CN102729987A - Hybrid bus energy management method - Google Patents
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Abstract
The invention discloses a hybrid bus energy management method, which comprises the following steps of: acquiring a speed transition probability model according to historical data of a bus on the same line, current speed information and bus position information by a bus control unit, then estimating the speed, and optimizing energy distribution of the hybrid bus in a prediction zone according to the estimated speed to acquire current optimal power distribution ratio of a fuel oil engine to a motor; and finally determining the actual oil injection quantity and the power provided by the motor, and sending message to a motor control unit and an engine control unit through a controller area network (CAN) bus. Energy management of the hybrid bus is optimized by fully using the characteristics of the bus on the same line; and the method has the characteristics of reasonable energy distribution, high economical efficiency of fuel oil, low exhaust emission, high robustness, energy conservation and environment friendliness.
Description
Technical field
The present invention relates to hybrid power management control technique field, be specifically related to a kind of hybrid-power bus energy management method.
Background technology
Developing of automobile industry is accompanied by problem of environmental pollution and energy scarcity problem.In order to alleviate these problems, new-energy automobile is a kind of valid approach.Because that is that all right is ripe for the relevant a lot of technology of pure electric automobile, has bottlenecks such as energy content of battery density and big electric capacity safety, current want to promote also premature.And hybrid vehicle (HEV) can design based on the prior fuel oil automobile, therefore has more practical significance.This patent mainly is exactly to launch around hybrid vehicle.Hybrid vehicle has adopted at least two kinds of propulsions source, therefore can let engine operation at more excellent operation interval, reaches purposes such as reducing fuel oil consumption, minimizing pollutant emission, recycling braking kinetic energy.
City bus has a fairly large number of characteristics as the main vehicle in city, and is bigger for the pollution effect of urban air, so people begin, and interpolation hybrid power technology reduces the aerial contamination in city in the city bus field.At present the energy management strategy of hybrid-power bus is based on some logic determines, however the specific aim of most of control policies a little less than, do not consider the operation characteristic of every circuit, the control effect that obtains like this is not very good.Some researchers formulate the energy management strategy through means such as dynamic programmings, and this way can utilize state of cyclic operation to estimate the power demand of each time period well, reaches the purpose of global optimization.Yet this strategy does not have universality, just can reach the control effect when having only bus to operate according to the model track definitely.But actual conditions are operations of bus has very strong randomness, the path motion in the time of hardly can be according to custom strategies, and each consuming time also be different, control policy is if probably can't reach the control effect of expection based on fixed model.Some researchists have made up probabilistic model to the operation conditions of vehicle, adopt stochastic dynamic programming to find the solution then, because the time complexity that calculates is not adapted at optimizing in the real-time vehicle operating.
Summary of the invention
The objective of the invention is to overcome the deficiency of prior art; A kind of hybrid-power bus energy management method is provided, and the method energy of the present invention distributes rationally, fuel economy is high, robustness good, find the solution simultaneously efficient, be adapted at implementing in the actual vehicle operational process.
The objective of the invention is to realize through following technical scheme: a kind of hybrid-power bus energy management method, hybrid-power bus have accelerator pedal position sensor, car speed sensor, GPS module, control unit for vehicle, control unit of engine, motor control unit and battery management unit; Accelerator pedal position sensor, car speed sensor and GPS module all link to each other with control unit for vehicle, and control unit for vehicle, control unit of engine, motor control unit and battery management unit are through the total wire joint of CAN; This method comprises the steps:
(1) car speed sensor is gathered current speed information, and the GPS module is obtained current vehicle location information, and the pedal position sensor of throttle obtains the location information of current throttle pedal, the battery charge state that the battery management unit estimation is current; The location information of current speed information, vehicle position information, Das Gaspedal and battery charge state all transfer to control unit for vehicle;
(2) estimate the speed of a motor vehicle after the current vehicle speed information that obtains according to speed of a motor vehicle transition probability model that early stage, historical data obtained and step 1 of control unit for vehicle and vehicle position information calculate;
(3) control unit for vehicle is optimized the energy distribution of hybrid vehicle in the prediction section according to the speed of a motor vehicle of estimating that step 2 obtains, and obtains the fuel engines of current optimal and the power-division ratios of motor;
(4) confirm the power that actual fuel injection amount and motor should provide according to the location information of the Das Gaspedal of gathering in power-division ratios that obtains in the step 3 and the step 1, send message to electric machine controller and engine controller through the CAN bus then; The message that control unit of engine sends over according to the reception control unit for vehicle is regulated the horsepower output of driving engine; Motor control unit is according to the horsepower output that receives the message control motor that control unit for vehicle sends over.
