CN115179922A - Multi-lane internet-of-vehicles traffic light road hybrid electric vehicle energy management method - Google Patents

Multi-lane internet-of-vehicles traffic light road hybrid electric vehicle energy management method Download PDF

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Publication number
CN115179922A
CN115179922A CN202210979630.2A CN202210979630A CN115179922A CN 115179922 A CN115179922 A CN 115179922A CN 202210979630 A CN202210979630 A CN 202210979630A CN 115179922 A CN115179922 A CN 115179922A
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vehicle
lane
speed
bat
max
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胡晓松
彭景辉
李佳承
赵楠
韩杰
李亚鹏
龙豪
肖文轩
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Chongqing University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
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Abstract

The invention relates to a method for managing energy of a multi-lane internet-of-vehicles traffic light road hybrid electric vehicle, belonging to the field of new energy vehicles. The method comprises the following steps: s1: planning target speeds in different lanes according to the traffic light timing information and the surrounding vehicle information; s2: determining a target function and constraint, and obtaining a driving lane and speed by using MPC tracking; s3: establishing a longitudinal dynamic model of the vehicle and models of all components of a vehicle transmission system according to parameters of the vehicle; s4: calculating the required torque and the required power of the vehicle by combining a longitudinal dynamics model according to the running speed of the vehicle; s5: and determining an objective function, and calculating the optimal power distribution by using the ECMS under the condition of ensuring the effective constraint.

Description

Energy management method for multi-lane internet-of-vehicles traffic light road hybrid electric vehicle
Technical Field
The invention belongs to the field of new energy automobiles, and relates to an energy management method for a multi-lane internet-of-vehicles traffic light road hybrid electric vehicle.
Background
Compared with the traditional fuel oil automobile and the pure electric automobile, the hybrid electric automobile has the advantages of good dynamic property and low pollution emission, and the battery charging technology has not yet made a breakthrough development stage, and the conventional hybrid electric automobile is a key object for the development of the automobile industry at the present stage. Compared with a fuel automobile, the hybrid electric vehicle can effectively improve the working efficiency of the engine, reduce the idling time of the engine, prolong the working life of the engine and improve the working state of a clutch. However, the conventional energy management method for the hybrid electric vehicle is mainly focused on the working efficiency of the system, and the influence of the physical state of each component on the working efficiency is neglected. The thermal state of each component plays a decisive role in the working performance of the battery pack, for example, the maximum output current of the battery pack is limited even the potential safety hazard is caused due to overhigh temperature; the output torque of the motor can be directly influenced by overhigh temperature of the motor, so that the working performance of the motor is reduced, and the problem of insufficient dynamic property is caused. How to improve the vehicle fuel economy while ensuring stable work of each part of a power system has important research significance and complex technical challenges. Most of the existing research focuses on battery pack thermal management, and less research is done on the thermal state of the motor as a main driving component.
Because the hybrid electric vehicle energy management method has a plurality of variables and is complex to control, the solving process is slow, and the optimization efficiency and the optimization result cannot reach an ideal state at the same time. The consideration of multi-component thermal state control undoubtedly brings about a more complex calculation process, so that the design of an energy management method which gives consideration to both calculation efficiency and optimization performance and also considers the thermal state of the motor has important scientific research and engineering application values.
Disclosure of Invention
In view of the above, the present invention provides an energy management method for a hybrid electric vehicle on a multi-lane internet-of-vehicles traffic light road, which realizes selection of an optimal traffic lane on the multi-lane traffic light road, realizes that the vehicle runs at a higher traffic speed, and has better fuel economy.
In order to achieve the purpose, the invention provides the following technical scheme:
a multi-lane Internet of vehicles traffic light road hybrid electric vehicle energy management method comprises the following steps:
s1: planning target vehicle speed V in different lanes according to traffic light timing information and surrounding vehicle information target (k)；
S2: determining objective functions and constraints, tracking V using model predictive control MPC target (k) Determining a driving lane L (k) and a speed v (k);
s3: establishing a longitudinal dynamic model of the vehicle and models of all components of a vehicle transmission system according to parameters of the vehicle;
s4: calculating the required torque T of the vehicle according to the running speed of the vehicle and combining a longitudinal dynamics model dem (k) Required power P dem (k)；
S5: and determining an objective function, and calculating the optimal power distribution by using an Equivalent Consumption Minimization Strategy (ECMS) under the condition of ensuring the effective constraint.
