WO2021159660A1 - 一种混合动力汽车能量管理方法和系统 - Google Patents

一种混合动力汽车能量管理方法和系统 Download PDF

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WO2021159660A1
WO2021159660A1 PCT/CN2020/100569 CN2020100569W WO2021159660A1 WO 2021159660 A1 WO2021159660 A1 WO 2021159660A1 CN 2020100569 W CN2020100569 W CN 2020100569W WO 2021159660 A1 WO2021159660 A1 WO 2021159660A1
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hybrid electric
electric vehicle
power
energy
vehicle speed
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PCT/CN2020/100569
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English (en)
French (fr)
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李卫民
王海滨
李丽娟
张景菁
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山东中科先进技术研究院有限公司
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Priority to US17/617,595 priority Critical patent/US20220242390A1/en
Publication of WO2021159660A1 publication Critical patent/WO2021159660A1/zh

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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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • 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
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K6/00—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00
    • B60K6/20—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs
    • B60K6/42—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by the architecture of the hybrid electric vehicle
    • B60K6/46—Series type
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/13—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
    • 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
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B60W2050/0039—Mathematical models of vehicle sub-units of the propulsion unit
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0666—Engine power
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration

Definitions

  • the invention relates to the field of automobile energy management, in particular to a hybrid electric automobile energy management method and system.
  • Hybrid electric vehicles not only have the advantages of pure electric vehicles with high efficiency and low emissions, but also have the advantages of long driving mileage and rapid fuel replenishment of traditional internal combustion engine vehicles. It is currently one of the effective ways to solve vehicle energy consumption and air pollution. Its control strategy is used to solve the energy management problem when the car is driving, to effectively use the power according to the driving demand, to achieve the purpose of energy saving and environmental protection.
  • Hybrid Electric Vehicle HEV
  • the energy management methods used in the prior art mainly include the following:
  • Hybrid electric vehicle adaptive proportional-integral-derivative (PID) dynamic control method based on improved gray prediction (patent number CN109635433A), which mainly combines gray prediction with adaptive PID control, and combines the two
  • the sub-type performance index is introduced into the PID controller's tuning process, and the weighting coefficient is automatically adjustable to realize the optimal control of the adaptive PID.
  • the prediction of the gray prediction model based on the exponential rate does not consider the randomness of the system, and the medium and long-term prediction accuracy is poor. In the actual control process, the error of the prediction accuracy easily causes the deviation of the control amount, and it is even difficult to achieve the optimization goal of the HEV control strategy.
  • the dynamic programming algorithm is used to generate the long-term battery state of charge trajectory
  • the neural network model is used to predict the short-term future vehicle speed and the power output of the vehicle energy source is allocated and managed .
  • it only outputs the energy of the battery uniformly without considering the fuel economy issue, so that the engine and the electric motor are kept working in the high-efficiency area as much as possible.
  • HEV Hybrid Electric Vehicles
  • the purpose of the present invention is to provide a hybrid electric vehicle energy management method and system, which can improve the accuracy of HEV energy control.
  • the present invention provides the following solutions:
  • An energy management method for hybrid electric vehicles including:
  • the state variables include: the vehicle speed at the current moment, the acceleration at the current moment, and the engine power;
  • a Markov model is used to determine the vehicle speed of the hybrid electric vehicle at the next moment
  • the energy cost includes fuel cost and electric power consumption cost
  • the energy management model of the hybrid electric vehicle is determined according to the energy optimization scheduling model and the dynamic model of battery charging and discharging, so as to manage the energy of the hybrid electric vehicle.
  • the use of a Markov model to determine the vehicle speed of the hybrid electric vehicle at the next time according to the vehicle speed at the current moment and the acceleration at the current moment specifically includes:
  • the number of second preset intervals is the number of divided intervals of the acceleration of the vehicle speed at the next moment;
  • a Markov model is used to determine the probability that the acceleration at the current moment changes to the acceleration at the vehicle speed at the next moment;
  • the acceleration of the vehicle speed at the next moment is determined according to the probability.
  • the determining the battery power of the hybrid electric vehicle according to the required power of the hybrid electric vehicle at the next moment and the engine power specifically includes:
  • P ba (k) P req (k)-P eng (k) + P miss ( k) Determine the battery power P ba (k) of the hybrid electric vehicle; where P req (k) is the required power at the next moment, P eng (k) is the engine power, and P miss (k) Is the power consumed by the friction brake.
  • the dynamic model of battery charging and discharging is:
  • the energy optimization scheduling model of the hybrid electric vehicle is:
  • G is the energy optimization target
  • C oil (t) is the fuel cost
  • F oil (t) is the power consumption cost
  • ⁇ 1 is the weight of fuel cost
  • ⁇ 2 is the weight of electricity consumption cost
  • ⁇ 3 is the weight of the lowest value of carbon dioxide emissions
  • ⁇ 1 + ⁇ 2 + ⁇ 3 1
  • t is Time
  • n is the total number of times.
