CN111823883A - Power distribution method of pure electric vehicle - Google Patents
Power distribution method of pure electric vehicle Download PDFInfo
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- CN111823883A CN111823883A CN202010661774.4A CN202010661774A CN111823883A CN 111823883 A CN111823883 A CN 111823883A CN 202010661774 A CN202010661774 A CN 202010661774A CN 111823883 A CN111823883 A CN 111823883A
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- 238000005206 flow analysis Methods 0.000 claims description 2
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- 238000012935 Averaging Methods 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract description 6
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- 238000007726 management method Methods 0.000 description 26
- 238000006243 chemical reaction Methods 0.000 description 4
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- LTPSRQRIPCVMKQ-UHFFFAOYSA-N 2-amino-5-methylbenzenesulfonic acid Chemical compound CC1=CC=C(N)C(S(O)(=O)=O)=C1 LTPSRQRIPCVMKQ-UHFFFAOYSA-N 0.000 description 1
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- 125000004122 cyclic group Chemical group 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L1/00—Supplying electric power to auxiliary equipment of vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
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- 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
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- 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
- Y02T10/72—Electric energy management in electromobility
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
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- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a power distribution method of a pure electric vehicle, and belongs to the technical field of energy management of pure electric vehicles. The method comprises the following steps: step 1: establishing a control model; step 2: defining a fuzzy subset; and step 3: determining a membership function; and 4, step 4: formulating an inference rule; and 5: selecting an inference method; step 6: a deblurring method is selected. The invention effectively reduces the cycle energy consumption rate of the whole vehicle under the working condition, optimizes the available output power of the power system, and improves the power performance and the economic performance of the whole vehicle for a long time period on the premise of not increasing or decreasing the whole energy of the power battery.
Description
Technical Field
The invention relates to a power distribution method of a pure electric vehicle, and belongs to the technical field of energy management of pure electric vehicles.
Background
And the software and hardware system is used for coordinating, distributing and controlling the working energy of the energy conversion device of the power system of the electric automobile. The hardware of the energy management system comprises a series of sensors, a control unit ECU, an execution element and the like, and the software system mainly has the functions of analyzing and processing signals of the sensors, optimizing and analyzing the working energy of the energy conversion device and sending instructions to the execution element. Therefore, it can be said that the energy management system of the electric vehicle has the function of realizing that energy flows between energy conversion devices (such as a motor, an energy storage device, a power conversion module, a thermal management system and the like) according to the optimal route and maximizing the energy utilization efficiency of the whole vehicle on the premise of meeting the requirements of basic technical performance (such as dynamic performance, driving stability and the like) and cost of the vehicle according to the characteristics of each component and the operation condition of the vehicle.
The current electric vehicle energy management adopts a threshold value control method, although the control is simple and reliable, the control rule is fixed, the power distribution problem cannot be optimally solved, and the power performance and the economic performance are influenced.
The confirmation and calibration of the membership function of the control model variable require a large amount of data, the actual effect is greatly influenced by the sample size and the scene, and the global optimization of working condition adaptability, cyclic working conditions, special working conditions and dynamic planning needs to be considered, so that the single driving cycle is globally optimal. In order to make an electric vehicle have good mechanical performance, good electric driving performance, reasonable energy distribution and the like, an energy management system of the electric vehicle must effectively monitor and control the work of an energy system, so that the energy of the electric vehicle flows optimally, the energy is utilized to the maximum extent, and the economic performance of the vehicle is improved.
Disclosure of Invention
The invention provides a power distribution method of a pure electric vehicle, which is characterized in that a control model input-output membership function is formulated through large-sample multi-scene data acquisition, and the optimization of an energy management control result is completed by applying a fuzzy control theory. The method has ideal dynamic property and economical efficiency.
The invention adopts the following technical scheme for solving the technical problems:
a power distribution method of a pure electric vehicle comprises the following steps:
step 1: building a control model
Through the whole vehicle energy flow analysis, input and output variables and a mutual relation are searched, and a two-layer fuzzy control model is established;
step 2: defining fuzzy subsets
Performing geometric definition and normalization processing on input variables through actual experience and effect;
and step 3: determining membership functions
Determining a membership function through data statistics of a large sample and multiple scenes in combination with expert experience;
and 4, step 4: formulating inference rules
Making an inference rule according to project experience, use scenes and subsystem requirements;
and 5: selection reasoning method
Determining a classic Mamdani reasoning method through project experience and simulation analysis;
step 6: selection of deblurring methods
And determining a classical mom ambiguity-resolving method through project experience and simulation analysis.
