CN112906296A - Method and system for optimizing energy of hybrid electric vehicle in full service period and storage medium - Google Patents
Method and system for optimizing energy of hybrid electric vehicle in full service period and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system and a storage medium for optimizing energy of a hybrid electric vehicle in a full service period, wherein the method comprises the following steps: constructing a mechanism model and a data driving model of the hybrid electric vehicle; determining a second hybrid electric vehicle mechanism and data hybrid model according to the hybrid electric vehicle data driving model and the hybrid electric vehicle mechanism model; constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with a second hybrid electric vehicle mechanism and data hybrid model; performing performance simulation on the hybrid electric vehicle by adopting a digital twin body of the hybrid electric vehicle to obtain simulation data; optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting simulation data and real-time operation data; and updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters. The invention can ensure that the energy of the hybrid electric vehicle in the full service period can be optimally distributed.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a method and a system for optimizing energy of a hybrid electric vehicle in a full service period and a storage medium.
Background
Energy management is one of the key technologies in the design and development of hybrid vehicles and is an important measure to achieve optimal fuel economy and emission performance. At present, energy management optimization aiming at a hybrid electric vehicle is mainly focused on a development and design stage, the performances of key components of the hybrid electric vehicle such as an engine, a motor, a battery and the like are in the best state at the stage, the running working condition of the vehicle is generally a regulation working condition, and the theoretically best fuel economy and emission performance can be obtained through various optimization algorithms. However, in the service process of the vehicle, the performance of key components such as a battery has certain decline, and the running condition of the vehicle has great randomness, so that local optimization is easily caused if the energy management strategy is not adjusted in time, and in addition, the control effect is not ideal due to long-time system accumulated errors. Therefore, the energy management optimization strategy formulated in the design stage cannot enable the energy of the hybrid electric vehicle in the full service period to achieve the optimal distribution.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method, a system and a storage medium for optimizing the energy of a hybrid electric vehicle in the full service period, which can ensure that the energy of the hybrid electric vehicle in the full service period can be optimally distributed.
According to the embodiment of the first aspect of the invention, the method for optimizing the energy of the hybrid electric vehicle in the full service period comprises the following steps:
constructing a mechanism model of the hybrid electric vehicle;
constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle;
compensating and correcting the hybrid electric vehicle mechanism model by adopting the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model;
performing characteristic test on the first hybrid electric vehicle mechanism and data hybrid model, and determining a second hybrid electric vehicle mechanism and data hybrid model meeting the requirements;
constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with the second hybrid electric vehicle mechanism and data hybrid model;
performing performance simulation on the hybrid electric vehicle by adopting the digital twin body of the hybrid electric vehicle to obtain simulation data;
optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting the simulation data and the real-time operation data;
and updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle.
The method for optimizing the energy of the hybrid electric vehicle in the full service period according to the embodiment of the invention at least has the following beneficial effects: the method comprises the steps of firstly constructing a hybrid electric vehicle mechanism model, constructing a hybrid electric vehicle data driving model according to real-time running data and historical running data of the hybrid electric vehicle, then compensating and correcting the hybrid electric vehicle mechanism model through the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data mixed model, determining a second hybrid electric vehicle mechanism and data mixed model meeting requirements after performing characteristic test on the first hybrid electric vehicle mechanism and data mixed model, and then constructing an energy management strategy model and a traffic scene model and forming a hybrid electric vehicle digital twin organism with the second hybrid electric vehicle mechanism and data mixed model; and finally, performing performance simulation on the hybrid electric vehicle through the hybrid electric vehicle digital twin body to obtain simulation data, optimizing energy management strategy parameters of the hybrid electric vehicle digital twin body according to the simulation data and the real-time operation data, and updating the energy management strategy parameters of the hybrid electric vehicle physical entity into the optimized parameters of the hybrid electric vehicle digital twin body, so that the energy distribution of the hybrid electric vehicle in the full service period can be adjusted according to the component state and the vehicle operation state, and the energy of the hybrid electric vehicle in the full service period can be optimally distributed.