  • An energy management system for hybrid electric vehicles including:
  • the state variable acquisition module is used to acquire the state variables of the hybrid electric vehicle; the state variables include: the vehicle speed at the current moment, the acceleration at the current moment, and the engine power;
  • a vehicle speed determination module configured to use a Markov model to determine the vehicle speed of the hybrid electric vehicle at the next time according to the vehicle speed at the current time and the acceleration at the current time;
  • the required power determining module is configured to determine the required power of the hybrid electric vehicle at the next moment according to the vehicle speed of the hybrid electric vehicle at the next moment;
  • a battery power determining module configured to determine the battery power of the hybrid electric vehicle according to the required power of the hybrid electric vehicle at the next moment and the engine power;
  • a dynamic model construction module which is used to construct a dynamic model of battery charging and discharging according to the battery power
  • An energy cost determining module configured to determine the energy cost of the hybrid electric vehicle according to the required power at the next moment; the energy cost includes fuel cost and electric power consumption cost;
  • An energy optimization scheduling model construction module configured to construct an energy optimization scheduling model of the hybrid electric vehicle according to the energy cost
  • the energy management model building module is used to determine the energy management model of the hybrid electric vehicle according to the energy optimization scheduling model and the dynamic model of battery charging and discharging, so as to manage the energy of the hybrid electric vehicle.
  • the vehicle speed determination module specifically includes:
  • a discrete grid space construction unit configured to construct a discrete grid space according to the number of first preset intervals according to the vehicle speed at the current moment and the acceleration at the current moment;
  • the second preset interval number acquiring unit is configured to acquire the second preset interval number; the second preset interval number is the number of divided intervals of the acceleration of the vehicle speed at the next moment;
  • An acceleration probability determination unit configured to use a Markov model to determine the probability that the acceleration at the current time changes to the acceleration at the next time vehicle speed according to the discrete grid space and the number of second preset intervals;
  • the acceleration determining unit is configured to determine the acceleration of the vehicle speed at the next moment according to the probability.
  • the vehicle speed determining unit is configured to determine the vehicle speed of the hybrid electric vehicle at the next time according to the acceleration of the vehicle speed at the next time.
  • the battery power determination module specifically includes:
  • the friction brake power consumption obtaining unit is used to obtain the power consumed by the friction brake of the hybrid electric vehicle when the regenerative braking is insufficient;
  • the present invention discloses the following technical effects:
  • the hybrid electric vehicle energy management method and system disclosed in the present invention predicts the vehicle speed and required power at the next time by using the state variables at the current time, and constructs an energy optimization scheduling model and battery according to the vehicle speed and required power at the next time
  • the dynamic model of charging and discharging, and finally the energy management model of the hybrid electric vehicle is determined through the energy optimization scheduling model and the dynamic model of battery charging and discharging, so as to accurately manage the energy of the hybrid electric vehicle.
  • FIG. 1 is a flowchart of a hybrid electric vehicle energy management method provided by an embodiment of the present invention
  • Figure 2 is a schematic diagram of the structure of an existing hybrid power system
  • FIG. 3 is another working flowchart of a hybrid electric vehicle energy management method provided by an embodiment of the present invention.
  • Figure 4 is a schematic diagram of a rolling solution process in an embodiment of the present invention.
  • Fig. 5 is a schematic structural diagram of a hybrid electric vehicle energy management system provided by an embodiment of the present invention.
  • the purpose of the present invention is to provide a hybrid electric vehicle energy management method and system, which can improve the accuracy of HEV energy control.
  • FIG. 1 is a flowchart of a hybrid electric vehicle energy management method provided by an embodiment of the present invention. As shown in FIG. 1, a hybrid electric vehicle energy management method includes:
  • the state variables include: vehicle speed at the current moment, acceleration at the current moment, and engine power.
  • S103 Determine the battery power of the hybrid electric vehicle according to the required power of the hybrid electric vehicle at the next moment and the engine power.
  • S105 Determine the energy cost of the hybrid electric vehicle according to the required power at the next moment.
  • the energy cost includes fuel cost and power consumption cost.
  • S107 Determine an energy management model of the hybrid electric vehicle according to the energy optimization scheduling model and the dynamic model of battery charging and discharging, so as to manage the energy of the hybrid electric vehicle.
  • S101 uses a Markov model to determine the vehicle speed of the hybrid electric vehicle at the next time according to the vehicle speed at the current moment and the acceleration at the current moment, which specifically includes:
  • a discrete grid space is constructed according to the number of first preset intervals.