Further, said step 2 can specifically be divided into following a few sub-steps:
(2.1) according to the historical speed of a motor vehicle of bus in the operation of same highway section; Calculate the speed of a motor vehicle transition probability matrix of bus at each displacement point; And make up speed of a motor vehicle transition probability model: earlier with the spatial discretization of speed of a motor vehicle value, it is 25 lattice speed of a motor vehicle values at interval that the speed interval with 0 to 60km/h is divided into 2.5km/h; According to historical speed of a motor vehicle path, each position on the vehicle operating path trains speed of a motor vehicle transition probability matrix respectively then, and these speed of a motor vehicle transition probability matrixs are stored in the control unit for vehicle; The value quantity of the state variable after the dimension of the state transition probability matrix P at position S place (S) and discretization is relevant, if after the speed of a motor vehicle discretization 25 possible values are arranged, then P (S) is 25 * 25 matrix; Then P (S) is 25 * 25 matrix; The element of the capable b row of a is represented the probability of transferring to b speed of a motor vehicle discrete value from a speed of a motor vehicle discrete value in P (S) matrix, and a and b are the natural number of 1-25; The element P of the capable b row of a in P (S) matrix (method of calculating b) is following for S, a:
Wherein, N
a(S) for the speed of a motor vehicle at S place is the number of a discrete value in the position in the historical path; N
b(S is a) for the speed of a motor vehicle at S place is the sum that the speed of a motor vehicle at a discrete value S+1 place in the position changes b discrete value in the position in the historical path.Constituted speed of a motor vehicle transition probability model from all state transition probability matrixs of the origin-to-destination of vehicle ';
(2.2) estimate the speed of a motor vehicle after the current vehicle speed information that obtains according to above-mentioned speed of a motor vehicle transition probability model and step 1 of control unit for vehicle and vehicle position information calculate: the form that at first current vehicle speed v (S) is converted into 1 * 25 vector; The 1st element is 0 probability corresponding to the speed of a motor vehicle in this vector; N element is the probability of (n-1) * 2.5 km/h corresponding to the speed of a motor vehicle, and n is the natural number of 1-25; V (S) multiplies each other with speed of a motor vehicle transition probability model can obtain to change into from current vehicle speed v (S) at current location S all probable values of next speed of a motor vehicle v (S+1):
；
Following formula gained v (S+1) as a result also is one 1 * 25 a vector, just obtained then in position S+1 might the speed of a motor vehicle set be { v
j(S+1) | j=1,2 ..., N
S+1, wherein, N
S+1Be illustrated in the sum of the non-vanishing possible speed of a motor vehicle of S+1 location probability, the speed of a motor vehicle v that each is possible
j(S+1) pairing probability is P
j(S), P
j(S) value is exactly j non-vanishing value in the vector v (S+1); Continue to obtain the follow-up speed of a motor vehicle and probability by each possible speed of a motor vehicle afterwards according to speed of a motor vehicle transition probability model recursion; In order to simplify follow-up computation complexity, the S+2 step and after estimation range in directly with the speed of a motor vehicle path trajectory (j) of probability maximum as v
j(S+1) follow-up speed of a motor vehicle prediction path, next step branch value of several speed of a motor vehicle and follow-up maximum possible path are combined into estimating of the speed of a motor vehicle.
