CN111581796A - Plug-in hybrid electric vehicle key technology evaluation system - Google Patents
Plug-in hybrid electric vehicle key technology evaluation system Download PDFInfo
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- CN111581796A CN111581796A CN202010350385.XA CN202010350385A CN111581796A CN 111581796 A CN111581796 A CN 111581796A CN 202010350385 A CN202010350385 A CN 202010350385A CN 111581796 A CN111581796 A CN 111581796A
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a key technology evaluation system of a plug-in hybrid electric vehicle, which comprises a vehicle static analysis unit, a test matrix construction unit, a signal acquisition unit, a vehicle test unit, a key technology analysis unit and a simulation optimization unit which are sequentially connected; the vehicle static analysis unit comprises static parameter query, configuration analysis, working mode analysis and electronic motor architecture analysis; the test matrix construction unit is constructed based on the basic result of the vehicle static analysis; the signal acquisition unit is used for screening based on a test target of the test matrix; the vehicle test unit collects related signals according to the test matrix and the test target, and performs rack test and road test; the key technology analysis unit comprises steady-state control, drive control, regenerative brake control and energy management key strategy analysis; and the simulation optimization unit constructs a simulation model based on the key technology analysis result, and performs extreme condition simulation and energy-saving potential analysis.
Description
Technical Field
The invention belongs to the technical field of new energy automobiles, and particularly relates to a key technology evaluation system of a plug-in hybrid electric vehicle.
Background
With the research and development and market promotion of the plug-in hybrid electric vehicle, the technical level of the whole plug-in hybrid electric vehicle is greatly improved, but in the aspect of the key technology of the hybrid electric vehicle, the plug-in hybrid electric vehicle has a certain gap with the international advanced vehicle type. For a long time, the test evaluation of the plug-in hybrid electric vehicle only stays at the external performance aspect of the whole vehicle, the key technology deep evaluation is lacked, and a system for deep evaluation of the key technology evaluation of the hybrid electric vehicle is lacked, so that a set of plug-in hybrid electric vehicle key technology evaluation system needs to be built to realize the test evaluation of the key technology from the whole vehicle to the component, and provide technical support for enterprise research and development verification.
Disclosure of Invention
In view of the above, the invention aims to provide a plug-in hybrid electric vehicle key technology evaluation system, which is used for designing a multidimensional test analysis matrix based on the configuration characteristics of a plug-in hybrid electric vehicle, deeply evaluating the plug-in hybrid electric vehicle key technology from aspects of steady-state control, drive control, brake control, energy management and the like, building a simulation mode according to strategy analysis, comprehensively analyzing the vehicle key technology, providing a method for research and development verification of the plug-in hybrid electric vehicle of an enterprise, and shortening the research and development period.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a key technology evaluation system of a plug-in hybrid electric vehicle comprises a vehicle static analysis unit, a test matrix construction unit, a signal acquisition unit, a vehicle test unit, a key technology analysis unit and a simulation optimization unit which are sequentially connected;
the vehicle static analysis unit comprises static parameter query, configuration analysis, working mode analysis and electronic motor architecture analysis;
the test matrix construction unit is constructed based on basic results of vehicle static analysis;
the signal acquisition unit is used for screening based on a test target of the test matrix;
the vehicle testing unit collects related signals according to the testing matrix and a testing target, and performs rack testing and road testing;
the key technology analysis unit comprises steady-state control, drive control, regenerative brake control and energy management key strategy analysis;
and the simulation optimization unit constructs a simulation model based on the key technology analysis result, and performs extreme condition simulation and energy-saving potential analysis.
Further, the static parameter query is used for acquiring vehicle key parameters, and specifically includes vehicle profile parameters: length, width, height, wheelbase, load and mass center; engine parameters: displacement, compression ratio, maximum power, torque and the like, motor parameters, rotating speed range, peak power and peak torque; battery parameters: such as battery type, battery capacity, maximum charge-discharge power.
Further, the configuration analysis comprises a series configuration, a parallel configuration, a series-parallel configuration and a power splitting configuration.
Further, the working mode analysis analyzes the working modes of the engine, the driving motor and the core components of the generator according to the configuration characteristics, wherein the working modes comprise a pure electric mode, a series or parallel mode, a regenerative braking mode and a parking charging mode.