  • the number of second preset intervals is the number of divided intervals of the acceleration of the vehicle speed at the next moment.
  • a Markov model is used to determine the probability that the acceleration at the current moment changes to the acceleration at the vehicle speed at the next moment.
  • the acceleration of the vehicle speed at the next moment is determined according to the probability.
  • a random process ⁇ (t) is used to simulate driving behavior.
  • ⁇ (t) represents the state of the hybrid vehicle at time t.
  • the variable ⁇ (t) can represent the required power, acceleration, speed, etc. or a combination of the above variables. All of this information can be measured by sensors on the vehicle.
  • the driving behavior at time t has nothing to do with historical information, and is only determined by current information, then the change of ⁇ (t) can be considered as a Markov process.
  • the Markov model can be used to simulate the change law of ⁇ (t) , And predict the vehicle speed at the next moment.
  • the vehicle speed ⁇ (0 ⁇ 36m/s) and acceleration a (-1.5 ⁇ 1.5m/s 2 ) are used to form a discrete grid space, and the vehicle speed is defined as the current state quantity, which is divided into p intervals, by i ⁇ 1, ..., p ⁇ index.
  • the vehicle acceleration as the output at the next moment, divide it into q intervals, indexed by j ⁇ 1,...,q ⁇ . Then the transition probability matrix of the Markov model is:
  • transition probability matrix can be calculated according to formula (1):
  • N i, j denotes the number of the current time vehicle speed v i is the time for the next vehicle acceleration of a j.
  • the vehicle acceleration at the next moment can be predicted at the current moment, and the speed at the next moment can be obtained:
  • T v(t),j is the prediction period.
  • S103 determines the battery power of the hybrid electric vehicle according to the required power of the hybrid electric vehicle at the next moment and the engine power, which specifically includes:
  • P miss (k) is the power consumed by the friction brake, and P miss (k) ⁇ 0.
  • the SOE of the battery is used to describe the battery status.
  • P miss (k)>0 the battery is discharged, when P miss (k) ⁇ 0, the battery is in a charged state, its dynamic model:
  • SOE( ⁇ ) is a dynamic model of the battery charging and discharging
  • P ba (k) is the battery power
  • k - ⁇ t/E ba
  • ⁇ t is the simulation step size
  • E ba is the total battery energy
  • the energy optimization scheduling model of hybrid electric vehicles constructed in S106 is:
  • G is the energy optimization target
  • C oil (t) is the fuel cost
  • F oil (t) is the power consumption cost
  • ⁇ 1 is the weight of fuel cost
  • ⁇ 2 is the weight of electricity consumption cost
  • ⁇ 3 is the weight of the lowest value of carbon dioxide emissions
  • ⁇ 1 + ⁇ 2 + ⁇ 3 1
  • t is Time
  • n is the total number of times.
  • the hybrid power vehicle energy management method provided by the present invention may further include the following processes (as shown in FIG. 3):
  • a state equation that reflects the real system is established.
  • a state quantity is used to represent the possible driving behavior of the driver.
  • the state transition matrix is used to simulate the behavior of the driver in actual driving.
  • Moment state transition probability the predicted vehicle speed in the predicted time domain is obtained.
  • the model is optimized by rolling through the simulated annealing algorithm, that is, at each sampling time, the first term of the optimal control sequence is used as the input variable of the system, and the solution process is repeated at the next time to obtain the control value at the next time. , And finally realize the real-time optimal control of hybrid electric vehicles.
  • the rolling optimization process using the simulated annealing algorithm specifically includes:
  • the simulated annealing algorithm is mainly used to solve the problem of local optimal solution. It can be decomposed into three parts: solution space, objective function and initial solution.
  • the termination condition is satisfied, the current solution is output as the optimal solution, and the rolling optimization procedure is ended.
  • the termination condition is usually a situation where several consecutive new solutions are not accepted.
  • the present invention uses the Markov model to predict the vehicle speed, and by simplifying the control model, it can predict the fuel economy, energy consumption and energy consumption in the domain.
  • the goal is to optimize the overall performance of CO 2 emissions.
  • the simulated annealing algorithm is used to solve the objective function. The calculation time is fast, and the adverse effects of its random characteristics on driving safety and performance are effectively avoided.
  • the present invention also provides a hybrid electric vehicle energy management system.
  • the specific structure of the hybrid electric vehicle energy management system is shown in FIG. 5.
  • the hybrid electric vehicle energy management system includes: state variable acquisition Module 1, vehicle speed determining module 2, demand power determining module 3, battery power determining module 4, dynamic model building module 5, energy cost determining module 6, energy optimization scheduling model building module 7, and energy management model building module 8.
  • the state variable acquisition module 1 is used to acquire the state variables of the hybrid electric vehicle.
