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Implementation methodology of powertrain for series-hybrid military vehicles applications equipped with hybrid energy storage
Energy 120 (2017) 229e240
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Implementation methodology of powertrain for series-hybrid military vehicles applications equipped with hybrid energy storage Seongjun Lee a, Jonghoon Kim b, * a
Power Electronics and Energy Conversion Laboratory, School of Mechanical System & Automotive Engineering, Chosun University, Gwangju, 61452, Republic of Korea b Energy Storage and Conversion Laboratory, Department of Electrical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 October 2015 Received in revised form 14 September 2016 Accepted 19 November 2016
This paper investigates a component-sizing method and a power-control algorithm for series-hybrid military vehicles equipped with hybrid energy storages that comprise batteries and super-capacitors. Component sizing of the powertrain is determined by the performance specification that is related to mission profiles and power-flow control methods. In order to minimize the effects of mission profiles and power-flow control methods, the linear programming (LP) technique is employed. The LP problem for minimizing the output energy from the engine under different conditions of driving cycles and capacities of the energy storage system (ESS) is solved to eliminate the effect of the power distribution. Through analyzing the effects of different power and energy ratings of the ESS, the optimal values of power and energy capacities of the ESS are determined. The design approaches are extensively verified with simulations and experimental results of a reduced-scale per-unit equivalent system of the 10-ton series-hybrid electric vehicle (SHEV). © 2016 Elsevier Ltd. All rights reserved.
Keywords: Component sizing Linear programming Power control Per-unit equivalent system Series-hybrid electric vehicle
1. Introduction Environmentally, friendly electric vehicles are classified into the hybrid electric vehicles (HEVs), battery-only electric vehicles (BEVs), and fuel-cell electric vehicles (FCEVs) depending on the energy source of the vehicle powertrain as shown in Table 1 [1]. Nowadays these are increasingly studied and manufactured at present to minimize environmental impacts and to increase the fuel economy. The propulsion sources of the HEVs are the engine and the electric motor and configured as the series hybrid electric vehicle (SHEV), parallel HEV and series-parallel HEV as shown in Fig. 1. The main energy of the vehicle comes from the internal combustion engine (ICE) and the battery and the super-capacitor are utilized as an auxiliary energy sources. The propulsion source of the SHEV is only the traction electric motors and the generated energy from the ICE is utilized to charge the high voltage battery and to supply the power of traction motor. The FCEV which uses a fuel cell instead of an engine is a type of SHEV. On the other hand, the parallel hybrid has two power sources such as the engine and traction motor.
* Corresponding author. E-mail address: [email protected] (J. Kim). http://dx.doi.org/10.1016/j.energy.2016.11.109 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
These are connected through the transmission, to the drive wheels. Each power source may supply some or all of power needed by the vehicle. The series-parallel HEV is known as a combined hybrid electric vehicle or a power-split HEV. This configuration is investigated and developed to overcome the drawbacks of series and parallel architectures. The powers of the engine and the electric motor are coupled to drive the vehicle in parallel operation. While the power generated from the ICE flows into the battery and the traction motor in series operation. The one of the main research field in the electric vehicle is the development of components of the vehicle and the energy management algorithm in order to increase the fuel consumption. Among them, the studies of the energy management algorithm in the FCEV application [2e5] are carried out. The power distribution method between the fuel cell and the battery is studied considering the driving cycle, and the manufactured vehicle has been empirically tested. And in order to increase the efficiency and to reduce the cost of the component, the researches of the electric motor type [6,7] and the active suspension have been progressed [8]. As in the previous researches, the studies about the energy management algorithm and more efficient component are important in the existing electric vehicle, and especially in the case of designing of the new type of the electric vehicle, the component sizing considering the driving profile and the power distribution
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S. Lee, J. Kim / Energy 120 (2017) 229e240
PI AP BP HPF LPF EMU OOL HILS DC AC UDDS SOC
Nomenclature HEV BEV FCEV SHEV ICE ESS IPM LP CPSR GVW
Hybrid electric vehicle Battery-only electric vehicle Fuel-cell electric vehicle Series-hybrid electric vehicle Internal Combustion Engine Energy storage system Interior permanent-magnet Linear programming Constant-power speed ratio Gross vehicle weight
Proportional and integral (controller) Accelerator pedal Brake pedal High-pass filter Low-pass filter Engine management unit Optimal operating line Hardware-in-the-loop system Direct current Alternating current Urban Dynamometer Driving Schedule State of Charge
Table 1 Characteristics of the HEV, BEV and FCEV. Types of EV
HEV
BEV
FCEV
Propulsion Source
-
- Electric motor
- Electric motor
- Battery - Super-capacitor
- Fuel cell - Battery - Super-capacitor
Energy Source
Internal combustion engine Electric motor Internal combustion engine Battery Super-capacitor
Fig. 1. Configuration of the hybrid electric vehicle.
algorithm is more important to achieving higher fuel economy. In these days, the HEVs have been extensively developed for military applications to ensure better performance in terms of the maneuver, survivability, and lethality, as well as the fuel economy [9e13]. For achievement of these performance requirements, military HEVs require features such as high grade-ability, fast acceleration, silent watch, and silent mobility. Therefore, various hybrid components, including the electric motor, primary source such as diesel engine and generator, and energy storage element such as battery, super-capacitor, should be appropriately designed considering the performance requirements, fuel economy, as well as
mission completeness [12,13]. Several studies [14e19] have been carried out to improve the performance and efficiency of devices used in commercial vehicles to obtain better fuel economy and to reduce emissions. In the case of hybrid military vehicles, because of its specificity, some constraints should be included at the design phase of the vehicles. As military vehicles have high performance requirements and are operated under poor driving conditions, new study that focuses on aspects such as component sizing and power control is important and necessary. In general, the component sizing of the powertrain is
S. Lee, J. Kim / Energy 120 (2017) 229e240
determined by the performance specification, which is related to mission profiles and power-flow control methods. In order to minimize the effects of mission profiles and control methods, an optimization algorithm is absolutely required to determine an optimal component sizing. In the design phase of the SHEV, because the efficiency of components is set to the constant target value, the power and energy relationships from the engine and the energy storage system (ESS) to the wheel are described as linear equations. Therefore, in this paper, the linear programming (LP) technique is applied to find an optimal power distribution algorithm considering the driving cycle and the power and energy capacity variations of the ESS [20]. In Ref. [21], authors investigated the power distribution method for minimizing the fuel consumption of powertrain which has the high voltage battery directly connected to the DC link in a series-hybrid electric vehicle (SHEV). In this study, we employ the LP technique to determine the optimal component sizing of the more complex powertrain with the battery and the super-capacitor as well as to find the basic distribution algorithm for power-flow control. The steps for sizing the components in the proposed approach are as follows. Firstly, the LP problem for minimizing the output energy from the engine under the conditions of different driving cycles and energy capacity variations of the ESS is solved. Thus, the effect of the power distribution method can be well eliminated. Through analyzing the effects of different power and energy ratings of the ESS, optimal values of power and energy capacities of the ESS are determined. In order to verify the component sizing method and the power distribution algorithm, 6 6 wheeled series-hybrid military vehicle that includes a diesel engine-generator, battery, and supercapacitor is designed as an example. A reduced-scale per-unit equivalent system is constructed and the newly proposed powercontrol algorithm is extensively verified by comparing the simulations and experimental results of the full-scale simulation system and the per-unit equivalent reduced-scale system. The remainder of this paper is organized as follows. Section II
231
describes the sizing method of components such as primary sources, secondary sources, and the traction electric motor. The power control algorithm of the SHEV with multiple power sources is described in Section 3. In the following section, the simulation and experimental results with a detailed discussion are presented. In the final section, some conclusions and final remarks are given. 2. Component sizing of the powertrain of the SHEV Fig. 2 shows the powertrain structure of the 6 6 wheeled SHEV and the parameters of the SHEV are listed in Table 2. The primary source of the vehicle is the diesel engine and an interior permanent-magnet (IPM) type generator. Secondary sources of lithium-type battery and super-capacitor are used in the vehicle. In order to achieve high driving performance and ensure a wide indoor space of the vehicle, in-wheel drive systems are applied to each wheel of the vehicle. The in-wheel drive system consists of a motor and a single reduction gear. 2.1. Sizing of the engine-generator The rating of the diesel engine, which is a primary source, should be determined to be able to supply an average power during driving mission profiles and to assist in meeting the required peak road-load demand. As a military vehicle characteristic, in order to maintain the driving performance in the case of any failures of secondary sources, the engine should be sized to meet the conditions such as moving for a specific mission of a high-speed driving. In this example, the engine is sized to supply the power that will be consumed when the vehicle is driven at the maximum speed. In this case, the tractive power which is defined to Ptr is same as the road-load power and is calculated from Eq. (1) and the engine should be determined to a value greater than the power calculated using Eq. (2). In this case, the engine power rating can be determined to 180 kW.
