CN106427990A - Hybrid power system and energy management method thereof - Google Patents

Abstract

The invention discloses an energy management method of a hybrid power system. The method comprises the steps that signal data needed by energy management strategy calculation is obtained, working condition adaptive calculation is performed on to-be-operated working conditions through a working condition power spectrum self-adaptive algorithm, a corresponding equivalence factor is obtained, the equivalence factor obtained by means of calculation and a working condition equivalence factor in a database are subjected to similarity matching, the needed signal data and the equivalence factors are calculated according to the received energy management strategy, an output power distribution combination is obtained through a whole car energy consumption cost minimum algorithm, adjustment and correction are performed on the output power distribution combination, the output power distribution combination is sent, and output power distribution of each energy source is completed. The invention further discloses the hybrid power system adopting the energy management method.

Description

Hybrid power system and its energy management method

Technical field

The present invention relates to the control technology of hybrid power system, more particularly, it relates to the energy pipe of hybrid power system Reason technology.

Background technology

Hybrid vehicle provides power by two kinds of energy sources, generally comprises electromotor and the electricity by driven by power of fuel oil Motivation.Dual intensity origin system, under the coordination of car load EMS controls, is cooperated with miscellaneous part, can carry out multiple Optimum organization, forms different power system operational patterns, to adapt to different driving cycles.

The target of energy management strategies typically has multiple input variables and the multi-target non-linear of multiple constraints Optimization problem, its control strategy all has a significant impact to the power performance and the economy performance of vehicle.According to preferable design object, When reaching the vehicle operating range of design, vehicle-mounted energy-storage system (battery of power system) should reach spent condition.On the one hand, If battery effect is too fast, excessive car load electrokinetic cell electricity exhaust may result in Full Vehicle System high-voltage electrical apparatus loss or It is distance increasing unit energy residual, the overall energy efficiency of impact automobile.On the other hand, if battery consumption is excessively slow, vehicle electricity disappears The insufficient purpose that possibly cannot obtain the minimizing fuel consumption being pre-designed of consumption, the ability of electrokinetic cell system is far from reaching The available limit, causes the waste of electric energy and the excessive consumption of gasoline.Therefore how to obtain in the application of hybrid vehicle Power between suitable different-energy source and energy stream distribution are one of root problems of energy management strategies.In practical application In, because driving cycle can not accurately be predicted, therefore suitable energy management strategies are to realize hybrid vehicle energy-saving ring The key point protected.

Study most commonly used four class hybrid vehicle energy management strategies at present：Rule-based control strategy, wink When Optimal Control Strategy, global optimization control strategy and the ADAPTIVE CONTROL based on optimized algorithm.

The working mechanism of rule-based control strategy is：Set a series of in advance with theory analysis and working experience intuition Vehicle program operation state values, by its working region divide.Judge what vehicle was worked according to the critical operating point of setting Region, thus take corresponding control mode.Rule-based logic threshold algorithm is relatively easy, can be applied to real vehicle control Device, in conjunction with offline optimization as a result, it is possible to be optimized to parameter, thus obtaining more reasonable, economic mode of operation switching rule Then.The advantage of the maximum of this kind of strategy is to be easy to Project Realization.But, rule-based energy management strategies, due to its rule It is that therefore regardless of whether carrying out Optimization about control parameter, the program exists based on theory analysis and working experience, not practical situation Still there is larger limitation in the raising aspect of fuel economy.

Instantaneous optimization control strategy generally adopts equivalent fuel consumption minimum or power loss min algorithm, by by two The energy expenditure of energy source ad hoc approach carries out quantifying unification, calculates the instantaneous least energy consumption of car load.This strategy can guarantee that It is optimum in each step-length, but cannot ensure is optimum in whole driving cycle, and the method needs substantial amounts of floating Point processing and more accurate vehicle and dynamical system model, computationally intensive, realize difficult.This kind of energy management strategies exist at present Good fuel economy effect is achieved on Computer Simulation, but does not extensively apply on real vehicle.It is primarily due to it right Higher in the collection of real-time vehicle running state parameter, analysis and processing requirement, current mobile unit cannot meet its requirement, The change of car load power system performance simultaneously affects larger on the real-time update of basic database.

Global optimization control strategy, know in advance running car all during under conditions of all duty parameters, The global optimization of energy management can be realized.Global optimization pattern achieves optimization truly, but realizes this plan Algorithm slightly is often all more complicated, and amount of calculation is also very big, and needs all of road information to be obtained ahead of time, in actual vehicle Real-time control in know in advance running car all during all duty parameters be impossible, therefore global optimization control Strategy processed also is difficult to effectively be applied.

