Accepted Manuscript Research Paper Extension control strategy of a single converter for hybrid PEMFC/battery power source Xueqin Lü, Xing Miao, Wenming Liu, Jian Lü PII: DOI: Reference:
S1359-4311(17)30423-4 http://dx.doi.org/10.1016/j.applthermaleng.2017.09.003 ATE 11060
To appear in:
Applied Thermal Engineering
Received Date: Revised Date: Accepted Date:
20 January 2017 8 June 2017 1 September 2017
Please cite this article as: X. Lü, X. Miao, W. Liu, J. Lü, Extension control strategy of a single converter for hybrid PEMFC/battery power source, Applied Thermal Engineering (2017), doi: http://dx.doi.org/10.1016/ j.applthermaleng.2017.09.003
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Extension control strategy of a single converter for hybrid PEMFC/battery power source Xueqin Lü*a, Xing Miao a, Wenming Liub, Jian Lüa a.School of Automation Engineering，Shanghai University of Electric Power，Shanghai 200090，China b. Tai'an power supply company, Shandong power company, China Power Grid, Tai'an 271000，China
Abstract: This paper describes a control scheme to improve the dynamic characteristics of a PEMFC/ battery hybrid power system. The proposed control scheme includes a power control strategy to control the power flow of the PEMFC/ battery hybrid system and an extension control for the DC/DC converter of the PEMFC. The power control strategy enables the fuel cell to operate within the high efficiency region. The DC/DC converter with the extension control is applied to control the fuel cell output power. In order to solve the problem that the parameter tuning of the extension control depends on human experience and is difficult to be determined, the extension control method is improved, which uses the state distance Ds to replace the original parameters to solve the difficult problem of parameter tuning. Finally, the experimental results on a 500W fuel cell/ 24V, 10Ah Li-ion battery hybrid system are given to verify the good tracking response, low ripple of output current and short response time of the hybrid system outputs. KEY WORDS: PEMFC; hybrid power source, Power control; Extension control
1 Introduction In order to meet the high demand for the advanced manufacturing, more and more autonomous robots are needed to work under special circumstance such as underwater, high altitude and so on[1-3]. However, the motion and the flexibility of the robots are limited by the cable power. While fuel cells are a promising renewable electrical power source for application. Among the various fuel cells, proton exchange membrane fuel cells (PEMFCS) were regarded as the most suitable for robots and vehicle application because of their low operating temperature, high power density, high efficiency, fast startup, quick response, and zero emission of greenhouse gases [4-5]. To improve the dynamics and power density of fuel cell systems, hybridization of fuel cells with new energy storage devices, such as Li-ion batteries and supercapacitors is required. The Li-ion battery has the advantage of high power density and long life when it compared with the supercapacitors. So the combination of fuel cell and Li-ion battery is the best strategy for the mobile robots, which not only satisfy the peak power demand, but also recover the braking energy. Thus, such configurations greatly reduce the hydrogen consumption, improve efficiency and meet the power driving requirements under all driving conditions [6-8]. However, this optimization is accomplished through an energy management strategy (EMS), which distributes the load power among the energy sources. Different energy management strategies for the fuel cell hybrid power systems have been reported in the literature. Zhang C et al.  developed a predictive power management and proposed a neural network, which performed with a better efficiency performance. Hemi H et al.  presented a real time fuzzy logical controller to determine the fuel cell output power and protected the battery from overcharging. Kraa O et al.  used a sliding mode controller to control the fuel cell and supercapacitor’s currents, which satisfied the load requirements with a stable and robust performances. Xu L et al.  proposed a multi-mode real-time control strategy for a fuel cell electrical vehicle (FCEV), experiments proved that it can reduce the degradation rate of the fuel cell. Geng B et al.  designed the energy management strategy of FCEV based on two-level control and realized the purpose of reducing the consumption of hydrogen under the premise of protecting the safe operation of fuel cell stack. *
Corresponding author. Tel.:+86 21 35303136; fax:+86 21 35303294 E-mail: [email protected]
Autonomous robots work under special operating environment, such as welding robot, EOD robot (explosive disposal robot), need faster response speed and higher stability. But at present, the research of the energy management is focus on the fuel cell hybrid power system, especially the energy efficiency and the optimal allocation of energy. The electric vehicle energy management strategy can’t meet the robot with special task based on the response speed and stability requirements. So the important study is to propose a new control algorithm of fuel cell/ battery hybrid source for autonomous robots applications. The simplicity and high performance of the control is that a fuel cell generator is functioned for supplying energy to keep batteries charged with respect to fuel cell dynamics, the battery storage devices is naturally operating for supplying the energy required by the robot. The main goals of this