CN110395245A - Hybrid electric vehicle energy management system based on fixed route driving information - Google Patents
Hybrid electric vehicle energy management system based on fixed route driving information Download PDFInfo
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- CN110395245A CN110395245A CN201910678022.6A CN201910678022A CN110395245A CN 110395245 A CN110395245 A CN 110395245A CN 201910678022 A CN201910678022 A CN 201910678022A CN 110395245 A CN110395245 A CN 110395245A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
-
- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
-
- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
-
- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
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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
-
- 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
- B60W2050/0019—Control system elements or transfer functions
-
- 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
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0027—Minimum/maximum value selectors
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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
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/84—Data processing systems or methods, management, administration
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Abstract
The invention provides a hybrid electric vehicle energy management system based on fixed route driving information, which comprises an information acquisition module and an information processing module, wherein the information acquisition module is used for acquiring a plurality of index data of all passengers in a vehicle and transmitting the index data to the information processing module; the information processing module comprises an information collecting unit and a control parameter planning unit, the information collecting unit is used for receiving a plurality of index data collected by the information collecting module and transmitting the index data to the control parameter planning unit, the control parameter planning unit analyzes the relation between information in the fixed driving route and control parameters in the energy management system by combining the weather type, the time characteristic and the passenger characteristic, determines parameters needing to be modified during the trip according to the index data, the weather type and the time characteristic, and performs energy management by using the optimized control parameters to reduce the energy consumption of the whole vehicle.
Description
Technical field
The present invention relates to hybrid vehicle field of energy management, and in particular to a kind of based on route driving information
Mixed electrical automobile Energy Management System.
Background technique
Hybrid vehicle has improvement environmental pollution, oil crisis and can reduce the advantages such as vehicle use cost, therefore
Current global automobile vendor is using hybrid vehicle as development object emphatically.Technology of Hybrid Electric Vehicle increases the dynamic of vehicle
Power source increases the flexibility of energy management.Energy management strategies are on improving plug-in hybrid-power automobile fuel economy
Significant effect, at present in the actual development project of most of vehicle enterprises and research institution, algorithm engineering teacher is according to driving habit, profession
Knowledge and the data model that measures of experiment design rule-based control strategy, and it is most widely used in hair to be that practical commercial melts
One of method.These rules are accumulated from the experiment of a large amount of member calibration, parameter match test and engineering experience, however
The poor effect of rule-based Energy Management System optimization, reason first is that part control parameter can not look after all works
Condition.
In the driving of real world, although vehicle can not follow strictly fixed and known driving cycle, permitted
More vehicles travel in route.For example, the private car of public transport bus, some multi-function vehicle and commuter are all
It is run in route, the information of entire stroke is more, can design energy management strategy goodly, during route drives
Information facilitate improve hybrid vehicle control strategy.
Summary of the invention
It is an object of the invention to overcome in the prior art, provide a kind of mixed based on route driving information
Electrical automobile Energy Management System is changed using factors such as occupant information, weather pattern, temporal characteristics in route driving procedure
Into existing rule-based Energy Management System.
The purpose of the present invention is achieved through the following technical solutions: a kind of mixed dynamic based on route driving information
Automobile energy management system, which is characterized in that including information acquisition module and message processing module, the information acquisition module is used
In several achievement datas of occupants all in collecting vehicle, and it is transmitted to the message processing module；The message processing module packet
Information taken unit and control parameter planning unit are included, the information taken unit is for receiving the information acquisition module acquisition
Several achievement datas, and be transferred to the control parameter planning unit, the control parameter planning unit is according to several described
Achievement data and weather pattern, temporal characteristics calculate analysis and Control parameter.
By above-mentioned technological means, it is transmitted to by several achievement datas of all occupants in information acquisition module collecting vehicle
Message processing module, message processing module is according to occupant's data of acquisition, weather pattern, temporal characteristics and history route row
Car data finds out optimum operating condition in common driving cycle, and the weather pattern stored in message processing module, temporal characteristics, which combine, to be multiplied
Contacting between the control parameter in information and Energy Management System that member's signature analysis goes out in fixed travel route, determines this
The parameter that secondary trip needs to modify, carrying out energy management with the control parameter after optimization reduces vehicle energy consumption.