According to some embodiments of the invention, the constructing the hybrid vehicle mechanism model comprises:
acquiring working principles of a hybrid electric vehicle and preset components of the hybrid electric vehicle;
and constructing a mechanism model of the hybrid electric vehicle according to the working principle.
According to some embodiments of the invention, the building of the data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle comprises:
acquiring real-time operation data and historical operation data of the hybrid electric vehicle;
constructing a preset component data driving model of the hybrid electric vehicle in a machine learning mode;
and constructing a data driving model of the hybrid electric vehicle according to the preset component data driving model.
According to some embodiments of the invention, the compensating and correcting comprises a parallel error compensating mode and a series error correcting mode; the first hybrid electric vehicle mechanism and data hybrid model comprises hybrid electric vehicle mechanism and data hybrid models with various structure types.
According to some embodiments of the present invention, the performing a characteristic test on the first hybrid vehicle mechanism and data hybrid model to determine a second hybrid vehicle mechanism and data hybrid model meeting requirements includes:
performing characteristic tests of a hybrid electric vehicle system and a preset component on the first hybrid electric vehicle mechanism and data mixed model by adopting a preset dynamic test platform;
determining the precision and the self-adaptive degree of the hybrid electric vehicle mechanism and data hybrid model with various structure types according to the special effect test result;
and determining a hybrid electric vehicle mechanism and data hybrid model meeting the requirements as a second hybrid electric vehicle mechanism and data hybrid model according to the precision and the self-adaptive degree.
According to some embodiments of the present invention, the constructing the energy management policy model and the traffic scenario model specifically includes:
constructing an energy management strategy model which is the same as the energy management strategy of the physical entity of the hybrid electric vehicle; and constructing a traffic scene model according to the road traffic state, the road terrain features and the driving behavior features.
According to some embodiments of the invention, the optimizing the energy management strategy parameters of the hybrid electric vehicle digital twin using the simulation data and the real-time operation data comprises:
fusing the simulation data and the real-time operation data by adopting a preset cloud computing platform;
and dynamically optimizing the energy management strategy parameters of the digital twin body of the hybrid electric vehicle according to the fusion processing result.
According to the second aspect of the invention, the system for optimizing the energy of the hybrid electric vehicle in the full service period comprises the following steps:
the first construction module is used for constructing a mechanism model of the hybrid electric vehicle; constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle;
the model processing module is used for compensating and correcting the hybrid electric vehicle mechanism model by adopting the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model;
the special effect test module is used for performing characteristic test on the first hybrid electric vehicle mechanism and data mixed model and determining a second hybrid electric vehicle mechanism and data mixed model meeting the requirements;
the second construction module is used for constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with the second hybrid electric vehicle mechanism and data hybrid model;
the simulation module is used for performing performance simulation on the hybrid electric vehicle by adopting the digital twin body of the hybrid electric vehicle to obtain simulation data;
the parameter optimization module is used for optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting the simulation data and the real-time operation data;
and the parameter updating module is used for updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle.
According to the third aspect of the invention, the hybrid electric vehicle full-service-period energy optimization system comprises:
at least one memory for storing a program;
at least one processor, configured to load the program to execute the method for optimizing the energy of the hybrid vehicle in the full service period according to the embodiment of the first aspect.
A storage medium according to an embodiment of a fourth aspect of the present invention stores therein a program executable by a processor, and the program executable by the processor is used for executing the method for optimizing the energy of the hybrid vehicle during the full service life of the hybrid vehicle described in the embodiment of the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described with reference to the following figures and examples, in which:
FIG. 1 is a flowchart of a method for optimizing energy of a hybrid electric vehicle in a full service period according to an embodiment of the present invention;
fig. 2 is an application schematic diagram of an embodiment of a hybrid electric vehicle full-service-period energy optimization method.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present numbers, and the above, below, within, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly defined, terms such as set, etc. should be broadly construed, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the detailed contents of the technical solutions.
In the description of the present invention, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Referring to fig. 1, an embodiment of the present invention provides a method for optimizing energy of a hybrid electric vehicle in a full service period, and the embodiment may be applied to a server or a background processor corresponding to a simulation platform.
In the application process, the embodiment includes the following steps:
and S11, constructing a mechanism model of the hybrid electric vehicle.
