CN113942491B - Series hybrid power system and networking hybrid power vehicle energy management method - Google Patents
Series hybrid power system and networking hybrid power vehicle energy management method Download PDFInfo
- Publication number
- CN113942491B CN113942491B CN202111433971.1A CN202111433971A CN113942491B CN 113942491 B CN113942491 B CN 113942491B CN 202111433971 A CN202111433971 A CN 202111433971A CN 113942491 B CN113942491 B CN 113942491B
- Authority
- CN
- China
- Prior art keywords
- power
- generator
- vehicle
- engine
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000006855 networking Effects 0.000 title claims abstract description 49
- 238000007726 management method Methods 0.000 title claims abstract description 20
- 238000004146 energy storage Methods 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 30
- 230000008859 change Effects 0.000 claims description 35
- 238000013528 artificial neural network Methods 0.000 claims description 25
- 230000001105 regulatory effect Effects 0.000 claims description 17
- 239000003990 capacitor Substances 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000004804 winding Methods 0.000 claims description 11
- 238000000034 method Methods 0.000 claims description 10
- 230000008878 coupling Effects 0.000 claims description 7
- 238000010168 coupling process Methods 0.000 claims description 7
- 238000005859 coupling reaction Methods 0.000 claims description 7
- 238000013135 deep learning Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 4
- 239000004973 liquid crystal related substance Substances 0.000 claims description 4
- 229910052744 lithium Inorganic materials 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 4
- 230000001537 neural effect Effects 0.000 claims description 4
- 230000008844 regulatory mechanism Effects 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 7
- 238000010248 power generation Methods 0.000 description 5
- 239000012634 fragment Substances 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 230000005284 excitation Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000005036 nerve Anatomy 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007363 regulatory process Effects 0.000 description 1
Classifications
-
- 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
- B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
-
- 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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
- B60W2710/0644—Engine speed
-
- 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
- B60W2710/08—Electric propulsion units
- B60W2710/081—Speed
-
- 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
Abstract
The invention provides a serial hybrid power system and a networking hybrid power vehicle energy management method thereof. The series hybrid power system comprises an engine-generator set, a rectifying module, an energy storage module, a driving load module, a networking power prediction module and a central processing unit. According to the energy management method, the vehicle driving demand power of a future road is predicted according to networking information such as cloud historical traffic data and real-time traffic data, the power output level mode of the hybrid power system is adjusted according to the power demand level and the current vehicle driving state, and the output power of a generator is further adjusted by reasonably adjusting the rotating speed of the engine, so that the use requirement of the system is met. The invention can effectively and quickly adjust the output power level of the hybrid power system by combining the predicted driving power and match the power demand of the vehicle.
Description
Technical Field
The invention belongs to the technical field of vehicle networking driving energy management, and particularly relates to a serial hybrid power system and a networking hybrid power vehicle energy management method thereof.
Background
At the end of the 20 th century, energy crisis and environmental pollution have become hot spot problems to be solved in the world, and electric automobile technology is increasingly emphasized and studied. The hybrid electric vehicle has the characteristics of an internal combustion engine power driving system and an electrified driving system, can effectively reduce tail gas emission and improve fuel economy, is not constrained by the construction of charging facilities and the endurance of batteries, and is a technology with great market competitiveness. Common hybrid powertrain topologies include series, parallel, and series-parallel. In the tandem structure, the engine drives the generator to generate electricity, and the generator and the electric energy output by the battery are supplied to the motor to drive the vehicle to run, and the power transmission way completely depends on the cable, so that the space arrangement is easy, and the special use requirements of heavy transportation vehicles, such as large buses, trucks, armored vehicles and the like, can be met.
In a common series hybrid power system, an engine is maintained to operate at a fixed rotating speed in a high-efficiency area to drive a generator to generate electricity efficiently, and alternating current output by the generator is rectified and regulated into constant-voltage direct current through an inverter device. Because the inversion rectifying voltage regulating device has larger volume, higher cost and difficult maintenance, the control difficulty and the manufacturing cost of the hybrid power system are increased. On the other hand, the power generation system is too little involved in the driving process of the vehicle, resulting in the vehicle power system needing to be equipped with a larger capacity battery pack to match the required power level in the pure electric mode, which also increases the cost of the vehicle hybrid system accordingly.
Disclosure of Invention
First, the technical problem to be solved
The invention provides a series hybrid power system and a networking hybrid power vehicle energy management method thereof, which replace a complex motor inversion rectification voltage regulating device by utilizing a simple diode rectifying device, and simultaneously solve the technical problem of how to solve the problem of controllable output power following of a generator by adjusting the rotation speed of an engine.
