CN106647251A - Self-adaptive fuzzy control method for energy management of vehicle system - Google Patents

Abstract

The invention discloses a self-adaptive fuzzy control method for energy management of a vehicle system. According to the characteristics that dynamic response of a vehicle main energy system is slow and load required power changes are fast, the distribution algorithm estimates some characteristic parameters in the system on the basis of self-adaptive control and fuzzy control theories, and corresponding self-adaptive control laws and fuzzy control laws are designed according to different work conditions such as regenerative braking conditions, normal conditions and overload conditions to distribute output power of a hybrid power system. During running of an actual vehicle, the method is capable of tracking load required power in real time, meets demands of limits of the main energy system and auxiliary energy, and guarantees stability and safety of the system.

Description

A kind of adaptive fuzzy control method of onboard system energy management

Technical field

The invention belongs to onboard system field of energy management, relate to a kind of adaptive fuzzy of onboard system energy management Control method, the safety issue of tracking, the On-line Estimation of system core parameter and system to load demand power.

Background technology

In recent years, energy crisis and problem of environmental pollution are on the rise, and reproducible new energy technology is in people's life Increasingly it is widely applied, such as fuel cell system, it is directly to turn the energy that the chemical energy of fuel is converted to electric energy Changing device, with energy conversion efficiency is high, renewable, no pollution the features such as, therefore be used widely in Vehicular system.But Due to these new forms of energy it is partially soft as vehicle-mounted main source of energy system power characteristic, it is therefore desirable to it is auxiliary with lithium battery or super capacitor etc. The energy is helped to carry out being mixed into vehicle offer power, so as to lift the dynamic response capability of Vehicular system.These supplementary energies have Dynamic characteristic is good, power density is high, it is almost pollution-free the features such as.But it is how effective in the vehicle dynamical system being continually changing Management main source of energy and supplementary energy between power output, the focus of always current research, this energy profit to lift system With rate, keep intact stability significant.

It is the structural representation of the hybrid electric vehicle energy system that the present invention is studied, wherein main source of energy system shown in Fig. 1 It is connected with load by a unidirectional DC/DC converter, and auxiliary energy system is directly connected on system bus.This vehicle The configuration of dynamical system has low cost, fast to the response of vertiginous loading demand, the features such as capacity usage ratio is higher, therefore In being widely used in hybrid vehicle system.

During vehicle actual travel, duty requirements are continually changing, (regenerative braking operating mode, normal under different operating modes Operating mode and overload conditions), the output of main source of energy system and supplementary energy has different dynamic characteristic and system to limit, therefore in car In energy system, for different operating mode and system dynamic characteristic, formulation meets the power management policies of its feature to being lifted Controller performance and raising energy utilization rate are significant.

In the case where supplementary energy is battery (such as lithium battery) system, the state-of-charge (SOC) of battery is reaction cell The important indicator of state, but because battery SOC cannot be measured, existing observer also cannot accurately be estimated to battery SOC Meter.Therefore it is significant to the control of battery SOC, while also contributing to the stability and security of support vehicles system.

The content of the invention

Present invention aims to the deficiencies in the prior art, there is provided a kind of self adaptation of onboard system energy management Fuzzy control method.

The technical solution used in the present invention is to comprise the following steps, as shown in Figure 1：

1) On-line Estimation is carried out to the key characterization parameter of onboard system；

2) self adaptation online updating is carried out to load current according to the On-line Estimation value of key characterization parameter, with to each energy Power management between source；

3) for different duty requirements and the system status of supplementary energy, using Fuzzy Control Law control key feature ginseng Number.

Described key characterization parameter includes the equivalent internal resistance of load equivalent electric current and supplementary energy.