3 Sets
Generator
AC / DC Converter
BAT
x 10
DC / AC DC / AC
600
Peak Torque (60sec)
Torque (Nm)
Motor / Gear
Bi-directional DC/DC Converter
SC
8
Fig. 2. 6 6 powertrain structure of the series hybrid electric vehicle (SHEV).
6
Peak Power
Continuous Power
60 % Road 400
4
200
Table 2 Parameters of the target vehicle.
2
Max. Speed
Continuous Torque
Parameter
Value
Parameter
Value
Gross vehicle weight (GVW) Wheel radius Rolling resistance
10,000 kg Frontal area 0.5 m Aerodynamic drag coeff. 0.01 þ 0.0001 v (km/h)
4.5 m2 1
0 0
Power (W)
Engine
4
Motor Torque-Speed Characteristic Curve 800
Motor M t / Gear / Gear G Motor
1000
2000
3000 4000 ωrpm (r/min)
5000
0 6000
Fig. 3. Motor torque-speed characteristic curve.
Table 3 Specification of the demand torque and power about required driving performance. Performance specification
Acceleration Max. Speed Grade-ability EV driving
Speed (km/h)
Grade (%)
Time (s)
0e48 100 10 30
0 0 60 0
8 e e 1800
Motor torque (Nm)
Motor power (kW)
e 34.8 526 12.6
25.9 19.3 27.9 2.1
S. Lee, J. Kim / Energy 120 (2017) 229e240
þ paux
Pbat
(1)
! ch
hbat
(2)
where M is the mass of the vehicle, g is the gravitational acceleration coefficient. fr_init is a constant coefficient of tire's rolling resistance, whereas fr_inc is a rolling resistance coefficient that is proportional to the vehicle's speed, vcr is the cruising speed, r is the air density, and Cd is an aerodynamic drag coefficient. In addition, Af is the frontal area of the vehicle, hengen is the engine-generator coupling efficiency, hgen is the AC-DC converter efficiency including the generator efficiency, hmot is the motor efficiency including the DC-AC inverter loss, hgr is the gear efficiency including losses from the motor output shaft to the driving wheel, and hbat is the efficiency of the battery. Lastly, Paux is the basic consumed power of the vehicle with a control power supply, cooling system etc., and Pbat_ch is the battery charging power.
feasible regions at given driving profiles are chosen. Finally, the optimal values of power and energy ratings of the ESS are determined that can be minimized to achieve the objective function of achieving the minimum output energy from the engine. The objective function of the LP for minimizing the output energy from engine on given driving cycles is defined as Eq. (4). The constraints of the problem consist of the power balance by Eq. (5),
(a)
2.2. Sizing of the electric motor The electric motor is the only propulsion device in a SHEV and should be appropriately designed to satisfy the demanded torque and speed of road conditions [21e23]. The tractive powers for a cruising speed and acceleration can be derived based on the second law of Newton as Eqs. (1) and (3), respectively [21].
(b)
2.3. Sizing of the battery and super-capacitor The secondary source that includes the battery and supercapacitor system assists in accomplishing the tractive power together with the primary source when a vehicle accelerates and also absorbs the regenerative power from the motor when a vehicle decelerates. The power and energy capacities of multiple secondary sources can be determined according to the power management algorithm and driving profiles. With the above conditions, in order to size the battery and super-capacitor optimally, an optimization technique considering given driving profiles and the power distribution method should be employed. To minimize the effects of mission profiles and control methods, the LP technique is employed. First, the LP problem for minimizing the output energy from the engine related to various power and energy capacities of the ESS is solved. By analyzing the results, the
50 0
200
400 600 Acceleration
800
1000
4 2 0 -2 0
200
400 600 Travelled distance
800
1000
200
400
800
1000
4000 2000 0
0
600 Time (s)
Altitude (m)
Travelled distance
500
1000 Time (s)
1500
Altitude variation 150 100 50 0 -50
0
500
1000 Time (s)
1500
Velocity profile 100
3 1.5 0 -1.5 -3
2
(c)
0
15000 10000 5000 0 0
(3)
where Meq is the equivalent vehicle mass including inertia, ta is the required acceleration time, vf is the final speed of acceleration, and vb is the vehicle speed corresponding to the motor base speed. In military applications, as the performance constraints for the grade-ability is more severe than for commercial vehicles, the maximum torque of the motor should be carefully sized. In the case of the multiple in-wheel motor, the torque rating can be determined from the maximum tractive force related to the normal load of each wheel on an inclined road [24]. In order to reduce the power and torque rating of the motor, the gear ratio and the constantpower speed ratio (CPSR) should be appropriately selected depending on the motor type [25]. Table 3 shows the demand torque and power for the required performance specifications, and Fig. 3 shows the designed continuous and maximum ratings of each motor when the gear ratio is 10.
Velocity profile 100
6000
Distance (m)
2 Meq 2 1 vf þ v2b þ Mgfr vf þ rCd Af v3f ptr ¼ 3 5 2ta
Speed (km/h)
hengen hgen hmot hgr
þ 0:5rCd Af v2cr vcr
2
Ptr
Accel. (m/s )
inc vcr
Distance (m)
þ fr
Distance (m)
Peng
1
init
Speed (km/h)
Ptr ¼ Mg fr
Accel. (m/s )
232
50 0
9000 6000 3000 0
0
200
400 600 Acceleration
800
1000
0
200
400 600 Travelled distance
800
1000
0
200
400
800
1000
600 Time (s)
Fig. 4. Two current profiles of the hybrid pulse power characterization (HPPC) technique. (a) City driving cycle. (b) Tactical driving cycle. (c) HD-UDDS driving cycle.
Table 4 Specification of three driving profiles considered at design phase of the SHEV.
Duration time (s) Traveled distance (km) Max. Speed (km/h) Avg. Speed (km/h) Max. Acceleration (m/s2) Max. Deceleration (m/s2) Altitude variation (m)
City driving cycle
Tactical driving cycle
HD-UDDS driving cycle
1065 5.352 75.7 18.09 3.51 2.14 e
1060 8.935 93.34 30.34 1.958 2.0697 e
1610 14.07 91.83 31.46 1.33 1.18 119.7
S. Lee, J. Kim / Energy 120 (2017) 229e240
233
Fig. 5. Engine output energy according to power rating variations of battery and super-capacitor on three driving cycles. (a) Engine output energy for tactical driving cycle. (b) Engine output energy for city driving cycle. (c) Engine output energy for HD-UDDS driving cycle. (d) Feasibility region for tactical driving cycle. (e) Feasibility region for city driving cycle. (f) Feasibility region for HD-UDDS driving cycle.
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S. Lee, J. Kim / Energy 120 (2017) 229e240
energy balance equation of each secondary energy device given by Eq. (6), physical dynamic characteristics of each power source given by Eqs. (7)e(10), maximum power limitations of each power source given by Eqs. (11)e(14), and charge sustaining conditions of ESS given by Eq. (15). The equations of the LP are simply discretized using the Euler method as Eq. (16) and converted into a standard form. Thus, the problem of the LP can be solved by the simplex method using MATLAB [13].