paper are to design an energy management for the autonomous robot based on the extension control. Extension control method deals with the control problem from the perspective of information transformation, and the correlation degree of the input information act as the control output, which make some of the parameters of the hybrid energy system from uncontrollable region into a stable and controllable region. This provides a new way to solve the contradiction problem in control engineering and improve the accuracy, stability and rapidity of control [14-16]. Extension control method can get rid of the limitations of the conventional control, not be restricted by the specific control means, and is not depended on the detailed mathematical models of the control objects . After expanding the original control area and dividing the global control area to three parts, different control functions are implemented in the three control areas and the corresponding control strategy can achieve the control effect which cannot be realized by the conventional control methods [18-19]. The main contribution of this paper is to propose a control strategy for fuel cell based hybrid energy management system using the improved extension control framework. A parallel hybrid structure of fuel cell and Li-ion battery is considered. During transients in DC bus voltage fuel cell is unable to provide sufficient current duo to slow dynamics. Li-ion battery provides this additional power requirement. The main objective of the controller design is to regulate the fuel cell voltage so as to track the reference bus voltage and to control charging/ discharging of the Li-ion battery. To validate the proposed control algorithm, the hardware system is realized. Experiments results show that the proposed energy management strategy enables the management of transient power demand, power peaks, and regenerative braking. While the rule-based fuzzy logic energy management is widely used in the electrical vehicle, where the power distribution is accomplished through membership functions and the set of IF-THEN rules [20-21]. So, when it compared with the fuzzy PID control, the proposed improved extension control has a better dynamics performance and this method will provide a new contribution to the field of autonomous robots. This paper is organized as follows. In Sec.3, the mathematical models of the hybrid power system will be created. In Sec.4, the energy management controllers will be designed. In Sec.5, the validation results will be shown. The conclusion will be discussed in Sec.6. Finally, the acknowledgments will be described in Sec.7.
2. The components of the hybrid power system 2.1 The selection of the primary and secondary energy sources Among all kinds of fuel cells, the PEMFC are very useful for the transport zone [22-24]. PEMFCs provide many benefits such as quick start up and high efficiency. Therefore, this paper chooses the PEMFC as the main energy of hybrid power system. The mechanism and mathematical model of PEMFC are described in many literatures. The characteristic of the PEMFC used in this paper are shown in Tab. 1. Among the several energy-storage devices, if using super capacitance as the secondary source of energy, when the hybrid power system meets sudden power increase during startup, acceleration and load fluctuation, the capacitance of shunt capacitor increases with the decrease of harmonic frequency. Therefore, large capacitors are needed to meet the requirements of energy buffering and suppress the fluctuation of output voltage, so that the volume and cost of the hybrid power system are greatly increased [25-26]. Compared with the supercapacitor, Li-ion battery is a good choice 2
due to the advantages such as high power density, long life cycle, and excellent charge-discharge properties, and can supply large instantaneous power . Tab.1 The characteristics of the PEMFC PEMFC
Number of cells
Maximum operation point
Rated operation point
Rated H2 pressure
The battery SOC is expressed as
SOC I Q V SOC , T I bat
V SOC , T 4 R SOC , T Pbat 2
2 RV SOC , T
Here, Qbat, Ibat, T and Pbat are charge capacity, battery current, temperature and power at the battery terminals respectively. 2.2 The size of the PEMFC and the Li-ion battery The size of the fuel cell is determined by the power requirements of the load. If the system has big power requirements, then the fuel cell size is relatively large; the battery size is determined by the power fluctuations, if the system power fluctuations is strong, larger size battery is need to adjust the power system. In turn, smaller power batteries are needed. For example, a fuel cell hybrid electric vehicle, the selection of the motor power and fuel cell size is related to the performance of the electric vehicle, such as the maximum speed, endurance mileage, acceleration, acceleration time and gradient. Power, speed, torque and other parameters determine the size of motor power. Of course, these calculations will use the knowledge of automotive theory, or the calculation software support. After the motor is selected, then the corresponding fuel cell and battery are matched. The matching voltage is consistent with the fuel cell voltage, and the maximum current of the motor matches the fuel cell current. According to these conditions, the fuel cell capacity is determined, and the corresponding voltage and energy are obtained. The battery capacity is determined by the maximum acceleration, acceleration time, etc. The characteristic of the Li-ion battery used in this paper are shown in Tab.2.