Optionally, the S1 specifically includes the following steps:
s11: speed V for planning no-stop traffic lights for vehicle passing according to signal light timing signal (k) The vehicle can pass through the traffic light at the fastest speed without stopping;
s12: obtaining the position, speed and acceleration information of other vehicles in the communication range of the Internet of vehicles, and determining the safe vehicle speed V of the vehicles in different lanes safe (k)；
S13: comparing V of each lane signal (k)，V safe (k) And the maximum speed limit v of the road max Selecting the minimum as the target speed V of each lane of the vehicle target (k)。
Optionally, the S2 specifically includes the following steps:
s21: calculating the minimum cost of the vehicle for driving on each lane in the prediction domain of M steps, and the acceleration a (k) and the speed v (k) on each lane by solving the cost function of each lane; wherein the cost function for each lane is:
Figure BDA0003799861350000021
where t is the current operating time of the vehicle, k is each step in the prediction domain, ω v 、ω a 、ω p 、ω e Each represents a weight of each term, v (k) is a speed of the vehicle, a (k) is an acceleration of the vehicle, pe (k) is a power of the vehicle, and the formula Pe (k) = m tot V (k) a (k) to m tot Is the total mass of the vehicle, V target_max The average vehicle speed of the lane with the largest average vehicle speed in the predicted domain;
s22: the constraints for solving the lane and the vehicle speed are specifically as follows:
v(k)∈[0,v max ]
a(k)∈[a min ,a max ]
Figure BDA0003799861350000022
wherein v is max For maximum speed limit of the road, a min ，a max Respectively, a minimum value and a maximum value of the vehicle acceleration, D (k) is a vehicle distance from a preceding vehicle,
Figure BDA0003799861350000023
is a reference headway, R 0 Is the minimum distance from the leading vehicle;
s23: comparing the minimum cost J calculated by each lane, and taking the lane with the minimum cost as a lane L (k) for the vehicle to run; the velocity solved for this lane is taken as the vehicle velocity v (k).
Optionally, in S3, the established vehicle longitudinal dynamic model is:
Figure BDA0003799861350000031
wherein, F t (k) Which is indicative of the tractive effort of the vehicle,
Figure BDA0003799861350000032
represents the air resistance of the automobile during running, c d Is the coefficient of air resistance, A f Is the windward area of the automobile, rho is the air density, v is the running speed of the automobile, k represents the running time of the automobile, g is the gravity acceleration, c r Is the rolling resistance coefficient of the road and beta is the road grade.
Optionally, in S3, the models of the components of the vehicle transmission system are established as follows:
P EM,out ＝T EM *ω EM
P EM,tot ＝P EM,out +P EM,loss
P ICE，out ＝T ICE *ω ICE
P ICE,tot ＝P ICE,out +P ICE,loss
P bat,tot ＝P bat,out +P bat,loss
wherein, P EM,out ，T EM ，ω EM Respectively, motor output power, torque and speed, P EM,tot And P EM,loss The subscripts are representative engine parameters of the ICE for the total power and the power loss of the electric machine.
Optionally, in S4, the required torque T of the vehicle is calculated dem (k) Required power P dem (k) Comprises the following steps:
P dem (k)＝F t (k)*v(k)
T dem (k)＝F t (k)*r wheel 。
optionally, in S5, the objective function is:
Figure BDA0003799861350000033
Figure BDA0003799861350000034
Figure BDA0003799861350000035
wherein the content of the first and second substances,
Figure BDA0003799861350000036
in order to achieve a high fuel consumption rate,
Figure BDA0003799861350000037
in order to obtain the fuel consumption rate of the engine,
Figure BDA0003799861350000038
λ is the equivalence factor for the equivalent specific fuel consumption of the battery.