In some embodiments, the working principle of the hybrid system of the hybrid vehicle and the preset components of the hybrid vehicle can be obtained. The preset components comprise key components such as an engine, a motor and a power battery. And then the working principle is combined with the characteristic data of the hybrid system and key components to construct a hybrid vehicle mechanism model.
And S12, constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle.
In some embodiments, step S12 may be implemented by:
and acquiring real-time operation data and historical operation data of the hybrid electric vehicle. The real-time operation data is real-time operation data of the hybrid electric vehicle during service.
Constructing a preset component data driving model of the hybrid electric vehicle in a machine learning mode; the preset components comprise key components such as an engine, a motor, a power battery and the like.
And constructing a data driving model of the hybrid electric vehicle according to the data driving models of the plurality of preset components.
And S13, compensating and correcting the hybrid electric vehicle mechanism model by adopting the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model. The compensation and correction comprises a parallel error compensation mode and a series error correction mode. The first hybrid electric vehicle mechanism and data hybrid model comprises a hybrid electric vehicle mechanism and data hybrid model with multiple structure types. Specifically, this step may be understood as performing different compensation and correction processes on the hybrid vehicle mechanism model by using the hybrid vehicle data driving model, so as to design a plurality of different hybrid vehicle mechanism and data hybrid models from a system level and a component level as the first hybrid vehicle mechanism and data hybrid model.
And S14, performing characteristic test on the first hybrid electric vehicle mechanism and data hybrid model, and determining a second hybrid electric vehicle mechanism and data hybrid model meeting the requirements.
In some embodiments, step S14 may be implemented by:
firstly, a preset dynamic test platform is adopted to perform characteristic tests of a hybrid electric vehicle system and preset components on a first hybrid electric vehicle mechanism and data hybrid model. Specifically, the steps are that a hybrid electric vehicle and key components thereof such as an engine, a motor and a power battery test platform are built by adopting a preset dynamic test platform frame, and then a dynamic characteristic test is carried out on a hybrid electric vehicle system and the preset components through the test platform. Determining the accuracy and the self-adaptive degree of the hybrid electric vehicle mechanism and data hybrid model with various structure types according to the special effect test result; and determining a hybrid electric vehicle mechanism and data hybrid model meeting the requirements as a second hybrid electric vehicle mechanism and data hybrid model according to the precision and the self-adaptive degree. The hybrid electric vehicle mechanism and data hybrid model meeting the requirements is the hybrid electric vehicle mechanism and data hybrid model with the optimal performance.
And S15, constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with a second hybrid electric vehicle mechanism and data hybrid model.
In some embodiments, an energy management strategy model is constructed that is identical to the energy management strategy of the hybrid vehicle physical entity; and constructing a traffic scene model according to the road traffic state, the road terrain features and the driving behavior features. The control strategy of the energy management strategy model is the same as that of a physical entity of the hybrid electric vehicle; and the traffic scene model is consistent with the actual operation scene of the physical entity of the hybrid electric vehicle, so that the simulation precision of the subsequent steps is improved.
And S16, performing performance simulation on the hybrid electric vehicle by adopting the digital twin body of the hybrid electric vehicle to obtain simulation data. In the step, preset simulation software can be adopted for simulation processing.
And S17, optimizing the energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting the simulation data and the real-time operation data.
In some embodiments, the step S17 can be implemented by:
and fusing the simulation data and the real-time operation data by adopting a preset cloud computing platform. The preset cloud computing platform can be a pre-built hybrid electric vehicle digital twin cloud computing platform. The platform can also store simulation data and acquire real-time operation data of the hybrid electric vehicle. And then dynamically optimizing the energy management strategy parameters of the digital twin body of the hybrid electric vehicle according to the fusion processing result. The energy management strategy parameter is an optimal energy management strategy parameter corresponding to the current state of the hybrid electric vehicle obtained according to the simulation data.
And S18, updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle. Specifically, the energy management strategy parameters of the controller of the physical entity of the hybrid electric vehicle are updated to the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle.