Based on the ADAPTIVE CONTROL of optimized algorithm, can according to Current vehicle transport condition and road conditions automatic Prediction not Carry out the power in a period of time and energy requirement carrys out adjust automatically control parameter to adapt to the change of driving cycle.So-called adaptive Should be it is simply that in each time step, requiring to adjust part working method according to current driving conditions and road conditions, being calculated by optimizing Method, under the premise of ensureing that object function is optimized, energy requirement is reasonably distributed to each energy source.Although it is self-adaptive controlled Target function model optimized algorithm of system strategy etc. is different, but because the substantial amounts of dynamical system of Real-time Collection is wanted in Self Adaptive Control System service data, calculates energy consumption, the following operating mode of prediction, and optimization process is complicated, computationally intensive, also due to current mobile unit Computing capability idle and lead to it cannot effectively be applied in practice at present.

Content of the invention

According to one embodiment of the invention, a kind of energy management method of hybrid power system is proposed, including：

First step, System self-test, if fault-free, enter third step, if faulty, enter second step；

Second step, carries out troubleshooting, after completing troubleshooting, returns first step and carries out System self-test again；

Third step, obtains energy management strategies and calculates required signal data；

Four steps, judges whether the signal data receiving is complete, if signal data is complete, next execution the 5th step Suddenly, if signal data is imperfect, return third step and reacquire the signal data needed for energy management strategies calculate；

5th step, carries out adaptability for working condition calculating by operating mode power spectrum adaptive algorithm to following operating condition, obtains Go out corresponding Reliability equivalence factor；

6th step, carries out similarity using the Reliability equivalence factor calculating with the operating mode Reliability equivalence factor in data base Join；

7th step, the signal data according to needed for the energy management strategies receiving calculate and Reliability equivalence factor, by whole Car energy consumption cost min algorithm obtains output distribution combination；

8th step, is adjusted revising to output distribution combination；

9th step, sends output distribution combination, completes each energy source output distribution.

In one embodiment, in first step, entire car controller VMS, drive motor controller PEU, electrokinetic cell control Device BMS processed, distance increasing unit controller RES, long distance control system carry out self-inspection to respective subsystem respectively, determine whether fault.

In one embodiment, in third step, entire car controller VMS passes through CAN to electrokinetic cell controller BMS, distance increasing unit controller RES, long distance control system, drive motor controller PEU send calling-on signal, therefrom obtain energy pipe Signal data needed for reason policy calculation.

In one embodiment, the signal data needed for energy management strategies calculate includes current vehicle speed V, electrokinetic cell SOC (t), range extender system Fuel ConsumptionCar load demand power P_vehicle, car load dynamical system accessory power P_auxiliary, Vehicle program travel total kilometrage L, vehicle travelled apart from l (t).

In one embodiment, in four steps, judge whether the signal data receiving is complete by entire car controller VMS Whole.

In one embodiment, in the 5th step, entire car controller VMS will pass through operating mode power spectrum adaptive algorithm to future Operating condition carries out adaptability for working condition calculating, draws corresponding Reliability equivalence factor；

Wherein operating mode power spectrum adaptive algorithm is as follows：

WhereinFor Reliability equivalence factor, a_ζ,b_ζFor constant, calculated by all duty parameters at present, SOC (t) is to run SOC value after time t, Q_bFor battery capacity, V_nomFor electrokinetic cell nominal voltage,For electrokinetic cell average discharge efficiency,Electrokinetic cell average charge efficiency,For car load energy wide gap,For whole stroke starting stage t Energy wide gap when=0, E_gT () is remaining gross energy after run time t,For car load bus requirements driving power；For car load braking power, T_totalTime used by whole stroke, the distance that l (t) has travelled for vehicle, L is stroke Total distance, K (ζ) is expressed as every traveling energy wide gap slip travelling after a segment distance, and K reduces estimation for energy wide gap Value, ζ₀For marginal value, χ ∈ [1,2].

In one embodiment, in the 6th step by entire car controller VMS using the Reliability equivalence factor calculating and data Operating mode Reliability equivalence factor in storehouse carries out similarity coupling.

In one embodiment, in the 7th step, calculated according to the energy management strategies receiving by entire car controller VMS Required signal data, asks power P by calculating car load in real time_vehicle, real by car load energy consumption cost min algorithm When calculate electrokinetic cell output P_b, the output P of distance increasing unit_RE, show that output divides through real-time adjustment correction Combo closes P_vehicle=F (P_b,P_RE,P_auxiliary).