2 min Econs
eng ðtÞ
6 ¼ min4
Ztf
dc ðtÞ
þ Pbat
dc ðtÞ
þ Psc
Peng
dc ðtÞ
hengen hgen
t0
Peng
3
dc ðtÞ
7 dt 5
Pbr ðtÞ ¼ Pmot
(4)
dc ðtÞ
(5)
E_ ess ¼ f ðPess ðtÞÞ
(6)
P_ eng
dc
engine slew rate up
(7)
P_ eng
dc
engine slew rate down
(8)
P_ bat
dc
battery slew rate up
(9)
P_ bat
dc
battery slew rate down
(10)
Peng
dc
0;
(11)
Peng
dc
Peng
dc max
Pbat Psc
dc
dc
Pbat
Psc
Pbr 0;
dc max ;
dc max ;
Pbr Pbr
Ebat ðt0 Þ ¼ Ebat tf ;
Pbat Psc
dc
dc
Pbat
Psc
(12)
dc min
(13)
dc min
(14)
max
Esc ðt0 Þ ¼ Esc tf
(15)
x_ ¼ fxðk þ 1Þ xðkÞg=DT
(16)
In this work, three different driving cycles are considered for designing the system of a wheeled vehicle used for military applications. Fig. 4 shows each characteristic of the driving cycles. The specification of three driving cycles considered at design phase of SHEV is listed in Table 4. One is an urban driving cycle that is representative normal city driving. Another is the tactical driving cycle designed for artificial mission areas. The third driving cycle is
Table 5 Specific power and energy, power and energy density for each component.
Engine Motor/Generator Battery Super-capacitor DC-DC converter DC-AC/AC-DC inverter
Specific power (W/kg)
Specific energy (Wh/kg)
Power density (W/l)
Energy density (Wh/l)
1000 1300 1000 2000 1000 5000
e e 120 4.5 e e
420 3190 2000 6400 1600 35000
e e 230 7.1 e e
Fig. 6. Engine output energy according to energy density variations of battery and super-capacitor. (a) Energy distribution between battery and super-capacitor. (b) Gross vehicle weight (GVW) according to the energy distribution of the ESS. (c) Engine output energy on different driving cycles.
S. Lee, J. Kim / Energy 120 (2017) 229e240
the HD-UDDS cycle mainly utilized in the area of a heavy-duty vehicle [26]. Fig. 5 shows some results of the engine output energy and feasible region about power rating variations of battery and supercapacitor on three different driving cycles. The (a), (b) and (c) of Fig. 5 present the results of the objective functions. As we can observe from the figure, the minimal engine output energy according to the optimal power distribution exists on three different driving cycles. Thus, the selection of the power control method and driving cycles is important in the design phase of the powertrain for SHEV. We can also notice the difference of the feasible regions of the required power of the battery and super-capacitor system from (d), (e), and (f) of Fig. 5. Finally, the system should be designed to ensure a power rating greater than the minimum value of feasible regions in order to obtain satisfactory performance and the optimal values for minimizing the objective function. Fig. 6 shows the results of the engine output energy about energy capacity variations in the case of the demanded energy requirement from the ESS. As the specific power (W/kg) and specific energy (Wh/kg) of the battery and super-capacitor as listed in Table 5 are used to calculate the gross vehicle weight (GVW), the energy distribution determined by the relationship of Fig. 6(a) and (b) causes the change in weight of the vehicle. From Fig. 6(c), in this case, as the energy capacity of the super-capacitor is increasing, the system efficiency is generally decreasing owing to an increase in the vehicle's weight. Nevertheless, in some cases, there exists an optimal value in terms of system efficiency. Thus, from above analysis, the power and energy ratings of the battery and super-
235
capacitor are designed to be optimal values so that the battery has the power and energy rating of 80 kW and 11.55 kWh at 620 V and the super-capacitor has the power and energy of maximum 190 kW and 750 Wh at maximum voltage of 400 V. 3. Power control algorithm design The power control algorithm for multiple power sources consisting of the engine-generator, the battery and the super-capacitor system in SHEV is shown in Fig. 7. At the top of this figure is the torque reference generation block together with a driver model. The driver model is applied to represent the acceleration or deceleration intention of a vehicle driver in the forward-facing simulation model [23,27]. The output of driver model controller is the acceleration pedal and brake pedal and it is composed of the proportional and integral (PI) controller whose inputs are the speed profile of Fig. 4 (Vwref) and measured vehicle speed (Vw), and outputs are the depressed positions of the accelerator pedal (AP) and brake pedal (BP). The Tmotoring and Tbraking is determined by considering the maximum motor torque (Tm,max) and mechanical braking torque (Tbr). Thus the generated torque reference is finally limited within an available maximum motor torque in a certain motor speed. The Tmot_ref is the torque reference for traction motors and the Tbr_ref is the mechanical braking torque reference. In the middle of the figure is a power reference generation block of the super-capacitor which is denoted as the Psc_ref. The supercapacitor is operated to supply a peak and fast transient power among the required power from the ESS occurring during the
Fig. 7. Control block diagram of the power system.
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S. Lee, J. Kim / Energy 120 (2017) 229e240
acceleration and deceleration of the vehicle. Therefore, the highpass filtered power of the load power (Pw) which is the traction power for vehicle driving is determined as a power reference of the super-capacitor from Eq. (17).
0
Psc
HPF Pess ; fc
B B B B ¼B B HPF Pess ; fc B B @ HPF Pess ; fc
ðaÞ SOCsc SOCsc ðcÞ SOCsc < SOCsc
sc
þ Psc
dis
SOCsc SOCsc max $ set 1 SOCsc max
max ; min
LPF Pess ; fc
ðaÞ
þ Psc
cha
SOCsc SOCsc $ set SOCsc min
ðbÞ SOCsc
min
min
SOCsc < SOCsc
ðcÞ
max ;
½Three conditions of SOCsc (17)
igen
Power Supply (Engine)
¼ 1 LPF Pess ; fc
sc
sc
¼
sc
;
2p$fc sc s þ 2p$fc sc
(18)
In Eq. (17), Pess which is the subtraction between the load power and the engine power is the required power from the ESS, fc_sc is the cut-off frequency of the high-pass filter (HPF) which is defined as Eq. (18), Psc_dis_set is the discharging power set value, SOCsc is the state of charge (SOC) of the super-capacitor, SOCsc_max is the upper limit value of SOCsc, Psc_cha_set is the charging power set value, SOCsc_min is the lower limit value of SOCsc. The second term of the right-side of Eq. (17) is the compensation power reference for maintaining the energy of the super-capacitor. The bottom of Fig. 7 presents the power reference generation and control methods of the engine-generator. The power reference of the engine-generator comes from the low-pass filtered value of
ðbÞ
sc
sc
HPF Pess ; fc
imot
Power Supply (generating)
vbat ibat
DC-DC Converter
Battery
isc
Analog output
igen imot isc ibat
RS232
DSP 56F807 vbat
- Engine control - Road load power control - BMS algorithm - Regenerative braking energy control - System Monitoring
RS232
S1, S2, S3
iL1,iL2,iL3
vsc GPIB
Electric load (motoring)
Super Cap Analog output Analog output
vsc
DSP TMS320F2812
Gating signals S1, S2, S3
iL1,iL2,iL3
Fig. 8. System configuration of the reduced-scale system.