Further, the objective function that in the said step 3 energy distribution of hybrid vehicle is optimized is:
Wherein, u is a controlling quantity, the ratio of engine power and total power demand during u (i) expression i section moves; Fuel oil consumption during F (i, u (i)) expression i section moves under the effect of controlling quantity u (i), Δ SOC (i; U (i)) the battery charge state reduction during expression i section moves under the effect of controlling quantity u (i); P is the length (being the total prediction step number of estimating the speed of a motor vehicle that obtains in the step 2) of forecast interval, and G is weights, and E is a symbol of asking for expectation value; Target is in the prediction time domain, to ask for an optimal control sequence (being the power-division ratios of each position in the estimation range); The concrete method of calculating of above-mentioned F (i, u (i)) is:
Wherein, map_f is the bivariate table of a fuel consumption rate, and the concrete data in this bivariate table are relevant with driving engine, T
ReqBe the torque of the required output of torsion coupler, ω
ICEBe the rotating speed of driving engine, T
ReqWith ω
ICECan obtain according to the automobile longitudinal kinetics equation, Δ t (i) expression vehicle moves the time of consumption at the i section.
The concrete method of calculating of Δ SOC (i, u (i)) is:
Wherein, P
ReqBe total demand power, U is the equivalent open-loop voltage of battery, and R is the equivalent internal resistance of battery, and Q is total electric weight of battery.
The initial speed of a motor vehicle and initial state-of-charge during optimization are all taken from the value that step 1 collects.Concrete optimization method is divided into two stages: in F/s, only consider driving engine independent drive pattern, electrical motor independent drive pattern, these three kinds of mode of operations of combination drive pattern; Power-division ratios under these three kinds of mode of operations (ratio of engine power and total power demand) is smaller or equal to 1; Then with power-division ratios discretization in current feasible zone; In forecast interval, the power-division ratios of all positions is initially 1 earlier, and the power-division ratios of tasting then each point is lowered to next possible values, and then for each point calculates the influence that adjustment is brought, influence specifically is to represent with following decision variable:
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
jBe illustrated under the operating mode of j bar prediction curve the adjustment oil consumption reduction that power-division ratios caused, Δ SOC
jThe state-of-charge that expression reduces; Whether decision variable k can be used for judging in current point is worth the cost electric energy to reduce oil consumption; It is big more that k is illustrated in the identical benefit that electric energy brought of this some consumption more greatly; So after calculating the decision variable of each point in the forecast interval, select that maximum some downward modulation power-division ratios of value of k, above-mentioned is the searching process of an iteration; Each optimizing all is to select the maximum position downward modulation power-division ratios of k value, and the end condition of iteration is that k=0 or SOC reach lower limit; Just get into the optimizing process in next stage afterwards; The mode of operation that the optimization in second stage is considered is power generation mode or keeps the last pattern of F/s.Still those points that are in later driving engine independent drive pattern in F/s optimization just might change power generation mode in subordinate phase; With those position candidate as the subordinate phase generating.After the position candidate that obtains generating electricity, will determine optimum electric energy generated.The optimizing process of subordinate phase is specially: the power-division ratios of at first tasting each point rises to next possible values; Power-division ratios (ratio of engine power and total power demand) greater than 1 o'clock then for each point calculates the influence that adjustment is brought, influencing specifically is to represent with following decision variable:
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
j' be illustrated under the operating mode of j bar prediction curve the fuel oil consumption for the additive incrementation that generates electricity, Δ SOC '
jRepresent corresponding SOC increment; Whether decision variable k ' can be used for judging in current point is worth the extra fuel oil of cost to produce electric energy; From candidate point, select the maximum point of value of k ' to regulate power-division ratios; Get into next iteration then, calculate the value of k ' once more, and in the maximum position adjustments power-division ratios of k '; The position of generating all is the highest position of cost performance like this; Through the optimization in top two stages, the power-division ratios of each position all has been set to the optimum position in the forecast interval.
The present invention has following technique effect: the present invention makes full use of the characteristics of bus in the operation of same highway section, and the energy management of hybrid-power bus is optimized.The historical speed of a motor vehicle-the displacement relation that the present invention is based on statistics obtains the speed of a motor vehicle of estimating after the current displacement point; And optimize power-division ratios according to the speed of a motor vehicle of estimating, this invention has that the energy distributes rationally, fuel economy is high, exhaust emissions is few, robustness is good, the advantage of energy-conserving and environment-protective.