  • the state variables include: vehicle speed at the current moment, acceleration at the current moment, and engine power.
  • the vehicle speed determination module 2 is configured to use a Markov model to determine the vehicle speed of the hybrid electric vehicle at the next time according to the vehicle speed at the current moment and the acceleration at the current moment.
  • the required power determining module 3 is used for determining the required power of the hybrid electric vehicle at the next moment according to the vehicle speed of the hybrid electric vehicle at the next moment.
  • the battery power determining module 4 is configured to determine the battery power of the hybrid electric vehicle according to the required power of the hybrid electric vehicle at the next moment and the engine power.
  • the dynamic model construction module 5 is used to construct a dynamic model of battery charging and discharging according to the battery power.
  • the energy cost determining module 6 is configured to determine the energy cost of the hybrid electric vehicle according to the required power at the next moment.
  • the energy cost includes fuel cost and power consumption cost.
  • the energy optimization scheduling model construction module 7 is used to construct an energy optimization scheduling model of the hybrid electric vehicle according to the energy cost.
  • the energy management model construction module 8 is configured to determine the energy management model of the hybrid electric vehicle according to the energy optimization scheduling model and the dynamic model of battery charging and discharging, so as to manage the energy of the hybrid electric vehicle.
  • the vehicle speed determination module 2 specifically includes: a discrete grid space construction unit, a second preset interval number acquisition unit, an acceleration probability determination unit, an acceleration determination unit, and a vehicle speed determination unit.
  • the discrete grid space construction unit is configured to construct a discrete grid space according to the vehicle speed at the current moment and the acceleration at the current moment according to the number of first preset intervals.
  • the second preset interval number acquiring unit is used for acquiring the second preset interval number.
  • the number of second preset intervals is the number of divided intervals of the acceleration of the vehicle speed at the next moment.
  • the acceleration probability determination unit is configured to determine the probability of the acceleration from the current moment of acceleration to the vehicle speed at the next moment by using a Markov model according to the number of discrete grid spaces and the second preset interval.
  • the acceleration determining unit is configured to determine the acceleration of the vehicle speed at the next moment according to the probability.
  • the vehicle speed determining unit is configured to determine the vehicle speed of the hybrid electric vehicle at the next time according to the acceleration of the vehicle speed at the next time.