Optionally, in S5, the operation state of each component of the transmission system is specifically constrained as follows:
T EM (k)∈[T EM,min ,T EM,max ]
T ICE (k)∈[0,T ICE,max ]
P bat (k)∈[P bat,min ,P bat,max ]
E bat ∈[SOC min ,SOC max ]*V oc *Q
E bat (0)＝E bat (N)
wherein T is EM (k) Is the output torque of the motor at time k, P bat (k) Is the power of the battery at time k, E bat For storing the charge of the battery, P bat,min ,P bat,max Minimum and maximum values of battery power, SOC, respectively min ,SOC max Minimum and maximum values of the battery state of charge, V, respectively oc Is the open circuit voltage of the battery and Q is the capacity of the battery.
The invention has the beneficial effects that:
1. considering lane change operation, the method has more degrees of freedom in vehicle speed planning;
2. the information of surrounding vehicles is combined, so that the traffic condition is more consistent with the actual traffic condition;
3. the upper layer uses MPC to plan speed, and the lower layer uses ECMS to manage energy, thus ensuring real-time performance;
4. the stability of the cooperative work of all parts of the power system is improved, and the economy and the safety are improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general logic diagram of the method of the present invention;
FIG. 2 is a schematic view of a vehicle lane change;
FIG. 3 is a vehicle powertrain for use in the present invention.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and the specific meaning of the terms described above will be understood by those skilled in the art according to the specific circumstances.
FIG. 1 is a general logic diagram of the method of the present invention; FIG. 2 is a schematic diagram of a lane change operation of a vehicle; FIG. 3 is a vehicle powertrain used in the invention.
The energy management method of the multi-lane internet-of-vehicles traffic light road hybrid electric vehicle comprises the following steps:
s1: planning target vehicle speed V in different lanes according to traffic light timing information and surrounding vehicle information target (k)；
S2: determining objective function and constraint, tracking V using MPC (Model Predictive Control) target (k) Determining a driving lane L (k) and a speed v (k);
s3: establishing a longitudinal dynamic model of the vehicle and models of all components of a vehicle transmission system according to parameters of the vehicle;
s4: according to the running speed of the vehicleCalculating the required torque T of the vehicle by combining the longitudinal dynamics model dem (k) Required power P dem (k)；
S5: determining an objective function, and calculating the optimal power allocation by using an ECMS (optimal meeting Minimization Stratagy) under the condition of ensuring the effective constraint;
the step S1 specifically includes:
s11: speed V for planning no-stop traffic lights for vehicle passing according to signal light timing signal (k) The vehicle can pass through the traffic light at the fastest speed without stopping;
s12: obtaining the position, speed and acceleration information of other vehicles in the communication range of the Internet of vehicles, and determining the safe vehicle speed V of the vehicles in different lanes safe (k)；
S13: comparing V of each lane signal (k)，V safe (k) And the maximum speed limit v of the road max Selecting the minimum as the target speed V of each lane of the vehicle target (k)；
The step S2 specifically comprises the following steps:
s21: by solving the cost function of each lane, the minimum cost of the vehicle to travel on each lane in the prediction domain of M steps, and the acceleration a (k) and the speed v (k) on each lane are calculated. Wherein the cost function for each lane is:
Figure BDA0003799861350000051
where t is the current operating time of the vehicle, k is each step in the prediction domain, ω v 、ω a 、ω p 、ω e Each represents a weight of each term, v (k) is a velocity of the vehicle, a (k) is an acceleration of the vehicle, pe (k) is a power of the vehicle, and the equation Pe (k) = m tot V (k) a (k) to m tot Is the total mass of the vehicle, V target_max The average vehicle speed of the lane in which the average vehicle speed in the predicted domain is the largest.
S22: the constraints for solving the lane and the vehicle speed are specifically as follows:
v(k)∈[0,v max ]
a(k)∈[a min ,a max ]
Figure BDA0003799861350000061
wherein v is max For maximum speed limit of the road, a min ，a max Respectively, a minimum value and a maximum value of the vehicle acceleration, D (k) is a vehicle distance from a preceding vehicle,
Figure BDA0003799861350000062
is a reference headway, R 0 Is the minimum distance from the leading vehicle.