Specifically, the above steps S11-S18 are all performed in real time, that is, the obtained energy management strategy parameter is an optimal energy management strategy parameter corresponding to the latest state of the hybrid electric vehicle, and by the above real-time performing manner, the energy distribution of the hybrid electric vehicle in the full service period can be adjusted according to the component state and the vehicle operating state, so that the energy of the hybrid electric vehicle in the full service period can be optimally distributed.
In some embodiments, as shown in fig. 2, the practical application process of the present embodiment is as follows:
and constructing a hybrid electric vehicle digital twin body corresponding to a physical entity of the hybrid electric vehicle, wherein the hybrid electric vehicle digital twin body comprises a traffic scene model, an energy management strategy model and a hybrid electric vehicle mechanism and data hybrid model, and the hybrid electric vehicle mechanism and data hybrid model is a hybrid electric vehicle mechanism and data hybrid model with optimal performance determined after a hybrid electric vehicle data driving model compensates and modifies the hybrid electric vehicle mechanism model and a characteristic test is carried out. The hybrid electric vehicle data driving model is obtained by constructing according to real-time operation data and historical operation data of the hybrid electric vehicle. The real-time operation data and the historical operation data comprise the real traffic environment of the hybrid electric vehicle in the running process and the driving behaviors of drivers.
The method comprises the steps of dynamically optimizing energy management strategy parameters of the hybrid electric vehicle on line by adopting a digital twin body of the hybrid electric vehicle through a digital twin cloud computing platform, wherein the optimization process comprises the steps of obtaining real-time running data of the hybrid electric vehicle, preprocessing the real-time running data and simulation data of the digital twin body of the hybrid electric vehicle, optimizing the energy management strategy parameters, updating parameters according to the optimized data, and generating a new control model, wherein the control parameters generated by the new control model are the optimal control parameters of the hybrid electric vehicle in the current state.
And updating the vehicle control unit of the physical entity of the hybrid electric vehicle into the optimal control parameter. The vehicle control unit and the component controller of the hybrid electric vehicle are communicated through a CAN network, control commands of the components of the hybrid electric vehicle are sent to the component controller through the CAN network, and all the component controllers jointly act on the power assembly of the hybrid electric vehicle.
The embodiment of the invention provides a system for optimizing energy of a hybrid electric vehicle in a full service period, which comprises:
the first construction module is used for constructing a mechanism model of the hybrid electric vehicle; constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle;
the model processing module is used for compensating and correcting the hybrid electric vehicle mechanism model by adopting a hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model;
the special effect test module is used for performing characteristic test on the first hybrid electric vehicle mechanism and data hybrid model and determining a second hybrid electric vehicle mechanism and data hybrid model meeting the requirements;
the second construction module is used for constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with a second hybrid electric vehicle mechanism and data hybrid model;
the simulation module is used for performing performance simulation on the hybrid electric vehicle by adopting a digital twin body of the hybrid electric vehicle to obtain simulation data;
the parameter optimization module is used for optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting simulation data and real-time operation data;
and the parameter updating module is used for updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized parameters of the digital twin of the hybrid electric vehicle.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
The embodiment of the invention provides a system for optimizing energy of a hybrid electric vehicle in a full service period, which comprises:
at least one memory for storing a program;
at least one processor for loading the program to execute a hybrid vehicle full-service-period energy optimization method shown in fig. 1.
The content of the embodiment of the method of the invention is all applicable to the embodiment of the system, the function of the embodiment of the system is the same as the embodiment of the method, and the beneficial effect achieved by the embodiment of the system is the same as the beneficial effect achieved by the method.
An embodiment of the present invention provides a storage medium, in which a processor-executable program is stored, and the processor-executable program is used for executing the hybrid vehicle full-service-period energy optimization method shown in fig. 1 when executed by a processor.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read from a storage medium by a processor of a computer device, and the computer instructions executed by the processor cause the computer device to perform the method shown in fig. 1.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
Claims (10)
1. A hybrid electric vehicle full-service-period energy optimization method is characterized by comprising the following steps:
constructing a mechanism model of the hybrid electric vehicle;
constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle;
compensating and correcting the hybrid electric vehicle mechanism model by adopting the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model;
performing characteristic test on the first hybrid electric vehicle mechanism and data hybrid model, and determining a second hybrid electric vehicle mechanism and data hybrid model meeting the requirements;
constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with the second hybrid electric vehicle mechanism and data hybrid model;
performing performance simulation on the hybrid electric vehicle by adopting the digital twin body of the hybrid electric vehicle to obtain simulation data;
optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting the simulation data and the real-time operation data;
and updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle.