Description of drawings
Fig. 1 is the implementing procedure scheme drawing of the embodiment of the invention;
Fig. 2 is the framed structure scheme drawing of the embodiment of the invention;
Fig. 3 is that the speed of a motor vehicle of the present invention is estimated scheme drawing;
The diagram of circuit of the F/s when Fig. 4 optimizes for the present invention;
The diagram of circuit of the subordinate phase when Fig. 5 optimizes for the present invention.
The specific embodiment
Have accelerator pedal position sensor, car speed sensor, GPS module, control unit for vehicle, control unit of engine, motor control unit and battery management unit in the hybrid-power bus; Accelerator pedal position sensor, car speed sensor and GPS module all link to each other with control unit for vehicle, and control unit for vehicle, control unit of engine, motor control unit and battery management unit are through the total wire joint of CAN.Wherein, control unit for vehicle receives speed information through accelerator pedal position sensor and car speed sensor and accelerator pedal position information draws the optimum power distribution ratio, passes to control unit of engine, motor control unit and battery management unit then; The variablees such as instruction control throttle opening that control unit of engine sends over according to the control unit for vehicle that receives, thus engine output regulated; The horsepower output of the instruction control electrical motor that motor control unit sends over according to the control unit for vehicle that receives; The state-of-charge that the battery management unit estimating battery is current is also passed to automobile control unit.
As shown in Figure 1, a kind of hybrid-power bus energy management method of the embodiment of the invention, implementation step is following:
1, car speed sensor is gathered current speed information, and the GPS module is obtained current vehicle location information, and the pedal position sensor of throttle obtains the location information of current throttle pedal, the battery charge state that the battery management unit estimation is current; The location information and the battery charge state of current speed information, vehicle position information, Das Gaspedal transfer to control unit for vehicle.
2, estimate the speed of a motor vehicle after the current vehicle speed information that obtains according to speed of a motor vehicle transition probability model that early stage, historical data obtained and step 1 of control unit for vehicle and vehicle position information calculate.
2.1, according to the historical speed of a motor vehicle of bus in the operation of same highway section, calculate the speed of a motor vehicle transition probability matrix of bus, and make up speed of a motor vehicle transition probability model at each displacement point.Off-line modeling is earlier with the spatial discretization of speed of a motor vehicle value, and it is 25 lattice speed of a motor vehicle values at interval that the speed interval with 0 to 60km/h is divided into 2.5km/h.According to historical speed of a motor vehicle path, each position on the vehicle operating path trains speed of a motor vehicle transition probability matrix respectively then, and these speed of a motor vehicle transition probability matrixs are stored in the control unit for vehicle.The value quantity of the state variable after the dimension of the state transition probability matrix P at position S place (S) and discretization is relevant, if after the speed of a motor vehicle discretization 25 possible values are arranged, then P (S) is 25 * 25 matrix.The element of the capable b row of a is represented the probability of transferring to b speed of a motor vehicle discrete value from a speed of a motor vehicle discrete value in P (S) matrix, and concrete probable value is various possibly acquisition the through the speed of a motor vehicle variation at same position place in the statistical history speed of a motor vehicle path.The state transition probability matrix of each position all should be different.Starting stage for example, there is a strong possibility is higher than current vehicle speed for the next speed of a motor vehicle; The probability that the speed of a motor vehicle descends when soon running into the crossing is bigger.Constituted speed of a motor vehicle transition probability model from all state transition probability matrixs of the origin-to-destination of vehicle ', a and b are the natural number of 1-25.The element P of the capable b row of a in P (S) matrix (method of calculating b) is following for S, a:
Wherein, N
a(S) for the speed of a motor vehicle at S place is the number of a discrete value in the position in the historical path; N
b(S is a) for the speed of a motor vehicle at S place is the sum that the speed of a motor vehicle at a discrete value S+1 place in the position changes b discrete value in the position in the historical path.Constituted speed of a motor vehicle transition probability model from all state transition probability matrixs of the origin-to-destination of vehicle ';
2.2, the current vehicle speed information that obtains according to above-mentioned speed of a motor vehicle transition probability model and step 1 of control unit for vehicle and vehicle position information estimate the speed of a motor vehicle after calculating.