Table 6 Experimental setup for reduced-scale system. Name of device/company
Rating
Remarks
Power supply
DLM60-66E/Sorensen
For engine-generator
Power supply
DLM60-50E/Sorensen
Electric load
SLL-5K/Unicorn-TMI
Battery
SLPB55205130H/Kokam
Super-capacitor
ESHSP-1700C0-002R7/Nesscap
DC-DC converter
3-phase bi-directional buck-boost converter
0e60 V, 0e66 A Max. power 3.3 kW 0e60 V, 0e50 A Max. power 3 kW 0e80 V, 0e500 A Max. power 5 kW 4.2 Ve2.7 V, 11 Ah Li-polymer cell 12S1P Vnom_pack ¼ 44.4 V Discharge: cont. 55 A, peak 110 A Charge: max. 22 A 1700F/2.7 V, 56S1P Rated current: 371 A Vsc_max: 36 V Max. power 1.5 kW Imax ¼ 39 A, L ¼ 235 mH, fsw ¼ 20 kHz
For regenerative road load power For monitoring road load power e
e
e
S. Lee, J. Kim / Energy 120 (2017) 229e240
(a)
237
Vehicle Speed & Power Distribution over City driving cycle
80
Speed profile Speed
Speed (km/h)
60 40 20 0 0
100
200
300
400
500 600 Time (s)
700
800
900
1000
1100
100
200
300
400
500 600 Time (s)
700
800
900
1000 Pmot 1100
Speed error (km/h)
2 1 0 -1 0
dc
Pengdc
Power (kW)
300 250
Pscdc Pbatdc
100 -50 -200 0
100
200
300
400
500 600 Time (s)
700
(b)
800
900
1000
1100
Imot_dc (15A/div)
Vbat (10V/div)
Ibat (15A/div)
Isc_dc (15A/div)
100s/div
Fig. 9. Simulation and experimental results on city driving cycle. (a) Simulation result of the full scale vehicle model. (b) Experimental result of the reduced-scale system.
the load power (Pw) such as that given by Eq. (19). Besides, the engine-generator is responsible for managing the state of charge of the battery. Therefore, the SOC of the battery is maintained within a certain boundary. The determined power from the enginegenerator is controlled through a speed control mechanism using the engine management unit (EMU) and a torque control
mechanism using the generator control unit. The detailed operating point in a speed-torque plane is conventionally determined according to an optimal operating line (OOL) of the engine and generator in SHEV [28] and the 1-D table which is denoted as a Table 1 given at the bottom of the figure is used to generate the speed reference.
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S. Lee, J. Kim / Energy 120 (2017) 229e240
0
Peng
ref
LPF Pw ; fc
B B B B B ¼ B LPF Pw ; fc B B B @ LPF Pw ; fc
ðaÞ SOCbat SOCbat ðcÞ SOCbat < SOCbat
eng
eng
eng
max ; min
þ Paux Pbat
dis set $
SOCbat SOCbat max 1 SOCbat max
þ Paux
ðbÞ
þ Paux þ Pbat
ðbÞ SOCbat
ðaÞ
min
cha
SOCbat min SOCbat $ set SOCbat min
SOCbat < SOCbat
(19) ðcÞ
max ;
½Three conditions of SOCbat
In Eq. (19), Pw is the road load power for the motor inverter, fc_eng is the cut-off frequency of the low-pass filter (LPF), Paux is the auxiliary power, Pbat_dis_set is the discharging power set value, SOCbat is the state of charge of the battery, SOCbat_max is the upper limit value of SOCbat, Pbat_cha_set is the charging power set value, and SOCbat_min is the lower limit value of SOCbat. The second term of the right-side of Eq. (19) is the compensation power reference for maintaining the energy of the battery denoted as the SOCbat compensator in Fig. 7. 4. Simulation and experimental results for verification of the proposed work As elaborately described in Section 3, although the detailed control design methods of each power converter are omitted, we
assume that all components with their controllers have wellcontrolled dynamic characteristics. In this case, because the measured real responses about the reference inputs such as voltage, current, speed etc. can be well tracked, the functional model designed as the relationship of the input and output with time delay can be applied to the simulation model [29]. Thus, the forward-facing software testbed with the functional model is used to simulate and verify the power and energy management algorithm because of the long simulation time required for the driving cycle. For verification of the proposed power control algorithm, a reduced-scale system which is equivalent in terms of the voltage and power of each component is constructed. Fig. 8 presents the constructed experimental setup and Table 6 lists the used devices. The engine-generator is substituted through the power supply, and
Fig. 10. Comparison of power distribution through simulation and experimental results based on the per-unit values (blue solid line: full-scale simulation, green dotted line: reduced-scale experiment). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
S. Lee, J. Kim / Energy 120 (2017) 229e240
239
Fig. 11. Comparison of voltage response through simulation and experimental results based on the per-unit values (battery and super-capacitor).
Table 7 Operating conditions of the battery and super-capacitor. Battery
Voltage Per-unit voltage
Super-capacitor
Full-scale simulation
Reduced-scale experiment
Full-scale simulation
Reduced-scale experiment
504e705.6 V 0.8e1.12 p.u.
36e50.4 V
200e400 V 0.5e1 p.u.
18e36 V
the tractive motoring power is supplied by the electric load while the tractive generating power is supplied by the power supply. Reduced-scale devices of the super-capacitor, battery, and a bidirectional DC-DC converter are applied to the system. The road load power of the simulation model is used as an input for tractive power substituted by the power supply and the electric load of the experimental setup. Thus, the battery, super-capacitor, and enginegenerator are controlled by the proposed power distribution algorithm, and the distributed results are compared to the simulation results. Figs. 9e11 show the simulation results of the full-scale vehicle model and the experimental results of the reduced-scale system. As shown in Fig. 9(a), the vehicle is well tracked to a speed reference and speed error is within ±2 km/h. The power distribution in the DC-link side is well balanced through the proposed power control algorithm. Therefore, the vehicle can travel the driving cycle through the powertrain with the designed capacity and dynamics of each component. Fig. 9(b) shows the experimental result of the reduced-scale system with the input of a scaled load power. The traction power (load power) is scaled as 1/126 of the required load power from the simulation result. By applying the same rate, the maximum power of the engine, battery and super-capacitor are scaled as 1.42 kW, 634 W, and 1.5 kW respectively. In order to make both the simulation and experimental system equivalent in terms of the per-unit, the impedance of the battery and super-capacitor in the reducedscale system is adjusted by applying Ohm's law and the definition of the per-unit of Eqs. (20) and (21).