(II) technical scheme
In order to solve the technical problems, the invention provides a serial hybrid power system, which comprises an engine-generator set, a rectifying module, an energy storage module, a driving load module, a networking power prediction module and a central processing unit; wherein, the liquid crystal display device comprises a liquid crystal display device,
an engine crankshaft in the engine-generator set is connected with a generator rotor through a coupler and drives a generator to generate electricity; the generator is a permanent magnet generator;
the rectifying module comprises a rectifying diode and a buffer filter capacitor device and is used for rectifying alternating current generated by the generator into direct current and eliminating the peak at the moment of voltage change;
the energy storage module is used for outputting energy to drive the load module to work and storing the energy output by the generator; the maximum output power of the energy storage module is lower than that of the generator; when the output power of the generator exceeds the driving power requirement of the vehicle, the electric energy flows back to the energy storage module; when the output power of the generator is lower than the driving power requirement of the vehicle, the energy storage module supplements the output electric energy for driving the load module;
the driving load module is used for driving the vehicle to run through the driving motor;
the networking power prediction module obtains road traffic information stored in the cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking time as a reference, fits to obtain a correlation curve of traffic flow and corresponding road vehicle density, and predicts future short-time traffic flow and corresponding road vehicle density by using a neural network; estimating the trend of the change of the future short-time driving speed by combining the correlation curve with the predicted future short-time vehicle flow and the corresponding road vehicle density, and further predicting to obtain the trend of the change of the future short-time driving power demand of the vehicle;
the central processing unit is used for adjusting the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and combining the working mode instruction of the hybrid power system, so as to change the output power of the generator.
Further, the coupling is an elastic coupling, a rigid connection structure or a gear transmission.
Further, the generator winding is in the form of a single-phase winding or a three-phase winding.
Further, the rectifying module consists of a rectifying diode and a buffer filter capacitor device; the buffer filter capacitor device is used for consuming peak voltage generated when the generator suddenly drops load, the output alternating voltage of the generator is converted into direct voltage through the rectifying module, the amplitude of output power is not changed, the voltage is regulated by adjusting the rotating speed of the engine, and the rotating speed and the output power are in positive correlation output relation.
Further, the energy storage module is a lithium battery pack or a super capacitor energy storage module.
Further, the hybrid system operation modes include a pure electric operation mode, a park charge mode, and a hybrid operation mode.
In addition, the invention also provides a networking hybrid power vehicle energy management method based on the series hybrid power system, which comprises the following steps:
s1, a networking power prediction module receives road traffic information stored by a cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking time as a reference, and fits to obtain a correlation curve of traffic flow and corresponding road vehicle density;
s2, a networking power prediction module combines the limited Boltzmann machine network with a support vector regression network to construct a combined neural network of a deep learning regression model, and trains the combined neural network by utilizing the historical traffic flow information of the cloud; after the neural network training is finished, inputting the time sequence state value segments of the acquired real-time traffic flow information into a combined neural network, wherein the output of the network is the predicted future short-time traffic flow value;
s3, estimating a trend of the future short-time driving speed change by the networking power prediction module according to the predicted future short-time vehicle flow and the corresponding road vehicle density, and further obtaining a trend of the future short-time driving power demand change of the vehicle;
s4, the central processing unit adjusts the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and by combining with the working mode instruction of the hybrid power system, so that the output power of the generator is changed;
s5, dividing the engine speed into a high-speed section and a low-speed section by the central processing unit according to a pre-calibrated working speed-output voltage-power output curve of the generator set and combining a voltage section of a current energy storage module and a vehicle running power demand change section;
s6, the central processing unit performs energy management according to the driver instruction
When the driver command is in a pure electric mode, the central processing unit applies a stop command to the engine, and the power generator does not have rotating speed and voltage output after the engine stops, so that the energy storage module provides power required by the driving load module;
when the driver command is in a hybrid working mode, the central processing unit judges the current required power level P1 of the vehicle according to the real-time vehicle speed information and the accelerator opening, and meanwhile judges whether the sum of the P1 and the P2 is larger than a judging threshold value or not by combining the predicted short-time running power required level P2 in the future, and adjusts the engine to enter different rotation speed adjusting intervals; when the low-speed time of the vehicle is longer than a certain time and the accelerator opening exceeds a certain value and is longer than a certain time, the current required power grade zone bit P1=1 is considered, otherwise P1=0; when the future short-time driving power demand exceeds the current output power by a certain percentage, the future short-time driving power demand level P2=1, otherwise P2=0; when the sum of the P1 and the P2 is judged to be more than or equal to 1, the engine is considered to be required to be switched into a high-rotation-speed regulation interval, otherwise, the engine is maintained to work in a low-rotation-speed regulation interval; selecting a proper engine unit rotating speed value according to power requirements by combining a generator unit working rotating speed-output voltage-power output curve and current energy storage module voltage so as to ensure that the energy output by the engine-generator unit meets the power requirements of a driving load module;
when the driver commands a hybrid working mode, the rotation speed of the engine changes in real time along with the change of the vehicle speed, when the actual consumption power of the vehicle is lower than the generated power, the charging current is prevented from exceeding the limit of the energy storage module, and an engine self-adaptive speed regulation mechanism based on the charging current is arranged to ensure that the system properly reduces the rotation speed when the charging current value is overlarge; the CPU detects whether the actual rotation speed of the engine reaches the instruction required rotation speed, if the error is larger than a certain percentage, the speed regulating instruction is continuously sent, otherwise, whether the charging current exceeds the battery limit value is continuously judged, if so, the rotation speed instruction is low until the charging current is within the safety limit;
when the driver command is in a parking charging mode, the central processing unit adjusts the engine rotating speed command according to the charging power level, the output voltage of the generator is ensured to be higher than the current voltage of the energy storage module in the adjusting process, and meanwhile, the charging current is ensured to be smaller than the charging capacity of the energy storage module by utilizing an engine self-adaptive speed regulating mechanism.