  • the battery power determining module 4 specifically includes: a friction brake power consumption obtaining unit and a battery power determining unit.
  • the friction brake power consumption obtaining unit is used to obtain the power consumed by the friction brake of the hybrid vehicle when the regenerative braking is insufficient.
  • P req (k) is the required power at the next moment
  • P eng (k) is the engine power
  • P miss (k) is the power consumed by the friction brake.

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Abstract

本发明涉及一种混合动力汽车能量管理方法和系统，通过获取混合动力汽车的状态变量；根据当前时刻的车速和当前时刻的加速度，采用马尔科夫模型确定混合动力汽车下一时刻的车速；根据混合动力汽车下一时刻的车速确定混合动力汽车下一时刻的需求功率；根据混合动力汽车下一时刻的需求功率和发动机功率确定混合动力汽车的电池功率；根据电池功率构建电池充放电的动态模型；根据下一时刻的需求功率确定混合动力汽车的能量成本；能量成本包括燃油成本和电能消耗成本；根据能量成本构建混合动力汽车的能量优化调度模型；根据能量优化调度模型和电池充放电的动态模型确定混合动力汽车的能量管理模型，以对混合动力汽车的能量进行精确管理。

Description

一种混合动力汽车能量管理方法和系统
本申请要求于2020年2月13日提交中国专利局、申请号为202010090351.1、发明名称为“一种混合动力汽车能量管理方法和系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及汽车能量管理领域，特别是涉及一种混合动力汽车能量管理方法和系统。
背景技术
传统内燃机汽车在带来便捷交通的同时，所引起的环境污染与能源短缺问题也日益彰显，因此，开发新一代的清洁节能型汽车成为了全球的必然趋势和开发热点。混合动力汽车既具有纯电动汽车高效率和低排放的优点，又具有传统内燃机汽车行驶里程长和快速补充燃料的优点，是目前解决车辆能源消耗过度和空气污染的有效途径之一。其控制策略用于解决汽车行驶时的能量管理问题，以行驶需求有效地利用动力，达到节能与环保的目的。
传统混合动力汽车(Hybrid Electric Vehicle，HEV)的能量管理方法有很多，现有技术中采用的能量管理方法主要有以下几种：
1、基于改进灰色预测的混合动力汽车自适应比例-积分-微分控制器(proportion integral derivative，PID)动态控制方法(专利号CN109635433A)，主要是将灰色预测与自适应PID控制结合，并将二次型性能指标引入到PID控制器的整定过程中，加权系数自动可调，实现自适应PID的最优控制。然而灰色预测模型基于指数率的预测没有考虑 系统的随机性，中长期预测精度较差，在实际控制过程中预测精度的误差容易引起控制量的偏离，甚至最终难以实现HEV控制策略的优化目标。
2、基于插电式混合动力汽车能量管理方法及系统(专利号CN108909702A)，利用动态规划算法生成长期电池荷电状态轨迹，基于神经网络模型预测短期未来车速并对车载能量源动力输出进行分配管理。但其仅仅是将电池的能量均匀输出而并未考虑到燃油经济性问题，以使发动机与电机尽可能保持工作在高效区。
因传统混合动力汽车(Hybrid Electric Vehicle，HEV)在行驶过程中，HEV整车系统存在非线性，HEV车速存在时变性，因此，现有技术中所采用的以上能量管理方法难以实现对HEV能量的精确控制。
发明内容
本发明的目的是提供一种混合动力汽车能量管理方法和系统，能够提高对HEV能量控制的精确度。
为实现上述目的，本发明提供了如下方案：
一种混合动力汽车能量管理方法，包括：
获取混合动力汽车的状态变量；所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率；
根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速；
根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率；
根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定 所述混合动力汽车的电池功率；
根据所述电池功率构建电池充放电的动态模型；
根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本；所述能量成本包括燃油成本和电能消耗成本；
根据所述能量成本构建所述混合动力汽车的能量优化调度模型；
根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
可选的，所述根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速，具体包括：
根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间；
获取第二预设区间个数；所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数；
根据所述离散网格空间和所述第二预设区间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率；
根据所述概率确定所述下一时刻车速的加速度。
可选的，所述根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率，具体包括：
获取所述混合动力汽车在再生制动不充分情况下摩擦制动器所消耗的功率；
根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)确定 所述混合动力汽车的电池功率P ba(k)；其中，P req(k)为所述下一时刻的需求功率，P eng(k)为所述发动机功率，P miss(k)为所述摩擦制动器所消耗的功率。
可选的，所述电池充放电的动态模型为：
SOE(k+1)＝SOE(k)-P ba(k)；其中，SOE(·)为所述电池充放电的动态模型，P ba(k)为所述电池功率，k＝-Δt/E ba，Δt为仿真步长，E ba为电池总能量。
可选的，所述混合动力汽车的能量优化调度模型为：
Figure PCTCN2020100569-appb-000001