Preferably, the control parameter planning unit is the model gone out based on algorithm and history feature database training, institute
It is one or more for stating algorithm.
By above-mentioned technological means, model is obtained using one or more algorithms, there is stronger specific aim, can more have
The optimum value of the control parameter under known history operating condition is solved to effect, the control parameter numerical value for making go on a journey next time is close to most
It is excellent.
Preferably, the algorithm includes one of algorithm globally optimal or a variety of.
By above-mentioned technological means, the algorithm globally optimal includes genetic algorithm, dynamic programming algorithm, population calculation
Method, simulated annealing.
Preferably, the history feature databases contain the operating condition of this vehicle difference history route, according to institute
The data stating the optimal control parameter of these operating conditions of algorithm globally optimal calculating and being acquired according to the information acquisition module
The expression containing occupant's influence factor, time effects factor, weather influence factor that binding time feature and weather pattern generate
Formula or the table that can be inquired.
By above-mentioned technological means, the control parameter for calculating optimization is looked by the expression formula of generation or the table that can be inquired
Value.
Preferably, the history feature database further includes multiple historical data matrixes, and the historical data matrix includes
The temporal characteristics of vehicle operation, speed, acceleration, GPS data, total voltage, total current, state-of-charge, passenger's feature, day
Gas type.
Preferably, the information acquisition unit includes that pressure monitoring unit, camera monitoring unit and sound monitoring are single
Member, the pressure monitoring unit are used for collecting vehicle for pressure suffered by all seats in monitoring car, the camera monitoring unit
Sound characteristic of the quantity and facial characteristics, the sound monitoring unit of interior occupant for all occupants in collecting vehicle.
By above-mentioned technological means, it is special to the pressure of seat, the quantity of acquisition passenger and face to monitor passenger
The sound characteristic of sign and passenger distinguish usual occupant to facilitate, and analyze the probability that the vehicle walks certain fixed route.
Preferably, the control parameter includes pure electric drive upper limit of the power value, electric quantity consumption stage SOC lower limit value, target
SOC value, SOC minimum value, SOC maximum value.
By above-mentioned technological means, based on the weather in existing mixed electrical automobile Energy Management System and route stroke
The model that influence factor, occupant's influence factor, time effects factor etc. train finds the control of this trip optimum modification
Parameter processed reduces energy consumption by modifying these control parameters.
Preferably, the GPS data of the control parameter planning unit analysis of history route, analysis starting point, destination with
And the GPS numerical value in path determines route.
By above-mentioned technological means, when determining that starting point is similar with the GPS data of destination, the GPS in path is compared
Data, it is most of similar, it is determined as fixed route.
Preferably, the information taken unit is used for the identity and quantity analyzed obtained data, judge passenger.
Preferably, the weather pattern includes fine day, dense fog, blast, hail, brash, and the weather pattern passes through interconnection
Net or camera obtain.
By above-mentioned technological means, substitutes road traffic condition by weather pattern, temporal characteristics and predicted, from network
Upper acquisition information requirement is less, improves reliability and convenience.
The beneficial effects of the present invention are:
1. control parameter planning unit of the invention is the model gone out based on algorithm and history feature database training, described
Algorithm is one or more.Model is obtained using one or more algorithms, there is stronger specific aim, can more effectively solve
Out under known history operating condition control parameter optimum value, the control parameter numerical value for making go on a journey next time is close to optimal；
2. the control parameter of the invention includes pure electric drive upper limit of the power value, electric quantity consumption stage SOC lower limit value, mesh
SOC value, SOC minimum value, SOC maximum value are marked, based in existing mixed electrical automobile Energy Management System and route stroke
The model that weather influence factor, occupant's influence factor, time effects factor etc. train looks into this trip optimum modification of calculating
Control parameter, by modify these control parameters reduce energy consumption.
Detailed description of the invention
Fig. 1 is the system schematic of one embodiment of the invention；
Fig. 2 is a kind of flow chart of the mixed electrical automobile energy management method based on route driving information of the present invention.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing, but protection scope of the present invention is not limited to
Below.