It is bus current vehicle speed and current location that the data that the speed of a motor vehicle need import are estimated in said calculating, then according to step 2) in speed of a motor vehicle transition probability model, output estimating to the following speed of a motor vehicle.Concrete method of calculating is: at first current vehicle speed v (S) is converted into the form of 1 * 25 vector, the 1st element is 0 probability corresponding to the speed of a motor vehicle in this vector, and n element is the probability of (n-1) * 2.5 km/h corresponding to the speed of a motor vehicle, and n is the natural number of 1-25.V (S) multiplies each other with speed of a motor vehicle transition probability model can obtain to change into from current vehicle speed v (S) at current location S all probable values of next speed of a motor vehicle v (S+1):
Following formula gained v (S+1) as a result also is the vector of a 1 * n, just obtained then in position S+1 might the speed of a motor vehicle set be { v
j(S+1) | j=1,2 ..., N
S+1, wherein, N
S+1Be illustrated in the sum of the non-vanishing possible speed of a motor vehicle of S+1 location probability, the speed of a motor vehicle v that each is possible
j(S+1) pairing probability is P
j(S), P
j(S) value is exactly j non-vanishing value in the vector v (S+1).
Continue to obtain the follow-up speed of a motor vehicle and probability by each possible speed of a motor vehicle afterwards according to speed of a motor vehicle transition probability model recursion; In order to simplify follow-up computation complexity, the S+2 step and after estimation range in directly with the speed of a motor vehicle path trajectory (j) of probability maximum as v
j(S+1) follow-up speed of a motor vehicle prediction path, next step branch value of several speed of a motor vehicle and follow-up maximum possible path are combined into estimating of the speed of a motor vehicle, and be roughly as shown in Figure 3.
3, control unit for vehicle is optimized the energy distribution of hybrid vehicle in the prediction section according to the speed of a motor vehicle of estimating that step 2 obtains, and obtains the fuel engines of current optimal and the power-division ratios of motor.
The objective function that in the said step 3 energy distribution of hybrid vehicle is optimized is:
Wherein, u is a controlling quantity, the ratio of engine power and total power demand during u (i) expression i section moves; Fuel oil consumption during F (i, u (i)) expression i section moves under the effect of controlling quantity u (i), Δ SOC (i; U (i)) the battery charge state reduction during expression i section moves under the effect of controlling quantity u (i); P is the length (being the total prediction step number of estimating the speed of a motor vehicle that obtains in the step 2) of forecast interval, and G is weights, and E is a symbol of asking for expectation value.Target is in the prediction time domain, to ask for an optimal control sequence (being the power-division ratios of each position in the estimation range).The concrete method of calculating of above-mentioned F (i, u (i)) is:
Wherein, map_f is the bivariate table of a fuel consumption rate, and the concrete data in this bivariate table are relevant with driving engine, T
ReqBe the torque of the required output of torsion coupler, ω
ICEBe the rotating speed of driving engine, T
ReqWith ω
ICECan obtain according to the automobile longitudinal kinetics equation, Δ t (i) expression vehicle moves the time of consumption at the i section.
The concrete method of calculating of Δ SOC (i, u (i)) is:
Wherein, P
ReqBe total demand power, U is the equivalent open-loop voltage of battery, and R is the equivalent internal resistance of battery, and Q is total electric weight of battery.