Tab.2 The characteristics of the Li-ion battery Li-ion battery
23.6 / V
Fully charged voltage
Rated discharge current
2.3 DC-DC converter Under supervision of the energy management controller, the fuel cell current is controlled by the DC-DC converter. A non-isolated buck converter is adopted [28-29]. The buck converter is represented in Fig.1. A capacitor is adopted to reduce the current ripples. The most prominent feature of this DC/DC converter is that the output voltage differential signal is introduced in the control loop, and the one cycle control circuit is adopted, which is called output voltage differential coefficient and one cycle control (OVDC-OCC). The converter is an output voltage double loop control method, which can greatly improve the dynamic load performance of the converter. Moreover, due to the introduction of input voltage feedforward, its most prominent advantage is the strong ability of resisting input voltage disturbance, which can eliminate the influence of input voltage variation on output in one switching cycle. Ifuel
+ Ufuel _
Kiuab Kdurd + +
+ uo _
+ u _ cd
+ urd _
I R INT O
_ + Uref
Fig. 1 The OVDC-OCC controlled buck converter The characteristic of this DC/DC converter is shown as: Uo=24V, L=10μH, Co=10μF, fs=100 kHz, Rd=10kΩ, Cd=0.1μF, K=0.5, Ki=0.5, KD=1.
3 Energy operation modes In order to carry out energy management of the hybrid power system, four energy operation modes are identified as follows: (1) Battery driven mode. At the system startup stage, the battery provides instantaneous energy to the load. The battery discharge power: Pbat= Pdemand-Pfuel. (2) Battery charge mode. After the system running, if the battery SOC is less than the charge threshold and the load 4
power less than the fuel cell rated power, the remaining energy of the fuel cell is charged to the Li-ion battery, that is: Pbat= Pfuel - Pdemand. (3) Battery discharge mode. When the load power has a sudden high peak and exceeds the fuel cell maximum power of the, the Li-ion battery provides the fluctuating energy to the load. The instantaneous power of the load is supplied by the battery: Pbat= Pdemand-Pfuelmax. (4) Energy recovery mode. The load supplies energy to the battery when the load power requirement is reduced, that is Pbat= Pload.
4. Energy management strategies The formulation of control strategy should comply with the following principles: (1) So it is strictly prohibited the fluctuation of output power on fuel cell, which has greater impact on the fuel cell. (2) Excessive charging and discharging will cause damage to the battery, it is necessary to control the current of the battery to avoid over charging or discharging. 4.1 The fuel cell control strategy The parameters are defined by following: The fuel cell reference current, Ifc; The load power demand, Pdemand; The rate power of the fuel cell, PFC,rate; The fuel cell maximum output power, Pfuelmax; The fuel cell minimum output power, Pfuelmin; the fuel cell actual output power, Pfuel; The actual battery output power, Pbat; The battery charging set value, SOCmin. The actual fuel cell voltage ufuel can be detected in real time, and then the reference current of the fuel cell calculated through the formula Iref1=Pfuel/ufuel. The power management strategy is shown in below: (1) Pdemand is detected, which is fed back through current sensor and voltage sensor, and is compared with Pfuelmax. a.
If Pdemand <= Pfuelmax, Pfuel = Pdemand.
If Pdemand<=PFC, rate, the fuel cell supplies energy to the load separately.
(2) And the DC/DC buck converter will measure SOC of the battery. a.
If SOC<=SOCmin, the controller will switch the battery into the charge state.
If SOC>SOCmin and Pdemand>Pfuelmax, Pfc = Pfuelmax, and the instantaneous power of the load is supplied by the battery.
Based on the above analysis, the flow diagram of the power selection is given out in Fig. 2.