In the step 1, the input variables are battery electric quantity, temperature offset, torque change rate, motor temperature and vehicle speed, and the output variables are power distribution coefficient and motor power.
The two layers of fuzzy control models in the step 1 are a power distribution coefficient fuzzy control model and a motor output power fuzzy control model.
Step 2 the fuzzy subsets comprise an input fuzzy subset and an output fuzzy subset,
the input fuzzy subset is:
fuzzy set of torque change rate (zone division) { small, medium, large }; domain (value range) {0:0.1:1}
SOC fuzzy set { small, medium, large }; discourse domain {0:0.1:1}
Fuzzy set of temperature deviation rate { small, medium and large }; discourse domain {0:0.1:1}
A fuzzy set of motor temperature { small, medium and large }; discourse domain {0:0.1:1}
Vehicle speed fuzzy set { small, medium, large }; discourse domain {0:0.1:1}
The output fuzzy subset is:
a fuzzy set of power distribution coefficients { small, medium, large }; the universe of discourse {0:0.1:1 }.
Step 3 the membership function is
Pbat＝Pmot+KTMSPTMS-MAX+PLP(1)
Pmot＝f(V,T,KTMS) (3)
Wherein: pbatIs the battery power, PTMS-MAXFor maximum power of the thermal management system, PLPFor low-voltage system power, PLP_iFor low voltage subsystem power, Δ PiThe variable quantity of the low-voltage subsystem power is represented, lambda is a low-voltage system power adjusting coefficient, and f () represents a functional mapping relation between the motor power and the vehicle speed, the motor temperature and the thermal management temperature; kTMSDistributing coefficients for the power; t is the motor temperature; v is the vehicle speed; pmotThe output power is available for the motor.
And 4, the inference rule is as follows:
(1) when the vehicle starts, if the working temperature Tbat of the power battery is lower, P is preferentially metTMSIn which P isTMSThermal management system power is indicated.
(2) When the vehicle starts, if the working temperature Tbat of the power battery is normal, P is preferentially metmotorIn which P ismotorRepresenting the motor power.
(3) When the automobile accelerates suddenly or runs on a slope, namely d theta/dt is larger than a certain set threshold value, P is started preferentiallymotorTo meet the high power requirement of the driving motor, and then P is startedTMS。
(4) When the automobile runs at normal speed, namely d theta/dt is smaller than a certain set threshold value, P is preferentially metTMSImprove the charging and discharging efficiency and satisfy PmotorAnd the common power requirement of the driving motor is ensured.
(5) If the battery SOC is low, P should be satisfied preferentiallymotor。
(6) If the battery SOC is high, shouldPreferably satisfies PTMS。
(7) If the motor temperature is high, P should be satisfied preferentiallyTMS。
(8) If the vehicle speed is high, P should be satisfied preferentiallyTMS。
(9) If the vehicle speed is medium, P should be satisfied preferentiallymotor。
And 6, the classical mom ambiguity resolution method is a maximum membership degree average method.
The invention has the following beneficial effects:
the invention can provide higher output power for the driving system when the electric quantity is low, can reduce the energy consumption rate when the electric quantity is high, and improves the power performance and the economic performance of the whole vehicle for a long time period on the premise of not increasing or decreasing the whole energy of the power battery.
Drawings
FIG. 1 is a topological diagram of high-voltage components of a pure electric vehicle.
FIG. 2 is a structure diagram of high-voltage components of a pure electric vehicle.
FIG. 3 is a control strategy model diagram.
Fig. 4 is a graph comparing energy consumption.
Fig. 5 is a graph of available power versus.