2. The method for optimizing the energy of the hybrid electric vehicle in the full service period according to claim 1, wherein the constructing of the hybrid electric vehicle mechanism model comprises the following steps:
acquiring working principles of a hybrid electric vehicle and preset components of the hybrid electric vehicle;
and constructing a mechanism model of the hybrid electric vehicle according to the working principle.
3. The method for optimizing the energy of the hybrid electric vehicle in the full service period according to claim 1, wherein the constructing the data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle comprises the following steps:
acquiring real-time operation data and historical operation data of the hybrid electric vehicle;
constructing a preset component data driving model of the hybrid electric vehicle in a machine learning mode;
and constructing a data driving model of the hybrid electric vehicle according to the preset component data driving model.
4. The hybrid electric vehicle full-service-period energy optimization method according to claim 2, wherein the compensation and correction comprises a parallel error compensation mode and a series error correction mode; the first hybrid electric vehicle mechanism and data hybrid model comprises hybrid electric vehicle mechanism and data hybrid models with various structure types.
5. The method for optimizing energy of a hybrid electric vehicle in the full service period according to claim 4, wherein the step of performing characteristic test on the first hybrid electric vehicle mechanism and data hybrid model to determine a second hybrid electric vehicle mechanism and data hybrid model meeting requirements comprises the following steps:
performing characteristic tests of a hybrid electric vehicle system and a preset component on the first hybrid electric vehicle mechanism and data mixed model by adopting a preset dynamic test platform;
determining the precision and the self-adaptive degree of the hybrid electric vehicle mechanism and data hybrid model with various structure types according to the special effect test result;
and determining a hybrid electric vehicle mechanism and data hybrid model meeting the requirements as a second hybrid electric vehicle mechanism and data hybrid model according to the precision and the self-adaptive degree.
6. The method for optimizing the energy of the hybrid electric vehicle in the full service period according to claim 1, wherein the constructing of the energy management strategy model and the traffic scene model specifically comprises:
constructing an energy management strategy model which is the same as the energy management strategy of the physical entity of the hybrid electric vehicle; and constructing a traffic scene model according to the road traffic state, the road terrain features and the driving behavior features.
7. The method for optimizing hybrid vehicle full-service-period energy according to claim 1, wherein the optimizing the energy management strategy parameters of the hybrid vehicle digital twin by using the simulation data and the real-time operation data comprises:
fusing the simulation data and the real-time operation data by adopting a preset cloud computing platform;
and dynamically optimizing the energy management strategy parameters of the digital twin body of the hybrid electric vehicle according to the fusion processing result.
8. A hybrid electric vehicle full-service-period energy optimization system is characterized by comprising:
the first construction module is used for constructing a mechanism model of the hybrid electric vehicle; constructing a data driving model of the hybrid electric vehicle according to the real-time operation data and the historical operation data of the hybrid electric vehicle;
the model processing module is used for compensating and correcting the hybrid electric vehicle mechanism model by adopting the hybrid electric vehicle data driving model to obtain a first hybrid electric vehicle mechanism and data hybrid model;
the special effect test module is used for performing characteristic test on the first hybrid electric vehicle mechanism and data mixed model and determining a second hybrid electric vehicle mechanism and data mixed model meeting the requirements;
the second construction module is used for constructing an energy management strategy model and a traffic scene model, and forming a hybrid electric vehicle digital twin body with the second hybrid electric vehicle mechanism and data hybrid model;
the simulation module is used for performing performance simulation on the hybrid electric vehicle by adopting the digital twin body of the hybrid electric vehicle to obtain simulation data;
the parameter optimization module is used for optimizing energy management strategy parameters of the digital twin body of the hybrid electric vehicle by adopting the simulation data and the real-time operation data;
and the parameter updating module is used for updating the energy management strategy parameters of the physical entity of the hybrid electric vehicle into the optimized energy management strategy parameters of the digital twin of the hybrid electric vehicle.