In one embodiment, the car load energy consumption cost min algorithm in the 7th step is as follows：

Sub-step a), calculates car load demand power Wherein G=mg, m are fully loaded with quality for car load, and f is coefficient of rolling resistance, and α is the gradient, C_DFor coefficient of air resistance, A meets for automobile Wind area, V is automobile current vehicle speed, η_tFor overall transmission efficiency, δ is car mass conversion coefficient, and α is the travel gradient Angle, cos α=1 when α is less than certain value, α=sin α=tan α=i, i are road grade, P_auxiliaryFor car load power accessories System power；

Sub-step b), in whole driving cycle, car load energy consumption cost is calculated as follows：

Wherein Cost is fuel consumption in whole stroke cycle and the totle drilling cost of power consumption, and Δ Cost is distance increasing unit to power Energy consumption cost difference when battery charges, T_totalTime used by whole stroke；ξ_fFor fuel market price,For distance increasing unit system The specific fuel consumption of system, ξ_eFor civil power price, η_elecEfficiency when charging from electrical network end for electrokinetic cell.P_elecFor electrokinetic cell Real-time output.For distance increasing unit with Fuel Consumption during maximum power output,For Charge efficiency when electrokinetic cell is exported with peak power output with distance increasing unit, SOC_minSet threshold value for SOC,For Distance increasing unit starts as SOC value of battery during power battery charging, V₀(SOC_min) for battery charge state be SOC_minBattery electricity Pressure, Q_bFor battery capacity；

Sub-step c), dynamical system power optimized solution is calculated as follows：

Wherein H is equation functions of minimizing, ξ_fFor fuel market price,For the specific fuel consumption of range extender system, ξ_eFor civil power price, soc is battery charge state, V₀For battery open circuit voltage, Q_bFor battery capacity, p is optimum association state ginseng Number.C_bFor constant, P_elecFor the real-time output of electrokinetic cell,For Reliability equivalence factor；

Sub-step d), car load output combination calculation is as follows：

Wherein, P_VehicleFor car load bus requirements power；η_convFor dc/dc converter efficiency；η_{dc/dc_discharging}It is Dc/dc converter efficiency during power battery discharge；η_{dc/dc_charging}It is in dc/dc converter efficiency during charged state for battery；P_b For electrokinetic cell output, P_b＞ 0 represents that electrokinetic cell is in discharge condition, P_b＜ 0 represents that electrokinetic cell is in charging shape State；P_REOutput for distance increasing unit；P_auxiliaryFor car load power accessories system power.

In one embodiment, in the 8th step, adjustment correction logic rule is as follows：

Wherein P_{b_min},P_{RR_min}It is respectively electrokinetic cell, the lower limit of distance increasing unit output, concrete numerical value is according to selection Default, P_{b_max},P_{RE_max}It is respectively electrokinetic cell, the upper limit of distance increasing unit output, concrete numerical value is according to the system selecting Set, η_convFor dc/dc converter efficiency；P_VehicleFor car load bus requirements power；P_bFor battery system output；P_REFor The output of distance increasing unit.

In one embodiment, in the 9th step, entire car controller VMS passes through CAN to electrokinetic cell controller BMS Send output distribution combination with distance increasing unit controller RES, complete entire car controller VMS each energy source output to dynamical system Power distribution.

According to one embodiment of the invention, propose a kind of hybrid power system, be suitable for the energy of aforesaid hybrid power system Quantity management method, this hybrid power system includes：Electrokinetic cell, distance increasing unit, long distance control system, motor, full-vehicle control Device VMS, electrokinetic cell controller BMS, distance increasing unit controller RES, drive motor controller PEU, car load dynamical system adnexa and CAN；

Entire car controller VMS is connected to distance increasing unit controller RES, electrokinetic cell controller BMS, driving by CAN Electric machine controller PEU, long distance control system and car load dynamical system adnexa；Distance increasing unit is connected with distance increasing unit controller RES, power Battery is connected with electrokinetic cell controller BMS；Distance increasing unit, electrokinetic cell, car load power accessories system and drive motor controller PEU is interconnected by high-tension bus-bar；Distance increasing unit accesses high-tension bus-bar by DC/DC transducer；

The control parameter of the energy management strategies that the energy management method of described hybrid power system uses passes through CAN Data interaction is completed between electrokinetic cell controller in entire car controller VMS and as energy source and distance increasing unit controller；Whole Vehicle controller VMS obtains after energy management strategies calculate desired data from CAN and provides operating mode power spectrum self adaptation and car load Energy consumption cost min algorithm formula calculates distance increasing unit output, then is sent to output combination by CAN each The controller of individual energy source, including electrokinetic cell controller and distance increasing unit controller, to complete dynamical system energy source power Real-time adjustment.

The energy management method of the hybrid power system of the present invention and application the method hybrid power system have as Under beneficial effect：

1) present invention is based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm can provide real-time detection to work as The energy expenditure rate of vehicle in front and the real-time status of each energy source, calculate car load power by operating mode power spectrum adaptive algorithm Request and Conditions Matching, the result of calculation according to car load energy consumption cost min algorithm and each energy of corresponding logic rule real-time adjustment The output in amount source, real-time is good；

2) the operating mode power spectrum self adaptation in the present invention and car load energy consumption cost min algorithm formula and logic rule have Hereinafter act on：A) according to car load transport condition and driver's real time information complete the automatic identification of different operating mode power spectrum with Join, can be to greatest extent close to Real-road Driving Cycle；B) with the minimum target of user's car load energy consumption cost, implement the adjustment energy The power output of system, makes the energy consumption use cost of user minimum；

3) should be solved based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm and mention in background technology Power battery for hybrid electric vehicle electricity consumes excessively or consumes insufficient problem；

4) should be relatively low to controller hardware requirement based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm, easily In realization on car load；

5) energy management algorithm of the present invention can be applicable to fuel cell-battery, and internal combustion engine-accumulator is interior The new-energy automobile hybrid power system of the various ways such as combustion engine-super capacitor, has good autgmentability.

Brief description

The above and other feature of the present invention, property and advantage are by by description with reference to the accompanying drawings and examples And become apparent, identical reference represents identical feature all the time in the accompanying drawings, wherein：

The flow chart that Fig. 1 discloses the energy management method of the hybrid power system according to one embodiment of the invention.

Fig. 2 discloses the topology knot of the hybrid power system of the energy management method of the hybrid power system being suitable for the present invention Composition.

Specific embodiment

After reading the detailed description of embodiment of the disclosure in conjunction with the following drawings, better understood when the present invention's Features described above and advantage, but following embodiments the invention is not limited in any way.In the accompanying drawings, each assembly be not necessarily by Ratio is drawn, and has similar correlation properties or the assembly of feature is likely to be of same or like reference.Should It is noted that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, if can also make Dry deformation and improvement, broadly fall into protection scope of the present invention.

The energy management method of the hybrid power system of the present invention is to be become with car load energy consumption based on operating mode power spectrum self adaptation This min algorithm, this operating mode power spectrum self adaptation and car load energy consumption cost min algorithm are by the energy management plan of hybrid power system Slightly it is reduced to the Capacity Management rule of one group of multi input, single output, applying working condition power spectrum self adaptation and car load energy consumption cost are Little algorithm is according to vehicle velocity V, electrokinetic cell SOC (t), range extender system Fuel ConsumptionCar load demand power P_vehicle, car load Dynamical system accessory power P_auxiliary, vehicle program traveling total kilometrage L, what vehicle had travelled passes through apart from parameters such as l (t) Calculate in real time to control the output of hybrid power system energy source, meeting user's normal driving custom and drive demand Under the premise of realize to car load energy consumption cost optimization.

Fig. 2 discloses the topology knot of the hybrid power system of the energy management method of the hybrid power system being suitable for the present invention Composition.With reference to shown in Fig. 2, this hybrid power system is extended-range electric vehicle hybrid power system.This hybrid power system includes Electrokinetic cell 201, distance increasing unit 202, long distance control system 203, motor 204, entire car controller VMS 205, electrokinetic cell Controller BMS 206, distance increasing unit controller RES 207, drive motor controller PEU 208, car load dynamical system adnexa 209 and CAN 210.

Entire car controller VMS 205 is connected to distance increasing unit controller RES 207 by CAN 210, electrokinetic cell controls Device BMS 206, drive motor controller PEU 208, long distance control system 203 and car load dynamical system adnexa 209.Distance increasing unit 202 are connected with distance increasing unit controller RES 207, and electrokinetic cell 201 is connected with electrokinetic cell controller BMS 206.Distance increasing unit 202nd, electrokinetic cell 201, car load power accessories system 209 are mutually connected by high-tension bus-bar with drive motor controller PEU 208 Connect.Distance increasing unit 202 accesses high-tension bus-bar by DC/DC transducer.In the embodiment shown in Figure 2, high-tension bus-bar also connects It is connected to high-low pressure junction box and charger.

The control parameter of the energy management strategies that the energy management method of the hybrid power system of the present invention uses passes through CAN Bus 210 entire car controller VMS 205 and the electrokinetic cell controller 206 as energy source and distance increasing unit controller 207 it Between complete data interaction.Entire car controller VMS 205 obtains after energy management strategies calculate desired data from CAN 210 and carries Calculate distance increasing unit output for operating mode power spectrum self adaptation and car load energy consumption cost min algorithm formula, more total by CAN Output combination is sent to the controller of each energy source by line 210, controls including electrokinetic cell controller 206 and distance increasing unit Device 207, to complete the real-time adjustment of dynamical system energy source power.

In one embodiment, car load dynamical system adnexa 209 includes car load heat dissipation subsystem, air conditioning subsystem and big The power consumption of the electrical appliances such as the electrical parts such as lamp, relay, instrument.

The flow chart that Fig. 1 discloses the energy management method of the hybrid power system according to one embodiment of the invention.Base Extended-range electric vehicle hybrid power system shown in Fig. 2, the present invention is become with car load energy consumption based on operating mode power spectrum self adaptation The flow process of the preferred embodiment of hybrid power system energy management method of this min algorithm is as shown in Figure 1.

In a step 101, entire car controller VMS, drive motor controller PEU, electrokinetic cell controller BMS, distance increasing unit The subsystem that controller RES, long distance control system are responsible for it respectively carries out self-inspection, determines whether fault, if no, enters each System ready state, execution step 103.If faulty, carry out failure handling mechanisms step 102.

In a step 102, carry out troubleshooting, after completing troubleshooting, return to step 101 carries out system certainly again Inspection.

In step 103, entire car controller VMS passes through CAN to electrokinetic cell controller BMS, distance increasing unit controller RES, long distance control system, drive motor controller PEU send calling-on signal, therefrom obtain needed for energy management strategies calculate Signal data.

In one embodiment, the signal data needed for energy management strategies calculate includes current vehicle speed V, electrokinetic cell SOC (t), range extender system Fuel ConsumptionCar load demand power P_vehicle, car load dynamical system accessory power P_auxiliary, vehicle program travel total kilometrage L, vehicle travelled apart from l (t).

At step 104, entire car controller VMS judges whether the signal data receiving is complete, if so, represents signal number According to complete, following execution step 105.If it is not, representing that signal data is imperfect, then return to step 103 reacquires energy pipe Signal data needed for reason policy calculation.

In step 105, entire car controller VMS carries out work by operating mode power spectrum adaptive algorithm to following operating condition Condition adaptive computation, draws corresponding Reliability equivalence factor.

In one embodiment, the operating mode power spectrum adaptive algorithm being related in step 105 is as follows：

WhereinFor Reliability equivalence factor, a_ζ,b_ζFor constant, calculated by all duty parameters at present, when SOC (t) is to run Between SOC value after t, Q_bFor battery capacity, V_nomFor electrokinetic cell nominal voltage,For electrokinetic cell average discharge efficiency,Electrokinetic cell average charge efficiency,For car load energy wide gap,For the whole stroke starting stage Energy wide gap during t=0, E_gT () is remaining gross energy after run time t,For car load bus requirements driving power；For car load braking power, T_totalTime used by whole stroke, the distance that l (t) has travelled for vehicle, L is stroke Total distance, K (ζ) is expressed as every traveling energy wide gap slip travelling after a segment distance, and K reduces estimation for energy wide gap Value, ζ₀For marginal value, χ ∈ [1,2].

In step 106, entire car controller VMS using the operating mode in the Reliability equivalence factor calculating and data base equivalent because Son carries out similarity coupling.

In step 107, signal data according to needed for the energy management strategies receiving calculate for the entire car controller VMS, Ask power P by calculating car load in real time_vehicle, electrokinetic cell is calculated in real time by car load energy consumption cost min algorithm Output P_b, the output P of distance increasing unit_RE.Obtain output distribution combination P through real-time adjustment correction_vehicle=F (P_b,P_RE,P_auxiliary).

In one embodiment, the car load energy consumption cost min algorithm being related in step 107 is as follows：

In sub-step a), calculate car load demand power Wherein G=mg, m are fully loaded with quality for car load, and f is coefficient of rolling resistance, and α is the gradient, C_DFor air Resistance coefficient, A is front face area of automobile, and V is automobile current vehicle speed, η_tFor overall transmission efficiency, δ is car mass conversion system Number, α is the travel angle of gradient, cos α=1 when α is less than certain value, and α=sin α=tan α=i, i are road grade, P_auxiliaryFor car load power accessories system power.

In sub-step b), in whole driving cycle, car load energy consumption cost is calculated as follows：

Wherein Cost is fuel consumption in whole stroke cycle and the totle drilling cost of power consumption, and Δ Cost is distance increasing unit to power Energy consumption cost difference when battery charges, T_totalTime used by whole stroke；ξ_fFor fuel market price,For distance increasing unit system The specific fuel consumption of system, ξ_eFor civil power price, η_elecEfficiency when charging from electrical network end for electrokinetic cell.P_elecFor electrokinetic cell Real-time output.For distance increasing unit with Fuel Consumption during maximum power output,For Charge efficiency when electrokinetic cell is exported with peak power output with distance increasing unit, SOC_minSet threshold value for SOC,For Distance increasing unit starts as SOC value of battery during power battery charging, V₀(SOC_min) for battery charge state be SOC_minBattery electricity Pressure, Q_bFor battery capacity.

In sub-step c), dynamical system power optimized solution is calculated as follows：

Wherein H is equation functions of minimizing, ξ_fFor fuel market price,For the specific fuel consumption of range extender system, ξ_eFor civil power price, soc is battery charge state, V₀For battery open circuit voltage, Q_bFor battery capacity, p is optimum association state ginseng Number.C_bFor constant, P_elecFor the real-time output of electrokinetic cell,For Reliability equivalence factor.

In sub-step d), output distribution combination calculation is as follows：

Wherein, P_VehicleFor car load bus requirements power；η_convFor dc/dc converter efficiency；η_{dc/dc_discharging}It is Dc/dc converter efficiency during power battery discharge；η_{dc/dc_charging}It is in dc/dc converter efficiency during charged state for battery；P_b For electrokinetic cell output, P_b＞ 0 represents that electrokinetic cell is in discharge condition, P_b＜ 0 represents that electrokinetic cell is in charging shape State；P_REOutput for distance increasing unit；P_auxiliaryFor car load power accessories system power.

In step 108, output distribution combination adjustment correction logic rule is as follows：

Wherein P_{b_min},P_{RE_min}It is respectively electrokinetic cell, the lower limit of distance increasing unit output, concrete numerical value is according to selection Default, P_{b_max},P_{RE_max}It is respectively electrokinetic cell, the upper limit of distance increasing unit output, concrete numerical value is according to the system selecting Set, η_convFor dc/dc converter efficiency；P_VehicleFor car load bus requirements power；P_bFor battery system output；P_REFor The output of distance increasing unit.

In step 109, entire car controller VMS passes through CAN to electrokinetic cell controller BMS and distance increasing unit controller RES sends output distribution combination, completes entire car controller VMS and dynamical system each energy source output is distributed.

The energy management method of the hybrid power system of the present invention and application the method hybrid power system have as Under beneficial effect：

1) present invention is based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm can provide real-time detection to work as The energy expenditure rate of vehicle in front and the real-time status of each energy source, calculate car load power by operating mode power spectrum adaptive algorithm Request and Conditions Matching, the result of calculation according to car load energy consumption cost min algorithm and each energy of corresponding logic rule real-time adjustment The output in amount source, real-time is good；

2) the operating mode power spectrum self adaptation in the present invention and car load energy consumption cost min algorithm formula and logic rule have Hereinafter act on：A) according to car load transport condition and driver's real time information complete the automatic identification of different operating mode power spectrum with Join, can be to greatest extent close to Real-road Driving Cycle；B) with the minimum target of user's car load energy consumption cost, implement the adjustment energy The power output of system, makes the energy consumption use cost of user minimum；

3) should be solved based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm and mention in background technology Power battery for hybrid electric vehicle electricity consumes excessively or consumes insufficient problem；

4) should be relatively low to controller hardware requirement based on operating mode power spectrum self adaptation and car load energy consumption cost min algorithm, easily In realization on car load；

5) energy management algorithm of the present invention can be applicable to fuel cell-battery, and internal combustion engine-accumulator is interior The new-energy automobile hybrid power system of the various ways such as combustion engine-super capacitor, has good autgmentability.

Above-described embodiment is available to be familiar with person in the art to realize or using the present invention, to be familiar with this area Personnel can make various modifications or change without departing from the present invention in the case of the inventive idea to above-described embodiment, thus this The protection domain of invention is not limited by above-described embodiment, and should be to meet the inventive features that claims mention On a large scale.

Claims (12)

1. a kind of energy management method of hybrid power system is it is characterised in that include：

First step, System self-test, if fault-free, enter third step, if faulty, enter second step；

Second step, carries out troubleshooting, after completing troubleshooting, returns first step and carries out System self-test again；

Third step, obtains energy management strategies and calculates required signal data；

Four steps, judges whether the signal data receiving is complete, if signal data is complete, next execution the 5th step, If signal data is imperfect, returns third step and reacquire the signal data needed for energy management strategies calculate；

5th step, carries out adaptability for working condition calculating by operating mode power spectrum adaptive algorithm to following operating condition, draws phase The Reliability equivalence factor answered；

6th step, carries out similarity using the Reliability equivalence factor calculating with the operating mode Reliability equivalence factor in data base and mates；

7th step, the signal data according to needed for the energy management strategies receiving calculate and Reliability equivalence factor, by car load energy Consumption cost minimization algorithm obtains output distribution combination；

8th step, is adjusted revising to output distribution combination；

9th step, sends output distribution combination, completes each energy source output distribution.

2. the energy management method of hybrid power system as claimed in claim 1 is it is characterised in that in described first step, Entire car controller VMS, drive motor controller PEU, electrokinetic cell controller BMS, distance increasing unit controller RES, remote monitoring system System carries out self-inspection to respective subsystem respectively, determines whether fault.

3. the energy management method of hybrid power system as claimed in claim 2 is it is characterised in that in described third step, Entire car controller VMS passes through CAN to electrokinetic cell controller BMS, distance increasing unit controller RES, long distance control system, driving Electric machine controller PEU sends calling-on signal, therefrom obtains energy management strategies and calculates required signal data.

4. the energy management method of hybrid power system as claimed in claim 3 is it is characterised in that described energy management strategies Calculate required signal data and include current vehicle speed V, electrokinetic cell SOC (t), range extender system Fuel ConsumptionCar load needs Seek power P_vehicle, car load dynamical system accessory power P_auxiliary, vehicle program travel total kilometrage L, vehicle travelled away from From l (t).

5. the energy management method of hybrid power system as claimed in claim 2 is it is characterised in that in described four steps, Judge whether the signal data receiving is complete by entire car controller VMS.

6. the energy management method of hybrid power system as claimed in claim 2 is it is characterised in that in described 5th step, Entire car controller VMS carries out adaptability for working condition calculating by operating mode power spectrum adaptive algorithm to following operating condition, draws phase The Reliability equivalence factor answered；

Wherein operating mode power spectrum adaptive algorithm is as follows：

WhereinFor Reliability equivalence factor, a_ζ,b_ζFor constant, calculated by all duty parameters at present, SOC (t) is run time t SOC value afterwards, Q_bFor battery capacity, V_nomFor electrokinetic cell nominal voltage,For electrokinetic cell average discharge efficiency,Dynamic Power battery average charge efficiency,For car load energy wide gap,During for whole stroke starting stage t=0 Energy wide gap, E_gT () is remaining gross energy after run time t,For car load bus requirements driving power；For car load braking power, T_totalTime used by whole stroke, the distance that l (t) has travelled for vehicle, L is stroke Total distance, K (ζ) is expressed as every traveling energy wide gap slip travelling after a segment distance, and K reduces estimation for energy wide gap Value, ζ₀For marginal value, X ∈ [1,2].

7. hybrid power system as claimed in claim 2 energy management method it is characterised in that in described 6th step by Entire car controller VMS carries out similarity using the Reliability equivalence factor calculating with the operating mode Reliability equivalence factor in data base and mates.

8. the energy management method of hybrid power system as claimed in claim 2 is it is characterised in that in described 7th step, By signal data according to needed for the energy management strategies receiving calculate for the entire car controller VMS, whole by calculating in real time Power P asked by car_vehicle, electrokinetic cell output P is calculated in real time by car load energy consumption cost min algorithm_b, distance increasing unit Output P_RE, draw output distribution combination P through real-time adjustment correction_vehicle=F (P_b,P_RE,P_auxiliary).

9. the energy management method of hybrid power system as claimed in claim 8 is it is characterised in that in described 7th step Car load energy consumption cost min algorithm is as follows：

Sub-step a), calculates car load demand power Wherein G=mg, m are fully loaded with quality for car load, and f is coefficient of rolling resistance, and α is the gradient, C_DFor coefficient of air resistance, A meets for automobile Wind area, V is automobile current vehicle speed, η_tFor overall transmission efficiency, δ is car mass conversion coefficient, and α is the travel gradient Angle, cos α=1 when α is less than certain value, α=sin α=tan α=i, i are road grade, P_auxiliaryFor car load power accessories System power；

Sub-step b), in whole driving cycle, car load energy consumption cost is calculated as follows：

$Cost={\mathrm{\ξ}}_f\underset{0}{\overset{T_{total}}{\∫}}{\stackrel{\·}{m}}_{f}dt+\frac{{\mathrm{\ξ}}_e}{{\mathrm{\η}}_{elec}}\underset{0}{\overset{T_{total}}{\∫}}{P}_{elec}dt+\mathrm{\Δ}Cost$

$\begin{array}{c}\mathrm{\Δ}Cost\\ =\left\{\begin{array}{cc}\left(\frac{{\mathrm{\ξ}}_f{\stackrel{\·}{m}}_{f\left(P_{RE\_\max }\right)}}{{\mathrm{\η}}_{chg\left(P_{RE\_\max }\right)}}-\frac{{\mathrm{\ξ}}_e}{{\mathrm{\η}}_{elec}}\right)*\[{\mathrm{SOC}}_{\min }-{\mathrm{SOC}}_{T_f}\]*V_0\left({\mathrm{SOC}}_{\min }\right)*Q_b,& {\mathrm{SOC}}_{\min }>{\mathrm{SOC}}_{T_f}\\ 0,& {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{T_f}\end{array}\right.\end{array}$

Wherein Cost is fuel consumption in whole stroke cycle and the totle drilling cost of power consumption, and Δ Cost is distance increasing unit to electrokinetic cell Energy consumption cost difference during charging, T_totalTime used by whole stroke；ξ_fFor fuel market price,For range extender system Specific fuel consumption, ξ_eFor civil power price, η_elecEfficiency when charging from electrical network end for electrokinetic cell.P_elecReality for electrokinetic cell When output.For distance increasing unit with Fuel Consumption during maximum power output,For power Charge efficiency when battery is exported with peak power output with distance increasing unit, SOC_minSet threshold value for SOC,For increasing journey Device starts as SOC value of battery during power battery charging, V₀(SOC_min) for battery charge state be SOC_minCell voltage, Q_b For battery capacity；

Sub-step c), dynamical system power optimized solution is calculated as follows：

Wherein H is equation functions of minimizing, ξ_fFor fuel market price,For the specific fuel consumption of range extender system, ξ_eFor Civil power price, soc is battery charge state, V₀For battery open circuit voltage, Q_bFor battery capacity, p is optimum association state parameter.C_b For constant, P_elecFor the real-time output of electrokinetic cell,For Reliability equivalence factor；

Sub-step d), car load output combination calculation is as follows：

$\left\{\begin{array}{c}{P}_{Vehicle}={\left({\mathrm{\&eta;}}_{conv}\right)}^{\mathrm{sgn}\left(P_b\right)}{P}_b+{\mathrm{\&eta;}}_{conv}{P}_{RE}+P_{auxiliary}\\ \left\{\begin{array}{c}{P}_b>0,{\mathrm{\&eta;}}_{conv}={\mathrm{\&eta;}}_{dc/dc\_disch\arg ing}\\ P_b<0,{\mathrm{\&eta;}}_{conv}={\mathrm{\&eta;}}_{dc/dc\_charging}\end{array}\right.\end{array}\right.$

Wherein, P_VehicleFor car load bus requirements power；η_convFor dc/dc converter efficiency；η_{dc/dc_discharging}For electrokinetic cell Dc/dc converter efficiency during electric discharge；η_{dc/dc_charging}It is in dc/dc converter efficiency during charged state for battery；P_bFor power Cell output, P_b＞ 0 represents that electrokinetic cell is in discharge condition, P_b＜ 0 represents that electrokinetic cell is in charged state；P_REFor The output of distance increasing unit；P_auxiliaryFor car load power accessories system power.

10. the energy management method of hybrid power system as claimed in claim 2 is it is characterised in that in described 8th step Adjustment correction logic rule is as follows：

$\begin{array}{c}{P}_{b\_\min },P_{RE\_\min }\&le;P_b,P_{RE}\&le;P_{b\_\max },P_{RE\_\max }\\ \max \left\{P_{RE\_\min },\frac{P_{Vehicle}}{{\mathrm{\&eta;}}_{conv}}-P_{b\_\max }\right\}\&le;P_{RE}\&le;\min \left\{P_{RE\_\max },\frac{P_{Vehicle}}{{\mathrm{\&eta;}}_{conv}}-\frac{P_{b\_\min }}{{\mathrm{\&eta;}}_{conv}^2}\right\}\\ {\mathrm{SOC}}_{\min }\&le;SOC\&le;{\mathrm{SOC}}_{\max }\\ SOC\left(T_{total}\right)={\mathrm{SOC}}_{\min }\end{array}$

Wherein P_{b_min,}P_{RE_min}It is respectively electrokinetic cell, the lower limit of distance increasing unit output, concrete numerical value is according to the system selecting Set, P_{b_max},P_{RE_max}It is respectively electrokinetic cell, the upper limit of distance increasing unit output, concrete numerical value sets according to the system selecting Fixed, η_convFor dc/dc converter efficiency；P_VehicleFor car load bus requirements power；P_bFor battery system output；P_REFor increasing The output of journey device.

The energy management method of 11. hybrid power systems as claimed in claim 2 it is characterised in that in described 9th step, Entire car controller VMS passes through CAN and sends output distribution to electrokinetic cell controller BMS and distance increasing unit controller RES Combination, completes entire car controller VMS and dynamical system each energy source output is distributed.

A kind of 12. hybrid power systems are it is characterised in that be suitable for the hybrid power system as any one of claim 1-11 The energy management method of system, described hybrid power system includes：Electrokinetic cell, distance increasing unit, long distance control system, motor, Entire car controller VMS, electrokinetic cell controller BMS, distance increasing unit controller RES, drive motor controller PEU, car load dynamical system System adnexa and CAN；

Entire car controller VMS is connected to distance increasing unit controller RES, electrokinetic cell controller BMS, motor by CAN Controller PEU, long distance control system and car load dynamical system adnexa；Distance increasing unit is connected with distance increasing unit controller RES, electrokinetic cell It is connected with electrokinetic cell controller BMS；Distance increasing unit, electrokinetic cell, car load power accessories system and drive motor controller PEU are led to Cross high-tension bus-bar to interconnect；Distance increasing unit accesses high-tension bus-bar by DC/DC transducer；

The control parameter of the energy management strategies that the energy management method of described hybrid power system uses passes through CAN whole Data interaction is completed between vehicle controller VMS and the electrokinetic cell controller as energy source and distance increasing unit controller；Car load control Device VMS processed obtains after energy management strategies calculate desired data from CAN and provides operating mode power spectrum self adaptation and car load energy consumption Cost minimization algorithmic formula calculates distance increasing unit output, then by CAN, output combination is sent to each energy The controller in amount source, including electrokinetic cell controller and distance increasing unit controller, to complete the real-time of dynamical system energy source power Adjustment.