Pbase ¼ Vbase $Ibase ; Pbase
sd
¼ Pbase =SD;
Zbase ¼ Vbase =Ibase Ri
pu
¼ Ri =Zbase
(20) (21)
In this case, since the battery base voltage (Vbase) of the reducedscale system is 44 V, Ibase and Pbase are calculated from Eq. (20). Therefore, the battery per-unit resistance of the reduced-scale system is calculated from Eq. (21) using the measured internal resistance of the battery denoted as Ri. In the same manner, the perunit resistance of the full-scale simulation system is calculated and to make the per-unit resistance same, the extra resistance is connected in series with the battery in the reduced-scale system. Then,
the added resistance is 0.3385 U. On the condition of the scaled load power, assuming that the vehicle travels the same driving cycle, the power sources of the engine, battery, and super-capacitor are stably distributed within the pre-determined boundary conditions of each power source through the proposed power control method. Fig. 10 shows the power distribution results that are represented as the per-unit values which are normalized based on each engine maximum power of both simulation and experimental results. Fig. 11 shows the per-unit voltage of the battery and supercapacitor from simulation and experiment results. From both results, we find that the per-unit power of each power source is almost equally distributed during the driving cycle. The per-unit voltages of the battery and the super-capacitor are almost same and the ESS is operated within the stable region as shown in Table 7. Thus, the validity of the designed powertrain and the control algorithm are verified before manufacturing the vehicle system. 5. Conclusion In this approach, the component-sizing method using linear programming (LP) and the power distribution algorithm for the series-hybrid electric vehicle are investigated. The proposed methods are verified through simulation and reduced-scale experiment. Although the SHEV has the structural type equipped with hybrid energy storage, the proposed component-sizing method can be used for other structural types as well. For example, an optimal structure and component capacity such as the fuel cell, battery and super-capacitor of the FCEV can be designed by applying the proposed method. The proposed reduced-scale system approach can also be utilized in hardware-in-the-loop (HILS) applications, especially in battery HILS. References [1] Chan CC. The state of the art of electric and hybrid vehicles. IEEE Journals Mag 2002;90(2):247e75. [2] Han J, Park Y, Kim D. Optimal adaptation of equivalent factor of equivalent consumption strategy for fuel cell hybrid electrical vehicles under active state inequality constraints. J Power Sources 2014;267:491e502. [3] Chopra S, Bauer P. Driving range extension of EV with on-road contactless power transfer-a case study. IEEE Trans Ind Electron 2013;60(1):329e38. Y. Enhanced fuel cell hybrid electric [4] Maalej K, Kelouwani S, Agbossou K, Dube
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S. Lee, J. Kim / Energy 120 (2017) 229e240 vehicle power sharing method based on fuel cost and mass estimation. J Power Sources 2014;248:668e78. Xu L, Li J, Ouyang M, Hua J, Yang G. Multi-mode control strategy for fuel cell electric vehicles regarding fuel economy and durability. Int J Hydrog Energy 2014;39:2374e89. Hu Y, Song X, Cao W, Ji B. New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs). IEEE Trans Ind Electron 2014;61(10): 5722e31. Tong C, Zheng P, Wu Q, Bai J, Zhao Q. A brushless claw-pole double-rotor machine for power-split hybrid electric vehicles. IEEE Trans Ind Electron 2014;61(8):4295e305. Montazeri-Gh M, Soleymani M, Hashemi S. Impact of traffic conditions on the active suspension energy regeneration in hybrid electric vehicles. IEEE Trans Ind Electron 2013;60(10):4546e53. Anderson AB, Palazzo AJ, Ayers PD, Fehmi JS, Shoop S, SullivanZheng P. Assessing the impacts of military vehicle traffic on natural areas. Introduction to the special issue and review of the relevant military vehicle impact literature. J Terramech 2005;42:143e58. Abdulmasih D, Oikonomidis PI, Annis R, Charchalakis P, Stipidis E. In-vehicle monitoring and management for military vehicles' integrated vetronics architecture. J Syst Archit 2014;60:405e18. Kane JR, Ayers P, Howard H, Anderson A, Koch D. Multipass coefficients for terrain impacts based on military vehicle type, size and dynamic operating properties. J Terramech 2013;50:175e83. Krylov VV, Pickup S, McNuff J. Calculation of ground vibration spectra from heavy military vehicles. J Sound Vibr 2010;329:3020e9. Liu K, Ayers P, Howard H, Anderson A. Influence of tuning radius on wheeled military vehicle induced rut formation. J Terramech 2009;46:45e55. Yu H, Kuang M, McGee R. Trip-oriented energy management control strategy for plug-in hybrid electric vehicles. IEEE Trans Control Syst Technol 2014;22(4):1323e36. Mohamed A, Salehi V, Tan M, Mohammed O. Real-time energy management algorithm for plug-in hybrid electric vehicle charging parks involving sustainable energy. IEEE Trans Sustain Energy 2014;5(2):577e86. Payri F, Guardiola C, Pla B, Blanco-Rodriguez DT. A stochastic method for the
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Implementation methodology of powertrain for series-hybrid military vehicles applications equipped with hybrid energy storage Seongjun Lee a, Jonghoon Kim b, * a
Power Electronics and Energy Conversion Laboratory, School of Mechanical System & Automotive Engineering, Chosun University, Gwangju, 61452, Republic of Korea b Energy Storage and Conversion Laboratory, Department of Electrical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 October 2015 Received in revised form 14 September 2016 Accepted 19 November 2016
This paper investigates a component-sizing method and a power-control algorithm for series-hybrid military vehicles equipped with hybrid energy storages that comprise batteries and super-capacitors. Component sizing of the powertrain is determined by the performance specification that is related to mission profiles and power-flow control methods. In order to minimize the effects of mission profiles and power-flow control methods, the linear programming (LP) technique is employed. The LP problem for minimizing the output energy from the engine under different conditions of driving cycles and capacities of the energy storage system (ESS) is solved to eliminate the effect of the power distribution. Through analyzing the effects of different power and energy ratings of the ESS, the optimal values of power and energy capacities of the ESS are determined. The design approaches are extensively verified with simulations and experimental results of a reduced-scale per-unit equivalent system of the 10-ton series-hybrid electric vehicle (SHEV). © 2016 Elsevier Ltd. All rights reserved.
Keywords: Component sizing Linear programming Power control Per-unit equivalent system Series-hybrid electric vehicle
1. Introduction Environmentally, friendly electric vehicles are classified into the hybrid electric vehicles (HEVs), battery-only electric vehicles (BEVs), and fuel-cell electric vehicles (FCEVs) depending on the energy source of the vehicle powertrain as shown in Table 1 [1]. Nowadays these are increasingly studied and manufactured at present to minimize environmental impacts and to increase the fuel economy. The propulsion sources of the HEVs are the engine and the electric motor and configured as the series hybrid electric vehicle (SHEV), parallel HEV and series-parallel HEV as shown in Fig. 1. The main energy of the vehicle comes from the internal combustion engine (ICE) and the battery and the super-capacitor are utilized as an auxiliary energy sources. The propulsion source of the SHEV is only the traction electric motors and the generated energy from the ICE is utilized to charge the high voltage battery and to supply the power of traction motor. The FCEV which uses a fuel cell instead of an engine is a type of SHEV. On the other hand, the parallel hybrid has two power sources such as the engine and traction motor.
* Corresponding author. E-mail address: [email protected] (J. Kim). http://dx.doi.org/10.1016/j.energy.2016.11.109 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
These are connected through the transmission, to the drive wheels. Each power source may supply some or all of power needed by the vehicle. The series-parallel HEV is known as a combined hybrid electric vehicle or a power-split HEV. This configuration is investigated and developed to overcome the drawbacks of series and parallel architectures. The powers of the engine and the electric motor are coupled to drive the vehicle in parallel operation. While the power generated from the ICE flows into the battery and the traction motor in series operation. The one of the main research field in the electric vehicle is the development of components of the vehicle and the energy management algorithm in order to increase the fuel consumption. Among them, the studies of the energy management algorithm in the FCEV application [2e5] are carried out. The power distribution method between the fuel cell and the battery is studied considering the driving cycle, and the manufactured vehicle has been empirically tested. And in order to increase the efficiency and to reduce the cost of the component, the researches of the electric motor type [6,7] and the active suspension have been progressed [8]. As in the previous researches, the studies about the energy management algorithm and more efficient component are important in the existing electric vehicle, and especially in the case of designing of the new type of the electric vehicle, the component sizing considering the driving profile and the power distribution
230
S. Lee, J. Kim / Energy 120 (2017) 229e240
PI AP BP HPF LPF EMU OOL HILS DC AC UDDS SOC
Nomenclature HEV BEV FCEV SHEV ICE ESS IPM LP CPSR GVW
Hybrid electric vehicle Battery-only electric vehicle Fuel-cell electric vehicle Series-hybrid electric vehicle Internal Combustion Engine Energy storage system Interior permanent-magnet Linear programming Constant-power speed ratio Gross vehicle weight
Proportional and integral (controller) Accelerator pedal Brake pedal High-pass filter Low-pass filter Engine management unit Optimal operating line Hardware-in-the-loop system Direct current Alternating current Urban Dynamometer Driving Schedule State of Charge
Table 1 Characteristics of the HEV, BEV and FCEV. Types of EV
HEV
BEV
FCEV
Propulsion Source
-
- Electric motor
- Electric motor
- Battery - Super-capacitor
- Fuel cell - Battery - Super-capacitor
Energy Source
Internal combustion engine Electric motor Internal combustion engine Battery Super-capacitor
Fig. 1. Configuration of the hybrid electric vehicle.
