CN113595220B - Power coordination method for super-capacitor-fuel cell hybrid power special vehicle - Google Patents
Power coordination method for super-capacitor-fuel cell hybrid power special vehicle Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other DC sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other DC sources, e.g. providing buffering using capacitors as storage or buffering devices
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0063—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
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Abstract
The patent relates to a power coordination method of a super capacitor-fuel cell hybrid power special vehicle, which is characterized by comprising the following steps: step 100, establishing a mathematical model of the hybrid powertrain architecture, comprising: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as hybrid power sources to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell; step 200, a model predictive controller system is built according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, energy management is carried out, and finally an optimal control sequence is obtained; and 300, adding the first value in the optimal control sequence obtained in the step 200 to a model predictive controller, updating the state value, and sequentially iterating.
Description
Technical Field
The invention belongs to the technical field of new energy hybrid electric vehicles, and relates to a power coordination method of a super capacitor-fuel cell hybrid power special vehicle.
Background
In recent years, new energy automobiles are favored by people due to the advantages of environmental protection, energy conservation and the like, and have rapid development. In the field of civil aviation, along with the start of the 'oil-to-gas' special test point work of special vehicles on the airport ground, how to deploy new energy automobiles in the airport environment is attracting attention in the industry. Compared with a general application scene, the vehicle running condition is complex under the airport background, the working time is long, the working frequency is high, the working load is large, and the load change is frequent. In particular, for guiding a vehicle by an aircraft in an airport, it is required to stably drag the aircraft in an extremely short time, which puts an extremely high demand on the instantaneous change of the power of the vehicle.
Based on the above-mentioned scene demands, there is an urgent need to design a reliable power system belonging to an airport special vehicle to adapt to airport working conditions.
The fuel cell is used as a novel vehicle power source, has the characteristics of high energy density, high discharge speed, cleanness, environmental protection and the like, and provides a new thought for meeting the operation requirements of special vehicles under airport working conditions. However, the fuel cell has problems of not recovering excessive energy and requiring a high dynamic performance and reliability of the fuel cell system, which requires to be matched with other power sources to improve the above-mentioned disadvantages of the fuel cell.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a power coordination method for a super capacitor-fuel cell hybrid power special vehicle, which is used for solving the problems of load change power system response, energy waste of a fuel cell and high requirements on the fuel cell system in the prior art.
The purpose of the invention is realized in the following way:
the power coordination method of the super capacitor-fuel cell hybrid power special vehicle comprises the following steps: step 100, establishing a mathematical model of the hybrid powertrain architecture, comprising: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as hybrid power sources to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell; the step 100 further comprises the following steps: step 101, from the perspective of power flow, establishing a power balance model of a total vehicle power sum node; 102, establishing a mathematical model according to the working principle and the circuit model of the super capacitor, and obtaining an expression of the residual electric quantity of the super capacitor; step 103, establishing a mathematical model according to the working principle and the circuit model of the fuel cell, and obtaining an expression of the electricity consumption of the fuel cell; step 200, a model predictive controller system is built according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, energy management is carried out, and finally an optimal control sequence is obtained; the step 200 includes the following steps: step 201, based on the super capacitor model and the fuel cell model obtained in step 200, establishing a mathematical model of a model predictive controller; step 202, predicting a controller system based on a model, and formulating system parameters of the system comprises: state variables, control variables, measurement inputs, outputs, and index functions; step 203, obtaining a calculation formula of an objective function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence; and 300, adding the first value in the optimal control sequence obtained in the step 200 to a model predictive controller, updating the state value, and sequentially iterating. The power system can play the functions of storing electric quantity and peak clipping and valley filling of the super capacitor in real time, achieves quick response of the power system under load change, and ensures the stability and reliability of the operation of the power system of the vehicle.
Further, the expression of the power balance model of the established total vehicle power sum node is as follows: p (P) C (t)＝P uc (t)+P req (t) wherein P C (t) is the instantaneous output power of the fuel cell, P uc (t) is the instantaneous input/output power of the super capacitor, and the value of the instantaneous input/output power is the charging state when the value is positive, P req (t) is the instantaneous output power of the vehicle load, including the motor and other vehicular electrical loads. From the aspect of system power flow, the power distribution among the vehicle load, the super capacitor and the fuel cell is coordinated.