Further, in step S2, the multi-layer limited boltzmann machine network is used as a first-stage network, and markov chain sampling is performed on the state of the hidden neural node according to the self-learning probability distribution of the input data, so as to realize the expected estimation of the input data; and the support vector regression network is used as a second-layer network to realize the classification of the change trend of the input data.
(III) beneficial effects
The invention provides a serial hybrid power system and a networking hybrid power vehicle energy management method thereof. The series hybrid power system comprises an engine-generator set, a rectifying module, an energy storage module, a driving load module, a networking power prediction module and a central processing unit. According to the energy management method, the vehicle driving demand power of a future road is predicted according to networking information such as cloud historical traffic data and real-time traffic data, the power output level mode of the hybrid power system is adjusted according to the power demand level and the current vehicle driving state, and the output power of a generator is further adjusted by reasonably adjusting the rotating speed of the engine, so that the use requirement of the system is met. The invention can effectively and quickly adjust the output power level of the hybrid power system by combining the predicted driving power and match the power demand of the vehicle.
Drawings
FIG. 1 is a schematic diagram of a series hybrid powertrain in accordance with an embodiment of the present invention;
fig. 2 (a) is a schematic diagram of a single-phase rectification mode of an alternating current according to the present invention, and fig. 2 (b) is a schematic diagram of a three-phase rectification mode of an alternating current according to the present invention;
FIG. 3 is a diagram illustrating power class determination logic according to the present invention;
FIG. 4 is a schematic diagram illustrating a method for limiting a rotational speed with excessive charging current according to the present invention;
FIG. 5 is a graph showing the relationship between the maximum output power and the output voltage of a permanent magnet generator set at different speeds according to an embodiment of the present invention;
FIG. 6 is a network prediction result of a deep learning regression machine according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a low rotation speed adjustment interval according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a high-speed regulation interval in an embodiment of the invention.
Detailed Description
To make the objects, contents and advantages of the present invention more apparent, the following detailed description of the present invention will be given with reference to the accompanying drawings and examples.
The embodiment provides a networking hybrid vehicle energy management method applied to a series hybrid system. As shown in fig. 1, the series hybrid power system has no reverse voltage regulation power generation device, and comprises an engine-generator set, a rectifying module, an energy storage module, a driving load module, a networking power prediction module and a central processing unit.
In the engine-generator set, an engine crankshaft is connected with a generator rotor through a coupling and drives a generator to generate electricity. The coupling comprises, but is not limited to, an elastic coupling, a rigid connection structure, a gear transmission device and the like. The generator is a permanent magnet generator, the generator winding form comprises, but is not limited to, a single-phase winding form and a three-phase winding form, more magnetic pole pairs are arranged in the generator rotor, and high-frequency alternating voltage can be independently and continuously output. The permanent magnet generator output power depends on the engine speed, the energy storage module voltage, and the load module demand power. The permanent magnet generator has simple structure and high reliability, omits the excitation winding, the carbon brush and the slip ring structure of the excitation generator, has simple whole structure, avoids the faults of easy burning and wire breakage of the excitation winding, easy abrasion of the carbon brush and the slip ring and the like, and has high reliability. Meanwhile, the device has the advantages of small volume, light weight and high specific power. The permanent magnet generator only performs motor output control without magnetic field control, and the engine speed determines the no-load output power of the generator, and the speed and the output power have positive correlation output relation.
The rectifying module comprises a rectifying diode and a buffer filter capacitor device and is used for rectifying alternating current generated by the generator into direct current and eliminating the peak at the moment of voltage change. The rectifying device adopted by the invention only comprises a rectifying diode and a buffer filter capacitor device, and has the advantages of simple function, small structure volume, and alternating current single-phase rectification and three-phase rectification modes, as shown in figure 2. The buffer filter capacitor device is used for consuming peak voltage generated when the generator suddenly drops. The output alternating voltage of the generator is converted into direct voltage through the rectifying module, the amplitude of the output power is not changed, the generator belongs to a voltage uncontrollable power generation mode, the voltage is regulated by adjusting the rotating speed of the engine, and the rotating speed and the output power are in positive correlation output relation.
The energy storage module is used for outputting energy for driving the load module to work and storing the energy output by the generator. The energy storage module includes, but is not limited to, a lithium battery pack, a super capacitor energy storage module, and the like. The maximum output power of the energy storage module is lower than that of the generator. When the output power of the generator exceeds the driving power requirement of the vehicle, the electric energy flows back to the energy storage module; when the output power of the generator is lower than the driving power requirement of the vehicle, the energy storage module supplements the output electric energy for driving the load module.
The driving load module is used for driving the vehicle to run through the driving motor. The drive load module may be used in any driving scheme including common front-drive, rear-drive, four-wheel drive, distributed drive, wheel-drive modes, and the like.
The networking power prediction module obtains road traffic information stored in the cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information (traffic flow=number of vehicles/time) and historical road vehicle density information (road vehicle density=number of vehicles/distance). The cloud server stores historical traffic flow information and historical road vehicle density information which have dynamic time-space correlation, the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information based on time, day, week and month, a correlation curve of traffic flow and corresponding road vehicle density is obtained by fitting, and future short-term traffic flow and corresponding road vehicle density are predicted by using a neural network; and estimating the trend of the change of the future short-time driving speed by combining the correlation curve with the predicted future short-time vehicle flow and the corresponding road vehicle density, and further predicting and obtaining the trend of the change of the future short-time driving power demand of the vehicle.