Fig. 2 The flowchart of the fuel cell controller Due to the converter constant output voltage, the fuel cell power can be controlled by the DC-DC converter output current. The sum of load current Iload and the current of the battery Ibattery are used as the reference current Iref to control the converter. Then the deviation of the converter output current Idc and the reference current Iref controls the duty ratio of the switch in the converter. 5
4.2 The battery charge-discharge control In the hybrid power system, the fuel cell is the main driving source, and the battery only provides a supplement to the sudden power demand during startup and acceleration, and it absorbs the energy when the power is reduced [30-31]. Assuming SOCmin and SOCmax are the charge and discharge thresholds respectively. At the stable state, the charge-discharge rules of the battery are: (1) If the SOCSOCmax, the battery supplies energy to the load and the fuel cell is not external power supply. The SOC is decreased until SOC
Fig.3 The diagram of the battery controller The charge-discharge status of the battery is affected by load power demand and the fuel cell power. The fuel cell charges the battery only when the load power is lower than the fuel cell maximum power. In order to avoid charge-discharge frequently, the deviation range of charge-discharge state is set to 5%. Fig.3 shows the battery charge-discharge controller. SOCref is the charge reference value. Kc is the regulation constant, which is determined by assuming that the charge-discharge adjustment of 5%. The “normalized” block normalizes the ΔSOC. |abs| gives a nonnegative output (|△SOC/SOCref|).The "relay" block is a lag block set to: it is set at "on" when the input is greater than 5%, until the input is less than 0.5%. Otherwise, the relay block outputs zero. When the battery SOC is below 95% of the rated value (above 105%), the battery will be charged-discharged with a constant current. When the SOC is within 0.5% of the rated value, the charge-discharge process will stop. Therefore, as long as the SOC is below 0.95 SOC ref, the positive additional reference signal I battery
0.05Cb SOCref 1800
, where Cb is
the battery capacity, is the current controller to generate. This reference value remains until SOC exceed 0.995 SOCref. When the SOC is higher than 1.05 SOCref, a negative reference current (-Ibattery) is generated, which is maintained until the SOC is lower than 1.005 SOCref. Otherwise, the battery output of the controller is zero. 4.3 Extension controller design for DC-DC converter 4.3.1 The basic concepts of extension control Firstly, the basic concepts related to extension control are introduced: (1) Characteristic variable (C), which represents the state of the system; (2) Characteristic state(S), which is described by the characteristic variable C; (3) Classical domain: the value range of the system characteristic state; (4) Extension domain, which the range of the characteristic state can be adjusted to the qualified range; (5) Non domain, which the range of the characteristic state can’t be adjusted to the qualified range; (6) Extension set: A characteristic states set established in the extension domain; (7) Correlation degree K(s) of the characteristic state, which is the relationship between the characteristic state current and the extension set. It will be divided into three types, which are K(s) = -1, -1=K(s)<0 and K(s)=0. Extension control study the type of -1 ≤K(s)≤0. 6
(8) Characteristic pattern: the typical mode described by the characteristic variable, marked as: Υi = fi (C1 , C2 , …, Cn), i = 1 , 2 , … , where Υi is the i characteristic pattern and fi is the Υi feature pattern division. (9) Measure pattern (Mi ): the pattern recognized by the correlation degree of the characteristic state (K (s)). 4.3.2 The traditional extension control algorithm
Fig.4 Extensible set of feature states The difference e, which is the error of the input current Iref and output current I of the DC-DC converter, and deviation differential e are used as the characteristic variable (C). The characteristic states are divided into 8 characteristic patterns. It is assumed that the allowable range of e and e is
eom and eom
respectively. The maximum
deviation and differential deviation of the system are em and em . On the characteristic state S (e, e ), the extension set can be expressed in Fig.4, where the shadow part represents the classical domain. Assumed the origin of the e and ec feature plane is S0 (0, 0), definition can be drowned as follow
M e 2 e 2 om om 0 2 M -1 em em 2
Where, [-eom, eom] is the scope of e and [-em, em] is the maximum extensible value of e. Define the distance from any point in the plane to the origin point Ds
e2 e2 , it is called the state distance,
then, D0=M0 and Dm=M-1. For any point in the e and ec feature plane S (e, e ), the correlation degree of the characteristic state is defined as
1 SS0 M 0 K S M 0 SS0 M 1 M 0
S Roy S Roy
Where, Roy is the classical domain, as shown in Fig.4, which is the shaded area. And |SS0| is defined as
k1e2 k2e 2
Where, K1 and K2 is the weighting coefficient, which depends on the characteristic pattern of the system. The correlation degree K(s) of the characteristic state(S) shows that the correlation degree of the extension set of the system characteristics S and characteristic state(e, e ) the extension association sets, thus the measure patterns, which the correlation degree is in the range of [-1, 0] can be expressed as follows: (1) Measure pattern M1: the correlation degree K(s) of the characteristic state is in the classical control domain. 7
M1 s | K (s) 0
(2) Measure pattern M2: the correlation degree K(s) of the characteristic state is in the extension domain.
M 2 s | -1 K ( s) 0 M 2i s | ai 1 K ( s) ai , S M 2, 1 a0 ai 1 ai am 0
(3) Measure pattern M3: the correlation degree K(s) of the characteristic state is in the non domain.
