CN112977450A - Energy management method for electric vehicle adaptive cruise - Google Patents
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Abstract
An energy management method for electric vehicle adaptive cruise comprehensively considers an energy management strategy of autonomous decision in the electric vehicle adaptive cruise, and solves a multi-target collaborative optimization problem about electric quantity consumption and battery life by respectively modeling an information layer, a physical layer and an energy management layer and taking multi-aspect factors such as driving safety and the like as constraints. The safety and the real-time performance in the following process can be ensured, the driving efficiency is improved, and the traffic pressure is relieved; meanwhile, the cost loss in the following process is reduced, the battery service life loss and the battery loss are added into the objective function, the electric quantity attenuation and the battery capacity decline are greatly balanced, the battery aging is effectively inhibited, the damage of the retired battery to the environment and the frequency of battery replacement are reduced, and the driving economy is greatly improved. With the development of 3C and the universal interconnection technology, the advantages and the beneficial effects of the invention can be further embodied.
Description
Technical Field
The invention relates to the technical field of electric vehicle energy management, in particular to an energy management method for multi-target real-time collaborative optimization aiming at Electric Vehicle (EV) adaptive cruise based on a physical information energy system (CPES).
Background
For electric vehicles, it is important to accurately acquire power consumption and battery life loss and to develop a suitable Energy Management Strategy (EMS), which has a significant influence on driving economy. With the development of technologies such as adaptive cruise and unmanned driving, the design of energy management strategies also needs to consider the influence of external factors such as the surrounding environment, future traffic information and road state information. At present, some driving road condition and environment monitoring modes realized based on a physical information system (CPS) have appeared, but a comprehensive control strategy capable of organically combining information such as traffic conditions and surrounding environments with energy management of electric vehicles is still lacked in the prior art, which causes certain limitation on efficient energy management of electric vehicles in a self-adaptive cruise process or automatic driving under an unmanned state.
Disclosure of Invention
In view of the above, the present invention aims to provide an energy management method for adaptive cruise of an electric vehicle based on a Cyber Physical Energy System (CPES) by combining an existing cyber physical system and an energy management strategy, and the method specifically includes the following steps:
step 1: carrying out information layer modeling aiming at the adaptive cruise process of the electric vehicle, acquiring information such as a vehicle speed sequence, road state information, road speed limit information, current distance between the vehicle and the vehicle ahead at historical time and current time, and predicting the upper limit and the lower limit of the adaptive cruise control speed sequence of the vehicle;
step 2: carrying out physical layer modeling aiming at the self-adaptive cruise process, namely establishing a longitudinal dynamic model of the vehicle;
and step 3: performing quantitative modeling of an energy management layer, namely electric quantity consumption and battery life, aiming at the self-adaptive cruise process; calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process;
and 4, step 4: the method comprises the steps of taking the vehicle economy mainly comprising electric quantity consumption and weighted battery life loss as a target to achieve the optimal effect together, solving a multi-target real-time collaborative optimization problem, and calculating a speed control sequence and a weight of the battery life, wherein the speed control sequence enables the comprehensive performance index of the vehicle to achieve the optimal effect;
and 5: and controlling the adaptive cruise driving based on the vehicle speed control sequence and the weight, and continuously updating the model parameters of the information layer and the physical layer.
Further, the information layer modeling in the step 1 comprises modeling of a perception layer and a decision layer respectively; the vehicle and elements such as pedestrians, vehicles and road environment objects in the actual traffic environment form a universal internet, so that a perception layer can acquire multi-source heterogeneous data through a 3C technology, a decision layer integrates the multi-source data through statistical analysis and machine learning, the multi-source data are used for accurately identifying the traffic environment and actively controlling safety, and a vehicle cruising speed range for ensuring the stability, safety and driving efficiency of the vehicle is obtained by combining the elements in a physical layer; the vehicle cruise speed range is expressed as:
vcruise_max＝min(vslip,vover,vmax,vh_safe)
Ft≤μ(0.5mg-m|ay|h/bave)
0≤(vh_safe(k)-vp(k))t+d(k-1)≤dismax
in the formula, vcruise_maxAt maximum cruising speed, vslipFor preventing vehicle from side-slip restraint, voverFor vehicle rollover prevention restraint, vmaxFor road speed limitation, vh_safeSafe vehicle speed, k, for maintaining a safe distance between the vehicle and its front and rear vehiclesslipAn anti-sideslip coefficient of less than 1, μ is a road surface friction coefficient, m is a vehicle mass, g is a gravity coefficient, α is a road surface gradient, L is a wheel base, δfFor actual front wheel of vehicleCorner, FtAs a running resistance of the vehicle, ayIs the lateral acceleration, h is the height of the center of mass, baveTo average track, koverIs a rollover prevention coefficient, R, of less than 1wIs the radius of curvature, dismaxThe maximum safe distance between the front and the rear vehicles, d is the distance between the two vehicles, vpFor the predicted forward speed, k is a time and t is a sampling time.