The initial speed of a motor vehicle and initial state-of-charge during optimization are all taken from the value that step 1 collects.Concrete optimization method is divided into two stages: the optimization flow process in first stage is as shown in Figure 4, in F/s, only considers driving engine independent drive pattern, electrical motor independent drive pattern, these three kinds of mode of operations of combination drive pattern.Power-division ratios under these three kinds of mode of operations (ratio of engine power and total power demand) is smaller or equal to 1.Then with power-division ratios discretization in current feasible zone.In forecast interval, the power-division ratios of all positions is initially 1 earlier, and the power-division ratios of tasting then each point is lowered to next possible values, and then for each point calculates the influence that adjustment is brought, influence specifically is to represent with following decision variable:
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
jBe illustrated under the operating mode of j bar prediction curve the adjustment oil consumption reduction that power-division ratios caused, Δ SOC
jThe state-of-charge that expression reduces.Whether decision variable k can be used for judging in current point is worth the cost electric energy to reduce oil consumption.It is big more that k is illustrated in the identical benefit that electric energy brought of this some consumption more greatly.So after calculating the decision variable of each point in the forecast interval, select that maximum some downward modulation power-division ratios of value of k.Above-mentioned is the searching process of an iteration, and each optimizing all is to select the maximum position downward modulation power-division ratios of k value.The end condition of iteration is that k=0 or SOC reach lower limit.Just get into the optimizing process in next stage afterwards.The mode of operation that the optimization in second stage is considered is power generation mode or keeps the last pattern of F/s.Still those points that are in later driving engine independent drive pattern in F/s optimization just might change power generation mode in subordinate phase; With those position candidate as the subordinate phase generating.After the position candidate that obtains generating electricity, will determine optimum electric energy generated.The optimizing process of subordinate phase is as shown in Figure 5; The process and the F/s of iteration optimizing are somewhat similar; The power-division ratios of at first tasting each point rises to next possible values; Power-division ratios (ratio of engine power and total power demand) greater than 1 o'clock then for each point calculates the influence that adjustment is brought, influencing specifically is to represent with following decision variable:
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
j' be illustrated under the operating mode of j bar prediction curve the fuel oil consumption for the additive incrementation that generates electricity, Δ SOC '
jRepresent corresponding SOC increment.Whether decision variable k ' can be used for judging in current point is worth the extra fuel oil of cost to produce electric energy.From candidate point, select the maximum point of value of k ' to regulate power-division ratios.Get into next iteration then, calculate the value of k ' once more, and in the maximum position adjustments power-division ratios of k '.The position of generating all is the highest position of cost performance like this.How to use the electric energy of new generation to be based on the Optimization result of F/s; If the optimization of F/s is because k < 0 when stopping; The so new electric energy that produces can not reallocated, if Fs optimization is because SOC is lower than lower limit when stopping, the so new electric energy that produces can be used in the maximum position of value of k; The value of upgrading k then gets into next iteration, and the end condition that subordinate phase is optimized iteration is k ' < 0.Through the optimization in top two stages, the power-division ratios of each position all has been set to the optimum position in the forecast interval.
4, confirm the power that actual fuel injection amount and motor should provide according to the location information of the Das Gaspedal of gathering in power-division ratios that obtains in the step 3 and the step 1; Send corresponding message to motor control unit and control unit of engine through the CAN bus then, concrete message form is relevant with the a2l file of each automobile vendor.The peripheral relevant framed structure scheme drawing of whole automobile control unit is as shown in Figure 2.Control unit of engine is responsible for receiving instruction that control unit for vehicle sends over and is controlled variable such as throttle opening and reach the effect of regulating engine power output; Motor control unit is used for the power output at control motor.
When vehicle operating can be upgraded the present speed of a motor vehicle, position and battery charge state SOC during to next displaced segments; Follow estimating according to the new state information updating speed of a motor vehicle; And the power-division ratios in the ensuing forecast interval is optimized, and correspondingly control the horsepower output of driving engine and motor.
The present invention has considered statistical law and the randomness that bus moves on same circuit; The control algorithm that adopts changes traditional multi-modal problem into two types of subproblems; The thought of dividing and ruling that embodies; And what when confirming electric energy generated and power consumption, utilize is the thought of greedy algorithm, has greatly accelerated the speed of finding the solution of optimization problem.Being fit to vehicle is optimized in real time execution.