M 3 s | K (s) -1
The extension controller is designed as follows
u t 1 u t y t k K ci K S sgn e um
K S 0 1 K S 0 K S 1
Where, t is the sampling time, u(t) is the output current, u(t-1) is the previous sample values; y(t) is the sampling of the DC-DC converter current; k is the static gain of the process; Kci is the control coefficient of the measurement model M2i; S is the characteristic state; K(s) is the correlation degree of the characteristic state, and sgn(e) is the symbolic function, which is shown in the follows
e0 e0 e0
1 sgn e 0 1
Where, is a small range correction, whose function is to eliminate the disturbance and the uncertainty of K, and it is defined as
t K I edt K P e 0 0
Where, KI and KP are the appropriate constants. 4.3.3 The improved extension control algorithm From the above extension control algorithm mechanism, we need to adjust K, K1, K2, Kci, KI, Kp,δand other parameters for extension control. Its tuning process depends on people's experience and knowledge, so parameters tuning is difficult, especially Kci tuning directly affects the extension control effect. On the other hand, the simulation experiments showed that the tuning effect of small range correction δ not obvious . Therefore, an improved control algorithm is proposed, which uses the state distance Ds e2 e 2 to replace the original parameters. State correlation characteristics of K(s) shows that the system characteristics state S and characteristics state (e, ec) extension association sets, thus dividing the Measurement model, namely the division of the characteristic state within the range of [-1, 0] can be expressed as follows: (1) Measure pattern M1. The classical control domain is in a fully controllable range, and extension control works in the extension region, so PID control algorithm is adopted to compensate the classical domain extension control effect is not ideal. At this point, the controller output is as follows:
u t K P e(t ) K I e( )d K D t
de(t ) dt
Where, e (t), u (t) are the inputs and outputs of the PID controller. The three parameters KP, KI, KD are tuned by Ziegler-Nichols method. For convenience, remember u (t) =u (PID). 8
(2) Measure pattern M2. Using the improved extension control algorithm, the controller output is:
u(t ) yt k K s p sgn e D(s) sgn e
Where, D(s) is the state distance, and p is the correction factor. (3) Measure pattern M3. The characteristic state corresponding to the measure pattern M3 deviates from the classical domain in a non-domain range. At this point, the amplitude is used as the output of the controller. To sum up, the extension controller can be changed to
u PID K S 0 u t yt k K S p sgn e D( s) sgn e 1 K S 0 um K S 1
The DC-DC converter controller based on extension control is shown in Fig. 5. The extension controller has two layers, which are basic and upper extension controllers respectively. The upper controller can optimize the parameters. The basic extension controller includes the characteristic variables selection, the characteristic pattern recognition, the correlation degree calculation, the measure pattern recognition and the extension control arithmetic . The key control parameters of this extension controller are shown as: eom=0.001, ecom=0.005, em=0.5, ecm=0.1, k1=0.3, k2=0.1, KI=0.026, KP=6.25,δ=0.002, um=1. The upper-layer Man-machine Interface(MMI) Knowledge base
Characteristic pattern recognition
Correlation degree calculation
Measure pattern recognition
Extension control arithmetic
The basic-layer DC/DC converter
Fig. 5 The DC/DC controller based on the extension control 4.4 The structure of the hybrid power system Based on the above analysis, the structure of the hybrid system is established in this paper is shown in Fig. 6. There are three controllers, namely, the PEMFC controller, the battery controller and the DC-DC controller. Idc is the actual output voltage of the DC-DC converter.