Described load equivalent electric current carries out On-line Estimation using the adaptive updates rule of below equation：

Wherein,Load current source internal resistance estimate is represented,Represent intermediate variable, p₁Represent equivalent Load current source adaptive factor, R_pRepresent load current source internal resistance estimate, i_lRepresent load current,Represent load current Estimate, based on the load current estimation value estimated obtained by parameterIt is really load current i_lLow-frequency component, i_pRepresent Equivalent load current source current value,Represent the estimate of equivalent load current source current, β₁Estimator gain is represented, is one The normal number that needs are adjusted；i_p,maxWith i_p,minThe supremum and infimum of equivalent load current source current value is represented respectively；So as to So that the estimate of load equivalent electric currentAlways in actual value i of load equivalent electric current_pBound within the scope of, that is, meet Below equation：

Wherein, i_p,maxAnd i_p,minBy i_pBound determine, according to load current condition in actual motion, can be according to above formula To i_pBound make determination.

The equivalent internal resistance of described supplementary energy carries out On-line Estimation using the adaptive updates rule of below equation：

Wherein, g₂Represent intermediate variable,μ is the positive parameter that need to adjust, p₂Represent auxiliary Help energy internal resistance adaptive factor, R₁Supplementary energy equivalent internal resistance is represented,Represent the estimate of accessory power supply equivalent internal resistance, i_B Represent auxiliary energy source output current, V_BRepresent bus voltage,The open-circuit voltage preset value of supplementary energy is represented, corresponding to auxiliary Help the SOC of the energy；β₂Internal resistance estimator gain is represented, is a normal number for needing to be adjusted according to emulation and experiment；R_1,maxWith R_1,minIt is to represent R respectively₁Supremum and infimum, be all on the occasion of so that the estimate of supplementary energy equivalent internal resistance Always within the scope of the bound of actual value, that is, meet below equation：

In actual applications, R_1,maxWith R_1,minBe chosen at and meet condition in the case of, while to consider control calculating The Actual Control Effect of Strong of method, this also means that R_1,maxSelection to be suitably larger than R₁Supremum, accordingly, R_1,minSelection will Suitably less than R₁Infimum.

The step 2) by below equation to load current estimation valueCarry out online updating：

Wherein,It is load inductance estimate, will be used to design the output current reference value of main source of energy,Represent auxiliary energy The estimate of source open-circuit voltage, V_BSystem bus voltages；By above-mentioned load current estimation valueBus voltage V_BAnd supplementary energy Open-circuit voltage preset valueSupplementary energy output current reference value can be calculated

Wherein,Rate of change after expression load current estimation value is smooth, V_fcSupplementary energy output voltage is represented, h (t) is Output after load current estimation value is smooth, this will be used for the output current design load of main source of energy, and T is smoothing factor, i_fcRepresent Main source of energy output current.V_fcRepresent main source of energy output current.

The step 3) with load current estimation value, load current estimation value changes rate and supplementary energy open-circuit voltage with The deviation (this voltage with supplementary energy SOC there are mapping relations) of preset value as the input of Fuzzy Control Law, by Fuzzy Control System rule output gain and supplementary energy refer to the increment of open-circuit voltage.

Be embodied as design fuzzy control input and output and membership function as shown in Figure 5, Figure 6, △ V in figure_BRepresent The actual value of bus voltage and the difference of desired value, △ V_flRepresent and expect bus voltage increment.

Described different operating modes include the operating modes such as regenerative braking operating mode, nominal situation and overload conditions.

By the inventive method, it is ensured that the vehicle-mounted main source of energy system power output speed of response protects ceiling restriction less than it, And ensure that the state-of-charge (SOC) of supplementary energy in certain safe range, prevents it from occurring overcharging and over-discharge Phenomenon, i.e., to ensure the state-of-charge (SOC) of supplementary energy near a setting value, so as to realize following two main mesh Mark：1) the dynamic tracking to duty requirements is realized；2) main source of energy and auxiliary energy system dynamic characteristic index are met, so as to ensure The stability of a system.

Beneficial effects of the present invention：

The model parameter of main source of energy system and auxiliary energy system need not accurately be distinguished in actual vehicular applications Know, so as to reduce the management strategy difficulty is realized；Amount of calculation is little, it is possible to achieve real-time control and On-line Estimation；Can be to not Measurable supplementary energy SOC is more accurately adjusted；Can be under different operating modes, i.e. regenerative braking operating mode, nominal situation And overload conditions, the power output of each energy source in system is effectively managed, and then meet the dynamic characteristic limit of system System, ensures its stability.