Further, the expression of the remaining capacity SOE (t) of the supercapacitor is represented by its differentiated form SOE (t)':wherein E is cap For maximum energy capacity, ζ cap Is super capacitor power. Providing a basis for calculating an optimal control sequence.
Further, the electricity consumption B of the fuel cell is obtained e The expression of (2) is:wherein P is C Is the power of the fuel electric car, t 0 Start-up time for fuel cell, +.>Is the electricity consumption rate. Providing a basis for calculating an optimal control sequence in the prediction time domain.
Further, the state variable of the model predictive controller system is x, the control variable thereof is u, the measurement input is v, the measurement output is y, wherein,u＝P uc ，v＝P req ，/>P uc output power of super capacitor, P req Is the output power of the vehicle load.
Further, the index function of the model predictive controller system is obtained by linearizing and discretizing the system.
Further, the state space after the model predictive controller system performs linearization and discretization is as follows:where k is the time of day, belonging to the time set {1,2, …, T }; x (k) is a state variable of the model predictive control system at the moment k; x (k+1) is a state variable of the model predictive control system at time k+1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; y (k) is the measurement output of the model predictive control system at the moment k;
A(k),B u (k),B v (k) And C (k) are respectively:
wherein, is the electricity consumption in unit time, m 1 A slope that is the linear function; m is m 2 Is a constant of the linear function.
Further, the index function J is the smallest difference between the measured output value and the reference track, and its expression is:wherein N is the length of the prediction horizon; q is a state weight value; r is input penalty; y is the measurement output; y is ref Is a reference value; i is a stage; y (k+i|k) is the output term; y is ref (k+i) is a reference term; u (k+i-1) is a control input; u is a control variable matrix, and the expression is: />N c Is the predicted length of the control variable.
Further, by the objective function minB e And (3) corresponding constraint conditions: y is min ≤y(k)≤y max ,k＝1,2,...,N，u min ≤u(k)≤u max K=1, 2,.. obtaining the optimal control sequence u 0 ,u 1 ,u 2 ,…,u N-1 。
Further, u is 0 After being input into the model predictive controller, the vehicle enters the next moment and obtains the current load demand power P of the vehicle req For the next moment B e And predicting the SOE, correcting the predicted value at the last moment, and repeating the steps of predicting, optimizing and correcting.
Compared with the prior art, the invention has at least one of the following beneficial effects:
a) The energy management method based on the model predictive controller is provided, power coordination among hybrid power sources is guaranteed, and quick response of the power system is realized when load changes.
b) And the running performance of the power system is improved while the quick response of the system is ensured under the condition of frequent load change.
c) Fully plays the functions of storing electric quantity and peak clipping and valley filling of the super capacitor in real time, realizes the quick response of the power system under load change and ensures the stability and reliability of the operation of the power system of the vehicle.
d) From the aspect of system power flow, the power distribution among the vehicle load, the super capacitor and the fuel cell is coordinated, so that real-time control is realized and the optimal control effect is achieved.
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In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present description, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the steps of a power coordination method of a super capacitor-fuel cell hybrid power special vehicle of the invention;
FIG. 2 is a schematic diagram of a power balance model of a super capacitor-fuel cell hybrid special vehicle according to the present invention;
FIG. 3 is a simplified model diagram of an equivalent RC of the supercapacitor of the present invention;
FIG. 4 is a diagram of a Rint equivalent circuit model of a fuel cell according to the present invention;
FIG. 5 is a step-wise flow chart of step 100 of the present invention;
FIG. 6 is a step-wise flow chart of step 200 of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For the purpose of facilitating an understanding of the embodiments of the present application, reference will now be made to the following description of specific embodiments, taken in conjunction with the accompanying drawings, in which the embodiments are not intended to limit the embodiments of the present application.
Example 1
The embodiment provides a power coordination method of a super capacitor-fuel cell hybrid power special vehicle.
As shown in fig. 1, the power coordination method of the super capacitor-fuel cell hybrid power special vehicle comprises the following steps:
step 100, establishing a mathematical model of the hybrid powertrain architecture, comprising: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as hybrid power sources to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell.
Step 101, from the point of view of power flow, a power balance model of the total vehicle power sum node is established.
The power balance model includes: power distribution among fuel cells, supercapacitors, motors, and other automotive electrical loads.
