV in the electric vehicle drive the wheels by the motor, and the extended range electric vehicle completes the operation control strategy by the whole vehicle controller. The battery pack can be charged by a ground charging pile or a vehicle-mounted charger, and the engine can adopt a fuel oil type or a gas type.
Referring to fig. 2 and 3, which are a detailed flowchart and a fuzzy inference model data processing diagram of step S3 in fig. 1 in an embodiment, as shown in fig. 2 and 3, step S3 of converting fuzzy feature data includes:
s31, extracting the characteristics of the engine battery in the hybrid electric quantity and power data, wherein in one embodiment, the characteristics of the engine battery can be, for example, the electric quantity remaining value of the power battery pack, the output power thereof and the corresponding relationship thereof;
s32, acquiring a preset simulation prediction model, wherein in one embodiment, fuzzy characteristics of an engine, an auxiliary power device and a power battery pack can be simulated according to a membership function of the required power of the motor, a membership function of the SOC of the power battery pack and a membership function of the output power of an APU;
and S33, processing the battery characteristics of the engine by using a preset simulation prediction model to obtain fuzzy characteristic data, wherein in one embodiment, as shown in FIG. 3, the fuzzy control system has a double-input single-output structure and adopts a Mamdani reasoning method.
Referring to fig. 4, fig. 5, fig. 6, fig. 7 and fig. 8, a schematic diagram of a specific flow of step S33 in fig. 2, a schematic diagram of a membership function of a required power of a motor, a schematic diagram of a membership function of a power battery pack, a schematic diagram of a membership function of an auxiliary power device and a schematic diagram of a fuzzy inference MAP for power distribution of an extended range electric logistics vehicle in an embodiment are shown, as shown in fig. 4 to fig. 8, the step S33 of calculating fuzzy characteristic data includes:
s331, obtaining fuzzy division data in the simulation prediction model, in one embodiment, as shown in FIG. 5, the required power P of the driving motor can be obtainedmotorThe division into e.g. 9 fuzzy subsets: NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB;
s332, dividing the engine battery characteristics into fuzzy subsets according to the fuzzy division data, and in one embodiment, as shown in fig. 6 and 7, the power battery SOC may be divided into, for example, 7 fuzzy subsets, which are EL, VL, LO, ST, HI, VH, EH. APU demanded power value PAPU_outDividing into 8 fuzzy subsets, ES, VS, SM, MI，BG，VB，EB， VEB；
S333, processing the fuzzy subset with the simulation prediction model to obtain fuzzy feature data, in an embodiment, the fuzzy feature data may be listed according to the fuzzy subset, for example, the membership function partition data as follows:
. In one embodiment, the power system Of the extended range vehicle may be composed Of a power battery system, a power driving system, a vehicle control system and an auxiliary power system (APU), in one embodiment, precise values Of required power Of a driving motor, a State Of Charge (SOC) Of a ternary lithium power battery, required power Of the APU and the like are converted into fuzzy language values by defining quantization coefficients through a fuzzy control strategy, in this embodiment, when the battery power is sufficient, the power battery drives the motor to provide the required driving power Of the vehicle, and at this time, the engine does not participate in the operation.