Further, the electronic and electric appliance architecture analysis comprises CAN network analysis and high-voltage architecture analysis.
Further, the test matrix generally includes, but is not limited to, ambient temperature, power battery level, driving mode, gear, and operating condition factors.
Further, the steady-state control strategy comprises acceleration intention identification and braking intention identification, wherein the acceleration intention identification comprises analyzing the relation between the opening degree of an accelerator pedal and the driving torque and the corresponding relation between the opening degree of the accelerator pedal and the acceleration under the multiple dimensions of different running modes, different SOCs and different vehicle speeds; the brake intention identification means that the corresponding relation between the opening degree of a brake pedal and the braking torque, the braking hydraulic pressure, the acceleration and the total braking torque of the motor under the multiple dimensions of different gears, different modes and different SOC is analyzed.
Further, the driving control comprises motor torque control, front and rear shaft torque distribution, a mode switching process and a mode switching threshold under the driving working conditions of creeping, accelerating, uniform speed, Tip in and Tip out under different modes, different gears and different SOC multiple dimensions.
Furthermore, the regenerative braking control is to analyze the relationship between the motor braking torque and the vehicle speed and the opening degree of the brake pedal under the sliding working condition, the braking working condition and the emergency braking working condition, analyze the coordination control relationship between the hydraulic pressure and the motor torque braking, and analyze the coordination control relationship between the motor braking torque and the ABS under the multiple dimensions of different modes, different gears, different SOCs, different vehicle speeds, different opening degrees of the brake pedal and the like.
Further, the energy management comprises the aspects of engine start-stop control, energy flow and component working points, the coordinated control of the start-stop process of the engine under various working conditions and the start-stop threshold value are analyzed, the energy flow under the circulating working conditions and the single working condition under different temperatures and different SOC is analyzed, the energy flow analysis comprises the aspects of energy transfer paths and efficiency, the working points of the engine and the motor are analyzed, and the control curve of the engine and the high-efficiency point are analyzed.
Compared with the prior art, the plug-in hybrid electric vehicle key technology evaluation system has the following advantages:
the key technology evaluation requirement of the plug-in hybrid electric vehicle provided by the invention provides an evaluation flow, which is from vehicle static analysis to key technology analysis to simulation research and deeply evaluates the key technology, provides a test method for enterprises to research and develop and optimize the plug-in hybrid electric vehicle, and effectively shortens the research and development period.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a vehicle evaluation flow chart of a plug-in hybrid electric vehicle key technology evaluation system according to an embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a plug-in hybrid electric vehicle key technology evaluation system includes aspects such as vehicle static analysis, test matrix construction, signal acquisition, vehicle testing, key technology analysis, simulation optimization, and the like;
as shown in fig. 1, the vehicle static analysis includes static parameter query, configuration analysis, operation mode analysis, and electronic motor architecture analysis;
as shown in fig. 1, the static parameter query refers to obtaining key parameters of a vehicle, and provides basic data for key technology analysis, specifically including overall vehicle profile parameters, such as length, width, height, wheelbase, load, center of mass, etc., engine parameters, such as displacement, compression ratio, maximum power, torque, etc., motor parameters, such as rotating speed range, peak power, peak torque, etc., and battery parameters, such as battery type, battery capacity, maximum charge-discharge power, etc.;
as shown in fig. 1, the configuration analysis is a basic judgment on the vehicle driving type, the main configurations include a series configuration, a parallel configuration, a series-parallel configuration, a power split configuration and the like, and the configuration analysis has an important meaning for subsequent working mode analysis and key technology analysis;
as shown in fig. 1, the working mode analysis is based on further analysis of the configuration analysis, and according to configuration characteristics, the working modes of core components such as an engine, a driving motor, a generator and the like are analyzed, and the main working modes include working modes such as a pure electric mode, a series (parallel) mode, a regenerative braking mode, a parking charging mode and the like;
as shown in fig. 1, the electronic appliance architecture analysis is a preliminary preparation for the signal acquisition, and specifically includes aspects such as CAN network analysis and high-voltage architecture analysis, which provide a basis for signal analysis and sensor arrangement schemes;