Referring to fig. 2, the power balance model may be represented by the following expression:
P C (t)＝P uc (t)+P req (t)
wherein P is C (t) is the instantaneous output power of the fuel cell in kw; p (P) uc (t) is the instantaneous input/output power of the super capacitor, and the value of the instantaneous input/output power is a charging state when the value is positive, and the unit is kw; p (P) req (t) is the instantaneous output power of the vehicle load in kw; t is the moment; p (P) req Mainly comprises an electric motor and other electric load requirements for vehicles.
Further, P can be deduced uc Constraints that need to be satisfied:
P C-min -P req (t)≤P uc (t)≤P C-max -P req (t)
wherein P is C-min Is burnt byInstantaneous minimum output power of the material battery; p (P) C-max Is the instantaneous maximum output power of the fuel cell.
In addition, in order to ensure safe and stable operation of the whole vehicle power system, the output P of the super capacitor uc And the supercapacitor SOE value must satisfy the physical constraints under hardware conditions, namely:
P uc-min ≤P uc (t)≤P uc-max
SOE min ≤SOE(t)≤SOE max
wherein P is uc-max Representing the maximum output power of the super capacitor; p (P) uc-min The minimum output power of the super capacitor; the SOE of the super capacitor is the highest in charging and discharging efficiency within the range of 40% -80%, so that the maximum residual capacity SOE of the super capacitor SOE max Set to 0.64; minimum residual capacity SOE of supercapacitor SOE min Set to 0.16.
And 102, establishing a mathematical model according to the working principle and the circuit model of the super capacitor, and obtaining an expression of the residual electric quantity of the super capacitor.
According to the Resistive-capacitance (RP) simplified circuit of the supercapacitor shown in fig. 3, ESR (Equivalent Series Resistance) is an equivalent series resistance, and a mathematical model of the supercapacitor can be obtained, where the expression is as follows:
P uc (t)＝V L (t)·I cap (t)
wherein P is uc (t) is the instantaneous input/output power of the super capacitor, the unit is kw, when P uc When the value of (t) is positive, the capacitor is representedIs in a charged state; v (V) L (t) is the terminal voltage of the super capacitor at the moment t, and the unit is V; i cap (t) is the real-time current flowing through the supercapacitor in a;is the first derivative of the voltage across the equivalent capacitor at time t, the unit is A/F; c is a super capacitor, and the unit is F; SOC (t) (State Of Charge) is the real-time state of charge of the supercapacitor; q (t) is the amount of charge stored in the capacitor, in C; q (Q) max The maximum storable charge quantity of the super capacitor at the time t is represented by C; v (V) cap (t) is the voltage across the equivalent capacitance at time t, in V; v (V) max The maximum voltage of the super capacitor is V; SOE (t) (State Of Energy) is the residual electric quantity of the super capacitor at the time t; e (t) represents the energy stored in the super capacitor at the moment t, and the unit is J; e (E) cap Is the maximum energy capacity in J.
The relationship between SOE differentiation, maximum energy capacity and supercapacitor power is as follows:
wherein SOE (t)' is the differentiation of SOE of the super capacitor at the moment t; zeta type toy cap The efficiency of the super capacitor is set to 98%.
And 103, establishing a mathematical model according to the working principle and the circuit model of the fuel cell, and obtaining an expression of the electric quantity consumption of the fuel cell.
Describing the mathematical model of the fuel cell according to the Rint equivalent circuit model shown in fig. 4 can be expressed by the following expression:
U′＝U DC -i·R
P C (t)＝U′(t)·i(t)
in the above formula, U' (t) is the terminal output voltage of the fuel cell at time t, and its unit is V; i (t) is the circuit current at time t, and its unit is A.
Defining fuel cell power consumption per unit timeIs thatThe calculation formula is as follows:
wherein P is C Represents the output power of the fuel cell in kw;the power consumption rate is expressed in V/(kw.h).
The amount of electricity consumed during the fuel cell operation time t can be expressed as:
wherein B is e Representing the amount of electricity consumed by the fuel cell; t is t 0 Indicating the start-up time of the fuel cell.
The DC/DC bi-directional converter is simplified and its conversion efficiency is set to 1.
And 200, establishing a model predictive controller system according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, and performing energy management to finally obtain an optimal control sequence.