Referring to fig. 9 and 10, which are a detailed flow chart of step S4 in fig. 1 and a simulation path processing data flow chart of the extended range electric logistics vehicle in an embodiment, as shown in fig. 9 and 10, the step S4 of iteratively acquiring the energy supply control data includes:
s41, acquiring preset iteration ratio parameters and iteration termination conditions, wherein in one embodiment, input data such as a target vehicle speed is processed by a vehicle power model, a wheel model, a traditional system model and a control strategy model to obtain input data of a motor model and an engine model, and actual vehicle speed is acquired by the motor model, the engine model and a battery model and the model processing;
s42, iterating the fuzzy characteristic data according to the iteration proportion parameters until an iteration termination condition is met, in one embodiment, optimizing a power distribution fuzzy control energy management strategy between a power battery and an APU of the extended range type electric logistics vehicle by adopting a genetic algorithm to reduce the comprehensive oil consumption and prolong the service life of the power battery, in the embodiment, the maximum iteration times of genetic operation can be set to be 40 generations, the initial population number is 40, the optimized variable number is 3, binary coding is adopted, a membership function and a fuzzy control rule are coded, and the length of a single variable code is 20; the total length of the individual is, for example, 60, and the gutter is set to, for example, 0.7. The fuzzy data in the foregoing table is digitized to obtain the following table. The fuzzy control rules in the following table are encoded and then converted into optimized variable parameters:
in this embodiment, in order to solve the optimal solution, a genetic algorithm toolbox such as a GATBX algorithm program and a complete vehicle simulation model established based on advsor software may be integrated through an adv _ no _ gui command, for example, to realize data interaction. The objective function is ObjV (n) ═ weight (1) ((200-max _ velocity) (n))/(200-90) + weight (2) ((oil) (n)) +1.85) -12)/(16-12) + weight (3) ("char _ coeff (n) - (-1.1))/((-0.9) -1.1)), where weight (i) is the weight coefficient value of the power battery charging coefficient, the power index maximum speed, the power index integrated fuel consumption and the power index of the logistics vehicle, respectively.
S43, storing the current fuzzy feature data as energy supply control data, in one embodiment, building an extended range electric logistics vehicle model based on Simulink, and performing joint simulation with Advisor. The next generation group repeats the above process until the evolution algebra is over.
Referring to fig. 11, which is a schematic diagram showing the connection between the energy supply control system devices of the present invention, as shown in fig. 11, an energy supply control system includes an induction acquisition device 1, a data converter 2, a data processor 3, an energy supply data processing device 4 and an energy supply controller 5,
the sensing and collecting device 1 is used for collecting and obtaining the Power data of the hybrid electric quantity, in one embodiment, the extended range type electric logistics vehicle comprises two energy sources of an APU (Auxiliary Power Unit) and a Power battery, and the whole vehicle running mode of the extended range type electric logistics vehicle can be according to the requirementOperating in an electric-only mode, a range-extended mode, or a hybrid electric mode (HEV). When the electric vehicle works in the range extending mode, the fuel saving rate is infinitely close to that of the pure electric vehicle along with the increase of the capacity of the battery pack, and the electric vehicle is a stable transition vehicle type of the pure electric vehicle; a data converter 2 for obtaining quantized converted data, in one embodiment by quantizing a coefficient thetaSOC、
Fuzzification operation is carried out on the required power of the driving motor and the SOC of the ternary lithium power battery from actual continuous values to obtain discrete values; the data processor 3 is used for discretely processing the hybrid electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data, the data processor 3 is connected with the induction acquisition device 1, the data processor 3 is connected with the data converter 2, and in one embodiment, accurate values Of required power Of a driving motor, SOC (State Of Charge), APU (auxiliary Power Unit) required power and the like are converted into fuzzy language values by defining quantization coefficients through a fuzzy control strategy; energy supply data processing means 4 for iteratively processing the fuzzy characteristic data to obtain energy supply control data, the energy supply data processing means 4 being connected to the data processor 3, in one embodiment, by a scaling factor
The discrete solution of the output variable APU required power is defuzzified to obtain an actual solution, and in the embodiment, a genetic algorithm can be adopted to optimize fuzzy control energy management strategy parameters of the extended range electric logistics vehicle; the energy controller 5 is used for controlling the power battery and the engine by the energy supply control data, the energy controller 5 is connected with the energy supply data processing device 4, in one embodiment, accurate values such as required power Of a driving motor, SOC (State Of Charge), APU (auxiliary Power Unit) required power and the like are converted into fuzzy language values by defining quantization coefficients through a fuzzy control strategy, in the embodiment, when the battery power is sufficient, the driving motor Of the power battery provides the driving power requirement Of the whole vehicle, and at the moment, the engine does not participate in the work. When the battery power is consumed to a certain levelAnd when the engine is started, the engine provides energy for the battery to charge the power battery. When the electric quantity of the battery is sufficient, the engine stops working, the battery drives the motor to provide the whole vehicle drive, the fuel cell vehicle FCV, the hybrid electric vehicle HEV and the pure electric vehicle EV in the electric vehicle drive the wheels by the motor, and the extended range electric vehicle completes the operation control strategy by the whole vehicle controller. The battery pack can be charged by a ground charging pile or a vehicle-mounted charger, and the engine can adopt a fuel oil type or a gas type.