as shown in fig. 1, the test matrix construction is based on the basic result of the vehicle static analysis, and the test matrix generally includes, but is not limited to, factors such as ambient temperature, power battery state of charge (SOC), driving mode, gear, and operating condition;
as shown in fig. 1, the environmental temperature generally includes temperatures of a high temperature, a low temperature, a Normal temperature, and the like, the SOC generally includes three kinds of SOCs, namely a high SOC, a medium SOC, a low SOC, and a limit SOC that is difficult to reach in a vehicle driving process, the driving model refers to a function key for vehicle driving, and generally includes modes such as an economy mode (ECO), a Normal mode (Normal), a Power mode (Power), and a Power active holding mode (Saving), and the gears generally include a D gear, a B gear, and other gears, such as a single pedal gear; the working conditions comprise a driving working condition, a braking working condition and a standard circulating working condition;
as shown in fig. 1, the signal acquisition is screened based on the test target of the test matrix, and mainly includes CAN signal acquisition and sensor data acquisition, even if the number, sampling frequency, reliability, synchronization and other aspects of the key signals are ensured;
the signal quantity screens out strong correlation degree signals, general correlation degree signals and auxiliary signals in a hierarchical mode according to the target of the test, and an acquisition mode and a signal acquisition scheme of the required signals are formulated, wherein the acquisition mode generally adopts CAN signal acquisition and sensor acquisition;
the sampling frequency is set according to the analysis requirement, for the steady-state working condition test, the sampling frequency can adopt a lower sampling frequency (such as 10Hz to 50 Hz) to save the storage space, and for the transient working condition, a higher sampling frequency (such as 100Hz to 1000Hz) is adopted to ensure the transient characteristic to be fully tested;
the reliability of the data acquisition needs to be tested in the limit working conditions of the vehicle, such as an emergency acceleration test, a maximum vehicle speed test, a low-temperature test and the like, after the sensor and the CAN bus are installed, the test time generally lasts for a long time (such as more than 3 hours), and the data acquisition is guaranteed not to be lost and have no errors in the limit working conditions for a long time;
the signal acquisition synchronism is ensured through the data acquisition module and the upper computer, all the sensor output lines and the CAN bus are connected to the same number of acquisition modules, the signal acquisition synchronism is ensured, and the upper computer CAN perform synchronous processing on signals and ensure the signal processing synchronism;
as shown in fig. 1, the CAN signal acquisition mode generally includes modes such as power CAN and OBD access, including vehicle controller signals, such as vehicle speed, accelerator pedal, brake pedal, gear, acceleration, mode, etc., engine signals, such as engine speed, torque, water temperature, fuel injection signal, ignition signal, etc., battery controller signals, such as battery voltage, current, SOC, temperature, etc., motor controller signals, such as motor speed, motor torque, water temperature, bus current, bus voltage, etc.;
as shown in fig. 1, the sensor data acquisition is performed, and a sensor arrangement scheme is constructed according to the vehicle analysis result, specifically including signals of a current sensor, a voltage sensor, a displacement sensor, a temperature sensor, a torque sensor, a hydraulic sensor and the like;
as shown in fig. 1, the vehicle test actually collects related signals according to the test matrix and the test target, and performs a bench test and a road test;
as shown in fig. 1, the key technology analysis of the core task of the key technology evaluation specifically includes key strategy analysis such as steady-state control, drive control, regenerative brake control, and energy management;
as shown in fig. 1, the steady-state control strategy mainly includes acceleration intention recognition and braking intention recognition, wherein the acceleration intention recognition includes analyzing the relationship between the opening degree of an accelerator pedal and the driving torque and the corresponding relationship between the opening degree of the accelerator pedal and the acceleration in multiple dimensions of different driving modes, different SOCs, different vehicle speeds and the like; the brake intention identification means analyzing the corresponding relation between the opening degree of a brake pedal and the braking torque, the braking hydraulic pressure, the acceleration and the total braking torque of the motor under the multiple dimensions of different gears, different modes, different SOCs and the like;
as shown in fig. 1, the driving control is motor torque control, front and rear axle torque distribution, mode switching process, and mode switching threshold under driving conditions of creep, acceleration, uniform speed, Tip in, Tip out, etc. under multiple dimensions of different modes, different gears, different SOCs, etc.;