The future short-time traffic flow prediction flow specifically comprises the following steps: and combining the limited Boltzmann machine network with a support vector regression network, constructing a combined neural network of a deep learning regression model, and training the combined neural network by utilizing cloud historical traffic flow information (sequence state value fragments based on time). The multi-layer limited Boltzmann machine network is used as a first-stage network, and Markov chain sampling can be carried out on the state of the hidden neural node according to the self-learning probability distribution of the input data, so that the expected estimation of the input data is realized. The support vector regression network is used as a second-layer network, and the core skill and maximum margin concepts are used in nonlinear and high-dimensional tasks to execute state value classification prediction so as to realize input data change trend classification. And inputting the time sequence state value fragments of the real-time traffic flow information into the trained combined neural network to output the predicted future short-time traffic flow value.
The central processing unit is used for adjusting the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and combining the working mode instruction of the hybrid power system, so as to change the output power of the generator. The central processing unit can adjust the working modes of the hybrid power system according to the instructions of the driver, including a pure electric working mode, a parking charging mode and a hybrid working mode.
Based on the series hybrid power system, the networking hybrid power vehicle energy management method comprises the following specific steps:
s1, a networking power prediction module receives road traffic information stored by a cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; and the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking the time, day, week and month as references, and fits to obtain a correlation curve of traffic flow and corresponding road vehicle density. Traffic flow is generally a concave function of road vehicle density.
S2, combining the limited Boltzmann machine network with the support vector regression network by the networking power prediction module, constructing a combined neural network of the deep learning regression model, and training the combined neural network by utilizing historical traffic flow information (sequence state value fragments based on time) of the cloud. The multi-layer limited Boltzmann machine network is used as a first-stage network, and Markov chain sampling can be carried out on the states of hidden nerve nodes according to the self-learning probability distribution of input data, so that expected estimation of the input data is realized. And the support vector regression network is used as a second-layer network to realize the classification of the change trend of the input data. After the neural network training is finished, the obtained time sequence state value segments of the real-time traffic flow information are input into a combined neural network, and the output of the network is the predicted future short-time traffic flow value.
S3, the networking power prediction module estimates the trend of the change of the future short-time running speed according to the predicted future short-time traffic flow and the corresponding road vehicle density, and further obtains the trend of the change of the future short-time running power demand of the vehicle.
S4, the central processing unit adjusts the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and by combining the working mode instruction of the hybrid power system, and then the output power of the generator is changed.
S5, the central processing unit divides the engine speed into a high-speed section and a low-speed section according to a previously calibrated working speed-output voltage-power output curve of the generator set and by combining a voltage section of the current energy storage module and a vehicle running power demand change section.
Because the inversion rectifying voltage regulating device is not arranged, the output voltage span of the generator set is large, and the working voltage range of the energy storage module is smaller. Because the voltage of the energy storage module is fixed, the output voltage of the generator set is increased along with the increase of the rotating speed of the engine, the potential difference between the two is gradually increased, and the generated power is increased.
And the rotating speed of the engine is regulated, so that the output power of the engine is ensured to reach an expected value, and the output voltage is ensured to meet the charging requirement. And when the load is low, the power generation voltage in the low-rotation-speed interval completely covers the change range of the energy storage voltage, the discharge requirement is met, and the discharge voltage in the high-rotation-speed interval of the generator set exceeds the upper limit of the battery. And under the condition of high load, the output voltage of the generator set drops more, and the power generation voltage in the low rotation speed interval cannot be matched with the system to work. Therefore, a proper working speed interval of the generator set needs to be selected according to the power demand and the working voltage of the energy storage module.
S6, the central processing unit performs energy management according to the driver instruction
When the driver command is in a pure electric mode, the central processing unit applies a stop command to the engine, and the power generator does not have rotating speed and voltage output after the engine stops, so that the energy storage module provides power required by the driving load module.
When the driver command is in a mixed working mode, the central processing unit judges the current required power level P1 of the vehicle according to real-time vehicle speed information and accelerator opening, meanwhile, judges whether the sum of P1 and P2 is larger than a judging threshold value or not by combining with the predicted future short-time running power required level P2, adjusts the engine to enter different rotation speed adjusting intervals, and judges that the current required power level flag bit P1=1 is considered when the low speed time of the vehicle flow is larger than 5s and the accelerator opening exceeds 10% and is larger than 5s as shown in a graph in fig. 3, otherwise P1=0; when the future short-time driving power demand exceeds the current output power by 20%, the future short-time driving power demand level P2=1 is considered, otherwise P2=0; when the sum of the P1 and the P2 is judged to be more than or equal to 1, the engine is considered to be required to be switched into a high-rotation-speed regulation interval, otherwise, the engine is maintained to work in a low-rotation-speed regulation interval.
And selecting a proper engine unit rotating speed value according to the power requirement by combining a generator unit working rotating speed-output voltage-power output curve and the current energy storage module voltage so as to ensure that the energy output by the engine-generator unit meets the power requirement of a driving load module.