S23: comparing the minimum cost J calculated by each lane, and taking the lane with the minimum cost as a lane L (k) for the vehicle to run; taking the speed solved on the lane as the vehicle speed v (k);
the vehicle longitudinal dynamic model established in the step S3 is as follows:
Figure BDA0003799861350000063
wherein, F t (k) Which represents the traction of the vehicle,
Figure BDA0003799861350000064
represents the air resistance of the automobile during running, c d Is the coefficient of air resistance, A f Is the windward area of the automobile, rho is the air density, v is the running speed of the automobile, k represents the running time of the automobile, g is the gravity acceleration, c r Beta is the road gradient, which is the rolling resistance coefficient of the road.
In the step S4, the required torque T of the automobile is calculated dem (k) Required power P dem (k) Comprises the following steps:
P dem (k)＝F t (k)*v(k)
T dem (k)＝F t (k)*r wheel
the objective function in step S5 is:
Figure BDA0003799861350000065
Figure BDA0003799861350000066
Figure BDA0003799861350000067
wherein the content of the first and second substances,
Figure BDA0003799861350000068
in order to obtain the specific consumption of fuel oil,
Figure BDA0003799861350000069
in order to achieve the fuel consumption rate of the engine,
Figure BDA00037998613500000610
λ is the equivalence factor for the equivalent specific fuel consumption of the battery.
The step S5 of constraining the operating states of the components of the transmission system specifically includes:
T EM (k)∈[T EM,min ,T EM,max ]
T ICE (k)∈[0,T ICE,max ]
P bat (k)∈[P bat,min ,P bat,max ]
E bat ∈[SOC min ,SOC max ]*V oc *Q
E bat (0)＝E bat (N)
wherein T is EM (k) Is the output torque of the motor at time k, P bat (k) Power of the battery at time k, E bat For storing the charge of the battery, P bat,min ,P bat,max Minimum and maximum values of battery power, SOC, respectively min ,SOC max Minimum value of battery state of charge andmaximum value, V oc Is the open circuit voltage of the battery and Q is the capacity of the battery.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (8)

1. A multi-lane internet-of-vehicles traffic light road hybrid electric vehicle energy management method is characterized in that: the method comprises the following steps:
s1: planning target vehicle speed V in different lanes according to traffic light timing information and surrounding vehicle information target (k)；
S2: determining objective functions and constraints, tracking V using model predictive control MPC target (k) Determining a driving lane L (k) and a speed v (k);
s3: establishing a longitudinal dynamic model of the vehicle and models of all components of a vehicle transmission system according to parameters of the vehicle;
s4: calculating the required torque T of the vehicle according to the running speed of the vehicle and combining with a longitudinal dynamics model dem (k) Required power P dem (k)；
S5: and determining an objective function, and calculating the optimal power distribution by using an Equivalent Consumption Minimization Strategy (ECMS) under the condition of ensuring the effective constraint.
2. The energy management method of the multi-lane internet of vehicles traffic light road hybrid electric vehicle as claimed in claim 1, characterized in that: the S1 specifically comprises the following steps:
s11: speed V for planning no-stop traffic lights for vehicle passing according to signal light timing signal (k) The vehicle can pass through the traffic light at the highest speed without stopping;
s12: obtaining the position, speed and acceleration information of other vehicles in the communication range of the Internet of vehicles, and determining whether to use the vehiclesVehicle safety speed V on same lane safe (k)；
S13: comparing V of each lane signal (k)，V safe (k) And the maximum speed limit v of the road max Selecting the minimum as the target speed V of each lane of the vehicle target (k)。
3. The energy management method of the multi-lane internet of vehicles traffic light road hybrid electric vehicle as claimed in claim 2, characterized in that: the S2 specifically comprises the following steps:
s21: calculating the minimum cost of the vehicle for driving on each lane in the prediction domain of M steps, and the acceleration a (k) and the speed v (k) on each lane by solving the cost function of each lane; wherein the cost function for each lane is:
Figure FDA0003799861340000011
where t is the current operating time of the vehicle, k is each step in the prediction domain, ω v 、ω a 、ω p 、ω e Each represents a weight of each term, v (k) is a velocity of the vehicle, a (k) is an acceleration of the vehicle, pe (k) is a power of the vehicle, and the equation Pe (k) = m tot V (k) a (k) to m tot Is the total mass of the vehicle, V target_max The average vehicle speed of the lane with the largest average vehicle speed in the predicted domain;
s22: the constraints for solving the lane and the vehicle speed are specifically as follows:
v(k)∈[0,v max ]
a(k)∈[a min ,a max ]
Figure FDA0003799861340000021
wherein v is max For maximum speed limit of the road, a min ，a max Respectively, the minimum and maximum values of the acceleration of the vehicle, and D (k) is the distanceThe distance between the front vehicle and the vehicle,
Figure FDA0003799861340000022
is a reference headway, R 0 Is the minimum distance from the leading vehicle;
s23: comparing the minimum cost J calculated by each lane, and taking the lane with the minimum cost as a lane L (k) for the vehicle to run; the velocity solved for this lane is taken as the vehicle velocity v (k).