as shown in fig. 1, the regenerative braking control is to analyze the relationship between the motor braking torque and the vehicle speed and the opening degree of the brake pedal under the sliding condition, the braking condition and the emergency braking condition, analyze the coordination control relationship between the hydraulic pressure and the motor torque braking, and analyze the coordination control relationship between the motor braking torque and the ABS under the different modes, different gears, different SOCs, different vehicle speeds, different opening degrees of the brake pedal, and the like;
as shown in fig. 1, the energy management includes aspects of engine start-stop control, energy flow, component operating points, and the like, analysis of coordinated control of the engine in start-stop processes under various operating conditions, and start-stop threshold values, analysis of energy flow under cyclic operating conditions and single operating conditions at different temperatures and different SOCs, analysis of energy flow including aspects of energy transfer paths, efficiency, and the like, analysis of operating points of the engine and the motor, analysis of engine control curves, and high efficiency points;
as shown in fig. 1, the optimization simulation analysis is based on a key technology analysis result, a simulation model is constructed, and extreme condition simulation and energy-saving potential analysis are performed, wherein the extreme condition simulation analysis refers to simulation analysis of conditions that are difficult to realize on a whole vehicle, such as wet road surfaces, extreme temperature analysis, limp condition analysis, regenerative braking failure protection and other conditions, the energy-saving potential analysis includes correlation between wind resistance optimization of the whole vehicle, quality reduction of the whole vehicle, component performance improvement, accessory performance optimization and other factors and vehicle energy saving performance, and the optimization direction of the vehicle energy saving is indicated by quantification of a simulation means.
Taking a certain plug-in hybrid electric vehicle as an example, the embodiment of the invention is detailed as follows:
the key technology evaluation system and method for a certain plug-in hybrid electric vehicle are subjected to aspects of vehicle static analysis, test matrix construction, signal acquisition, vehicle test, key technology analysis, simulation optimization and the like;
the vehicle static analysis comprises static parameter query, configuration analysis, working mode analysis and electronic motor architecture analysis;
the static parameter query is to obtain key parameters of a vehicle, provide basic data for key technology analysis, and the vehicle is provided with an engine, a generator and a driving motor, so the key parameter query specifically comprises overall vehicle appearance parameters such as length, width, height, wheelbase, load, mass center and the like, engine parameters such as displacement, compression ratio, maximum power, torque and the like, driving motor parameters and generator parameters such as rotating speed range, peak power, peak torque and the like, and power battery parameters such as battery type, battery capacity, maximum charge-discharge power and the like;
the configuration analysis is a basic judgment on the vehicle driving type, the main configurations comprise a series configuration, a parallel configuration, a series-parallel configuration, a power distribution configuration and the like, the vehicle type is judged to be the series-parallel configuration according to the power assembly structure, and the configuration analysis has important significance for subsequent working mode analysis and key technology analysis;
the working mode analysis is based on further analysis of the configuration analysis, and the working modes of core components such as an engine, a driving motor, a generator and the like are analyzed according to the configuration characteristics, wherein the working modes of the plug-in hybrid electric vehicle comprise a pure electric mode, a series mode, a parallel mode, a pure engine driving mode, a regenerative braking mode and a parking charging mode;
the electronic and electric appliance architecture analysis is carried out on the plug-in hybrid electric vehicle, so that the early preparation of signal acquisition is provided, specifically including aspects such as CAN network analysis and high-voltage architecture analysis, and a basis is provided for a signal analysis and sensor arrangement scheme;
constructing a test matrix based on the analysis result of the plug-in hybrid electric vehicle, wherein the test matrix comprises factors such as environment temperature, power battery electric quantity (SOC), driving mode, gear, working condition and the like;
the environment temperature generally comprises high temperature, low temperature, Normal temperature and other temperatures, the SOC generally comprises high, medium and low SOCs, and in addition, the SOC also comprises a limit SOC which is difficult to reach in the vehicle driving process, the driving model refers to a function key for vehicle driving, and generally comprises an economy mode (ECO), a Normal mode (Normal), a Power mode (Power), an electric quantity active keeping mode (Saving) and other modes, the gears generally comprise a D gear, a B gear, B1, B2, B3 and other gears, and the working conditions comprise a driving working condition, a braking working condition and a standard cycle working condition; the factors are combined in a cross mode, a test matrix is designed, for example, in a Normal mode, a driving condition test of a D gear is carried out under the condition that the SOC is 80% high SOC in a Normal environment;
the signal acquisition is screened based on a test target of the test matrix, and the signal acquisition mainly comprises CAN signal acquisition and sensor data acquisition, wherein the number, sampling frequency, reliability, synchronism and other aspects of key signals are ensured;
the signal quantity is according to the target of this test, screens out strong correlation degree signal, general correlation degree signal and auxiliary signal in the hierarchy to formulate the acquisition mode and the signal acquisition scheme of required signal, acquire acquisition mode and adopt CAN signal acquisition and sensor acquisition generally. For example, when the acceleration performance of the plug-in hybrid electric vehicle is tested, the core signals comprise vehicle speed, accelerator pedal opening, motor rotating speed torque, engine rotating speed torque, power battery SOC current, voltage, generator torque and acceleration, the general correlation signals comprise signals of vehicle gear, mode, brake pedal opening, engine water temperature, power battery discharging power limitation and the like, and the auxiliary signals comprise signals of engine starting request signal, oil injection signal, driving motor water temperature and the like
The sampling frequency is set according to the analysis requirement, for the steady-state working condition test, the sampling frequency can adopt a lower sampling frequency (such as 10Hz to 50 Hz) to save the storage space, and for the transient working condition, a higher sampling frequency (such as 100Hz to 1000Hz) is adopted to ensure the transient characteristic to be fully tested;
the reliability of the data acquisition needs to be tested in the limit working conditions of the vehicle, such as an emergency acceleration test, a maximum vehicle speed test, a low-temperature test and the like, after the sensor and the CAN bus are installed, the test time generally lasts for a long time (such as more than 3 hours), and the data acquisition is guaranteed not to be lost and have no errors in the limit working conditions for a long time;
the signal acquisition synchronism is ensured through the data acquisition module and the upper computer, all the sensor output lines and the CAN bus are connected to the same number of acquisition modules, the signal acquisition synchronism is ensured, and the upper computer CAN perform synchronous processing on signals and ensure the signal processing synchronism;
the CAN signal acquisition of the vehicle generally comprises the modes of power CAN, OBD access and the like, and comprises vehicle control unit signals, such as vehicle speed, an accelerator pedal, a brake pedal, gears, acceleration, modes and the like, engine signals, such as engine rotating speed, torque, water temperature, oil injection signals, ignition signals and the like, battery controller signals, such as battery voltage, current, SOC, temperature and the like, motor controller signals, such as motor rotating speed, motor torque, water temperature, bus current, bus voltage and the like;
aiming at the vehicle, a sensor arrangement scheme is constructed according to the vehicle analysis result by a sensor signal acquisition mode, and specifically comprises signals of a current sensor, a voltage sensor, a displacement sensor, a temperature sensor, a torque sensor, a hydraulic sensor and the like;
according to the vehicle test, relevant signals are collected according to the test matrix and a test target, and a bench test and a road test are carried out;
for the vehicle, key technology analysis of a core task of the key technology evaluation specifically comprises key strategy analysis such as steady-state control, drive control, regenerative brake control and energy management;
the steady-state control strategy mainly comprises acceleration intention identification and braking intention identification, wherein the acceleration intention identification comprises the steps of analyzing the relation between the opening degree of an accelerator pedal and the driving torque and the corresponding relation between the opening degree of the accelerator pedal and the acceleration under multiple dimensions of different running modes, different SOCs, different vehicle speeds and the like; the brake intention identification means analyzing the corresponding relation between the opening degree of a brake pedal and the braking torque, the braking hydraulic pressure, the acceleration and the total braking torque of the motor under the multiple dimensions of different gears, different modes, different SOCs and the like;
the driving control is to analyze motor torque control, front and rear shaft torque distribution, a mode switching process and a mode switching threshold under driving conditions of creeping, accelerating, constant speed, Tip in, Tip out and the like under multiple dimensions of different modes, different gears, different SOC and the like;
the regenerative braking control is to analyze the relation between the motor braking torque and the vehicle speed and the brake pedal opening degree of the sliding working condition, the braking working condition and the emergency braking working condition, analyze the coordination control relation between the hydraulic pressure and the motor torque braking and the coordination control relation between the motor braking torque and the ABS under the multiple dimensions of different modes, different gears, different SOC, different vehicle speeds, different brake pedal opening degrees and the like;
the energy management comprises the aspects of engine start-stop control, energy flow, part working points and the like, the coordinated control of the start-stop process of the engine under various working conditions and the start-stop threshold value are analyzed, the circulating working conditions and the energy flow under single working conditions under different temperatures and different SOCs are analyzed, the energy flow analysis comprises the aspects of energy transfer paths, efficiency and the like, the working points of the engine and the motor are analyzed, and the control curve and the high-efficiency points of the engine are analyzed;
the optimization simulation analysis is used for constructing a simulation model based on a key technology analysis result, and performing extreme condition simulation and energy-saving potential analysis, wherein the extreme condition simulation analysis refers to simulation analysis of conditions which are difficult to realize on the whole vehicle, such as wet and slippery road surfaces, extreme temperature analysis, limping condition analysis, regenerative braking failure protection and other conditions, the energy-saving potential analysis comprises the correlation degree of vehicle energy saving and factors such as whole vehicle wind resistance optimization, whole vehicle quality reduction, component performance improvement, accessory performance optimization and the like, and the optimization direction of the vehicle energy saving is indicated by means of quantification of simulation.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. The utility model provides a plug-in hybrid vehicle key technology evaluation system which characterized in that: the system comprises a vehicle static analysis unit, a test matrix construction unit, a signal acquisition unit, a vehicle test unit, a key technology analysis unit and a simulation optimization unit which are connected in sequence;
the vehicle static analysis unit comprises static parameter query, configuration analysis, working mode analysis and electronic motor architecture analysis;
the test matrix construction unit is constructed based on basic results of vehicle static analysis;
the signal acquisition unit is used for screening based on a test target of the test matrix;
the vehicle testing unit collects related signals according to the testing matrix and a testing target, and performs rack testing and road testing;
the key technology analysis unit comprises steady-state control, drive control, regenerative brake control and energy management key strategy analysis;
and the simulation optimization unit constructs a simulation model based on the key technology analysis result, and performs extreme condition simulation and energy-saving potential analysis.
2. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the static parameter query is used for obtaining vehicle key parameters, and specifically comprises vehicle appearance parameters: length, width, height, wheelbase, load and mass center; engine parameters: displacement, compression ratio, maximum power, torque and the like, motor parameters, rotating speed range, peak power and peak torque; battery parameters: such as battery type, battery capacity, maximum charge-discharge power.
3. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the configuration analysis comprises a series configuration, a parallel configuration, a series-parallel configuration and a power splitting configuration.
4. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the working mode analysis analyzes the working modes of the core components of the engine, the driving motor and the generator according to the configuration characteristics, and comprises a pure electric mode, a series or parallel mode, a regenerative braking mode and a parking charging mode.
5. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the electronic and electric appliance architecture analysis comprises CAN network analysis and high-voltage architecture analysis.
6. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the test matrix generally includes, but is not limited to, ambient temperature, power battery level, driving mode, gear, and operating condition factors.
7. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the steady-state control strategy comprises acceleration intention identification and braking intention identification, wherein the acceleration intention identification comprises analyzing the relation between the opening degree of an accelerator pedal and the driving torque and the corresponding relation between the opening degree of the accelerator pedal and the acceleration under the multiple dimensions of different running modes, different SOCs and different vehicle speeds; the brake intention identification means that the corresponding relation between the opening degree of a brake pedal and the braking torque, the braking hydraulic pressure, the acceleration and the total braking torque of the motor under the multiple dimensions of different gears, different modes and different SOC is analyzed.
8. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the driving control is motor torque control, front and rear shaft torque distribution, a mode switching process and a mode switching threshold under the driving conditions of crawling, accelerating, constant speed, Tip in and Tip out under the multiple dimensions of different modes, different gears and different SOC.
9. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the regenerative braking control is to analyze the relation between the motor braking torque and the vehicle speed and the brake pedal opening degree of the sliding working condition, the braking working condition and the emergency braking working condition, analyze the coordination control relation between the hydraulic pressure and the motor torque braking and the coordination control relation between the motor braking torque and the ABS under a plurality of dimensions of different modes, different gears, different SOC, different vehicle speeds, different brake pedal opening degrees and the like.
10. The plug-in hybrid electric vehicle key technology evaluation system of claim 1, characterized in that: the energy management comprises the aspects of engine start-stop control, energy flow and component working points, the coordination control of the start-stop process of the engine under various working conditions is analyzed, the start-stop threshold value is analyzed, the energy flow under the circulating working conditions and the single working condition under different temperatures and different SOC is analyzed, the energy flow analysis comprises the aspects of energy transfer paths and efficiency, the working points of the engine and the motor are analyzed, and the control curve of the engine and the high-efficiency point are analyzed.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112539937A (en) * | 2020-12-11 | 2021-03-23 | 奇瑞汽车股份有限公司 | Method for testing start-stop reliability of 48V system |
| CN113029590A (en) * | 2021-03-03 | 2021-06-25 | 中国汽车技术研究中心有限公司 | Device and method for testing power transmission smoothness of hybrid vehicle |
| GB2617697A (en) * | 2022-04-11 | 2023-10-18 | Hydrogen Vehicle Systems Ltd | A system and method for the management of regenerative-braking for battery recharging in a hybrid electric vehicle |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105021406A (en) * | 2015-07-23 | 2015-11-04 | 江苏大学 | Driving-condition-based testing method for regenerative brake system of electric automobile |
| CN106570200A (en) * | 2015-10-12 | 2017-04-19 | 南京赛宝工业技术研究院 | Virtual simulation test method |
| CN107145082A (en) * | 2017-07-11 | 2017-09-08 | 河北工业大学 | A kind of plug-in hybrid-power automobile entire car controller emulation platform |
| CN108255649A (en) * | 2017-11-30 | 2018-07-06 | 中国航空综合技术研究所 | A kind of Diagnosis Strategy Design method based on modeling and simulating Cooperative Analysis |
| WO2019153850A1 (en) * | 2018-02-09 | 2019-08-15 | 浙江吉利控股集团有限公司 | Series-parallel hybrid power system and vehicle operating mode decision-making method |
-
2020
- 2020-04-28 CN CN202010350385.XA patent/CN111581796B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105021406A (en) * | 2015-07-23 | 2015-11-04 | 江苏大学 | Driving-condition-based testing method for regenerative brake system of electric automobile |
| CN106570200A (en) * | 2015-10-12 | 2017-04-19 | 南京赛宝工业技术研究院 | Virtual simulation test method |
| CN107145082A (en) * | 2017-07-11 | 2017-09-08 | 河北工业大学 | A kind of plug-in hybrid-power automobile entire car controller emulation platform |
| CN108255649A (en) * | 2017-11-30 | 2018-07-06 | 中国航空综合技术研究所 | A kind of Diagnosis Strategy Design method based on modeling and simulating Cooperative Analysis |
| WO2019153850A1 (en) * | 2018-02-09 | 2019-08-15 | 浙江吉利控股集团有限公司 | Series-parallel hybrid power system and vehicle operating mode decision-making method |
Non-Patent Citations (1)
| Title |
|---|
| 李高伟;王显会;张明;周云波;孙晓旺;樊晓都;: "基于强化路面的车身疲劳联合仿真" * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112539937A (en) * | 2020-12-11 | 2021-03-23 | 奇瑞汽车股份有限公司 | Method for testing start-stop reliability of 48V system |
| CN113029590A (en) * | 2021-03-03 | 2021-06-25 | 中国汽车技术研究中心有限公司 | Device and method for testing power transmission smoothness of hybrid vehicle |
| CN113029590B (en) * | 2021-03-03 | 2023-02-21 | 中国汽车技术研究中心有限公司 | Device and method for testing power transmission smoothness of hybrid vehicle |
| GB2617697A (en) * | 2022-04-11 | 2023-10-18 | Hydrogen Vehicle Systems Ltd | A system and method for the management of regenerative-braking for battery recharging in a hybrid electric vehicle |
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