Fig. 6 is a driving range simulation diagram.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the electric power appliance of the pure electric vehicle mainly comprises an air conditioner, a motor, a heat management system and a low-voltage power utilization system; the energy management system adjusts the power of the air conditioner according to the available power of the battery and the required power of the motor so as to adapt to the power requirement and the economic requirement of the power performance, and the power management controller is used as a central control unit of the power/energy management and distribution system and is responsible for commanding, controlling and coordinating various components. The communication between the power management controller and all of the power converters and power control units is to send control commands and to receive sensor signals and status reports from these devices. It also communicates with the vehicle system controller to enable interaction with other systems of the vehicle. All power and energy management algorithms are implemented in the power manager.
As shown in fig. 2, the battery provides the energy supply required by all the components to the heat management system motor, the power system and the low-voltage power supply system.
Pbat＝Pmot+KTMSPTMS-MAX+PLP(1)
Pmot＝f(V,T,KTMS) (3)
Wherein: pbatIs the battery power, PTMS-MAXFor maximum power of the thermal management system, PLPFor low-voltage system power, PLP_iFor low voltage subsystem power, Δ PiThe variable quantity of the low-voltage subsystem power is represented, lambda is a low-voltage system power adjusting coefficient, and f () represents a functional mapping relation between the motor power and the vehicle speed, the motor temperature and the thermal management temperature; kTMSDistributing coefficients for the power; t is the motor temperature; v is the vehicle speed; pmotThe output power is available for the motor.
As shown in fig. 3, the energy flow distribution for the electric drive system and the high-voltage accessories is used to coordinate the output powers of the power system and the high-voltage accessories in different driving scenarios under the constraint conditions of vehicle dynamics, economy and smoothness, so as to reduce the vehicle energy consumption; and introducing a power distribution coefficient of an energy management system according to the characteristics of the whole vehicle and the battery, and establishing a two-stage fuzzy control model.
SOC represents the battery electric quantity, and 0 represents the lowest residual electric quantity of the power battery; and 1 represents that the residual capacity of the power battery is highest. Δ T represents a battery temperature deviation rate, and 0 represents that a temperature difference between the current battery temperature and the optimal battery temperature is minimum; 1 denotes that the temperature difference between the current battery temperature and the optimum battery temperature is the largest. KTMSRepresents a power distribution coefficient; 0 indicates that thermal management is required with the lowest available power; 1 indicates that the highest power available requires thermal management. T represents the motor temperature; 0 represents the motor temperature minimum; 1 indicates that the motor temperature is highest. V represents a vehicle speed; 0 indicates that the vehicle speed is low; 1 represents the maximum vehicle speed.PmotRepresenting the maximum output power of the motor. d θ/dt represents the rate of change of torque; 0 represents that the frequency of the driver stepping on the accelerator pedal is low, namely the vehicle runs normally; 1 means that the driver has a high frequency of depressing the accelerator pedal, i.e., the vehicle accelerates suddenly or runs uphill.
When Topi _ min is less than or equal to Tbat is less than or equal to Topti _ max, delta T is 0;
when Tbat ≧ Topti _ max, Δ T ═ Tbat-Topti _ max)/(Tmax-Topti _ max);
tbat < Topti _ min, (Topti _ min-Tbat)/(Topti _ min-Tmin).
Wherein Topti _ max is the upper limit value of the optimal working temperature interval of the battery; topti _ min is the lower limit value of the optimal working temperature interval of the battery; tbat is the battery temperature median.
Compared with the threshold control method and the non-thermal management control method, the energy consumption of the fuzzy control in fig. 4 is better improved, and the system economy is saved to a certain extent. The available power of the battery under fuzzy control in fig. 5 is better improved than that of the threshold control method and the non-thermal management control method under the condition that the power requirement is met, and the system dynamic property is saved to a certain extent. FIG. 6 shows the relationship between driving range, vehicle speed and air conditioner, and the effect of vehicle speed change and air conditioner power change on driving range reflects the effective strategy function.
The vehicle energy consumption of the fuzzy control method is lower than the threshold value control method and the non-thermal management control method, and compared with the two methods, the fuzzy control method has better economical efficiency. In a low SOC interval, the output power of the energy system using the method is higher than a threshold value control method and a non-thermal management control method, which shows that the energy system can provide higher output power for a driving system in the later running period of the vehicle, so that the whole vehicle has ideal power performance for a long time on the premise of not increasing the overall energy of a power battery.