其中，G为能量优化目标，C oil(t)为燃油成本，F oil(t)为电能消耗成本，
Figure PCTCN2020100569-appb-000002
为二氧化碳排放量的最低值，ω 1为燃油成本的权重，ω 2为电能消耗成本的权重，ω 3为二氧化碳排放量的最低值的权重，ω 1+ω 2+ω 3＝1，t为时刻，n为时刻总数。
一种混合动力汽车能量管理系统，包括：
状态变量获取模块，用于获取混合动力汽车的状态变量；所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率；
车速确定模块，用于根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速；
需求功率确定模块，用于根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率；
电池功率确定模块，用于根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率；
动态模型构建模块，用于根据所述电池功率构建电池充放电的动态模 型；
能量成本确定模块，用于根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本；所述能量成本包括燃油成本和电能消耗成本；
能量优化调度模型构建模块，用于根据所述能量成本构建所述混合动力汽车的能量优化调度模型；
能量管理模型构建模块，用于根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
可选的，所述车速确定模块具体包括：
离散网格空间构建单元，用于根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间；
第二预设区间个数获取单元，用于获取第二预设区间个数；所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数；
加速度概率确定单元，用于根据所述离散网格空间和所述第二预设区间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率；
加速度确定单元，用于根据所述概率确定所述下一时刻车速的加速度。
车速确定单元，用于根据所述下一时刻车速的加速度确定所述混合动力汽车下一时刻的车速。
可选的，所述电池功率确定模块具体包括：
摩擦制动器消耗功率获取单元，用于获取所述混合动力汽车在再生制 动不充分情况下摩擦制动器所消耗的功率；
电池功率确定单元，用于根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)确定所述混合动力汽车的电池功率P ba(k)；其中，P req(k)为所述下一时刻的需求功率，P eng(k)为所述发动机功率，P miss(k)为所述摩擦制动器所消耗的功率。
根据本发明提供的具体实施例，本发明公开了以下技术效果：
本发明公开的混合动力汽车能量管理方法和系统，通过采用当前时刻的状态变量来对下一时刻的车速和需求功率进行预测，并根据下一时刻的车速和需求功率构建能量优化调度模型和电池充放电的动态模型，最后通过能量优化调度模型和电池充放电的动态模型确定混合动力汽车的能量管理模型，以对混合动力汽车的能量进行精确管理。
说明书附图
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例提供的混合动力汽车能量管理方法的流程图；
图2为现有的混合动力系统的结构示意图；
图3为本发明实施例提供的混合动力汽车能量管理方法的另一工作流程图；
图4为本发明实施例中滚动求解过程的示意图；
图5为本发明实施例提供的混合动力汽车能量管理系统的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的目的是提供一种混合动力汽车能量管理方法和系统，能够提高对HEV能量控制的精确度。
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
图1为本发明实施例提供的混合动力汽车能量管理方法的流程图，如图1所示，一种混合动力汽车能量管理方法，包括：
S100、获取混合动力汽车的状态变量。所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率。
S101、根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速。
S102、根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率。
S103、根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率。
S104、根据所述电池功率构建电池充放电的动态模型。
S105、根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本。所述能量成本包括燃油成本和电能消耗成本。
S106、根据所述能量成本构建所述混合动力汽车的能量优化调度模型。
S107、根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
其中，S101根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速的过程，具体包括：
根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间。
获取第二预设区间个数。所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数。
根据所述离散网格空间和所述第二预设区间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率。
根据所述概率确定所述下一时刻车速的加速度。
采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率的具体过程为：
采用一个随机过程ω(t)来模拟驾驶行为。ω(t)表示混合动力汽车在t时刻的状态。变量ω(t)可以表示需求功率、加速度、速度等或者是上述变 量的组合。所有的这些信息均可以通过车辆上的传感器测得。t时刻的驾驶行为与历史信息无关，只由当前信息决定，则可以认为ω(t)的变化是一个马尔科夫过程，此时即可使用马尔科夫模型来模拟ω(t)的变化规律，并对下一时刻的车速进行预测。
采用车速ν(0～36m/s)和加速度a(-1.5～1.5m/s 2)构成离散网格空间，定义车速为当前状态量，将其划分为p个区间，由i∈{1，…，p}索引。定义车辆加速度为下一刻输出量，将其划分为q个区间，由j∈{1，…，q}索引。则马尔科夫模型的转移概率矩阵为：
X i,j＝Pr[a(t+1)＝a j|v(t)＝v i]      (1)
式中，X i,j表示在当前时刻车速v(t)＝v i的情况下车辆加速度在下一时刻变化至a j的概率。因此可以根据式(1)计算转移概率矩阵：
Figure PCTCN2020100569-appb-000003