Further, the establishing of the vehicle longitudinal dynamics model in the step 2 specifically includes:
Ft(k)＝0.5ρCdAv(k)2+mg(fcosα(k)+sinα(k))+δmdv(k)/dt
where ρ is the air density, CdIs the coefficient of air resistance, A is the windward area, f is the coefficient of road rolling resistance, delta is the coefficient of rotating mass conversion, v is the speed of the vehicle, I0Is the main transmission ratio, etatFor mechanical system efficiency, TmFor the output torque of the motor, rwIs the radius of the vehicle;
further, the power consumption model of the battery established in the step 3 is represented as:
Vbatt(k)＝Voc(k)-Rint(k)Ibatt(k)
Pbatt(k)＝Ibatt(k)Vbatt(k)
in the formula, VocIs the open circuit voltage, V, of the batterybattFor the output voltage of the battery, RintIs the internal resistance of the battery, IbattFor outputting current, Q, to the batterybattIs the battery capacity, PbattFor battery output power, SoC is state of charge;
calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process, specifically:
Ctotal＝Cele+Cbatt_aging+Cbatt_mana
Cele＝ρeleEele
Cbatt_aging＝QlossρBQbattVbatt/1000
Cbatt_mana＝Qlossmbattρrep
in the formula, CtotalFor the total cost, CeleFor electricity consumption cost, Cbatt_agingFor the aging cost of the battery, Cbatt_manaCost of ex-service battery management, ρeleTo the electricity price, EeleFor total power consumption, ρBUnit price for battery aging, mbattIs the total weight of the battery, ρrepIs the decommissioning management cost of the battery per unit weight.
Further, the multi-objective real-time collaborative optimization problem in step 4 is specifically expressed in the following form:
and satisfies the following constraints:
in the formula, ZsocIn order to be a rate of consumption of electric power,
is the rate of decay of battery life, alpha is the weight, Tm_minIs the minimum output torque, T, of the motormFor output of torque of the motor, Tm_maxIs the maximum output torque of the motor,
and
limit values, v, of the coefficient of adhesion provided to the vehicle for the road surface, respectivelycruise_maxAnd vcruise_minUpper and lower limits, I, respectively, of cruising speedminAnd ImaxRespectively represent the maximum charging and discharging current limit value of the battery, and deltad is the distance between two vehicles.
Further, the multi-target real-time collaborative optimization problem is solved based on a particle swarm optimization PSO algorithm, and an optimal vehicle speed control sequence and the weight alpha are obtained through calculation.
The method provided by the invention provides an energy management strategy comprehensively considering autonomous decision in the self-adaptive cruise of the electric vehicle, and solves the multi-target collaborative optimization problem about electric quantity consumption and battery life loss by respectively modeling an information layer, a physical layer and an energy layer and taking various factors such as driving safety and the like as constraints. The safety and the real-time performance in the following process can be ensured, the driving efficiency is improved, and the traffic pressure is relieved; meanwhile, the cost loss in the following process is reduced, the battery service life loss and the battery loss are added into the objective function, the electric quantity attenuation and the battery capacity decline are greatly balanced, the battery aging is effectively inhibited, the damage of the retired battery to the environment and the frequency of battery replacement are reduced, and the driving economy is greatly improved. With the development of 3C and the universal interconnection technology, the advantages and the beneficial effects of the invention can be further embodied.