Buck DC/DC converter Idc
PEMFC controller Pdemand
SOCref Battery controller
SOC Ibat Battery Ubat
Fig. 6 The structure of the fuel cell/Li-ion battery hybrid power system
5 Effectiveness of control strategy 5.1 Simulation and analysis To test the control characteristics of the extension control method in DC-DC converter, simulations are carried out with the extension control and the fuzzy PID control respectively, which are studied in case of the SOC initial value SOC0=50%, SOC0=70% and SOC0=95%. Throughout the validation process, the charge threshold is all set to 60%, and the discharge threshold is 90%. In addition to the control strategy of the cooperative power supply, the battery charging/discharging principle is: the Li-ion battery charging/discharging is not controlled when the current SOC is in the range [60%-90%]. Only when the fuel cell maximum power is greater than the load power and the SOC is less than 60%, the battery is charged by fuel cell. When the SOC is more than 90%, the battery is discharged to the load; however fuel cell is in idle state. In order to prevent frequent charge-discharge operation, when the current SOC is in the range [57%-63%] and [85.5%-94.5%], the above action is not carry out. The switching frequency of the buck converter is set to 10 KHz. The capacity of battery is 10A*h. The power demand of the load is increased at T=2s and keeping constant to T=6s, the power demand is reduced, after T>6s. The output currents of the converter are shown in Fig. 7 and the control effect comparison is shown in Tab.3, which are controlled by extension control and fuzzy PID control method respectively. The response of the extension control is faster than fuzzy PID control while the current ripple of the DC-DC converter, which is under the control of the extension control, is smaller than that under the control of fuzzy PID control. When the load is increased, and has improved the dynamic response characteristics of the hybrid power system. At the stable state, the fluctuation of the output current can be suppressed effectively. This is avoided the changes frequently of the fuel cell power, and improved the fuel cell tracking accuracy to the load power. 20
20 15 Fuzzy PID control for SOC 50 Extension control for SOC 50 Fuzzy PID control for SOC 60 Extension control for SOC 60 Fuzzy PID control for SOC 70 Extension control for SOC 70
10 5 0 -5 0
Fig. 7 The DC-DC converter output currents using two methods in case of three SOC0 values 10
Tab.3 The hybrid system performance under different control methods Response time (s)
Ripple current (A)
Fuzzy PID control Extension
Fuzzy PID control Extension
Fuzzy PID control
5.2 Experiment and analysis Hydrogen cylinders
Variable load CAN bus DC/DC converter
Fig.8 Experimental setup of the hybrid system in the laboratory Fig.8 shows the platform and layout of the hybrid fuel cell/battery. The platform is composed of a load, a PEMFC stack a Li-ion battery, a DC-DC converter and a controller system. In order to improve the reliability and reduce the cost of the PEMFC system, the assistant devices are connected to the output terminals of the battery and powered by the battery. The devices of the hybrid power system powertrain can communicate with each other via a CAN bus. Experiments are carried out in three cases, such as heavy load, light load and complex load, separately. During the experiments, the sample time is 1 second. 5.2.1 System characteristics start-up with light load Before the system starts, 2 incandescent lamps are taken as the load and connected to the output terminal of the hybrid power system, and the rated voltage and power of the incandescent lamps are 24V and 100W respectively. During the operation of the hybrid power system, a lamp is removed in 100 seconds and the other is removed in 130 seconds. The experiment results are shown in Fig.9. It can be see that, at the start-up stage of the system, the battery can 11
reach the rated voltage requirement of the load due to the sufficient power of the battery. But with the increase of the output current of the battery, the internal voltage drop of the battery is increased, which leads to the decrease of the terminal voltage of the battery. Before the fuel cell output power has not reached the load power, the hybrid power system works in the combined power mode of the fuel cell and the battery. At this stage, the power output of the battery decreases with the increase of the power output of the fuel cell.
Fig.9 The system characteristics with light load As there is a voltage stabilizing capacitor in the DC-DC converter, the output power of the DC-DC converter has appeared the stepping-up phenomenon at the start of the hybrid system instantly, which only lasts for a short time because the size of the capacitor is small and the stored energy is less. When the output power of the PEMFC is equal to the load power, the system will detect SOC of the battery, which the detection methods as shown in formula (1), if it is less than the set value, the fuel cell will supplies energy to the load and battery simultaneously. So the fuel cell output power will continue to increase. The load power decreases sharply when the load is removed, and the charge current of the battery is suddenly increased to absorb the braking energy, which is the fuel cell output power, that is because the dynamic characteristics of fuel cell are slow and can’t reduce the output energy quickly. After that the loads (two incandescent lamps) are completely removed, the fuel cell still charges the battery until the SOC reaches the set value, and the fuel cell output power is equal to the input power of the battery. 5.2.2 System characteristics start-up with heavy load Before the start-up of the system, 8 incandescent lamps are taken as the load and connected to the output terminal 12
of the hybrid power system, of which rated voltage is 24V and rated power is 100W. After the start of the hybrid power system, the incandescent lamps are gradually removed, and the system is shut down when all the incandescent lamps are removed. In this process, the total load exceeds the maximum power of the fuel cell, which is different from the start of the light load.