式中，N i,j表示当前时刻车速为v i的情况下车辆加速度在下一个时刻为a j的次数。
根据以上马尔科夫模型，即可在当前时刻预测出下一时刻的车辆加速度，并求出下一时刻的速度：
Figure PCTCN2020100569-appb-000004
其中，T v(t),j为预测周期。
S103根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率，具体包括：
获取所述混合动力汽车在再生制动不充分情况下摩擦制动器所消耗 的功率。
根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)(公式(4))确定所述混合动力汽车的电池功率P ba(k)。其中，P req(k)为所述下一时刻的需求功率，可以根据S101中利用马尔科夫模型预测出的速度及加速度计算得到，P eng(k)为所述发动机功率，其功率变化如式(5)所示：
ΔP neg(t)＝P neg(t+1)-P neg(t)    (5)
其中，P miss(k)为所述摩擦制动器所消耗的功率，P miss(k)≥0。
在S104中，采用电池的SOE来描述电池状态，当SOE＝1时表示电池完全充满，当SOE＝0时表示电池放电完全。当P miss(k)＞0时电池放电，当P miss(k)＜0时电池处于充电状态，其动态模型：
SOE(k+1)＝SOE(k)-P ba(k)。         (6)
其中，SOE(·)为所述电池充放电的动态模型，P ba(k)为所述电池功率，k＝-Δt/E ba，Δt为仿真步长，E ba为电池总能量。
在预测域上S106中所构建的混合动力汽车的能量优化调度模型为：
Figure PCTCN2020100569-appb-000005
其中，G为能量优化目标，C oil(t)为燃油成本，F oil(t)为电能消耗成本，
Figure PCTCN2020100569-appb-000006
为二氧化碳排放量的最低值，ω 1为燃油成本的权重，ω 2为电能消耗成本的权重，ω 3为二氧化碳排放量的最低值的权重，ω 1+ω 2+ω 3＝1，t为时刻，n为时刻总数。
作为本发明的另一实施例，基于图2所公开的混合动力系统结构，本发明所提供的混合动力汽车能量管理方法还可以包括如下过程(如图3 所示)：
首先，基于混合动力系统结构建立一个反应真实系统的状态方程，用一个状态量表示驾驶员可能的驾驶行为，用状态转移矩阵来模拟驾驶员在实际驾驶中的行为，利用马尔科夫模型计算转矩状态转移概率，得到预测时域范围内的预测车速。
然后，以预测域内能量消耗量、燃油成本及CO 2(二氧化碳)排放量最低为综合优化目标建立混合动力汽车优化控制模型。
最后，通过模拟退火算法对该模型进行滚动寻优，即在每一个采样时刻采用最优控制序列的第一项作为系统的输入变量，在下一时刻重复该求解过程以得到下一时刻的控制量，最终实现混合动力汽车的实时最优控制。
其中，采用模拟退火算法滚动寻优的过程具体包括：
模拟退火算法作为一种通用的随机搜索算法，主要用来解决局部最优解问题，可以分解为解空间、目标函数和初始解三部分。
(1)初始化：初始温度T(充分大)，T＝α×T,α∈(0,1)，为了保证较大的搜索空间，α一般取接近于1的值，如0.95。
获取初始解状态S(S是算法迭代的起点)和每个T值所对应的预设迭代次数L。
(2)使k＝1,…,L重复第(3)至第(6)步。
(3)产生新解S'。
(4)计算增量ΔT＝G(S')-G(S)，其中G(S)为目标函数。
(5)若ΔT<0则接受S'作为新的当前解，否则以概率exp(-ΔT/T)接受S' 作为新的当前解。
(6)如果满足终止条件则输出当前解作为最优解，结束滚动寻优程序。所述终止条件通常为连续若干个新解都没有被接受的情况。
(7)T逐渐减少，且T趋近于0，然后转第(2)步。
在每一个采样时刻，根据获得的当前信息，如车辆行驶的加速度、速度及需求功率等实时信息，将模拟退火算法代入已建立的系统模型在线求解，得到一个有限时间开环最优控制序列，即S'，并将S'的第一个元素S'(1)作用于被控对象。在下一个采样时刻重复上述过程，以此类推，图4为滚动求解过程的示意图。
本发明所提供技术方案的优点在于：
针对传统混合动力汽车控制策略缺乏对汽车在实际驾驶过程中的实时性和随机性的考虑，本发明利用马尔科夫模型预测车速，通过简化控制模型，以预测域内的燃油经济性、能量消耗及CO 2排放综合性能最优为目标，采用模拟退火算法对目标函数进行求解，计算时间快，且有效避免了其随机特性对行驶安全及性能带来的不利影响。
此外，针对本发明所公开的混合动力汽车能量管理方法，本发明还对应提供了一种混合动力汽车能量管理系统，其具体结构如图5所示，混合动力汽车能量管理系统包括：状态变量获取模块1、车速确定模块2、需求功率确定模块3、电池功率确定模块4、动态模型构建模块5、能量成本确定模块6、能量优化调度模型构建模块7和能量管理模型构建模块8。
其中，状态变量获取模块1用于获取混合动力汽车的状态变量。所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率。
车速确定模块2用于根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速。
需求功率确定模块3用于根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率。
电池功率确定模块4用于根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率。
动态模型构建模块5用于根据所述电池功率构建电池充放电的动态模型。
能量成本确定模块6用于根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本。所述能量成本包括燃油成本和电能消耗成本。
能量优化调度模型构建模块7用于根据所述能量成本构建所述混合动力汽车的能量优化调度模型。
能量管理模型构建模块8用于根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
所述车速确定模块2具体包括：离散网格空间构建单元、第二预设区间个数获取单元、加速度概率确定单元、加速度确定单元和车速确定单元。
离散网格空间构建单元用于根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间。
第二预设区间个数获取单元用于获取第二预设区间个数。所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数。
加速度概率确定单元用于根据所述离散网格空间和所述第二预设区 间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率。
加速度确定单元用于根据所述概率确定所述下一时刻车速的加速度。
车速确定单元用于根据所述下一时刻车速的加速度确定所述混合动力汽车下一时刻的车速。
所述电池功率确定模块4具体包括：摩擦制动器消耗功率获取单元和电池功率确定单元。
摩擦制动器消耗功率获取单元用于获取所述混合动力汽车在再生制动不充分情况下摩擦制动器所消耗的功率。