Drawings
FIG. 1 is a flowchart of the overall method of the present invention;
FIG. 2 is a schematic diagram of modeling of an information layer;
FIG. 3 is a diagram of a particular driving route;
FIG. 4 is a schematic diagram of information layer multi-source information;
FIG. 5 is a Map of a corresponding motor Map in an embodiment of the present invention;
FIG. 6 is a flow chart of a solution algorithm for a multi-objective collaborative optimization problem;
FIG. 7 is a reference diagram of an optimal speed planning sequence;
FIG. 8 is a reference diagram of optimal weighting factors for power consumption and battery life loss.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an energy management method for electric vehicle adaptive cruise, as shown in fig. 1, the method specifically comprises the following steps:
step 1: carrying out information layer modeling aiming at the adaptive cruise process of the electric vehicle, acquiring information such as a vehicle speed sequence, road state information, road speed limit information, current distance between the vehicle and the vehicle ahead at historical time and current time, and predicting the upper limit and the lower limit of the adaptive cruise control speed sequence of the vehicle;
step 2: carrying out physical layer modeling aiming at the self-adaptive cruise process, namely establishing a longitudinal dynamic model of the vehicle;
and step 3: performing quantitative modeling of an energy management layer, namely electric quantity consumption and battery life, aiming at the self-adaptive cruise process; calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process;
and 4, step 4: the method comprises the steps of taking the vehicle economy mainly comprising electric quantity consumption and weighted battery life loss as a target to achieve the optimal effect together, solving a multi-target real-time collaborative optimization problem, and calculating a speed control sequence and a weight of the battery life, wherein the speed control sequence enables the comprehensive performance index of the vehicle to achieve the optimal effect;
and 5: and controlling the adaptive cruise driving based on the vehicle speed control sequence and the weight, and continuously updating the model parameters of the information layer and the physical layer.
In a preferred embodiment of the present invention, the information layer modeling in step 1 includes separate modeling of a perception layer and a decision layer; the sensing layer acquires multi-source heterogeneous data as shown in fig. 2 and 3, the multi-source heterogeneous data are respectively road speed limit information, road information, a preceding vehicle speed sequence, a distance between the sensing layer and a preceding vehicle, a selected driving route and other information acquired by the sensing layer, and the decision layer integrates the multi-source data through statistical analysis and machine learning as shown in fig. 4; the vehicle cruise speed range is expressed as:
vcruise_max＝min(vslip,vover,vmax,vh_safe)
Ft≤μ(0.5mg-m|ay|h/bave)
0≤(vh_safe(k)-vp(k))t+d(k-1)≤dismax
in the formula, vcruise_maxAt maximum cruising speed, vslipFor preventing vehicle from side-slip restraint, voverFor vehicle rollover prevention restraint, vmaxFor road speed limitation, vh_safeSafe vehicle speed, k, for maintaining a safe distance between the vehicle and its front and rear vehiclesslipAn anti-sideslip coefficient of less than 1, μ is a road surface friction coefficient, m is a vehicle mass, g is a gravity coefficient, α is a road surface gradient, L is a wheel base, δfIs the actual front wheel angle of the vehicle, FtAs a running resistance of the vehicle, ayIs the lateral acceleration, h is the height of the center of mass, baveTo average track, koverIs a rollover prevention coefficient, R, of less than 1wIs the radius of curvature, dismaxThe maximum safe distance between the front and the rear vehicles, d is the distance between the two vehicles, vpFor the predicted forward speed, k is a time and t is a sampling time.
The step 2 of establishing the vehicle longitudinal dynamics model specifically comprises the following steps:
Ft(k)＝0.5ρCdAv(k)2+mg(fcosα(k)+sinα(k))+δmdv(k)/dt
where ρ is the air density, CdIs the coefficient of air resistance, A is the windward area, f is the coefficient of road rolling resistance, delta is the coefficient of rotating mass conversion, v is the speed of the vehicle, I0Is the main transmission ratio, etatFor mechanical system efficiency, TmFor the output torque of the motor, rwIs the radius of the vehicle;
referring to fig. 5, the motor model can be expressed as:
ηm(nm,Tm)＝f(nm,Tm)
in the formula, nm、TmThe rotational speed and the torque of the motor, respectively.