Claims (3)
1. hybrid-power bus energy management method, hybrid-power bus has accelerator pedal position sensor, car speed sensor, GPS module, control unit for vehicle, control unit of engine, motor control unit and battery management unit; Accelerator pedal position sensor, car speed sensor and GPS module all link to each other with control unit for vehicle, and control unit for vehicle, control unit of engine, motor control unit and battery management unit are through the total wire joint of CAN; It is characterized in that this method comprises the steps:
(1) car speed sensor is gathered current speed information, and the GPS module is obtained current vehicle location information, and the pedal position sensor of throttle obtains the location information of current throttle pedal, the battery charge state that the battery management unit estimation is current; The location information of current speed information, vehicle position information, Das Gaspedal and battery charge state all transfer to control unit for vehicle;
(2) estimate the speed of a motor vehicle after the current vehicle speed information that obtains according to speed of a motor vehicle transition probability model that early stage, historical data obtained and step 1 of control unit for vehicle and vehicle position information calculate;
(3) control unit for vehicle is optimized the energy distribution of hybrid vehicle in the prediction section according to the speed of a motor vehicle of estimating that step 2 obtains, and obtains the fuel engines of current optimal and the power-division ratios of motor;
(4) confirm the power that actual fuel injection amount and motor should provide according to the location information of the Das Gaspedal of gathering in power-division ratios that obtains in the step 3 and the step 1, send message to electric machine controller and engine controller through the CAN bus then; The message that control unit of engine sends over according to the reception control unit for vehicle is regulated the horsepower output of driving engine; Motor control unit is according to the horsepower output that receives the message control motor that control unit for vehicle sends over.
2. according to the said hybrid-power bus energy management method of claim 1, it is characterized in that said step 2 can specifically be divided into following a few sub-steps:
(2.1) according to the historical speed of a motor vehicle of bus in the operation of same highway section; Calculate the speed of a motor vehicle transition probability matrix of bus at each displacement point; And make up speed of a motor vehicle transition probability model: earlier with the spatial discretization of speed of a motor vehicle value, it is 25 lattice speed of a motor vehicle values at interval that the speed interval with 0 to 60km/h is divided into 2.5km/h; According to historical speed of a motor vehicle path, each position on the vehicle operating path trains speed of a motor vehicle transition probability matrix respectively then, and these speed of a motor vehicle transition probability matrixs are stored in the control unit for vehicle; The value quantity of the state variable after the dimension of the state transition probability matrix P at position S place (S) and discretization is relevant, if after the speed of a motor vehicle discretization 25 possible values are arranged, then P (S) is 25 * 25 matrix; Then P (S) is 25 * 25 matrix; The element of the capable b row of a is represented the probability of transferring to b speed of a motor vehicle discrete value from a speed of a motor vehicle discrete value in P (S) matrix, and a and b are the natural number of 1-25; The element P of the capable b row of a in P (S) matrix (method of calculating b) is following for S, a:
Wherein, N
a(S) for the speed of a motor vehicle at S place is the number of a discrete value in the position in the historical path; N
b(S is a) for the speed of a motor vehicle at S place is the sum that the speed of a motor vehicle at a discrete value S+1 place in the position changes b discrete value in the position in the historical path; Constituted speed of a motor vehicle transition probability model from all state transition probability matrixs of the origin-to-destination of vehicle ';
(2.2) estimate the speed of a motor vehicle after the current vehicle speed information that obtains according to above-mentioned speed of a motor vehicle transition probability model and step 1 of control unit for vehicle and vehicle position information calculate: the form that at first current vehicle speed v (S) is converted into 1 * 25 vector; The 1st element is 0 probability corresponding to the speed of a motor vehicle in this vector; N element is the probability of (n-1) * 2.5 km/h corresponding to the speed of a motor vehicle, and n is the natural number of 1-25; V (S) multiplies each other with speed of a motor vehicle transition probability model can obtain to change into from current vehicle speed v (S) at current location S all probable values of next speed of a motor vehicle v (S+1):
Following formula gained v (S+1) as a result also is one 1 * 25 a vector, just obtained then in position S+1 might the speed of a motor vehicle set be { v
j(S+1) | j=1,2 ..., N
S+1, wherein, N
S+1Be illustrated in the sum of the non-vanishing possible speed of a motor vehicle of S+1 location probability, the speed of a motor vehicle v that each is possible
j(S+1) pairing probability is P
j(S), P
j(S) value is exactly j non-vanishing value in the vector v (S+1); Continue to obtain the follow-up speed of a motor vehicle and probability by each possible speed of a motor vehicle afterwards according to speed of a motor vehicle transition probability model recursion; In order to simplify follow-up computation complexity, the S+2 step and after estimation range in directly with the speed of a motor vehicle path trajectory (j) of probability maximum as v
j(S+1) follow-up speed of a motor vehicle prediction path, next step branch value of several speed of a motor vehicle and follow-up maximum possible path are combined into estimating of the speed of a motor vehicle.