Referring to fig. 12, which is a schematic diagram showing the connection of specific units of the data processor 3 in fig. 11 in an embodiment, as shown in fig. 12, the data processor 3 includes a power-electric-quantity unit 31, a processing unit 32 and a feature data unit 33, the power-electric-quantity unit 31 is used for extracting the engine power feature and the battery power feature in the hybrid electric-quantity power data; the processing unit 32 is used for acquiring a preset simulation prediction model, and the processing unit 32 is connected with the power and electric quantity unit 31, and in one embodiment, fuzzy characteristics of an engine, an auxiliary power device and a power battery pack can be simulated according to a membership function of required power of a motor, a membership function of SOC of the power battery pack and a membership function of output power of an APU (auxiliary Power Unit); a characteristic data unit 33, configured to process the engine power characteristic and the battery power characteristic with the preset simulation prediction model to obtain the fuzzy characteristic data, where the characteristic data unit 33 is connected to the processing unit 32, and in an embodiment, as shown in fig. 3, the fuzzy control system has a dual-input and single-output structure, and a Mamdani inference method is adopted.
Referring to fig. 13, which is a schematic diagram showing the connection of specific components of the feature data unit 33 in fig. 12 in an embodiment, as shown in fig. 13, the feature data unit 33 includes a dividing component 331, an engine power component 332, a battery level component 333, and a feature processing component 334, where the dividing component 331 is used to obtain fuzzy division data in the simulation pre-model; an engine power component 332 for dividing the engine power characteristic into engine fuzzy subsets according to the fuzzy division data, the engine power component 332 being connected to the dividing component 331; a battery power component 333, configured to divide the battery power characteristics into fuzzy subsets of power according to the fuzzy division data, where the battery power component 333 is connected to the division component 331; a feature processing component 334, configured to process the fuzzy subset of the engine and the fuzzy subset of the battery power with the simulation prediction model to obtain the fuzzy feature data, wherein the feature processing component 334 is connected to the battery power component 333. In one embodiment, the power system of the extended range vehicle may be composed of a power battery system, a power drive system, a vehicle control system, and an Auxiliary Power Unit (APU).
Referring to fig. 14, which is a schematic diagram showing the connection of specific units of the energy supply data processing device 4 in fig. 11 in an embodiment, as shown in fig. 14, the energy supply data processing device 4 includes a strategy receiving unit 41, an extracting unit 42, an iterating unit 43 and a control storage unit 44, the strategy receiving unit 41 is used for obtaining preset genetic strategy data, in one embodiment, input data such as a vehicle power model, a wheel model, a conventional system model and a control strategy model are processed by the strategy receiving unit 41, and actual vehicle speed is obtained by the motor model and the engine model, in one embodiment, the input data such as a target vehicle speed is processed by the motor model and the engine model, and the actual vehicle speed is obtained by the motor model, the; an extracting unit 42, configured to extract an iteration proportion parameter, a proportion parameter, and an iteration termination condition in the preset genetic strategy data, where the extracting unit 42 is connected to the strategy receiving unit 41, and in an embodiment, the fuzzy control energy management strategy for power distribution between the power battery and the APU of the extended range electric logistics vehicle is optimized by using a genetic algorithm to reduce the integrated oil consumption and improve the service life of the power battery, in this embodiment, a maximum iteration number of genetic operations may be set to be, for example, 40 generations, an initial population number is, for example, 40, an optimized variable number is, for example, 3, binary coding is used to code the membership function and the fuzzy control rule, and a single variable coding length is, for example, 20; the total length of the individual is, for example, 60, and the surrogate groove is set to, for example, 0.7; the iteration unit 43 is configured to iterate the fuzzy feature data according to the iteration parameter and the proportion parameter until the fuzzy feature data meets the iteration termination condition, and the iteration unit 43 is connected to the extraction unit 42. Repeating the process for the next generation group until the evolution algebra is finished; and the control storage unit 44 is used for storing the current fuzzy characteristic data as energy supply control data, and the control storage unit 44 is connected with the iteration unit 43.