Further, the power consumption model of the battery established in the step 3 is represented as:
Vbatt(k)＝Voc(k)-Rint(k)Ibatt(k)
Pbatt(k)＝Ibatt(k)Vbatt(k)
in the formula, VocIs the open circuit voltage, V, of the batterybattFor the output voltage of the battery, RintIs the internal resistance of the battery, IbattFor outputting current, Q, to the batterybattIs the battery capacity, PbattFor battery output power, SoC is state of charge;
in one embodiment of the present invention using LiFePO4 cells, based on Arrhenius attenuation model and accumulated loss theory, the cell dynamic attenuation model can be expressed as:
in the formula, QlossAs a batteryCapacity attenuation, CrateFor the charge-discharge multiplying power of the battery, R is the internal resistance of the battery, TbattIs the operating temperature of the battery, AhIs the total battery capacity of the flowing battery
Calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process, specifically:
Ctotal＝Cele+Cbatt_aging+Cbatt_mana
Cele＝ρeleEele
Cbatt_aging＝QlossρBQbattVbatt/1000
Cbatt_mana＝Qlossmbattρrep
in the formula, CtotalFor the total cost, CeleFor electricity consumption cost, Cbatt_agingFor the aging cost of the battery, Cbatt_manaCost of ex-service battery management, ρeleTo the electricity price, EeleFor total power consumption, ρBUnit price for battery aging, mbattIs the total weight of the battery, ρrepIs the decommissioning management cost of the battery per unit weight.
The multi-objective real-time collaborative optimization problem in the step 4 is specifically expressed in the following form:
and satisfies the following constraints:
in the formula, ZsocIn order to be a rate of consumption of electric power,
is the rate of decay of battery life, alpha is the weight, Tm_minIs the minimum output torque, T, of the motormFor motor transmissionTorque out, Tm_maxIs the maximum output torque of the motor,
and
limit values, v, of the coefficient of adhesion provided to the vehicle for the road surface, respectivelycruise_maxAnd vcruise_minUpper and lower limits, I, respectively, of cruising speedminAnd ImaxRespectively represent the maximum charging and discharging current limit value of the battery, and deltad is the distance between two vehicles.
Solving the multi-target real-time collaborative optimization problem based on the particle swarm optimization PSO algorithm is composed of a real-time calculation process and an information layer data updating process, and is shown in FIG. 6. Safe and reliable cooperation and interoperation between the heterogeneous information system and the physical system are applied to energy management of the vehicle, the system takes corresponding actions according to a result of the first-layer optimization solution, accordingly, the external environment of the vehicle changes, a result of information-layer data updating is used as input of the first-layer optimization, and the system is iteratively optimized and updated, so that the optimal vehicle speed planning sequence and the optimal distribution coefficient of electric quantity consumption and battery life loss of the vehicle are continuously obtained, as shown in fig. 7 and 8.
It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. An energy management method for adaptive cruise of an electric vehicle, characterized by: the method specifically comprises the following steps:
step 1: carrying out information layer modeling aiming at the adaptive cruise process of the electric vehicle, acquiring a speed sequence, road state information, road speed limit information and current distance information between the vehicle and the vehicle ahead at historical time and current time, and predicting the upper limit and the lower limit of the adaptive cruise control speed sequence of the vehicle;
step 2: carrying out physical layer modeling aiming at the self-adaptive cruise process, namely establishing a longitudinal dynamic model of the vehicle;
and step 3: performing quantitative modeling of an energy management layer, namely electric quantity consumption and battery life, aiming at the self-adaptive cruise process; calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process;
and 4, step 4: the method comprises the steps of taking the vehicle economy mainly comprising electric quantity consumption and weighted battery life loss as a target to achieve the optimal effect together, solving a multi-target real-time collaborative optimization problem, and calculating a speed control sequence and a weight of the battery life, wherein the speed control sequence enables the comprehensive performance index of the vehicle to achieve the optimal effect;
and 5: and controlling the adaptive cruise driving based on the vehicle speed control sequence and the weight, and continuously updating the model parameters of the information layer and the physical layer.