When the driver command is a hybrid working mode, the engine speed changes in real time along with the change of the vehicle speed, when the actual consumption power of the vehicle is lower than the generated power, the charging current is prevented from exceeding the limit of the energy storage module, an engine self-adaptive speed regulating mechanism based on the charging current needs to be arranged, the system properly reduces the speed when the charging current value is too large, the regulating process is as shown in fig. 4, the CPU detects whether the actual engine speed reaches the command required speed, if the error is greater than 5%, the speed regulating command is continuously sent, otherwise, whether the charging current exceeds the battery limit value is continuously judged, if so, the speed regulating command is low until the charging current is within the safety limit.
When the driver command is in a parking charging mode, the central processing unit adjusts the engine rotating speed command according to the charging power level, the output voltage of the generator is ensured to be higher than the current voltage of the energy storage module in the adjusting process, and meanwhile, the charging current is ensured to be smaller than the charging capacity of the energy storage module by utilizing an engine self-adaptive speed regulating mechanism.
The networked hybrid vehicle energy management method of the present invention is described below with specific examples. In the series hybrid power system of this embodiment, the effective speed regulation range of the engine in the engine-generator set is 1200 rpm to 3000 rpm, and the maximum output power and voltage of the permanent magnet generator set at different rotational speeds are shown in fig. 5. The energy storage module is a ternary lithium battery pack, the capacity is 50 kilowatt-hours, and the rated voltage class is 378V-588V.
Based on the above-mentioned series hybrid system, the networking hybrid vehicle energy management method of the present embodiment includes the steps of:
s1, a networking power prediction module receives road traffic information stored by a cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; and the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking the time, day, week and month as references, and fits to obtain a correlation curve of traffic flow and corresponding road vehicle density. Traffic flow is generally a concave function of road vehicle density.
S2, combining the limited Boltzmann machine network with the support vector regression network by the networking power prediction module, constructing a combined neural network of the deep learning regression model, and training the combined neural network by utilizing historical traffic flow information (sequence state value fragments based on time) of the cloud. The multi-layer limited Boltzmann machine network is used as a first-stage network, and Markov chain sampling can be carried out on the states of hidden nerve nodes according to the self-learning probability distribution of input data, so that expected estimation of the input data is realized. And the support vector regression network is used as a second-layer network to realize the classification of the change trend of the input data. After the neural network training is finished, the obtained time sequence state value segments of the real-time traffic flow information are input into a combined neural network, and the output of the network is the predicted future short-time traffic flow value.
In the method, when the networking power prediction module constructs a combined neural network, an input layer network is a limited Boltzmann machine network, an output layer network is a support vector regression network, collected historical traffic flow information is divided into a training set and a test set, the combined neural network is trained by using the training set, and the training effect is evaluated by using the test set. The number of nodes per neural layer in the combined neural network needs to be determined according to the training effect. And when the prediction result error meets the requirement, the network training is considered to be qualified. Fig. 6 shows that a predicted data value within a certain time range is obtained by inputting a starting segment by taking a data segment at a certain time point in a test set as a predicted object, the predicted result is similar to the actual value variation trend, and the error is within an acceptable range, so that it can be considered that the future traffic flow variation trend can be estimated.
S3, the networking power prediction module estimates the trend of the change of the future short-time running speed according to the predicted future short-time traffic flow and the corresponding road vehicle density, and further obtains the trend of the change of the future short-time running power demand of the vehicle.
In the method, the predicted short-time traffic flow is a discrete time series parameter point, and the discrete short-time vehicle speed time series parameter point is obtained by combining the determined vehicle flow-vehicle density curve. And obtaining a speed curve through a time axis, and further obtaining a vehicle running power demand change curve.
S4, the central processing unit adjusts the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and by combining the working mode instruction of the hybrid power system, and then the output power of the generator is changed.
And S5, dividing the engine speed into a high-speed section and a low-speed section by the central processing unit according to a pre-calibrated working speed-output voltage-power output curve of the generator set and combining a voltage section of the current energy storage module and a vehicle running power demand change section.
The working speed of the generator set is 1200-3200rpm, and the output voltage range is 200-750V. The operating voltage range of the high voltage battery is 378-588V. The low rotation speed interval is [1600-2800] and the high rotation speed interval is [1800,3200] according to the voltage grade and the power grade.
S6, the central processing unit performs energy management according to the driver instruction
And according to the current running state of the vehicle, predicting that the vehicle is in a high-power demand state (P1 state value) such as acceleration or climbing, and determining the output power level of the current hybrid power system by matching with the predicted power level (P2 state value) required by the driving load module in a short time in the future. When p1=1, it indicates that the vehicle is still at a lower speed when the vehicle gets a long-time accelerator signal, i.e. the current output power of the hybrid power system fails to meet the real-time power demand of the vehicle, so the hybrid power system needs to output more power to meet the vehicle use. When p2=1, it is indicated that the vehicle demand power is greater than the current hybrid output power in a short time in the future, so it is necessary to adjust the power level to cope with the future high-power demand scenario. Assuming that the output voltage of the battery is 450V at this time, the power requirements corresponding to different speeds of the vehicle are shown in the following table.
| Vehicle speed (km/h) | 20 | 40 | 60 | 80 | 100 |
| Power requirement (kw) | 5 | 10 | 20 | 30 | 40 |
Assuming that the vehicle speed is uniform at 20km/h, the power level of the stable operation of the system is 5kw, and p1=0 and p2=0, at this time, the hybrid power generator operates in a low rotation speed regulation section shown in fig. 7, the rotation speed is within [1600,1800], and the output power range of the generator satisfies the level of 5 kw. When the vehicle speed in the future 30s needs to be increased to 40km/h according to the change of the vehicle flow, the power in the accelerating process of the vehicle is ensured to meet the accelerating requirement in the future, the power generator changes the working strategy and operates in the high-rotation speed regulating section shown in the figure 8, the rotation speed is within [1800,2000], the maximum value of the output power of the power generator exceeds 10kw, and the accelerating requirement is met.