Fig.10 The system characteristics with heavy load The powers, currents and voltages of the hybrid power system are shown in Fig.10. It can be seen that the power demand of the load is floating between 560W and 600W, and not reach to the actual rated power demand of 800W. By observing the corresponding voltage change curve, it is found that the reason of this phenomenon is that the load terminal voltage can't reach the rated voltage of the load. This is because there is an internal voltage drop in the battery, which varies with the change of the output current and the remaining capacity of battery. The dynamic response of the fuel cell to a high step increase of power demand is slow and can’t meet the instantaneous power demand, but with the characteristics of fast dynamic response of the battery, the system can meet the power demand mainly by the supplication of the battery, which leads to the decrease of the remaining capacity. When system starts with heavy load, due to the response latency of fuel cell, the system is mainly supplied by the battery, which leads to the decrease of the remaining capacity. When the energy of the regulated capacitor releases completely, yet the fuel cell has not reached its maximum power. At this time, the hybrid power system works in the mode that fuel cell and battery are combined to supply the power to the load. At this stage, the power output of the battery decreases with the increase of the fuel cell power output. After that the fuel cell power output arrives to the maximum value, the hybrid power system is still in the coordinated 13
power supply mode because the power demand of the load is greater than the fuel cell maximum power, and during all these processes, the power outputs are stable. When reaches to the maximum output power, its voltage changes to the opposite direction of the power and current. Thus, the voltage drops with the increasing of the power and current, which is consistent with the electrical characteristics of the fuel cell. During the whole running process, the bus voltage will remain stable and the fuel cell output power will be increased. Except that the bus voltage of the hybrid power system has a slight drop, which is caused by the increase of the internal pressure drop caused by the battery. With the increase of the FC power output, the terminal voltage will rise and return to the set value until the loads (Incandescent lamps) are all removed. The fuel cell is connected with the DC-DC converter, which can be equal to a power generation, so the fuel cell port voltage is less affected by the load. But under the heavy load condition, there will be an output voltage drop of the battery. This is why the output voltage of the DC-DC converter is slightly higher than that of the battery terminal voltage. When removing the incandescent light bulbs one by one, if the battery SOC is less than the discharge threshold, the fuel cell will charge to the battery. So when the load power is lower than the fuel cell maximum power, the fuel cell is still working at the maximum power point. When all incandescent lamps are removed and the system turns into empty load, the fuel cell continues to charge the battery. Due to the battery charging power can’t exceed the limit, the fuel cell output power should be reduced to meet the charging requirements of the battery. Finally, the hybrid system is closed manually, and the fuel cell output power is suddenly dropped to zero. As is shown in Fig.10, the voltages of the hybrid power system changes similarly to that of light load. The difference is that the bus voltage drop is higher than that of the light load. That is because, with the increase of load power demand, the output current of the battery is also raised, and the internal voltage drop of the battery is also raised accordingly. 5.2.3 System characteristics start-up with complex load In the experiment of complex load conditions, adding load to the system when starting with free-load firstly. When system is stable, increase the load gradually until the total load exceeds the fuel cell maximum power. When system is stable again, reduce the load in turn with the same way until the total load is zero. After running with free-load for a period of time, the whole process of light load starting repeats. The powers, currents and voltages of each part of the fuel cell hybrid power system are shown in Fig.11. Under the condition that hybrid power system starts with free-load, due to the condition that the SOC is lower than the discharge threshold, the fuel cell output power increases to charge the battery. During the charging process, the charging power is equal to the power output of the converter. When the load of the system is suddenly increased, the dynamic response of the fuel cell to the high step increase of power demand is slow and can’t meet the instantaneous power demand, and taken that the battery has the characteristics of fast dynamic response, so the system power demand is supplied by the fuel cell and the battery cooperatively. When the system begins to stabilize, the fuel cell output power is gradually increased, and the battery is decreased accordingly. Synchronously, the battery output power, after a sharp increase, decreases slowly with the increase of the fuel cell power. After that, incandescent lamps are accessed into the system gradually to add the load, the dynamic responses of the fuel cell and battery repeat the adjustment process if the load is less than the fuel cell maximum power output. After connecting incandescent lamps to the hybrid power system at the third time, the load power exceeds the fuel cell maximum power. The power demand of the system is still supplied by the fuel cell and the battery cooperatively. However, it is different from the previous process that the fuel cell and the battery have steady output power when the system works steadily. The fuel cell is used as the main power supply, and the battery provides the power difference between the load power and the fuel cell output power. 14
Fig.11 The system characteristics with complex load When the system load is removed for the first time, the response of the hybrid power system is similar to that of removing the load at the second time in starting with light-load, and the fuel cell output power is reduced accordingly, but is still charging the battery with constant power. During the process of operation, according to the changes of the powers, the conversion efficiency of the DC-DC converter is decreased with the increase of the fuel cell output power. From Fig.11, it can be seen that the bus voltage will decrease when the load is increased gradually. After that the load power exceeds the fuel cell maximum power, the bus voltage is higher than that in the heavy load direct start. During the operation process, the voltages of the DC-DC converter and the battery have the same trend. With the gradual removal of the load, the bus voltage is gradually restored. It can be seen from the comparison of Fuzzy-PID and extension control that the energy management control system based on the improved algorithm has advantages in stabilization and response time.