2. The method of claim 1, wherein: the information layer modeling in the step 1 comprises the respective modeling of a perception layer and a decision layer, and the cruising speed range of the vehicle is obtained and is specifically represented as follows:
vcruise_max＝min(vslip,vover,vmax,vh_safe)
Ft≤μ(0.5mg-m|ay|h/bave)
0≤(vh_safe(k)-vp(k))t+d(k-1)≤dismax
in the formula, vcruise_maxAt maximum cruising speed, vslipFor preventing vehicle from side-slip restraint, voverFor vehicle rollover prevention restraint, vmaxFor road speed limitation, vh_safeSafe vehicle speed, k, for maintaining a safe distance between the vehicle and its front and rear vehiclesslipAn anti-sideslip coefficient of less than 1, μ is a road surface friction coefficient, m is a vehicle mass, g is a gravity coefficient, α is a road surface gradient, L is a wheel base, δfIs the actual front wheel angle of the vehicle, FtAs a running resistance of the vehicle, ayIs the lateral acceleration, h is the height of the center of mass, baveTo average track, koverIs a rollover prevention coefficient, R, of less than 1wIs the radius of curvature, dismaxThe maximum safe distance between the front and the rear vehicles, d is the distance between the two vehicles, vpFor the predicted forward speed, k is a time and t is a sampling time.
3. The method of claim 2, wherein: the step 2 of establishing the vehicle longitudinal dynamics model specifically comprises the following steps:
Ft(k)＝0.5ρCdAv(k)2+mg(fcosα(k)+sinα(k))+δmdv(k)/dt
where ρ is the air density, CdIs the coefficient of air resistance, A is the windward area, f is the coefficient of road rolling resistance, delta is the coefficient of rotating mass conversion, v is the speed of the vehicle, I0Is the main transmission ratio, etatFor mechanical system efficiency, TmFor the output torque of the motor, rwIs the radius of the vehicle.
4. The method of claim 3, wherein: the electric quantity consumption model of the battery established in the step 3 is expressed as:
Vbatt(k)＝Voc(k)-Rint(k)Ibatt(k)
Pbatt(k)＝Ibatt(k)Vbatt(k)
in the formula, VocIs the open circuit voltage, V, of the batterybattFor the output voltage of the battery, RintIs the internal resistance of the battery, IbattFor outputting current, Q, to the batterybattIs the battery capacity, PbattFor battery output power, SoC is state of charge;
calculating the cost related to battery aging and retired battery management corresponding to the self-adaptive cruise process, specifically:
Ctotal＝Cele+Cbatt_aging+Cbatt_mana
Cele＝ρeleEele
Cbatt_aging＝QlossρBQbattVbatt/1000
Cbatt_mana＝Qlossmbattρrep
in the formula, CtotalFor the total cost, CeleFor electricity consumption cost, Cbatt_agingFor the aging cost of the battery, Cbatt_manaCost of ex-service battery management, ρeleTo the electricity price, EeleFor total power consumption, ρBUnit price for battery aging, mbattIs the total weight of the battery, ρrepIs the decommissioning management cost of the battery per unit weight.
5. The method of claim 4, wherein: the multi-objective real-time collaborative optimization problem in the step 4 is specifically expressed in the following form:
and satisfies the following constraints:
in the formula, ZsocIn order to be a rate of consumption of electric power,
is the rate of decay of battery life, alpha is the weight, Tm_minIs the minimum output torque, T, of the motormFor output of torque of the motor, Tm_maxIs the maximum output torque of the motor,
and
limit values, v, of the coefficient of adhesion provided to the vehicle for the road surface, respectivelycruise_maxAnd vcruise_minUpper and lower limits, I, respectively, of cruising speedminAnd ImaxRespectively represent the maximum charging and discharging current limit value of the battery, and deltad is the distance between two vehicles.
6. The method of claim 1, wherein: and solving the multi-target real-time collaborative optimization problem based on a particle swarm optimization PSO algorithm, and calculating to obtain an optimal vehicle speed control sequence and the weight alpha.
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| US20070142996A1 (en) * | 2005-12-17 | 2007-06-21 | Chankyu Lee | Adaptive cruise control system and method for vehicle |
| CN107117170A (en) * | 2017-04-28 | 2017-09-01 | 吉林大学 | A kind of real-time estimate cruise control system driven based on economy |
| CN110329258A (en) * | 2019-07-23 | 2019-10-15 | 吉林大学 | Intelligent driving automotive energy-saving emission-reducing control method for coordinating |
| CN110641456A (en) * | 2019-10-29 | 2020-01-03 | 重庆大学 | Plug-in hybrid power system two-state self-adaptive control method based on PMP principle |
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