Meanwhile, when the power level and the rotating speed level are increased, the output power of the hybrid power system cannot be completely consumed due to the real-time power demand of the vehicle, and the energy storage module is charged by surplus power of the system. In the charging process, in order to protect the charging current from exceeding the maximum charging current which can be borne by the battery, an adaptive speed regulation mechanism is needed for the rotating speed of the engine-generator set, so that the system properly reduces the rotating speed when the charging current value is overlarge.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. The series hybrid power system is characterized by comprising an engine-generator set, a rectifying module, an energy storage module, a driving load module, a networking power prediction module and a central processing unit; wherein, the liquid crystal display device comprises a liquid crystal display device,
an engine crankshaft in the engine-generator set is connected with a generator rotor through a coupler and drives a generator to generate electricity; the generator is a permanent magnet generator;
the rectifying module comprises a rectifying diode and a buffer filter capacitor device and is used for rectifying alternating current generated by the generator into direct current and eliminating the peak at the moment of voltage change;
the energy storage module is used for outputting energy for driving the load module to work and storing the energy output by the generator; the maximum output power of the energy storage module is lower than that of the generator; when the output power of the generator exceeds the driving power requirement of the vehicle, the electric energy flows back to the energy storage module; when the output power of the generator is lower than the driving power requirement of the vehicle, the energy storage module supplements the output electric energy for driving the load module;
the driving load module is used for driving the vehicle to run through the driving motor;
the networking power prediction module obtains road traffic information stored in a cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking time as a reference, fits to obtain a correlation curve of traffic flow and corresponding road vehicle density, and predicts future short-time traffic flow and corresponding road vehicle density by using a neural network; estimating the trend of the change of the future short-time driving speed by combining the correlation curve with the predicted future short-time vehicle flow and the corresponding road vehicle density, and further predicting to obtain the trend of the change of the future short-time driving power demand of the vehicle;
the central processing unit is used for adjusting the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and combining the working mode instruction of the hybrid power system, so as to change the output power of the generator.
2. The tandem hybrid system of claim 1, wherein the coupling is an elastic coupling, a rigid connection structure, or a gear assembly.
3. The series hybrid system of claim 1, wherein the generator winding is in the form of a single phase winding or a three phase winding.
4. The series hybrid system of claim 1, wherein the rectifying module is comprised of a rectifying diode and a buffer filter capacitor device; the buffer filter capacitor device is used for consuming peak voltage generated when the generator suddenly drops load, the output alternating voltage of the generator is converted into direct voltage through the rectifying module, the amplitude of output power is not changed, the voltage is regulated by adjusting the rotating speed of the engine, and the rotating speed and the output power are in positive correlation output relation.
5. The series hybrid system of claim 1, wherein the energy storage module is a lithium battery pack or a super capacitor energy storage module.
6. The series hybrid system of claim 1, wherein the hybrid system operating modes include a battery-only operating mode, a park-charge mode, and a hybrid operating mode.
7. A networked hybrid vehicle energy management method based on the series hybrid system of any one of claims 1-6, the method comprising the steps of:
s1, a networking power prediction module receives road traffic information stored by a cloud server through networking equipment, wherein the road traffic information comprises real-time traffic flow information, historical traffic flow information and historical road vehicle density information; the networking power prediction module selects corresponding historical traffic flow information and historical road vehicle density information by taking time as a reference, and fits to obtain a correlation curve of traffic flow and corresponding road vehicle density;
s2, a networking power prediction module combines the limited Boltzmann machine network with a support vector regression network to construct a combined neural network of a deep learning regression model, and trains the combined neural network by utilizing the historical traffic flow information of the cloud; after the neural network training is finished, inputting the time sequence state value segments of the acquired real-time traffic flow information into a combined neural network, wherein the output of the network is the predicted future short-time traffic flow value;
s3, estimating a trend of the future short-time driving speed change by the networking power prediction module according to the predicted future short-time vehicle flow and the corresponding road vehicle density, and further obtaining a trend of the future short-time driving power demand change of the vehicle;
s4, the central processing unit adjusts the rotating speed of the engine according to the real-time vehicle speed information and the predicted future short-time running power demand and by combining with the working mode instruction of the hybrid power system, so that the output power of the generator is changed;