6 Conclusions A new control approach for a hybrid power system supplied by a hybrid source that uses Li-ion battery as the storage source, in association with a PEMFC as the main source, has been proposed. The most important contribution of this paper deals with the control methods of the hybrid power system, which focusing on improving the dynamic characteristics of the system. Therefore, the control principle describes how to realize the fast transition of the fuel cell power and the battery power, and to avoid large fluctuations in the fuel cell output power. This proposed hybrid system is controlled by a single loop control using the extension control theory based on the 15
DC/DC buck converter. In order to solve the problem that the parameter tuning of the extension control depends on human experience and is difficult to be determined, the extension control method is improved, which uses the state distance Ds to replace the original parameters to improve the robustness of the control system. The extension control mainly discusses the contradictory problems existing in the control system, and uses the formal methods to transform the contradictory problems. For this reason, the extension of mathematics and logic must be made, mainly as: (1) establishment of extension set; (2) establishment of the correlation function; (3) establishment of the primitive concepts; (4) establishment of the extension logic. The fuzzy PID control is a rule-based intelligent control method, which is based on fuzzy mathematics, mainly as: (1) establishment of the fuzzy set; (2) the fuzzy set are fuzzified by different membership functions; (3) establishment of the rules to describe the objectives and strategies; (4) using the fuzzy logic to deduce; (5) the fuzzy value of defuzzification. It is validated through the simulation and experimental results that the response of the extension control is faster and the current ripple smaller than fuzzy PID control, and the designed energy management adopted the extension control can fulfill the energy requirements for the unknown driving cycles in an efficient way.
7 Acknowledgments This project supported by the National Natural Science Foundation of China (Grant No. 51405286) and Shanghai Key Laboratory Power Station Automation Technology Laboratory (Grant No.13DZ2273800).
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Biographical notes Lü Xueqin, born in 1974, is an associate professor of automatic engineering at the Shanghai University of Electric Power. She received her Ph.D from the Shanghai Jiao Tong University in material process engineering. Her current major research interests include intelligent control of robots, automatic control of fuel cell hybrid robots. She has more than 30 technical publications. Tel: +86–21–35303136; E–mail: [email protected] Nomenclature: Nomenclature
battery power, W
small range correction
load power demand, W
fuel cell power, W
maximum fuel cell power, W
minimum fuel cell power, W
fuel cell rate power, W
correlation degree of the characteristic state
fuel cell voltage, V
reference current of the fuel cell, A
reference current of the battery, A
reference current of the converter, A
load current, A
battery current, A
maximum differential deviation
converter output current, A
state of charge
charge reference SOC, %
charge capacity, Ah
sampling time, s
battery current, A
output current, A
battery temperature, K
previous sample values, A
battery capacity, F
sampling of the DC-DC converter current, A
maximum SOC, %
static gain of the process
minimum SOC, %
Appendix Tab.1 The characteristics of the PEMFC Tab.2 The characteristics of the Li-ion battery Tab.3 The hybrid system performance under different control methods Fig.1 The OVDC-OCC controlled buck converter Fig.2 The flowchart of the fuel cell controller Fig.3 The diagram of the battery controller Fig.4 Extensible set of feature states Fig.5 The DC/DC controller based on the extension control Fig.6 The structure of the fuel cell/Li-ion battery hybrid power system Fig.7 The DC-DC converter output currents using two methods in case of three SOC0 value Fig.8 Experimental setup of the hybrid system in the laboratory Fig.9 The system characteristics with light load Fig.10 The system characteristics with heavy load Fig.11 The system characteristics with complex load
Highlights 1.Investigation of the dynamic behavior of the PEMFC/battery hybrid power system. 2.Energy management method of the PEMFC/ battery hybrid power system based on extension control technique. 3.Using the state distance Ds to replace the original parameters to improve the robustness of the control system.