algorithm is more important to achieving higher fuel economy. In these days, the HEVs have been extensively developed for military applications to ensure better performance in terms of the maneuver, survivability, and lethality, as well as the fuel economy [9e13]. For achievement of these performance requirements, military HEVs require features such as high grade-ability, fast acceleration, silent watch, and silent mobility. Therefore, various hybrid components, including the electric motor, primary source such as diesel engine and generator, and energy storage element such as battery, super-capacitor, should be appropriately designed considering the performance requirements, fuel economy, as well as
mission completeness [12,13]. Several studies [14e19] have been carried out to improve the performance and efficiency of devices used in commercial vehicles to obtain better fuel economy and to reduce emissions. In the case of hybrid military vehicles, because of its specificity, some constraints should be included at the design phase of the vehicles. As military vehicles have high performance requirements and are operated under poor driving conditions, new study that focuses on aspects such as component sizing and power control is important and necessary. In general, the component sizing of the powertrain is
S. Lee, J. Kim / Energy 120 (2017) 229e240
determined by the performance specification, which is related to mission profiles and power-flow control methods. In order to minimize the effects of mission profiles and control methods, an optimization algorithm is absolutely required to determine an optimal component sizing. In the design phase of the SHEV, because the efficiency of components is set to the constant target value, the power and energy relationships from the engine and the energy storage system (ESS) to the wheel are described as linear equations. Therefore, in this paper, the linear programming (LP) technique is applied to find an optimal power distribution algorithm considering the driving cycle and the power and energy capacity variations of the ESS [20]. In Ref. [21], authors investigated the power distribution method for minimizing the fuel consumption of powertrain which has the high voltage battery directly connected to the DC link in a series-hybrid electric vehicle (SHEV). In this study, we employ the LP technique to determine the optimal component sizing of the more complex powertrain with the battery and the super-capacitor as well as to find the basic distribution algorithm for power-flow control. The steps for sizing the components in the proposed approach are as follows. Firstly, the LP problem for minimizing the output energy from the engine under the conditions of different driving cycles and energy capacity variations of the ESS is solved. Thus, the effect of the power distribution method can be well eliminated. Through analyzing the effects of different power and energy ratings of the ESS, optimal values of power and energy capacities of the ESS are determined. In order to verify the component sizing method and the power distribution algorithm, 6 6 wheeled series-hybrid military vehicle that includes a diesel engine-generator, battery, and supercapacitor is designed as an example. A reduced-scale per-unit equivalent system is constructed and the newly proposed powercontrol algorithm is extensively verified by comparing the simulations and experimental results of the full-scale simulation system and the per-unit equivalent reduced-scale system. The remainder of this paper is organized as follows. Section II
231
describes the sizing method of components such as primary sources, secondary sources, and the traction electric motor. The power control algorithm of the SHEV with multiple power sources is described in Section 3. In the following section, the simulation and experimental results with a detailed discussion are presented. In the final section, some conclusions and final remarks are given. 2. Component sizing of the powertrain of the SHEV Fig. 2 shows the powertrain structure of the 6 6 wheeled SHEV and the parameters of the SHEV are listed in Table 2. The primary source of the vehicle is the diesel engine and an interior permanent-magnet (IPM) type generator. Secondary sources of lithium-type battery and super-capacitor are used in the vehicle. In order to achieve high driving performance and ensure a wide indoor space of the vehicle, in-wheel drive systems are applied to each wheel of the vehicle. The in-wheel drive system consists of a motor and a single reduction gear. 2.1. Sizing of the engine-generator The rating of the diesel engine, which is a primary source, should be determined to be able to supply an average power during driving mission profiles and to assist in meeting the required peak road-load demand. As a military vehicle characteristic, in order to maintain the driving performance in the case of any failures of secondary sources, the engine should be sized to meet the conditions such as moving for a specific mission of a high-speed driving. In this example, the engine is sized to supply the power that will be consumed when the vehicle is driven at the maximum speed. In this case, the tractive power which is defined to Ptr is same as the road-load power and is calculated from Eq. (1) and the engine should be determined to a value greater than the power calculated using Eq. (2). In this case, the engine power rating can be determined to 180 kW.
3 Sets
Generator
AC / DC Converter
BAT
x 10
DC / AC DC / AC
600
Peak Torque (60sec)
Torque (Nm)
Motor / Gear
Bi-directional DC/DC Converter
SC
8
Fig. 2. 6 6 powertrain structure of the series hybrid electric vehicle (SHEV).
6
Peak Power
Continuous Power
60 % Road 400
4
200
Table 2 Parameters of the target vehicle.
2
Max. Speed
Continuous Torque
Parameter
Value
Parameter
Value
Gross vehicle weight (GVW) Wheel radius Rolling resistance
10,000 kg Frontal area 0.5 m Aerodynamic drag coeff. 0.01 þ 0.0001 v (km/h)
4.5 m2 1
0 0
Power (W)
Engine
4
Motor Torque-Speed Characteristic Curve 800
Motor M t / Gear / Gear G Motor
1000
2000
3000 4000 ωrpm (r/min)
5000
0 6000
Fig. 3. Motor torque-speed characteristic curve.
Table 3 Specification of the demand torque and power about required driving performance. Performance specification
Acceleration Max. Speed Grade-ability EV driving
Speed (km/h)
Grade (%)
Time (s)
0e48 100 10 30
0 0 60 0
8 e e 1800
Motor torque (Nm)
Motor power (kW)
e 34.8 526 12.6
25.9 19.3 27.9 2.1
S. Lee, J. Kim / Energy 120 (2017) 229e240
þ paux
Pbat
(1)
! ch
hbat
(2)
where M is the mass of the vehicle, g is the gravitational acceleration coefficient. fr_init is a constant coefficient of tire's rolling resistance, whereas fr_inc is a rolling resistance coefficient that is proportional to the vehicle's speed, vcr is the cruising speed, r is the air density, and Cd is an aerodynamic drag coefficient. In addition, Af is the frontal area of the vehicle, hengen is the engine-generator coupling efficiency, hgen is the AC-DC converter efficiency including the generator efficiency, hmot is the motor efficiency including the DC-AC inverter loss, hgr is the gear efficiency including losses from the motor output shaft to the driving wheel, and hbat is the efficiency of the battery. Lastly, Paux is the basic consumed power of the vehicle with a control power supply, cooling system etc., and Pbat_ch is the battery charging power.
feasible regions at given driving profiles are chosen. Finally, the optimal values of power and energy ratings of the ESS are determined that can be minimized to achieve the objective function of achieving the minimum output energy from the engine. The objective function of the LP for minimizing the output energy from engine on given driving cycles is defined as Eq. (4). The constraints of the problem consist of the power balance by Eq. (5),
(a)
2.2. Sizing of the electric motor The electric motor is the only propulsion device in a SHEV and should be appropriately designed to satisfy the demanded torque and speed of road conditions [21e23]. The tractive powers for a cruising speed and acceleration can be derived based on the second law of Newton as Eqs. (1) and (3), respectively [21].
(b)
2.3. Sizing of the battery and super-capacitor The secondary source that includes the battery and supercapacitor system assists in accomplishing the tractive power together with the primary source when a vehicle accelerates and also absorbs the regenerative power from the motor when a vehicle decelerates. The power and energy capacities of multiple secondary sources can be determined according to the power management algorithm and driving profiles. With the above conditions, in order to size the battery and super-capacitor optimally, an optimization technique considering given driving profiles and the power distribution method should be employed. To minimize the effects of mission profiles and control methods, the LP technique is employed. First, the LP problem for minimizing the output energy from the engine related to various power and energy capacities of the ESS is solved. By analyzing the results, the
50 0
200
400 600 Acceleration
800
1000
4 2 0 -2 0
200
400 600 Travelled distance
800
1000
200
400
800
1000
4000 2000 0
0
600 Time (s)
Altitude (m)
Travelled distance
500
1000 Time (s)
1500
Altitude variation 150 100 50 0 -50
0
500
1000 Time (s)
1500
Velocity profile 100
3 1.5 0 -1.5 -3
2
(c)
0
15000 10000 5000 0 0
(3)
where Meq is the equivalent vehicle mass including inertia, ta is the required acceleration time, vf is the final speed of acceleration, and vb is the vehicle speed corresponding to the motor base speed. In military applications, as the performance constraints for the grade-ability is more severe than for commercial vehicles, the maximum torque of the motor should be carefully sized. In the case of the multiple in-wheel motor, the torque rating can be determined from the maximum tractive force related to the normal load of each wheel on an inclined road [24]. In order to reduce the power and torque rating of the motor, the gear ratio and the constantpower speed ratio (CPSR) should be appropriately selected depending on the motor type [25]. Table 3 shows the demand torque and power for the required performance specifications, and Fig. 3 shows the designed continuous and maximum ratings of each motor when the gear ratio is 10.