Referring to fig. 15, which is a schematic connection diagram of a logistics apparatus applied in the present invention, as shown in fig. 15, a logistics apparatus includes an equipment housing 10, an energy supply apparatus 20 and an energy supply control system 30, the equipment housing 10; the energy supply device 20 is arranged inside the equipment shell; the energy supply control system 30 of the logistics apparatus is installed in the energy supply apparatus 20, and includes: the induction acquisition device is used for acquiring and acquiring mixed electric quantity power data; a data converter for obtaining quantized converted data; a data processor for discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data; the energy supply data processing device is used for iteratively processing the fuzzy characteristic data to obtain energy supply control data; and the energy controller is used for controlling the power battery and the engine by the energy supply control data.
In summary, a method for determining an engine operating point is provided, which considers the influence of the engine operating point on the motor loss, and in the dynamic programming method, for a certain transfer path of the state variable battery SOC in two adjacent stages, because the variation of the SOC is determined, the corresponding target battery power can be obtained.
In summary, the invention provides an energy supply control method, an energy supply control system and a logistics device using the energy supply control system, and solves the technical problems of poor energy saving effect and short battery life of logistics transportation equipment in the prior art.
Claims (9)
1. An energy supply control method, comprising:
acquiring mixed electric quantity power data;
acquiring quantized transform data;
discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data;
iteratively processing the fuzzy characteristic data to obtain energy supply control data;
and controlling the power battery and the engine by the energy supply control data.
2. The method of claim 1, wherein the step of obtaining fuzzy feature data comprises:
extracting engine battery characteristics in the hybrid electric quantity power data;
acquiring a preset simulation prediction model;
and processing the characteristics of the engine battery by using the preset simulation prediction model to obtain the fuzzy characteristic data.
3. The method of claim 2, wherein the step of computing the fuzzy feature data comprises:
acquiring fuzzy division data in the preset simulation prediction model;
partitioning the engine battery characteristics into fuzzy subsets according to the fuzzy partition data;
and processing the fuzzy subset by using the preset simulation prediction model to calculate the fuzzy characteristic data.
4. The method of claim 1, wherein said step of iteratively obtaining said energization control data comprises:
acquiring a preset iteration proportion parameter and an iteration termination condition;
iterating the fuzzy characteristic data according to iteration proportion parameters until the iteration termination condition is met;
and saving the current fuzzy characteristic data as energy supply control data.
5. An energy supply control system, comprising:
the induction acquisition device is used for acquiring and acquiring mixed electric quantity power data;
a data converter for obtaining quantized converted data;
a data processor for discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data;
the energy supply data processing device is used for iteratively processing the fuzzy characteristic data to obtain energy supply control data;
and the energy controller is used for controlling the power battery and the engine by the energy supply control data.
6. The system of claim 5, wherein the data processor comprises:
the power and electric quantity unit is used for extracting an engine power characteristic and a battery electric quantity characteristic in the hybrid electric quantity and power data;
the processing unit is used for acquiring a preset simulation prediction model;
and the characteristic data unit is used for processing the engine power characteristic and the battery electric quantity characteristic by using the preset simulation prediction model to obtain the fuzzy characteristic data.