Embodiment 1
As shown in Figure 1, a kind of mixed electrical automobile Energy Management System based on route driving information, which is characterized in that
Including information acquisition module and message processing module, several index numbers of the information acquisition module for all occupants in collecting vehicle
According to, and it is transmitted to message processing module；Message processing module includes information taken unit and control parameter planning unit, and information is converged
Collection unit is used to receive several achievement datas of information acquisition module acquisition, and is transferred to control parameter planning unit, control ginseng
Number planning unit calculates analysis and Control parameter according to several achievement datas and weather pattern, temporal characteristics.
Message processing module, information are transmitted to by several achievement datas of all occupants in information acquisition module collecting vehicle
Processing module finds out optimum operating condition in common driving cycle according to the occupant's data and history route travelling data of acquisition,
The weather pattern that is stored in message processing module, temporal characteristics combination occupant's signature analysis go out information in fixed travel route with
The connection between control parameter in Energy Management System determines the parameter that this trip needs to modify, with the control after optimization
Parameter processed, which carries out energy management, reduces vehicle energy consumption.
Control parameter planning unit is the model gone out based on algorithm and history feature database training, and algorithm is a kind of or more
Kind.Model is obtained using one or more algorithms, there is stronger specific aim, can more effectively solve in known history work
The optimum value of control parameter under condition, the control parameter numerical value for making go on a journey next time is close to optimal.
Algorithm includes one of algorithm globally optimal or a variety of.Algorithm globally optimal includes genetic algorithm, Dynamic Programming
Algorithm, particle swarm algorithm, simulated annealing.
History feature databases contain the operating condition of this vehicle difference history route, are calculated according to algorithm globally optimal
The optimal control parameter of these operating conditions out, and the data binding time feature and weather class that are acquired according to information acquisition module
What type generated contains occupant's influence factor, time effects factor, the expression formula of weather influence factor or the table that can be inquired.Pass through
The value of the expression formula of generation or the control parameter of the table lookup that can be inquired optimization.
History travelling data under same route is put into whole vehicle model corresponding with the vehicle and is emulated, is easy to get
To Optimal Parameters.Occupant's feature, temporal characteristics, weather pattern are corresponding to it.The table listing expression formula or can inquiring.
Expression formula: result=control parameter fixed value+occupant's feature impact factor+temporal characteristics impact factor+weather class
Type impact factor, impact factor can carry out linear fit according to statistical history data and obtain.
It as shown in table 1, is each control parameter and impact factor correspondence table.
Table 1
History feature database further includes multiple historical data matrixes, and historical data matrix includes the time spy of vehicle operation
Sign, speed, acceleration, GPS data, total voltage, total current, state-of-charge, passenger's feature, weather pattern.Information collection
Unit includes pressure monitoring unit, camera monitoring unit and sound monitoring unit, and pressure monitoring unit is for institute in monitoring car
There are pressure suffered by seat, camera monitoring unit to be used to acquire the quantity and facial characteristics, sound monitoring unit of passenger
Sound characteristic for occupants all in collecting vehicle.Monitor passenger to the pressure of seat, acquire passenger quantity and
Facial characteristics and the sound characteristic of passenger analyze the vehicle and walk the general of certain fixed route to facilitate the usual occupant of discrimination
Rate.The input terminal access of pressure monitoring unit is mounted on the pressure of driver chair of automobile, co pilot's seat and back seat
Force snesor, pressure monitoring unit is using each seat pressure size as the information index data transmission of passenger to letter
The information taken unit of processing module is ceased, the camera monitoring unit input terminal access in information acquisition module is mounted in automobile
Camera, by the quantity and facial characteristics of camera acquisition passenger, camera monitoring unit is by the quantity of passenger and face
Portion's feature as passenger information index data transmission to message processing module information taken unit.Information acquisition module
In sound monitoring unit input terminal access the microphone that is mounted in automobile, the sound by microphone acquisition passenger is special
Sign, sound monitoring unit is using the sound characteristic of passenger as the information index data transmission of passenger to information processing mould
The information taken unit of block.
Control parameter include pure electric drive upper limit of the power value, electric quantity consumption stage SOC lower limit value, target SOC value, SOC most
Small value, SOC maximum value, based in existing mixed electrical automobile Energy Management System and route stroke weather influence factor,
The model that occupant's influence factor, time effects factor etc. train finds the control parameter of this trip optimum modification, leads to
It crosses and modifies these control parameters reduction energy consumption.