Step 201, based on the super capacitor model and the fuel cell model obtained in step 200, a mathematical model of the model predictive controller is established.
The model predictive controller may be expressed using the following equation:
wherein x is 1 =soe represents the remaining capacity of the supercapacitor; x is x 2 ＝B e Representation ofElectricity consumption of the fuel cell; u=p uc Representing the control variable and taking it as the power output of the super capacitor.
Step 202, predicting a controller system based on a model, and formulating system parameters of the system comprises: state variables, control variables, measurement inputs, outputs, and index functions.
Defining state variables, control variables, measurement inputs and outputs of a model predictive control system:
u＝P uc
v＝P req
wherein x is a state variable of the model predictive control system; u is a control variable; v is a measurement input; y is the measurement output.
Linearizing and discretizing the model predictive controller system to obtain a state space form as follows:
x(k+1)＝A(k)x(k)+B u (k)u(k)+B v (k)
y(k)＝C(k)x(k)
wherein k is the time of day, belonging to the time set {1,2, …, T }; x (k) is a state variable of the model predictive control system at the moment k; x (k+1) is a state variable of the model predictive control system at time k+1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; y (k) is the measurement output of the model predictive control system at the moment k; a (k), B u (k),B v (k) And C (k) are respectively:
wherein, among them,then m is 1 A slope that is the linear function; m is m 2 Is a constant of the linear function.
Defining the index function J of the model predictive controller system as the smallest difference between the measured output value of the process and the reference trajectory, the minimized index function J can be described as the sum of the differences of the staged output term and the reference term and the weighted norms of the control input terms:
wherein N is the length of the prediction horizon; q is a state weight value; r is input penalty; y is the measurement output; y is ref Is a reference value; i is a stage; y (k+i|k) is the output term; y is ref (k+i) is a reference term; u (k+i-1) is a control input; u is a control variable matrix, and the expression is:
wherein N is c Is the predicted length of the control variable.
For the weighted norm of the difference between the output term and the reference term +.>For controlling the weighted norms of the input items.
The expression of the weighted norm of the difference between the output term and the reference term is:
the expression of the weighted norms of the control inputs is:
and 203, obtaining a calculation formula of an objective function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence.
The super capacitor-fuel cell hybrid system targets the power consumption B of the fuel cell e The lowest, the calculation formula is as follows:
due to the physical constraints under which the measured output and the control variables must meet, namely:
y min ≤y(k)≤y max ,k＝1,2,...,N
u min ≤u(k)≤u max ,k＝1,2,...,N
calculating to obtain the optimal control sequence u of the control variable u under the given constraint condition 0 ,u 1 ,u 2 ,…,u N-1 。
And 300, adding the first value in the optimal control sequence obtained in the step 200 to a model predictive controller, updating the state value, and sequentially iterating.
Inputting the first numerical value in the calculated optimal control sequence into a model predictive controller, then entering the next moment, and continuously acquiring the current load demand power P of the vehicle req Information about the power consumption B of the fuel cell at the next time of the model predictive controller system e And the residual capacity SOE of the super capacitor, and simultaneously, predictingThe predicted value at the previous time is corrected. And repeating the above steps of predicting, optimizing, and correcting.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not meant to limit the scope of the invention, but to limit the scope of the invention.
Claims (10)
1. A method for power coordination of a super capacitor-fuel cell hybrid special vehicle, comprising:
step 100, establishing a mathematical model of the hybrid powertrain architecture, comprising: the power balance model, the mathematical model of the super capacitor and the mathematical model of the fuel cell are used as hybrid power sources to obtain the residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell;
the step 100 further comprises the following steps: step 101, from the perspective of power flow, establishing a power balance model of a total vehicle power sum node; 102, establishing a mathematical model according to the working principle and the circuit model of the super capacitor, and obtaining an expression of the residual electric quantity of the super capacitor; step 103, establishing a mathematical model according to the working principle and the circuit model of the fuel cell, and obtaining an expression of the electricity consumption of the fuel cell;
step 200, a model predictive controller system is built according to the obtained residual electric quantity of the super capacitor and the electric quantity consumption of the fuel cell, energy management is carried out, and finally an optimal control sequence is obtained;
the step 200 includes the following steps: step 201, based on the super capacitor model and the fuel cell model obtained in step 200, establishing a mathematical model of a model predictive controller; step 202, predicting a controller system based on a model, and formulating system parameters of the system comprises: state variables, control variables, measurement inputs, outputs, and index functions; step 203, obtaining a calculation formula of an objective function of the super capacitor-fuel cell hybrid power system, and obtaining an optimal control input sequence;
and 300, adding the first value in the optimal control sequence obtained in the step 200 to a model predictive controller, updating the state value, and sequentially iterating.