7. The system of claim 6, wherein the profile data unit comprises:
the dividing component is used for acquiring fuzzy dividing data in the preset simulation prediction model;
an engine power component to divide the engine power characteristic into an engine fuzzy subset according to the fuzzy partition data;
the battery electric quantity component is used for dividing the battery electric quantity characteristics into electric quantity fuzzy subsets according to the fuzzy division data;
and the characteristic processing component is used for processing the engine fuzzy subset and the electric quantity fuzzy subset by using the preset simulation prediction model so as to obtain the fuzzy characteristic data.
8. The system of claim 5, wherein the powered data processing device comprises:
the strategy receiving unit is used for acquiring preset genetic strategy data;
the extraction unit is used for extracting iteration parameters, proportion parameters and iteration termination conditions in the preset genetic strategy data; the iteration unit is used for iterating the fuzzy characteristic data according to iteration parameters and proportion parameters until the fuzzy characteristic data meets the iteration termination condition;
and the control storage unit is used for storing the current fuzzy characteristic data as energy supply control data.
9. A logistics apparatus, comprising:
an equipment housing;
the energy supply device is arranged inside the equipment shell;
the energy supply control system of the logistics device is installed in the energy supply device and comprises:
the induction acquisition device is used for acquiring and acquiring mixed electric quantity power data;
a data converter for obtaining quantized converted data;
a data processor for discretely processing the mixing electric quantity power data according to the quantized conversion data to obtain fuzzy characteristic data;
the energy supply data processing device is used for iteratively processing the fuzzy characteristic data to obtain energy supply control data;
and the energy controller is used for controlling the power battery and the engine by the energy supply control data.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105262147A (en) * | 2015-11-19 | 2016-01-20 | 中国电建集团华东勘测设计研究院有限公司 | Fuzzy decision method for island micro-grid including wind-solar-diesel-battery and seawater desalination system |
| US20160332616A1 (en) * | 2015-05-14 | 2016-11-17 | Ford Global Technologies, Llc | Battery State of Charge Control with Preview Information Classification |
| CN106274888A (en) * | 2015-05-28 | 2017-01-04 | 上海通用汽车有限公司 | The SOC control system of hybrid vehicle and control method thereof |
| CN108608876A (en) * | 2018-04-09 | 2018-10-02 | 江苏理工学院 | Extended-range mixed power electric car composite power source Energy Management System control strategy |
| US20190016230A1 (en) * | 2017-07-12 | 2019-01-17 | Ford Global Technologies, Llc | Battery thermal conditioning pump control for electric vehicle |
| CN109591659A (en) * | 2019-01-14 | 2019-04-09 | 吉林大学 | A kind of pure electric automobile energy management control method of intelligence learning |
-
2019
- 2019-06-27 CN CN201910572629.6A patent/CN110422161B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160332616A1 (en) * | 2015-05-14 | 2016-11-17 | Ford Global Technologies, Llc | Battery State of Charge Control with Preview Information Classification |
| CN106274888A (en) * | 2015-05-28 | 2017-01-04 | 上海通用汽车有限公司 | The SOC control system of hybrid vehicle and control method thereof |
| CN106274888B (en) * | 2015-05-28 | 2019-01-11 | 上海通用汽车有限公司 | The SOC control system and its control method of hybrid vehicle |
| CN105262147A (en) * | 2015-11-19 | 2016-01-20 | 中国电建集团华东勘测设计研究院有限公司 | Fuzzy decision method for island micro-grid including wind-solar-diesel-battery and seawater desalination system |
| US20190016230A1 (en) * | 2017-07-12 | 2019-01-17 | Ford Global Technologies, Llc | Battery thermal conditioning pump control for electric vehicle |
| CN108608876A (en) * | 2018-04-09 | 2018-10-02 | 江苏理工学院 | Extended-range mixed power electric car composite power source Energy Management System control strategy |
| CN109591659A (en) * | 2019-01-14 | 2019-04-09 | 吉林大学 | A kind of pure electric automobile energy management control method of intelligence learning |
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