9. A hybrid electric vehicle full-service-period energy optimization system is characterized by comprising:
at least one memory for storing a program;
at least one processor configured to load the program to perform a hybrid vehicle full-service-period energy optimization method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a program executable by a processor, wherein the program executable by the processor is configured to perform a hybrid vehicle energy optimization method according to any one of claims 1 to 7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102707627A (en) * | 2010-12-10 | 2012-10-03 | 帝斯贝思数字信号处理和控制工程有限公司 | Real-time capable battery cell simulation |
| CN104112036A (en) * | 2014-06-12 | 2014-10-22 | 湖南文理学院 | Method for simulating series-parallel hybrid power electromobile |
| CN106414146A (en) * | 2013-12-16 | 2017-02-15 | 雷诺股份公司 | Method and device for managing the energy of a hybrid vehicle |
| CN108622187A (en) * | 2018-05-09 | 2018-10-09 | 江苏大学 | The energy dynamics control system and method for composite power source EPS |
| CN110395245A (en) * | 2019-07-25 | 2019-11-01 | 西华大学 | A kind of mixed electrical automobile Energy Management System based on route driving information |
| CN110454290A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | A kind of automobile engine management-control method based on the twin technology of number |
| CN110456635A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | The control method of power system of electric automobile based on the twin technology of number |
| CN110488629A (en) * | 2019-07-02 | 2019-11-22 | 北京航空航天大学 | A kind of management-control method of the hybrid vehicle based on the twin technology of number |
| CN112116156A (en) * | 2020-09-18 | 2020-12-22 | 中南大学 | Hybrid train energy management method and system based on deep reinforcement learning |
| CN112265538A (en) * | 2020-10-10 | 2021-01-26 | 河北工业大学 | Vehicle component working condition construction method based on real-time optimal energy management strategy |
-
2021
- 2021-02-02 CN CN202110141702.1A patent/CN112906296B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102707627A (en) * | 2010-12-10 | 2012-10-03 | 帝斯贝思数字信号处理和控制工程有限公司 | Real-time capable battery cell simulation |
| CN106414146A (en) * | 2013-12-16 | 2017-02-15 | 雷诺股份公司 | Method and device for managing the energy of a hybrid vehicle |
| CN104112036A (en) * | 2014-06-12 | 2014-10-22 | 湖南文理学院 | Method for simulating series-parallel hybrid power electromobile |
| CN108622187A (en) * | 2018-05-09 | 2018-10-09 | 江苏大学 | The energy dynamics control system and method for composite power source EPS |
| CN110454290A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | A kind of automobile engine management-control method based on the twin technology of number |
| CN110456635A (en) * | 2019-07-02 | 2019-11-15 | 北京航空航天大学 | The control method of power system of electric automobile based on the twin technology of number |
| CN110488629A (en) * | 2019-07-02 | 2019-11-22 | 北京航空航天大学 | A kind of management-control method of the hybrid vehicle based on the twin technology of number |
| CN110395245A (en) * | 2019-07-25 | 2019-11-01 | 西华大学 | A kind of mixed electrical automobile Energy Management System based on route driving information |
| CN112116156A (en) * | 2020-09-18 | 2020-12-22 | 中南大学 | Hybrid train energy management method and system based on deep reinforcement learning |
| CN112265538A (en) * | 2020-10-10 | 2021-01-26 | 河北工业大学 | Vehicle component working condition construction method based on real-time optimal energy management strategy |
Non-Patent Citations (3)
| Title |
|---|
| 张佩 等: ""基于动态规划的混合动力重型货车能量管理策略研究"", 《机械传动》 * |
| 杜莎: ""离未来更进一步，ABB引领机器人数字化技术"", 《智能制造》 * |
| 邓涛 等: ""混合动力汽车工况识别自适应能量管理策略"", 《西安交通大学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642177A (en) * | 2021-08-16 | 2021-11-12 | 清华大学 | Digital twin virtual-real multi-vehicle mixed-driving simulation method and device |
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