s5, dividing the engine speed into a high-speed section and a low-speed section by the central processing unit according to a pre-calibrated working speed-output voltage-power output curve of the generator set and combining a voltage section of a current energy storage module and a vehicle running power demand change section;
s6, the central processing unit performs energy management according to the driver instruction
When the driver command is in a pure electric mode, the central processing unit applies a stop command to the engine, and the power generator does not have rotating speed and voltage output after the engine stops, so that the energy storage module provides power required by the driving load module;
when the driver command is in a hybrid working mode, the central processing unit judges the current required power level P1 of the vehicle according to the real-time vehicle speed information and the accelerator opening, and meanwhile judges whether the sum of the P1 and the P2 is larger than a judging threshold value or not by combining the predicted short-time running power required level P2 in the future, and adjusts the engine to enter different rotation speed adjusting intervals; when the low-speed time of the vehicle is longer than a certain time and the accelerator opening exceeds a certain value and is longer than a certain time, the current required power grade zone bit P1=1 is considered, otherwise P1=0; when the future short-time driving power demand exceeds the current output power by a certain percentage, the future short-time driving power demand level P2=1, otherwise P2=0; when the sum of the P1 and the P2 is judged to be more than or equal to 1, the engine is considered to be required to be switched into a high-rotation-speed regulation interval, otherwise, the engine is maintained to work in a low-rotation-speed regulation interval; selecting a proper engine unit rotating speed value according to power requirements by combining a generator unit working rotating speed-output voltage-power output curve and current energy storage module voltage so as to ensure that the energy output by the engine-generator unit meets the power requirements of a driving load module;
when the driver commands a hybrid working mode, the rotation speed of the engine changes in real time along with the change of the vehicle speed, when the actual consumption power of the vehicle is lower than the generated power, the charging current is prevented from exceeding the limit of the energy storage module, and an engine self-adaptive speed regulation mechanism based on the charging current is arranged to ensure that the system properly reduces the rotation speed when the charging current value is overlarge; the CPU detects whether the actual rotation speed of the engine reaches the instruction required rotation speed, if the error is larger than a certain percentage, the speed regulating instruction is continuously sent, otherwise, whether the charging current exceeds the battery limit value is continuously judged, if so, the rotation speed instruction is low until the charging current is within the safety limit;
when the driver command is in a parking charging mode, the central processing unit adjusts the engine rotating speed command according to the charging power level, the output voltage of the generator is ensured to be higher than the current voltage of the energy storage module in the adjusting process, and meanwhile, the charging current is ensured to be smaller than the charging capacity of the energy storage module by utilizing an engine self-adaptive speed regulating mechanism.
8. The networking hybrid vehicle energy management method of claim 7, wherein in step S2, the multi-layer limited boltzmann machine network is used as a first-level network, and the state of hidden neural nodes is subjected to markov chain sampling according to the self-learning probability distribution of input data to realize the expected estimation of the input data; and the support vector regression network is used as a second-layer network to realize the classification of the change trend of the input data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111433971.1A CN113942491B (en) | 2021-11-29 | 2021-11-29 | Series hybrid power system and networking hybrid power vehicle energy management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111433971.1A CN113942491B (en) | 2021-11-29 | 2021-11-29 | Series hybrid power system and networking hybrid power vehicle energy management method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113942491A CN113942491A (en) | 2022-01-18 |
| CN113942491B true CN113942491B (en) | 2023-10-31 |
Family
ID=79339023
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111433971.1A Active CN113942491B (en) | 2021-11-29 | 2021-11-29 | Series hybrid power system and networking hybrid power vehicle energy management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113942491B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114655147B (en) * | 2022-05-05 | 2022-08-23 | 浙江春风动力股份有限公司 | All-terrain vehicle |
| US11685323B2 (en) | 2021-08-31 | 2023-06-27 | Zhejiang CFMOTO Power Co., Ltd. | Off-road vehicle |
| CN116522779B (en) * | 2023-04-29 | 2023-10-20 | 中国人民解放军63963部队 | Armored vehicle dynamic performance and engine operation MAP graph matching system and method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1583444A (en) * | 2004-06-15 | 2005-02-23 | 嘉兴市恒久动力科技有限公司 | Fuel and electric mixed power system of light traffic tool |
| JP2011235838A (en) * | 2010-05-13 | 2011-11-24 | Mitsubishi Electric Corp | Device for control of motor generator for vehicle |
| CN102358198A (en) * | 2011-08-24 | 2012-02-22 | 南京航空航天大学 | Electric vehicle mounted power generation system and control method thereof |
| FR2975958A1 (en) * | 2011-06-03 | 2012-12-07 | Renault Sa | Method for controlling regulation speed of car, involves estimating parameter of dangerous situation from measurement value of separate speed value, and selecting activation of control systems based on estimated parameter |