The GPS data of control parameter planning unit analysis of history route is analyzed in starting point, destination and path
GPS numerical value determines route.When determining that starting point is similar with the GPS data of destination, the GPS data in path is compared,
It is most of similar, it is determined as fixed route.Available a plurality of route, the opposite route of Origin And Destination are not intended as together
One route, because their gradients are different.
Information taken unit is used for the identity and quantity analyzed obtained data, judge passenger.Weather pattern includes
Fine day, dense fog, blast, hail, brash, weather pattern are obtained by internet or camera.Temporal characteristics be 24 hours really
The festivals or holidays or memorable date that timing is carved and be can determine that are obtained by internet or interior timing device.
It substitutes road traffic condition by weather pattern, temporal characteristics and is predicted, obtain information requirement more from network
It is few, improve reliability and convenience.
A method of the mixed electrical automobile energy management based on route driving information, comprising the following steps:
S1: being input to model training for history feature database, obtains control parameter plan model, history feature database
Including passenger's information in route, temporal characteristics, weather pattern, control parameter model include several control parameters with
The table or formula of each influence factor.
S2: occupant information, temporal characteristics, weather pattern are input to trained control parameter plan model in collecting vehicle,
Find out the concrete outcome for needing the control parameter modified.Flow chart is as shown in Figure 2.
The above is only the preferred embodiment of the present invention, it should be understood that the present invention is not limited to shape described herein
Formula should not be regarded as an exclusion of other examples, and can be used for other combinations, modifications, and environments, and can be herein
In contemplated scope, modifications can be made through the above teachings or related fields of technology or knowledge.And what this field occupant was carried out changes
Dynamic and variation does not depart from the spirit and scope of the present invention, then all should be within the scope of protection of the appended claims of the present invention.
Claims (10)
1. a kind of mixed electrical automobile Energy Management System based on route driving information, which is characterized in that including information collection
Module and message processing module, several achievement datas of the information acquisition module for all occupants in collecting vehicle, and transmit
To the message processing module；The message processing module includes information taken unit and control parameter planning unit, the letter
Breath collects unit for receiving several achievement datas of the information acquisition module acquisition, and is transferred to the control parameter planning
Unit, the control parameter planning unit calculate analysis control according to several achievement datas and weather pattern, temporal characteristics
Parameter processed.
2. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the control parameter planning unit is the model gone out based on algorithm and history feature database training, and the algorithm is
It is one or more.
3. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 2, special
Sign is that the algorithm includes one of algorithm globally optimal or a variety of.
4. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 3, special
Sign is that the history feature databases contain the operating condition of this vehicle difference history route, most according to the overall situation
The optimal control parameter of these operating conditions that excellent algorithm calculates, and the data binding time acquired according to the information acquisition module
Feature and weather pattern generate containing occupant's influence factor, time effects factor, the expression formula of weather influence factor or can look into
The table of inquiry.
5. according to a kind of any mixed electrical automobile energy management system based on route driving information of claim 2-4
System, which is characterized in that the history feature database further includes multiple historical data matrixes, and the historical data matrix includes vehicle
Operation temporal characteristics, speed, acceleration, GPS data, total voltage, total current, state-of-charge, passenger's feature, weather
Type.
6. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the information acquisition unit includes pressure monitoring unit, camera monitoring unit and sound monitoring unit, the pressure
Power monitoring unit is used to acquire the number of passenger for pressure suffered by all seats in monitoring car, the camera monitoring unit
The sound characteristic of amount and facial characteristics, the sound monitoring unit for all occupants in collecting vehicle.
7. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the control parameter includes pure electric drive upper limit of the power value, electric quantity consumption stage SOC lower limit value, target SOC value, SOC
Minimum value, SOC maximum value.
8. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the control parameter planning unit analysis of history route GPS data is analyzed in starting point, destination and path
GPS numerical value determines route.
9. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the information taken unit is used for the identity and quantity analyzed obtained data, judge passenger.