电池功率确定单元用于根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)确定所述混合动力汽车的电池功率P ba(k)。其中，P req(k)为所述下一时刻的需求功率，P eng(k)为所述发动机功率，P miss(k)为所述摩擦制动器所消耗的功率。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的系统而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想。同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处。综上所述，本说明书内容不应理解为对本发明的限制。

Claims (8)

  1. 一种混合动力汽车能量管理方法，其特征在于，包括：
    获取混合动力汽车的状态变量；所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率；
    根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速；
    根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率；
    根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率；
    根据所述电池功率构建电池充放电的动态模型；
    根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本；所述能量成本包括燃油成本和电能消耗成本；
    根据所述能量成本构建所述混合动力汽车的能量优化调度模型；
    根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
  2. 根据权利要求1所述的一种混合动力汽车能量管理方法，其特征在于，所述根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速，具体包括：
    根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间；
    获取第二预设区间个数；所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数；
    根据所述离散网格空间和所述第二预设区间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率；
    根据所述概率确定所述下一时刻车速的加速度。
    根据所述下一时刻车速的加速度确定所述混合动力汽车下一时刻的车速。
  3. 根据权利要求1所述的一种混合动力汽车能量管理方法，其特征在于，所述根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率，具体包括：
    获取所述混合动力汽车在再生制动不充分情况下摩擦制动器所消耗的功率；
    根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)确定所述混合动力汽车的电池功率P ba(k)；其中，P req(k)为所述混合动力汽车下一时刻的需求功率，P eng(k)为所述发动机功率，P miss(k)为所述摩擦制动器所消耗的功率。
  4. 根据权利要求1所述的一种混合动力汽车能量管理方法，其特征在于，所述电池充放电的动态模型为：
    SOE(k+1)＝SOE(k)-P ba(k)；其中，SOE(·)为所述电池充放电的动态模型，P ba(k)为所述电池功率，k＝-Δt/E ba，Δt为仿真步长，E ba为电池总能量。
  5. 根据权利要求1所述的一种混合动力汽车能量管理方法，其特征在于，所述混合动力汽车的能量优化调度模型为：
    Figure PCTCN2020100569-appb-100001
    其中，G为能量优化目标，C oil(t)为燃油成本，F oil(t)为电能消耗成本，
    Figure PCTCN2020100569-appb-100002
    为二氧化碳排放量的最低值，ω 1为燃油成本的权重，ω 2为电能消耗成本的权重，ω 3为二氧化碳排放量的最低值的权重，ω 1+ω 2+ω 3＝1，t为时刻，n为时刻总数。
  6. 一种混合动力汽车能量管理系统，其特征在于，包括：
    状态变量获取模块，用于获取混合动力汽车的状态变量；所述状态变量包括：当前时刻的车速、当前时刻的加速度和发动机功率；
    车速确定模块，用于根据所述当前时刻的车速和所述当前时刻的加速度，采用马尔科夫模型确定所述混合动力汽车下一时刻的车速；
    需求功率确定模块，用于根据所述混合动力汽车下一时刻的车速确定所述混合动力汽车下一时刻的需求功率；
    电池功率确定模块，用于根据所述混合动力汽车下一时刻的需求功率和所述发动机功率确定所述混合动力汽车的电池功率；
    动态模型构建模块，用于根据所述电池功率构建电池充放电的动态模型；
    能量成本确定模块，用于根据所述下一时刻的需求功率确定所述混合动力汽车的能量成本；所述能量成本包括燃油成本和电能消耗成本；
    能量优化调度模型构建模块，用于根据所述能量成本构建所述混合动力汽车的能量优化调度模型；
    能量管理模型构建模块，用于根据所述能量优化调度模型和所述电池充放电的动态模型确定所述混合动力汽车的能量管理模型，以对所述混合动力汽车的能量进行管理。
  7. 根据权利要求6所述的一种混合动力汽车能量管理系统，其特征在于，所述车速确定模块具体包括：
    离散网格空间构建单元，用于根据所述当前时刻的车速和所述当前时刻的加速度，按照第一预设区间个数构建离散网格空间；
    第二预设区间个数获取单元，用于获取第二预设区间个数；所述第二预设区间个数为下一时刻车速的加速度的划分区间的个数；
    加速度概率确定单元，用于根据所述离散网格空间和所述第二预设区间个数，采用马尔科夫模型确定所述当前时刻的加速度变化至所述下一时刻车速的加速度的概率；
    加速度确定单元，用于根据所述概率确定所述下一时刻车速的加速度。
    车速确定单元，用于根据所述下一时刻车速的加速度确定所述混合动力汽车下一时刻的车速。
  8. 根据权利要求1所述的一种混合动力汽车能量管理系统，其特征在于，所述电池功率确定模块具体包括：
    摩擦制动器消耗功率获取单元，用于获取所述混合动力汽车在再生制动不充分情况下摩擦制动器所消耗的功率；
    电池功率确定单元，用于根据所述混合动力汽车下一时刻的需求功率、所述发动机功率和所述摩擦制动器所消耗的功率，采用公式P ba(k)＝P req(k)-P eng(k)+P miss(k)确定所述混合动力汽车的电池功率P ba(k)；其中，P req(k)为所述下一时刻的需求功率，P eng(k)为所述发动机功率，P miss(k)为所述摩擦制动器所消耗的功率。
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