Velocity profile 100
6000
Distance (m)
2 Meq 2 1 vf þ v2b þ Mgfr vf þ rCd Af v3f ptr ¼ 3 5 2ta
Speed (km/h)
hengen hgen hmot hgr
þ 0:5rCd Af v2cr vcr
2
Ptr
Accel. (m/s )
inc vcr
Distance (m)
þ fr
Distance (m)
Peng
1
init
Speed (km/h)
Ptr ¼ Mg fr
Accel. (m/s )
232
50 0
9000 6000 3000 0
0
200
400 600 Acceleration
800
1000
0
200
400 600 Travelled distance
800
1000
0
200
400
800
1000
600 Time (s)
Fig. 4. Two current profiles of the hybrid pulse power characterization (HPPC) technique. (a) City driving cycle. (b) Tactical driving cycle. (c) HD-UDDS driving cycle.
Table 4 Specification of three driving profiles considered at design phase of the SHEV.
Duration time (s) Traveled distance (km) Max. Speed (km/h) Avg. Speed (km/h) Max. Acceleration (m/s2) Max. Deceleration (m/s2) Altitude variation (m)
City driving cycle
Tactical driving cycle
HD-UDDS driving cycle
1065 5.352 75.7 18.09 3.51 2.14 e
1060 8.935 93.34 30.34 1.958 2.0697 e
1610 14.07 91.83 31.46 1.33 1.18 119.7
S. Lee, J. Kim / Energy 120 (2017) 229e240
233
Fig. 5. Engine output energy according to power rating variations of battery and super-capacitor on three driving cycles. (a) Engine output energy for tactical driving cycle. (b) Engine output energy for city driving cycle. (c) Engine output energy for HD-UDDS driving cycle. (d) Feasibility region for tactical driving cycle. (e) Feasibility region for city driving cycle. (f) Feasibility region for HD-UDDS driving cycle.
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S. Lee, J. Kim / Energy 120 (2017) 229e240
energy balance equation of each secondary energy device given by Eq. (6), physical dynamic characteristics of each power source given by Eqs. (7)e(10), maximum power limitations of each power source given by Eqs. (11)e(14), and charge sustaining conditions of ESS given by Eq. (15). The equations of the LP are simply discretized using the Euler method as Eq. (16) and converted into a standard form. Thus, the problem of the LP can be solved by the simplex method using MATLAB [13].
2 min Econs
eng ðtÞ
6 ¼ min4
Ztf
dc ðtÞ
þ Pbat
dc ðtÞ
þ Psc
Peng
dc ðtÞ
hengen hgen
t0
Peng
3
dc ðtÞ
7 dt 5
Pbr ðtÞ ¼ Pmot
(4)
dc ðtÞ
(5)
E_ ess ¼ f ðPess ðtÞÞ
(6)
P_ eng
dc
engine slew rate up
(7)
P_ eng
dc
engine slew rate down
(8)
P_ bat
dc
battery slew rate up
(9)
P_ bat
dc
battery slew rate down
(10)
Peng
dc
0;
(11)
Peng
dc
Peng
dc max
Pbat Psc
dc
dc
Pbat
Psc
Pbr 0;
dc max ;
dc max ;
Pbr Pbr
Ebat ðt0 Þ ¼ Ebat tf ;
Pbat Psc
dc
dc
Pbat
Psc
(12)
dc min
(13)
dc min
(14)
max
Esc ðt0 Þ ¼ Esc tf
(15)
x_ ¼ fxðk þ 1Þ xðkÞg=DT
(16)
In this work, three different driving cycles are considered for designing the system of a wheeled vehicle used for military applications. Fig. 4 shows each characteristic of the driving cycles. The specification of three driving cycles considered at design phase of SHEV is listed in Table 4. One is an urban driving cycle that is representative normal city driving. Another is the tactical driving cycle designed for artificial mission areas. The third driving cycle is
Table 5 Specific power and energy, power and energy density for each component.
Engine Motor/Generator Battery Super-capacitor DC-DC converter DC-AC/AC-DC inverter
Specific power (W/kg)
Specific energy (Wh/kg)
Power density (W/l)
Energy density (Wh/l)
1000 1300 1000 2000 1000 5000
e e 120 4.5 e e
420 3190 2000 6400 1600 35000
e e 230 7.1 e e
Fig. 6. Engine output energy according to energy density variations of battery and super-capacitor. (a) Energy distribution between battery and super-capacitor. (b) Gross vehicle weight (GVW) according to the energy distribution of the ESS. (c) Engine output energy on different driving cycles.
S. Lee, J. Kim / Energy 120 (2017) 229e240
the HD-UDDS cycle mainly utilized in the area of a heavy-duty vehicle [26]. Fig. 5 shows some results of the engine output energy and feasible region about power rating variations of battery and supercapacitor on three different driving cycles. The (a), (b) and (c) of Fig. 5 present the results of the objective functions. As we can observe from the figure, the minimal engine output energy according to the optimal power distribution exists on three different driving cycles. Thus, the selection of the power control method and driving cycles is important in the design phase of the powertrain for SHEV. We can also notice the difference of the feasible regions of the required power of the battery and super-capacitor system from (d), (e), and (f) of Fig. 5. Finally, the system should be designed to ensure a power rating greater than the minimum value of feasible regions in order to obtain satisfactory performance and the optimal values for minimizing the objective function. Fig. 6 shows the results of the engine output energy about energy capacity variations in the case of the demanded energy requirement from the ESS. As the specific power (W/kg) and specific energy (Wh/kg) of the battery and super-capacitor as listed in Table 5 are used to calculate the gross vehicle weight (GVW), the energy distribution determined by the relationship of Fig. 6(a) and (b) causes the change in weight of the vehicle. From Fig. 6(c), in this case, as the energy capacity of the super-capacitor is increasing, the system efficiency is generally decreasing owing to an increase in the vehicle's weight. Nevertheless, in some cases, there exists an optimal value in terms of system efficiency. Thus, from above analysis, the power and energy ratings of the battery and super-
235
capacitor are designed to be optimal values so that the battery has the power and energy rating of 80 kW and 11.55 kWh at 620 V and the super-capacitor has the power and energy of maximum 190 kW and 750 Wh at maximum voltage of 400 V. 3. Power control algorithm design The power control algorithm for multiple power sources consisting of the engine-generator, the battery and the super-capacitor system in SHEV is shown in Fig. 7. At the top of this figure is the torque reference generation block together with a driver model. The driver model is applied to represent the acceleration or deceleration intention of a vehicle driver in the forward-facing simulation model [23,27]. The output of driver model controller is the acceleration pedal and brake pedal and it is composed of the proportional and integral (PI) controller whose inputs are the speed profile of Fig. 4 (Vwref) and measured vehicle speed (Vw), and outputs are the depressed positions of the accelerator pedal (AP) and brake pedal (BP). The Tmotoring and Tbraking is determined by considering the maximum motor torque (Tm,max) and mechanical braking torque (Tbr). Thus the generated torque reference is finally limited within an available maximum motor torque in a certain motor speed. The Tmot_ref is the torque reference for traction motors and the Tbr_ref is the mechanical braking torque reference. In the middle of the figure is a power reference generation block of the super-capacitor which is denoted as the Psc_ref. The supercapacitor is operated to supply a peak and fast transient power among the required power from the ESS occurring during the
Fig. 7. Control block diagram of the power system.