3. according to the said hybrid-power bus energy management method of claim 1, it is characterized in that the objective function that in the said step 3 energy distribution of hybrid vehicle is optimized is:
Wherein, u is a controlling quantity, the ratio of engine power and total power demand during u (i) expression i section moves; Fuel oil consumption during F (i, u (i)) expression i section moves under the effect of controlling quantity u (i), Δ SOC (i; U (i)) the battery charge state reduction during expression i section moves under the effect of controlling quantity u (i); P is the length (being the total prediction step number of estimating the speed of a motor vehicle that obtains in the step 2) of forecast interval, and G is weights, and E is a symbol of asking for expectation value; Target is in the prediction time domain, to ask for an optimal control sequence (being the power-division ratios of each position in the estimation range); The concrete method of calculating of above-mentioned F (i, u (i)) is:
Wherein, map_f is the bivariate table of a fuel consumption rate, and the concrete data in this bivariate table are relevant with driving engine, T
ReqBe the torque of the required output of torsion coupler, ω
ICEBe the rotating speed of driving engine, T
ReqWith ω
ICECan obtain according to the automobile longitudinal kinetics equation, Δ t (i) expression vehicle moves the time of consumption at the i section;
The concrete method of calculating of Δ SOC (i, u (i)) is:
Wherein, P
ReqBe total demand power, U is the equivalent open-loop voltage of battery, and R is the equivalent internal resistance of battery, and Q is total electric weight of battery;
The initial speed of a motor vehicle and initial state-of-charge during optimization are all taken from the value that step 1 collects; Concrete optimization method is divided into two stages: in F/s, only consider driving engine independent drive pattern, electrical motor independent drive pattern, these three kinds of mode of operations of combination drive pattern; Power-division ratios under these three kinds of mode of operations (ratio of engine power and total power demand) is smaller or equal to 1; Then with power-division ratios discretization in current feasible zone; In forecast interval, the power-division ratios of all positions is initially 1 earlier, and the power-division ratios of tasting then each point is lowered to next possible values, and then for each point calculates the influence that adjustment is brought, influence specifically is to represent with following decision variable:
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
jBe illustrated under the operating mode of j bar prediction curve the adjustment oil consumption reduction that power-division ratios caused, Δ SOC
jThe state-of-charge that expression reduces; Whether decision variable k can be used for judging in current point is worth the cost electric energy to reduce oil consumption; It is big more that k is illustrated in the identical benefit that electric energy brought of this some consumption more greatly; So after calculating the decision variable of each point in the forecast interval, select that maximum some downward modulation power-division ratios of value of k, above-mentioned is the searching process of an iteration; Each optimizing all is to select the maximum position downward modulation power-division ratios of k value, and the end condition of iteration is that k=0 or SOC reach lower limit; Just get into the optimizing process in next stage afterwards; The mode of operation that the optimization in second stage is considered is power generation mode or keeps the last pattern of F/s; Still those points that are in later driving engine independent drive pattern in F/s optimization just might change power generation mode in subordinate phase; With those position candidate as the subordinate phase generating; After the position candidate that obtains generating electricity, will determine optimum electric energy generated; The optimizing process of subordinate phase is specially: the power-division ratios of at first tasting each point rises to next possible values; Power-division ratios (ratio of engine power and total power demand) greater than 1 o'clock then for each point calculates the influence that adjustment is brought, influencing specifically is to represent with following decision variable:
；
Wherein, P
jBe illustrated in the probability of transferring to j bar prediction curve in the speed of a motor vehicle prediction model, Δ f
j' be illustrated under the operating mode of j bar prediction curve the fuel oil consumption for the additive incrementation that generates electricity, Δ SOC '
jRepresent corresponding SOC increment; Whether decision variable k ' can be used for judging in current point is worth the extra fuel oil of cost to produce electric energy; From candidate point, select the maximum point of value of k ' to regulate power-division ratios; Get into next iteration then, calculate the value of k ' once more, and in the maximum position adjustments power-division ratios of k '; The position of generating all is the highest position of cost performance like this; Through the optimization in top two stages, the power-division ratios of each position all has been set to the optimum position in the forecast interval.
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