Claims (5)
1. A power distribution method of a pure electric vehicle is characterized by comprising the following steps:
step 1: building a control model
Through the whole vehicle energy flow analysis, input and output variables and a mutual relation are searched, and a two-layer fuzzy control model is established;
step 2: defining fuzzy subsets
Performing geometric definition and normalization processing on input variables through actual experience and effect;
and step 3: determining membership functions
Determining a membership function through data statistics of a large sample and multiple scenes in combination with expert experience;
and 4, step 4: formulating inference rules
Making an inference rule according to project experience, use scenes and subsystem requirements;
and 5: selection reasoning method
Determining a classic Mamdani reasoning method through project experience and simulation analysis;
step 6: selection of deblurring methods
And determining a classical mom ambiguity-resolving method through project experience and simulation analysis.
2. The power distribution method of the pure electric vehicle according to claim 1, wherein the input variables in step 1 are battery level, temperature offset, torque change rate, motor temperature and vehicle speed, and the output variables are power distribution coefficient and motor power.
3. The power distribution method of the pure electric vehicle according to claim 1, wherein the two-layer fuzzy control model in step 1 is a power distribution coefficient fuzzy control model and a motor output power fuzzy control model.
4. The power distribution method of the pure electric vehicle according to claim 1, wherein the membership function in step 3 is
Pbat＝Pmot+KTMSPTMS-MAX+PLP(1)
Pmot＝f(V,T,KTMS) (3)
Wherein: pbatIs the battery power, PTMS-MAXFor maximum power of the thermal management system, PLPFor low-voltage system power, PLP_iFor low voltage subsystem power, Δ PiThe variable quantity of the low-voltage subsystem power is represented, lambda is a low-voltage system power adjusting coefficient, and f () represents a functional mapping relation between the motor power and the vehicle speed, the motor temperature and the thermal management temperature; kTMSDistributing coefficients for the power; t is the motor temperature; v is the vehicle speed; pmotThe output power is available for the motor.
5. The power distribution method of the pure electric vehicle according to claim 1, wherein the classical mom deblurring method in step 6 is a maximum membership averaging method.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112455423A (en) * | 2020-11-27 | 2021-03-09 | 重庆青山工业有限责任公司 | Pure electric starting control method of double-motor hybrid electric vehicle |
| CN112829590A (en) * | 2021-02-04 | 2021-05-25 | 东风汽车集团股份有限公司 | Pure electric vehicle energy flow calculation method, system and medium based on vehicle economy |
| CN113071507A (en) * | 2021-03-22 | 2021-07-06 | 江铃汽车股份有限公司 | Electric automobile energy management control method based on fuzzy control |
| CN117341535A (en) * | 2023-12-04 | 2024-01-05 | 四川鼎鸿智电装备科技有限公司 | Intelligent electric engineering machinery energy management method and system |
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2020
- 2020-07-10 CN CN202010661774.4A patent/CN111823883A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112455423A (en) * | 2020-11-27 | 2021-03-09 | 重庆青山工业有限责任公司 | Pure electric starting control method of double-motor hybrid electric vehicle |
| CN112455423B (en) * | 2020-11-27 | 2022-11-01 | 重庆青山工业有限责任公司 | Pure electric starting control method of double-motor hybrid electric vehicle |
| CN112829590A (en) * | 2021-02-04 | 2021-05-25 | 东风汽车集团股份有限公司 | Pure electric vehicle energy flow calculation method, system and medium based on vehicle economy |
| CN113071507A (en) * | 2021-03-22 | 2021-07-06 | 江铃汽车股份有限公司 | Electric automobile energy management control method based on fuzzy control |
| CN113071507B (en) * | 2021-03-22 | 2022-03-01 | 江铃汽车股份有限公司 | Electric automobile energy management control method based on fuzzy control |
| CN117341535A (en) * | 2023-12-04 | 2024-01-05 | 四川鼎鸿智电装备科技有限公司 | Intelligent electric engineering machinery energy management method and system |
| CN117341535B (en) * | 2023-12-04 | 2024-02-06 | 四川鼎鸿智电装备科技有限公司 | Intelligent electric engineering machinery energy management method and system |
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