2. The power coordination method of the super capacitor-fuel cell hybrid power special vehicle according to claim 1, wherein the expression of the power balance model of the established total vehicle power sum node is: p (P) C (t)＝P uc (t)+P req (t) wherein P C (t) is the instantaneous output power of the fuel cell, P uc (t) is the instantaneous input/output power of the super capacitor, and the value of the instantaneous input/output power is the charging state when the value is positive, P req (t) is the instantaneous output power of the vehicle load, including the motor and other vehicular electrical loads.
3. The power coordination method of a hybrid special vehicle of a super capacitor-fuel cell according to claim 2, wherein the expression of the remaining capacity SOE (t) of the super capacitor is represented by its differentiated form SOE (t)':
wherein E is cap For maximum energy capacity, ζ cap Is super capacitor power.
4. The power coordination method of a super capacitor-fuel cell hybrid special vehicle according to claim 1, wherein the power consumption B of the fuel cell is obtained e The expression of (2) is:wherein P is C Is the power of the fuel electric car, t 0 Start-up time for fuel cell, +.>Is the electricity consumption rate.
5. The method for power coordination of a hybrid power special vehicle of a super capacitor-fuel cell as recited in claim 4, wherein the state variable of the model predictive controller system is x, the control variable thereof is u, the measurement input is v, the measurement output is y,u＝P uc ，v＝P req ，/>P uc output power of super capacitor, P req Is the output power of the vehicle load.
6. The power coordination method of the super capacitor-fuel cell hybrid power special vehicle according to claim 1, wherein the index function of the model predictive controller system is obtained by linearizing and discretizing the system.
7. The power coordination method of the super capacitor-fuel cell hybrid power special vehicle according to claim 1, wherein the state space form after the model predictive controller system performs linearization and discretization processing is as follows:
where k is the time of day, belonging to the time set {1,2, …, T }; x (k) is a state variable of the model predictive control system at the moment k; x (k+1) is a state variable of the model predictive control system at time k+1; u (k) is the output power of the super capacitor of the model predictive control system at the moment k; measurement output of model predictive control system with y (k) being k time；A(k),B u (k),B v (k) And C (k) are respectively:
wherein, is the electricity consumption in unit time, m 1 A slope that is the linear function; m is m 2 Is a constant of the linear function.
8. The power coordination method of a supercapacitor-fuel cell hybrid special vehicle according to claim 7, wherein the index function J is a minimum difference between a measured output value of a process and a reference track, and the expression is:
wherein N is the length of the prediction horizon; q is a state weight value; r is input penalty; y is the measurement output; y is ref Is a reference value; i is a stage; y (k+i|k) is the output term; y is ref (k+i) is a reference term; u (k+i-1) is a control input; u is a control variable matrix, and the expression is: />N c Is the predicted length of the control variable.
9. The method for power coordination of a super capacitor-fuel cell hybrid special vehicle of claim 6, wherein said objective function minB is used to coordinate power of said super capacitor-fuel cell hybrid special vehicle e And (3) corresponding constraint conditions: y is min ≤y(k)≤y max ,k＝1,2,...,N，u min ≤u(k)≤u max K=1, 2,.. obtaining the optimal control sequence u 0 ,u 1 ,u 2 ,…,u N-1 。
10. The method for power coordination of a super capacitor-fuel cell hybrid special vehicle of claim 9, wherein u is 0 After being input into the model predictive controller, the vehicle enters the next moment and obtains the current load demand power P of the vehicle req For the next moment B e And predicting the SOE, correcting the predicted value at the last moment, and repeating the steps of predicting, optimizing and correcting.
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| CN104015626A (en) * | 2014-05-29 | 2014-09-03 | 北京航空航天大学 | Hybrid power system for electric car |
| CN107901776A (en) * | 2017-11-15 | 2018-04-13 | 吉林大学 | Electric automobile composite power source fuel cell hybrid energy system power dividing method |
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