Description of the drawings

Fig. 1 is the system architecture diagram that the embodiment of the present invention is adopted.

Fig. 2 is the power response curve of main source of energy and supplementary energy in testing example.

Fig. 3 is to supplementary energy internal resistance estimation effect figure in testing example.

Fig. 4 is the control effect in the emulation of embodiment Matlab/Simulink to supplementary energy open-circuit voltage, i.e., right The control effect of SOC.

Fig. 5 is the fuzzy control input and output schematic diagram being embodied as.

Fig. 6 is the membership function figure of the fuzzy control input and output being embodied as.

Specific embodiment

Below in conjunction with specific embodiment and compare accompanying drawing to the present invention described in detail.

Three below embodiment is implemented using the inventive method, and its specific implementation process is as follows：

Embodiment 1

The present invention has carried out experimental verification on actual hybrid power tour bus.It is the fuel as main source of energy in Fig. 2 Power output response curve of the battery with the lithium battery as supplementary energy in an experimental period, it can be seen that main source of energy electricity The response speed of stream is slower, meet its output characteristics it is soft the characteristics of.Supplementary energy is then responded rapidly, there is provided bearing power Transient state composition in demand.

Embodiment 2

The present invention has carried out the contrast test of Adaptive Fuzzy Control algorithm on actual hybrid power tour bus, same Under one traffic route, main source of energy output current is measured under adaptive algorithm and Adaptive Fuzzy Control algorithm respectively with load The situation of change.

From figure 3, it can be seen that Adaptive Fuzzy Control algorithm proposed by the invention can more preferable adaptation condition change Change, i.e., main source of energy output current can more effectively track load current；Meanwhile, (the shadow part in Fig. 3 in the case where operating mode processed is regenerated Point), Adaptive Fuzzy Control algorithm proposed by the invention can preferably control main source of energy in the presence of fuzzy controller Power output, indicate the validity of put forward Fuzzy Control Law.

Embodiment 3

Although supplementary energy SOC in practice is not directly measured, supplementary energy open-circuit voltage is deposited with supplementary energy SOC In mapping relations, by building above-mentioned hybrid power tour bus system simulation model in Matlab/Simulink, and measure auxiliary The change for helping energy open-circuit voltage is verified to the control effect of the present invention.

Fig. 4 is control effect of the present invention to supplementary energy SOC.The setting value of supplementary energy SOC is 0.55, is mapped to auxiliary Energy open-circuit voltage setting value is helped to be 48V.Fig. 4 shows and reduce rapidly after load power demand first increases so that auxiliary energy Source open-circuit voltage deviates after setting value, and the present invention can readjust supplementary energy open-circuit voltage near setting value, i.e., right The supplementary energy SOC for answering is adjusted near setting value.

Claims (7)

1. a kind of adaptive fuzzy control method of onboard system energy management, it is characterised in that：

1) On-line Estimation is carried out to the key characterization parameter of onboard system；

2) self adaptation online updating is carried out to load current according to the On-line Estimation value of key characterization parameter, with to each energy source it Between power management；

3) for different duty requirements and the system status of supplementary energy, using Fuzzy Control Law key characterization parameter is controlled.

2. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 1, it is characterised in that： Described key characterization parameter includes the equivalent internal resistance of load equivalent electric current and supplementary energy.

3. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 2, it is characterised in that： Described load equivalent electric current carries out On-line Estimation using the adaptive updates rule of below equation：

${\stackrel{\·}{\hat{i}}}_p={\mathrm{\β}}_1{\hat{R}}_p(i_l-i_l^{*})+p_1\left(t\right),$

$p_1\left(t\right)=\left\{\begin{array}{cc}-g_1,& \begin{array}{cccc}if& {\hat{i}}_p\&GreaterEqual;i_{p,max}& and& g_1>0\end{array}\\ & \begin{array}{cccc}or& {\hat{i}}_p\&le;i_{p,\min }& and& g_1<0\end{array}\\ 0,& otherwise\end{array}\right.,$

Wherein,Represent load current source internal resistance estimate, g₁Represent intermediate variable,p₁(t) expression etc. Effect load current source adaptive factor, i_lRepresent load current,Represent load current estimation value, i_pRepresent equivalent load electric current Ource electric current value,Represent the estimate of equivalent load current source current, β₁Represent estimator gain；i_p,maxWith i_p,minRepresent respectively The supremum and infimum of equivalent load current source current value；

So that the estimate of load equivalent electric currentIn actual value i of load equivalent electric current_pBound within the scope of, i.e., Meet below equation：

$i_{p,min}\&le;{\hat{i}}_p\&le;i_{p,max}.$

4. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 2, it is characterised in that： The equivalent internal resistance of described supplementary energy carries out On-line Estimation using the adaptive updates rule of below equation：

${\stackrel{\&CenterDot;}{\hat{R}}}_1=-{\mathrm{\&beta;}}_2\mathrm{\&mu;}(V_B-V_{oc}^{*})(i_B+i_l-i_l^{*})+p_2\left(t\right),$

$p_2\left(t\right)=\left\{\begin{array}{cc}-g_2,& \begin{array}{cccc}if& {\hat{R}}_1\&GreaterEqual;R_{1,max}& and& g_2>0\end{array}\\ & \begin{array}{cccc}or& {\hat{R}}_1\&le;R_{1,\min }& and& g_2<0\end{array}\\ 0,& otherwise\end{array}\right.$

Wherein, g₂Represent intermediate variable,μ is the positive parameter that need to adjust, p₂T () represents auxiliary Help energy internal resistance adaptive factor, R₁Supplementary energy equivalent internal resistance is represented,Represent the estimate of accessory power supply equivalent internal resistance, i_B Represent auxiliary energy source output current, V_BRepresent bus voltage,The open-circuit voltage preset value of supplementary energy is represented, corresponding to auxiliary Help the SOC of the energy, β₂Represent internal resistance estimator gain, R_1,maxWith R_1,minIt is to represent R respectively₁Supremum and infimum；

So that the estimate of supplementary energy equivalent internal resistanceWithin the scope of the bound of actual value, that is, meet below equation：

$R_{1,min}\&le;{\hat{R}}_1\&le;R_{1,max}.$

5. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 1, it is characterised in that： The step 2) by below equation to load current estimation valueCarry out online updating：

$\hat{L}\stackrel{\&CenterDot;}{i_l^{*}}=V_{oc}^{*}-(i_l^{*}-{\hat{i}}_p){\hat{R}}_p-2\mathrm{\&mu;}{\hat{R}}_1(V_B-V_{oc}^{*})$

Wherein,It is load inductance estimate,Represent the estimate of supplementary energy open-circuit voltage, V_BSystem bus voltages；

By above-mentioned load current estimation valueBus voltage V_BAnd the open-circuit voltage preset value of supplementary energyCalculate auxiliary energy Source output current reference value

$i_{fc}^{*}=\frac{V_B}{V_{fc}}\&lsqb;h\left(t\right)-\mathrm{\&mu;}(V_B-V_{oc}^{*})\&rsqb;,$

$h+T\stackrel{\&CenterDot;}{h}=i_l^{*}$

Wherein, h (t) is the output after load current estimation value is smoothed,Rate of change after expression load current estimation value is smooth, V_fcSupplementary energy output voltage is represented, T is smoothing factor, i_fcRepresent main source of energy output current.

6. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 1, it is characterised in that： The step 3) with load current estimation value, load current estimation value changes rate and supplementary energy open-circuit voltage and preset value Deviation refers to the increment of open-circuit voltage as the input of Fuzzy Control Law by Fuzzy Control Law output gain and supplementary energy.

7. the adaptive fuzzy control method of a kind of onboard system energy management according to claim 1, it is characterised in that： Described different operating modes include the operating modes such as regenerative braking operating mode, nominal situation and overload conditions.