10. a kind of mixed electrical automobile Energy Management System based on route driving information according to claim 1, special
Sign is that the weather pattern includes fine day, dense fog, blast, hail, brash, and the weather pattern passes through networking or camera
It obtains.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111002975A (en) * | 2019-12-27 | 2020-04-14 | 延锋汽车饰件系统有限公司 | Vehicle energy management method, system, electronic device, and storage medium |
| CN111898895A (en) * | 2020-07-24 | 2020-11-06 | 重庆长安汽车股份有限公司 | Vehicle quality evaluation method and system based on big data fusion |
| CN112906296A (en) * | 2021-02-02 | 2021-06-04 | 武汉理工大学 | Method and system for optimizing energy of hybrid electric vehicle in full service period and storage medium |
| CN113392374A (en) * | 2021-06-03 | 2021-09-14 | 联合汽车电子有限公司 | Data extraction method, vehicle service method, vehicle control system, and storage medium |
| CN116714437A (en) * | 2023-06-01 | 2023-09-08 | 西华大学 | Hydrogen fuel cell automobile safety monitoring system and monitoring method based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102887144A (en) * | 2011-07-18 | 2013-01-23 | 通用汽车环球科技运作有限责任公司 | Adaptive energy management in a hybrid vehicle |
| CN105216782A (en) * | 2015-09-30 | 2016-01-06 | 上海凌翼动力科技有限公司 | Based on the plug-in hybrid-power automobile energy management method of energy predicting |
| WO2017207982A1 (en) * | 2016-05-31 | 2017-12-07 | Arrival Limited | Vehicle sleep mode |
| CN107818377A (en) * | 2016-09-12 | 2018-03-20 | 法乐第(北京)网络科技有限公司 | Vehicle global optimization control method, system, vehicle and cloud computing platform based on cloud computing platform |
| CN109733443A (en) * | 2019-01-11 | 2019-05-10 | 中车唐山机车车辆有限公司 | A kind of hybrid power tramcar braking optimization method and system |
-
2019
- 2019-07-25 CN CN201910678022.6A patent/CN110395245B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102887144A (en) * | 2011-07-18 | 2013-01-23 | 通用汽车环球科技运作有限责任公司 | Adaptive energy management in a hybrid vehicle |
| CN105216782A (en) * | 2015-09-30 | 2016-01-06 | 上海凌翼动力科技有限公司 | Based on the plug-in hybrid-power automobile energy management method of energy predicting |
| WO2017207982A1 (en) * | 2016-05-31 | 2017-12-07 | Arrival Limited | Vehicle sleep mode |
| CN107818377A (en) * | 2016-09-12 | 2018-03-20 | 法乐第(北京)网络科技有限公司 | Vehicle global optimization control method, system, vehicle and cloud computing platform based on cloud computing platform |
| CN109733443A (en) * | 2019-01-11 | 2019-05-10 | 中车唐山机车车辆有限公司 | A kind of hybrid power tramcar braking optimization method and system |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111002975A (en) * | 2019-12-27 | 2020-04-14 | 延锋汽车饰件系统有限公司 | Vehicle energy management method, system, electronic device, and storage medium |
| CN111002975B (en) * | 2019-12-27 | 2022-02-08 | 延锋汽车饰件系统有限公司 | Vehicle energy management method, system, electronic device, and storage medium |
| CN111898895A (en) * | 2020-07-24 | 2020-11-06 | 重庆长安汽车股份有限公司 | Vehicle quality evaluation method and system based on big data fusion |
| CN111898895B (en) * | 2020-07-24 | 2023-06-13 | 重庆长安汽车股份有限公司 | Vehicle quality evaluation method and system based on big data fusion |
| CN112906296A (en) * | 2021-02-02 | 2021-06-04 | 武汉理工大学 | Method and system for optimizing energy of hybrid electric vehicle in full service period and storage medium |
| CN112906296B (en) * | 2021-02-02 | 2022-05-10 | 武汉理工大学 | Method and system for optimizing energy of hybrid electric vehicle in full service period and storage medium |
| CN113392374A (en) * | 2021-06-03 | 2021-09-14 | 联合汽车电子有限公司 | Data extraction method, vehicle service method, vehicle control system, and storage medium |
| CN116714437A (en) * | 2023-06-01 | 2023-09-08 | 西华大学 | Hydrogen fuel cell automobile safety monitoring system and monitoring method based on big data |
| CN116714437B (en) * | 2023-06-01 | 2024-03-26 | 西华大学 | Hydrogen fuel cell automobile safety monitoring system and monitoring method based on big data |
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