| JP2013103563A (en) * | 2011-11-11 | 2013-05-30 | Toyota Motor Corp | Vehicle-mounted electric power generating device |
| CN103229388A (en) * | 2010-10-05 | 2013-07-31 | 潘先鹏 | Battery augmentation system and method |
| CN103718220A (en) * | 2010-07-29 | 2014-04-09 | 丰田自动车株式会社 | Traffic control system, vehicle control system, traffic regulation system, and traffic control method |
| CN107284452A (en) * | 2017-07-18 | 2017-10-24 | 吉林大学 | Merge the following operating mode forecasting system of hybrid vehicle of intelligent communication information |
| TWI707796B (en) * | 2019-07-01 | 2020-10-21 | 國立雲林科技大學 | Predict vehicle driving shockwave for active safe driving system and method thereof |
| CN112700642A (en) * | 2020-12-19 | 2021-04-23 | 北京工业大学 | Method for improving traffic passing efficiency by using intelligent internet vehicle |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6622804B2 (en) * | 2001-01-19 | 2003-09-23 | Transportation Techniques, Llc. | Hybrid electric vehicle and method of selectively operating the hybrid electric vehicle |
| US20110190970A1 (en) * | 2010-01-13 | 2011-08-04 | Kabushiki Kaisha Toyota Chuo Kenkyusho | Power generation device equipped on vehicle |
| US10202048B2 (en) * | 2017-06-28 | 2019-02-12 | Toyota Motor Engineering & Manufacturing North America, Inc. | Systems and methods for adjusting operation of a vehicle according to HOV lane detection in traffic |
| US11625624B2 (en) * | 2019-09-24 | 2023-04-11 | Ford Global Technologies, Llc | Vehicle-to-everything (V2X)-based real-time vehicular incident risk prediction |
| KR20210073638A (en) * | 2019-12-10 | 2021-06-21 | 엘지전자 주식회사 | Controlling platooning |
| US11210945B2 (en) * | 2020-03-18 | 2021-12-28 | Ford Global Technologies, Llc | Infrastructure system |
-
2021
- 2021-11-29 CN CN202111433971.1A patent/CN113942491B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1583444A (en) * | 2004-06-15 | 2005-02-23 | 嘉兴市恒久动力科技有限公司 | Fuel and electric mixed power system of light traffic tool |
| JP2011235838A (en) * | 2010-05-13 | 2011-11-24 | Mitsubishi Electric Corp | Device for control of motor generator for vehicle |
| CN103718220A (en) * | 2010-07-29 | 2014-04-09 | 丰田自动车株式会社 | Traffic control system, vehicle control system, traffic regulation system, and traffic control method |
| CN103229388A (en) * | 2010-10-05 | 2013-07-31 | 潘先鹏 | Battery augmentation system and method |
| FR2975958A1 (en) * | 2011-06-03 | 2012-12-07 | Renault Sa | Method for controlling regulation speed of car, involves estimating parameter of dangerous situation from measurement value of separate speed value, and selecting activation of control systems based on estimated parameter |
| CN102358198A (en) * | 2011-08-24 | 2012-02-22 | 南京航空航天大学 | Electric vehicle mounted power generation system and control method thereof |
| JP2013103563A (en) * | 2011-11-11 | 2013-05-30 | Toyota Motor Corp | Vehicle-mounted electric power generating device |
| CN107284452A (en) * | 2017-07-18 | 2017-10-24 | 吉林大学 | Merge the following operating mode forecasting system of hybrid vehicle of intelligent communication information |
| TWI707796B (en) * | 2019-07-01 | 2020-10-21 | 國立雲林科技大學 | Predict vehicle driving shockwave for active safe driving system and method thereof |
| CN112700642A (en) * | 2020-12-19 | 2021-04-23 | 北京工业大学 | Method for improving traffic passing efficiency by using intelligent internet vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 中国汽车工程学术研究综述・2017;《中国公路学报》编辑部;;中国公路学报(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113942491A (en) | 2022-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113942491B (en) | Series hybrid power system and networking hybrid power vehicle energy management method | |
| CN108382186B (en) | Series-parallel hybrid power system and vehicle working mode decision method | |
| US10081350B2 (en) | Vehicle propulsion system having an energy storage system and optimized method of controlling operation thereof | |
| Lee et al. | Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle | |
| US7267191B2 (en) | System and method for battery protection strategy for hybrid electric vehicles | |
| CN104139709B (en) | A kind of control system of fuel cell stroke lengthening unit and control method | |
| US7791216B2 (en) | Method and system for use with a vehicle electric storage system | |
| CN109606348A (en) | A kind of plug-in planet mixed connection automobile energy management control method | |
| CN103158711A (en) | Torque control method for hybrid vehicle and system thereof | |
| US7308958B2 (en) | Method for controlling a series hybrid electric vehicle | |
| Vallur et al. | Prescriptive modeling, simulation and performance analysis of mild hybrid vehicle and component optimization | |
| Mustafi | An overview of hybrid electric vehicle technology | |
| KR20100035265A (en) | Series hybrid driving system using flywheel and driving method | |
| Sadek et al. | Power sources sizing for a fuel cell hybrid vehicle | |
| Meintz et al. | Control strategy optimization for a parallel hybrid electric vehicle | |
| Van Mierlo et al. | Views on hybrid drivetrain power management strategies | |
| Frezza et al. | Ecological adaptive cruise controller for a parallel hybrid electric vehicle | |
| Al-Adsani et al. | An ICE/HPM generator range extender in a series hybrid electric vehicle | |
| Ehsani et al. | On board power management | |
| Alipour et al. | A heuristic power management strategy for plug-in hybrid electric vehicles | |
| Eragamreddy et al. | Design requirements of solar powered plug in hybrid electric vehicles | |
| CN116834613B (en) | Power battery assisted hydrogen fuel cell automobile system energy management method | |
| Ambaripeta et al. | Engine-generator sizing for re-engineering an electric vehicle into an extended range electric vehicle | |
| Wang et al. | Application of flywheel system in series hybrid transit bus | |
| Haryadi et al. | Modelling and optimization of energy range extended electric bus strategy management system using dynamic programming |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |