CN109910866B - Hybrid electric vehicle energy management method and system based on road condition prediction - Google Patents
Hybrid electric vehicle energy management method and system based on road condition prediction Download PDFInfo
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Abstract
The invention provides a hybrid electric vehicle energy management method based on road condition prediction, which comprises the following steps: determining key parameter threshold values of energy management based on planned path information, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid-state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point migration torque coefficient; and obtaining motor torque, battery power and engine torque based on the key parameter threshold value and a preset control strategy, and using the motor torque, the battery power and the engine torque for energy management of a power system. The invention also provides a hybrid electric vehicle energy management system based on road condition prediction. The invention adjusts the original key parameter threshold value of the vehicle based on the predicted road condition information, so that the fuel economy of the intelligent network type hybrid electric vehicle can be improved in the actual use process.
Description
Technical Field
The invention relates to a hybrid electric vehicle energy management system, in particular to a hybrid electric vehicle energy management method and system based on road condition prediction.
Background
With the rapid development of automobile power systems, the proportion of electric power in automobile power is higher and higher, and hybrid power becomes one of the most effective technologies for reducing oil consumption. Hybrid technology increases the power sources of the vehicle, increasing the flexibility of energy management. With the development of the car networking and intelligent networking cars, energy management technology based on road condition prediction gradually receives attention.
However, the current hybrid power prediction-based energy management system is mainly realized through a certain hardware system and a certain software algorithm, the algorithm is mainly used for planning the SOC, the hybrid power energy consumption control strategy relates to a plurality of parameters, and only the SOC is planned, so that the optimal fuel economy is difficult to realize. In view of this, under the trend that the intelligent networking system is gradually mature, it is necessary to provide a more efficient energy management strategy based on the current hybrid control system and the intelligent networking system.
Disclosure of Invention
Aiming at the technical problems, the invention provides a road condition prediction-based energy management method and system for a hybrid electric vehicle, and aims to improve the fuel economy of an intelligent network type hybrid electric vehicle in the actual use process of a user.
The technical scheme adopted by the invention is as follows:
the embodiment of the invention provides a hybrid electric vehicle energy management method based on road condition prediction, which comprises the following steps:
determining key parameter threshold values of energy management based on planned path information, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid-state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point migration torque coefficient;
and obtaining motor torque, battery power and engine torque based on the key parameter threshold value and a preset control strategy, and performing corresponding control based on the obtained motor torque, battery power and engine torque.
Optionally, the determining the critical parameter threshold value of energy management based on the planned path information includes:
extracting road condition characteristics of a planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of altitude along with the mileage;
dividing the planned path into a plurality of driving road sections based on the mileage or the number and the distribution of traffic lights;
on the basis of the extracted road condition characteristics of the route and the driving habits of the driver, under the condition of control according to a preset key parameter threshold value, carrying out prediction analysis on the relation between the battery SOC of each driving road section and the driving mileage or the driving time to obtain the predicted change characteristics of the battery SOC along with the mileage or the time;
according to the predicted characteristic that the battery SOC changes along with the mileage or the time and the minimum oil consumption principle, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize the key parameter threshold value of each running road section, wherein the first optimization scheme comprises the steps of obtaining the optimized battery SOC minimum value, the optimized battery SOC maximum value and the optimized mixed-power-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-power-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
Optionally, the first optimization scheme specifically includes:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node.
Optionally, the second optimization scheme specifically includes:
by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
Optionally, the third optimization scheme specifically includes:
by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
The embodiment of the invention also provides a hybrid electric vehicle energy management system based on road condition prediction, which comprises the following components: the intelligent network controller, the hybrid controller, the motor controller, the battery controller and the engine controller form a hybrid power system, wherein,
the intelligent network controller is connected with the intelligent driving sensor and the vehicle networking system, and is used for planning driving road conditions based on road condition information acquired by the intelligent driving sensor, calculating key parameter threshold values for energy management based on planned path information, and sending the key parameter threshold values to the hybrid controller, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point migration torque coefficient;
and the hybrid controller is in information interaction with the intelligent network controller, obtains motor torque, battery power and engine torque based on the received key parameter threshold value and a preset control strategy, and respectively sends corresponding torque instructions and power instructions to the motor controller, the battery controller and the engine controller so as to respectively control the motor, the battery and the engine.
Optionally, the determining, by the intelligent network controller, the key parameter threshold value of energy management based on the planned path information includes:
extracting road condition characteristics of a planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of altitude along with the mileage;
dividing the planned path into a plurality of driving road sections based on the mileage or the number and the distribution of traffic lights;
on the basis of the extracted route working condition characteristics and the driving habits of the driver, under the condition of control according to a preset key parameter threshold value, carrying out prediction analysis on the relation between the battery SOC and the driving mileage or the driving time of each driving road section to obtain the predicted change characteristics of the battery SOC along with the mileage or the driving time;
according to the predicted characteristic that the battery SOC changes along with the mileage or the time and the minimum oil consumption principle, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize the key parameter threshold value of each running road section, wherein the first optimization scheme comprises the steps of obtaining the optimized battery SOC minimum value, the optimized battery SOC maximum value and the optimized mixed-power-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-power-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
Optionally, the first optimization scheme specifically includes:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node.
Optionally, the second optimization scheme specifically includes:
by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
Optionally, the third optimization scheme specifically includes:
by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
According to the road condition prediction-based hybrid electric vehicle energy management method and system, the original key parameter threshold value of the vehicle is adjusted based on the predicted road condition information, so that the fuel economy of an intelligent network-connected hybrid electric vehicle can be improved in the actual use process.
Drawings
Fig. 1 is a schematic diagram of a hardware structure and data of a hybrid electric vehicle energy management system based on road condition prediction according to an embodiment of the present invention, in which a connection line linking each unit is a CAN line; arrows indicate data flow direction;
FIG. 2 is a schematic diagram of the programming of SOCmin and SOCmax;
FIG. 3 is a schematic diagram of the maximum speed planning of the hybrid operating mode EV;
FIG. 4 is a schematic diagram of a hybrid mode engine torque modulation factor map;
fig. 5 is a schematic flow chart of a hybrid electric vehicle energy management method based on road condition prediction according to an embodiment of the invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a road condition prediction-based energy management system for a plug-in hybrid electric vehicle, and aims to improve the fuel economy of an intelligent network type hybrid electric vehicle in the actual use process of a user. Fig. 1 is a road condition prediction-based hybrid electric vehicle energy management system provided in an embodiment of the present invention, and as shown in fig. 1, the system includes an intelligent grid-connected controller, a hybrid controller HCU, a motor controller MCU, a battery controller BMS, and an engine controller ECU, where the motor controller MCU, the battery controller BMS, and the engine controller ECU constitute a hybrid electric vehicle system, and the intelligent grid-connected controller is connected to an intelligent driving sensor and a vehicle networking system, and is configured to plan driving road conditions based on road condition information acquired by the intelligent driving sensor, calculate key parameter thresholds for energy management based on the planned path information, and send the key parameter thresholds to the hybrid controller MCU, where the key parameter thresholds include a battery SOC minimum value, a battery SOC maximum value, a hybrid state electric vehicle speed, an engine torque coefficient, a torque coefficient, and a vehicle speed An engine operating point migration torque coefficient; and the hybrid controller MCU is in information interaction with the intelligent network controller, obtains motor torque T-Motdem, battery power P-BatDem and engine torque T-EngDem based on the received key parameter threshold value and a preset control strategy, and respectively sends corresponding torque instructions and power instructions to the motor controller MCU, the battery controller BMS and the engine controller ECU so as to respectively control a motor, a battery and an engine.
In the embodiment of the invention, a preset key parameter threshold value and a preset control strategy are stored in the hybrid controller MCU, wherein the preset key parameter threshold value is a threshold value set when a vehicle leaves a factory, namely a factory standard quantity, and the preset control strategy is the existing energy management logic and is a control strategy for determining the output power of each power assembly according to the key parameter threshold values and the capacity of a power system. When the optimal adjustment is not performed, the hybrid controller MCU performs control based on a preset control strategy and an original threshold value, and when the optimized threshold value sent by the intelligent network connection controller is received, the hybrid controller MCU performs control according to the optimized threshold value. The intelligent network controller is also used for controlling an actuator of the intelligent driving system, the intelligent network controller can obtain road condition and path information according to traffic information input by the vehicle networking system and sensing information input by the intelligent driving sensor, plans a path according to the selection of a driver, and automatically drives or is driven by the driver according to the planned path after the path is planned. After the path planning, the intelligent network controller may determine the threshold value of the key parameter of the energy management based on the planned path information, which may specifically include the following steps:
s101, extracting road condition characteristics of the planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of the altitude along with the mileage.
In the step, the mileage is a distance from a starting point to a terminal point in the planned path, the distribution of different speed sections represents the relationship between the vehicle speed and the traveled mileage in the planned path, and the elevation changes along with the mileage represents the relationship between the gradient and the traveled mileage in the planned path.
And S102, dividing the planned path into a plurality of driving road sections based on the mileage or the number and the distribution of the traffic lights.
In this step, the intelligent network controller may divide the planned route into a plurality of driving sections according to the mileage uniform distribution or the vehicle speed characteristics, such as the vehicle speed of 0, i.e., the number and distribution of traffic lights.
S103, under the condition of control according to a preset key parameter threshold value based on the extracted route working condition characteristics and the driving habits of the driver, performing prediction analysis on the relation between the battery SOC and the driving mileage or the driving time of each driving road section to obtain the predicted change characteristics of the battery SOC along with the mileage or the driving time.
In the step, the intelligent network controller analyzes the change characteristics of the battery SOC of the target vehicle in the driving process, namely, the predicted battery SOC and the driving mileage or the driving time relation of each driving road section according to the extracted route working condition characteristics and in combination with the driving habits of the driver of the target vehicle, including the driving speed, the driving mileage and the driving time relation, and the like when the road sections in different forms are controlled through preset key parameter threshold values, namely, when the optimization adjustment control is not performed, so as to obtain the predicted battery SOC change characteristics along with the mileage or the driving time, wherein the characteristics represent the battery SOC and the driving mileage or the driving time relation.
S104, according to the predicted characteristic that the battery SOC changes along with the mileage or the time and the minimum oil consumption principle, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize a key parameter threshold value of each running road section, wherein the first optimization scheme comprises the steps of obtaining an optimized battery SOC minimum value, an optimized battery SOC maximum value and a optimized mixed-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
In the step, the intelligent network controller can select an optimal combination scheme from the three optimization schemes for optimization through an internal operation program, and after each optimization, the optimized battery SOC minimum value, the optimized battery SOC maximum value, the optimized hybrid state pure electric driving maximum speed, the engine minimum torque coefficient and the engine working condition point transfer torque coefficient are sent to the hybrid controller at the same time. Since the selected optimization schemes are not necessarily the same during each optimization, some threshold values of the key parameters transmitted each time are not optimized, that is, the threshold values are the same as the original threshold values.
The first optimization scheme may specifically include:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node. That is to say, the intelligent network controller uses the battery power at the high SOC of each running road section for low-speed running, and the power is applied at the low-speed in advance by reducing the minimum battery SOC and adjusting the maximum hybrid-state electric-only running speed, or the power is stored by increasing the maximum SOC and adjusting the maximum hybrid-state electric-only running speed, and applied at the low-speed in the future journey. For example, as shown in fig. 2, in the preset critical parameter threshold value of the hybrid controller, there is a margin in the battery SOC of a certain driving node, the windows of the SOCs corresponding to certain regions before or after the driving node are 10%, SOCmin and SOCmax are 20% and 30%, respectively, and when performing the optimization planning, the intelligent grid-connected controller plans the window of the battery SOC to be 20%, and SOCmin and SOCmax are 25% and 45%, respectively, the margin in the battery capacity before or after the driving node can be used. Therefore, the SOC window is changed while the SOCmin and the SOCmax are changed, and accordingly the energy management of the whole vehicle is changed. Therefore, by increasing the window of SOC from 10% to 20% for these regions, the change can be adjusted from 30% to 40% by keeping SOCmin constant, increasing SOCmax, thus increasing system flexibility and reducing the number of start-stops. Meanwhile, as shown in fig. 3, in the preset key parameter threshold value of the hybrid controller, the initial value of V _ EV _ Max is 50km/h, and after the hybrid controller is planned, it is determined that the oil consumption can be lowest at 60km/h, so that the V _ EV _ Max is increased from 50km/h to 60km/h, and is used as the V _ EV _ Max used in the driving area before or after the driving node in the actual driving process. Generally, the V _ EV _ Max range may be 30-80 km/h, if the engine is driven alone, the HCU performs judgment, and if the torque is lower than the product of the hybrid minimum torque TEngMix (factory standard amount) and the C _ TEngMin of the hybrid mode engine, a torque command is issued to the ECU, and the torque value is the product of the hybrid maximum torque TEngMax (factory standard amount) and the C _ TEngMin of the engine, as shown in fig. 4.
The second optimization scheme may specifically include: by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running. For example, in the predicted route, if the battery SOC of a certain travel node is lower than the battery SOC normal range, the preset engine minimum torque coefficient before the travel node is controlled is adjusted, for example, from 1 to 1.05, so that the amount of power generation is increased, and in actual travel, the battery SOC of the travel node is made to fall within the normal range by using the optimized engine minimum torque coefficient.
The third optimization scheme may specifically include: by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running. For example, in the predicted path, if the battery SOC of a certain driving node is lower than the battery SOC normal range, the preset engine operating point transition torque coefficient before the driving node is controlled is adjusted, for example, from 1 to 1.05, so that the amount of power generation is increased, and in actual driving, the battery SOC of the driving node is made to fall within the normal range by using the optimized engine operating point transition torque coefficient.
In summary, the road condition prediction-based energy management system for a hybrid electric vehicle according to the embodiment of the present invention adjusts the threshold value of the original key parameter of the vehicle based on the predicted road condition information, so that the fuel economy of the intelligent network-connected hybrid electric vehicle can be improved in the actual use process.
Based on the same inventive concept, the embodiment of the invention also provides a hybrid electric vehicle energy management method based on road condition prediction, and as the principle of the problem solved by the method is similar to that of the hybrid electric vehicle energy management system based on road condition prediction, the implementation of the method can be referred to the implementation of the system, and repeated details are omitted.
As shown in fig. 5, the hybrid electric vehicle energy management method based on road condition prediction according to the embodiment of the present invention may include the following steps:
s200, determining key parameter threshold values of energy management based on planned path information, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid-state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point transition torque coefficient;
s210, obtaining motor torque, battery power and engine torque based on the key parameter threshold value and a preset control strategy, and performing corresponding control based on the obtained motor torque, battery power and engine torque.
In step S200, the determining the threshold value of the key parameter of energy management based on the planned path information includes:
s201, extracting road condition characteristics of a planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of elevation along with the mileage;
s202, dividing a planned path into a plurality of driving road sections based on the mileage or the number and distribution of traffic lights;
s203, under the condition of control according to a preset key parameter threshold value based on the extracted route working condition characteristics and the driving habits of the driver, carrying out prediction analysis on the relation between the battery SOC and the driving mileage or the driving time of each driving road section to obtain the predicted change characteristics of the battery SOC along with the mileage or the driving time;
s204, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize a key parameter threshold value of each running road section according to the predicted characteristic of the battery SOC changing along with the mileage or the time and the minimum oil consumption principle, wherein the first optimization scheme comprises the steps of obtaining an optimized battery SOC minimum value, an optimized battery SOC maximum value and a optimized mixed-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
Wherein, the first optimization scheme specifically comprises:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node.
The second optimization scheme specifically includes:
by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
The third optimization scheme specifically includes:
by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
The functions of the above steps can be executed by the structures of the system, and are not described in detail herein.
The above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A hybrid electric vehicle energy management method based on road condition prediction is characterized by comprising the following steps:
determining key parameter threshold values of energy management based on planned path information, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid-state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point migration torque coefficient;
obtaining motor torque, battery power and engine torque based on the key parameter threshold value and a preset control strategy, and performing corresponding control based on the obtained motor torque, battery power and engine torque;
the determining key parameter threshold values for energy management based on the planned path information comprises:
extracting road condition characteristics of a planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of altitude along with the mileage;
dividing the planned path into a plurality of driving road sections based on the mileage or the number and the distribution of traffic lights;
on the basis of the extracted road condition characteristics of the route and the driving habits of the driver, under the condition of control according to a preset key parameter threshold value, carrying out prediction analysis on the relation between the battery SOC of each driving road section and the driving mileage or the driving time to obtain the predicted change characteristics of the battery SOC along with the mileage or the time;
according to the predicted characteristic that the battery SOC changes along with the mileage or the time and the minimum oil consumption principle, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize the key parameter threshold value of each running road section, wherein the first optimization scheme comprises the steps of obtaining the optimized battery SOC minimum value, the optimized battery SOC maximum value and the optimized mixed-power-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-power-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
2. The method according to claim 1, wherein the first optimization scheme specifically comprises:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node.
3. The method according to claim 1, wherein the second optimization scheme specifically comprises:
by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
4. The method according to claim 1, wherein the third optimization scheme specifically comprises:
by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
5. A hybrid electric vehicle energy management system based on road condition prediction is characterized by comprising: the intelligent network controller, the hybrid controller, the motor controller, the battery controller and the engine controller form a hybrid power system, wherein,
the intelligent network controller is connected with the intelligent driving sensor and the vehicle networking system, and is used for planning driving road conditions based on road condition information acquired by the intelligent driving sensor, calculating key parameter threshold values for energy management based on planned path information, and sending the key parameter threshold values to the hybrid controller, wherein the key parameter threshold values comprise a battery SOC minimum value, a battery SOC maximum value, a hybrid state pure electric driving maximum speed, an engine minimum torque coefficient and an engine working condition point migration torque coefficient;
the hybrid controller is in information interaction with the intelligent network controller, obtains motor torque, battery power and engine torque based on the received key parameter threshold value and a preset control strategy, and respectively sends corresponding torque instructions and power instructions to the motor controller, the battery controller and the engine controller so as to respectively control a motor, a battery and an engine;
the intelligent network controller determines the threshold value of the key parameter of energy management based on the planned path information, and the threshold value comprises the following steps:
extracting road condition characteristics of a planned route from the planned path information, wherein the road condition characteristics comprise mileage, the number and the distribution of traffic lights, the mileage distribution of different speed sections and the change of altitude along with the mileage;
dividing the planned path into a plurality of driving road sections based on the mileage or the number and the distribution of traffic lights;
on the basis of the extracted route working condition characteristics and the driving habits of the driver, under the condition of control according to a preset key parameter threshold value, carrying out prediction analysis on the relation between the battery SOC and the driving mileage or the driving time of each driving road section to obtain the predicted change characteristics of the battery SOC along with the mileage or the driving time;
according to the predicted characteristic that the battery SOC changes along with the mileage or the time and the minimum oil consumption principle, at least one of a first optimization scheme, a second optimization scheme and a third optimization scheme is selected to optimize the key parameter threshold value of each running road section, wherein the first optimization scheme comprises the steps of obtaining the optimized battery SOC minimum value, the optimized battery SOC maximum value and the optimized mixed-power-state pure electric running maximum speed by adjusting a preset battery SOC minimum value, a preset battery SOC maximum value and a preset mixed-power-state pure electric running maximum speed; the second optimization scheme comprises the steps that the optimized engine lowest torque coefficient is obtained by adjusting a preset engine lowest torque coefficient; the third optimization scheme comprises the step of obtaining an optimized engine working condition point migration torque coefficient by adjusting a preset engine working condition point migration torque coefficient.
6. The system according to claim 5, wherein the first optimization scheme specifically comprises:
by adjusting the preset battery SOC minimum value, the preset battery SOC maximum value and the hybrid state pure electric running maximum speed, under the control of the optimized battery SOC minimum value, the battery SOC maximum value and the hybrid state pure electric running maximum speed, when the control is carried out according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC exceeding the normal range preset value can be applied before or after the running node.
7. The system according to claim 5, wherein the second optimization scheme specifically comprises:
by adjusting the preset lowest torque coefficient of the engine, under the control of the optimized lowest torque coefficient of the engine and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
8. The system according to claim 5, characterized in that said third optimization scheme comprises in particular:
by adjusting the preset engine working condition point migration torque coefficient, under the control of the optimized engine working condition point migration torque coefficient, and according to the preset key parameter threshold value, the battery electric quantity corresponding to the running node with the battery SOC lower than or higher than the normal range can be within the normal range during actual running.
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| CN111114343B (en) * | 2020-01-03 | 2021-08-06 | 东风商用车有限公司 | Vehicle energy management method and system |
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