4. The energy management method for the multi-lane internet-of-vehicles traffic light road hybrid electric vehicle according to claim 3, characterized in that: in the step S3, the established vehicle longitudinal dynamic model is:
Figure FDA0003799861340000023
wherein, F t (k) Which is indicative of the tractive effort of the vehicle,
Figure FDA0003799861340000024
representing the air resistance of the vehicle during travel, c d Is the coefficient of air resistance, A f Is the windward area of the automobile, rho is the air density, v is the running speed of the automobile, k represents the running time of the automobile, g is the gravity acceleration, c r Is the rolling resistance coefficient of the road and beta is the road grade.
5. The energy management method for the multi-lane internet-of-vehicles traffic light road hybrid electric vehicle according to claim 4, characterized in that: in S3, the established models of all parts of the vehicle transmission system are as follows:
P EM,out ＝T EM *ω EM
P EM,tot ＝P EM,out +P EM,loss
P ICE，out ＝T ICE *ω ICE
P ICE,tot ＝P ICE,out +P ICE,loss
P bat,tot ＝P bat,out +P bat,loss
wherein, P EM,out ，T EM ，ω EM Respectively, the output power, torque and speed of the motor, P EM,tot And P EM,loss The overall power and the loss power of the motor are represented by ICE representing various parameters of the engine.
6. The energy management method of the multi-lane internet of vehicles traffic light road hybrid electric vehicle as claimed in claim 5, characterized in that: in S4, the required torque T of the automobile is calculated dem (k) Required power P dem (k) Comprises the following steps:
P dem (k)＝F t (k)*v(k)
T dem (k)＝F t (k)*r wheel 。
7. the energy management method for the multi-lane internet-of-vehicles traffic light road hybrid electric vehicle according to claim 6, characterized in that: in S5, the objective function is:
Figure FDA0003799861340000031
Figure FDA0003799861340000032
Figure FDA0003799861340000033
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003799861340000034
in order to obtain the specific consumption of fuel oil,
Figure FDA0003799861340000035
in order to obtain the fuel consumption rate of the engine,
Figure FDA0003799861340000036
λ is the equivalence factor for the equivalent specific fuel consumption of the battery.
8. The energy management method for the multi-lane internet-of-vehicles traffic light road hybrid electric vehicle according to claim 7, characterized in that: in S5, the operating states of the components of the transmission system are specifically constrained as follows:
T EM (k)∈[T EM,min ,T EM,max ]
T ICE (k)∈[0,T ICE,max ]
P bat (k)∈[P bat,min ,P bat,max ]
E bat ∈[SOC min ,SOC max ]*V oc *Q
E bat (0)＝E bat (N)
wherein T is EM (k) Is the output torque of the motor at time k, P bat (k) Is the power of the battery at time k, E bat For storing the charge of the battery, P bat,min ,P bat,max Minimum and maximum values of battery power, SOC, respectively min ,SOC max Minimum and maximum values of the battery state of charge, V, respectively oc Is the open circuit voltage of the battery and Q is the capacity of the battery.
CN202210979630.2A 2022-08-16 2022-08-16 Multi-lane internet-of-vehicles traffic light road hybrid electric vehicle energy management method Pending CN115179922A (en)

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