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S. Lee, J. Kim / Energy 120 (2017) 229e240
acceleration and deceleration of the vehicle. Therefore, the highpass filtered power of the load power (Pw) which is the traction power for vehicle driving is determined as a power reference of the super-capacitor from Eq. (17).
0
Psc
HPF Pess ; fc
B B B B ¼B B HPF Pess ; fc B B @ HPF Pess ; fc
ðaÞ SOCsc SOCsc ðcÞ SOCsc < SOCsc
sc
þ Psc
dis
SOCsc SOCsc max $ set 1 SOCsc max
max ; min
LPF Pess ; fc
ðaÞ
þ Psc
cha
SOCsc SOCsc $ set SOCsc min
ðbÞ SOCsc
min
min
SOCsc < SOCsc
ðcÞ
max ;
½Three conditions of SOCsc (17)
igen
Power Supply (Engine)
¼ 1 LPF Pess ; fc
sc
sc
¼
sc
;
2p$fc sc s þ 2p$fc sc
(18)
In Eq. (17), Pess which is the subtraction between the load power and the engine power is the required power from the ESS, fc_sc is the cut-off frequency of the high-pass filter (HPF) which is defined as Eq. (18), Psc_dis_set is the discharging power set value, SOCsc is the state of charge (SOC) of the super-capacitor, SOCsc_max is the upper limit value of SOCsc, Psc_cha_set is the charging power set value, SOCsc_min is the lower limit value of SOCsc. The second term of the right-side of Eq. (17) is the compensation power reference for maintaining the energy of the super-capacitor. The bottom of Fig. 7 presents the power reference generation and control methods of the engine-generator. The power reference of the engine-generator comes from the low-pass filtered value of
ðbÞ
sc
sc
HPF Pess ; fc
imot
Power Supply (generating)
vbat ibat
DC-DC Converter
Battery
isc
Analog output
igen imot isc ibat
RS232
DSP 56F807 vbat
- Engine control - Road load power control - BMS algorithm - Regenerative braking energy control - System Monitoring
RS232
S1, S2, S3
iL1,iL2,iL3
vsc GPIB
Electric load (motoring)
Super Cap Analog output Analog output
vsc
DSP TMS320F2812
Gating signals S1, S2, S3
iL1,iL2,iL3
Fig. 8. System configuration of the reduced-scale system.
Table 6 Experimental setup for reduced-scale system. Name of device/company
Rating
Remarks
Power supply
DLM60-66E/Sorensen
For engine-generator
Power supply
DLM60-50E/Sorensen
Electric load
SLL-5K/Unicorn-TMI
Battery
SLPB55205130H/Kokam
Super-capacitor
ESHSP-1700C0-002R7/Nesscap
DC-DC converter
3-phase bi-directional buck-boost converter
0e60 V, 0e66 A Max. power 3.3 kW 0e60 V, 0e50 A Max. power 3 kW 0e80 V, 0e500 A Max. power 5 kW 4.2 Ve2.7 V, 11 Ah Li-polymer cell 12S1P Vnom_pack ¼ 44.4 V Discharge: cont. 55 A, peak 110 A Charge: max. 22 A 1700F/2.7 V, 56S1P Rated current: 371 A Vsc_max: 36 V Max. power 1.5 kW Imax ¼ 39 A, L ¼ 235 mH, fsw ¼ 20 kHz
For regenerative road load power For monitoring road load power e
e
e
S. Lee, J. Kim / Energy 120 (2017) 229e240
(a)
237
Vehicle Speed & Power Distribution over City driving cycle
80
Speed profile Speed
Speed (km/h)
60 40 20 0 0
100
200
300
400
500 600 Time (s)
700
800
900
1000
1100
100
200
300
400
500 600 Time (s)
700
800
900
1000 Pmot 1100
Speed error (km/h)
2 1 0 -1 0
dc
Pengdc
Power (kW)
300 250
Pscdc Pbatdc
100 -50 -200 0
100
200
300
400
500 600 Time (s)
700
(b)
800
900
1000
1100
Imot_dc (15A/div)
Vbat (10V/div)
Ibat (15A/div)
Isc_dc (15A/div)
100s/div
Fig. 9. Simulation and experimental results on city driving cycle. (a) Simulation result of the full scale vehicle model. (b) Experimental result of the reduced-scale system.
the load power (Pw) such as that given by Eq. (19). Besides, the engine-generator is responsible for managing the state of charge of the battery. Therefore, the SOC of the battery is maintained within a certain boundary. The determined power from the enginegenerator is controlled through a speed control mechanism using the engine management unit (EMU) and a torque control
mechanism using the generator control unit. The detailed operating point in a speed-torque plane is conventionally determined according to an optimal operating line (OOL) of the engine and generator in SHEV [28] and the 1-D table which is denoted as a Table 1 given at the bottom of the figure is used to generate the speed reference.
238
S. Lee, J. Kim / Energy 120 (2017) 229e240
0
Peng
ref
LPF Pw ; fc
B B B B B ¼ B LPF Pw ; fc B B B @ LPF Pw ; fc
ðaÞ SOCbat SOCbat ðcÞ SOCbat < SOCbat
eng
eng
eng
max ; min
þ Paux Pbat
dis set $
SOCbat SOCbat max 1 SOCbat max
þ Paux
ðbÞ
þ Paux þ Pbat
ðbÞ SOCbat
ðaÞ
min
cha
SOCbat min SOCbat $ set SOCbat min
SOCbat < SOCbat
(19) ðcÞ
max ;
½Three conditions of SOCbat
In Eq. (19), Pw is the road load power for the motor inverter, fc_eng is the cut-off frequency of the low-pass filter (LPF), Paux is the auxiliary power, Pbat_dis_set is the discharging power set value, SOCbat is the state of charge of the battery, SOCbat_max is the upper limit value of SOCbat, Pbat_cha_set is the charging power set value, and SOCbat_min is the lower limit value of SOCbat. The second term of the right-side of Eq. (19) is the compensation power reference for maintaining the energy of the battery denoted as the SOCbat compensator in Fig. 7. 4. Simulation and experimental results for verification of the proposed work As elaborately described in Section 3, although the detailed control design methods of each power converter are omitted, we
assume that all components with their controllers have wellcontrolled dynamic characteristics. In this case, because the measured real responses about the reference inputs such as voltage, current, speed etc. can be well tracked, the functional model designed as the relationship of the input and output with time delay can be applied to the simulation model [29]. Thus, the forward-facing software testbed with the functional model is used to simulate and verify the power and energy management algorithm because of the long simulation time required for the driving cycle. For verification of the proposed power control algorithm, a reduced-scale system which is equivalent in terms of the voltage and power of each component is constructed. Fig. 8 presents the constructed experimental setup and Table 6 lists the used devices. The engine-generator is substituted through the power supply, and
Fig. 10. Comparison of power distribution through simulation and experimental results based on the per-unit values (blue solid line: full-scale simulation, green dotted line: reduced-scale experiment). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
S. Lee, J. Kim / Energy 120 (2017) 229e240
239
Fig. 11. Comparison of voltage response through simulation and experimental results based on the per-unit values (battery and super-capacitor).
Table 7 Operating conditions of the battery and super-capacitor. Battery
Voltage Per-unit voltage
Super-capacitor
Full-scale simulation
Reduced-scale experiment
Full-scale simulation
Reduced-scale experiment
504e705.6 V 0.8e1.12 p.u.
36e50.4 V
200e400 V 0.5e1 p.u.
18e36 V
the tractive motoring power is supplied by the electric load while the tractive generating power is supplied by the power supply. Reduced-scale devices of the super-capacitor, battery, and a bidirectional DC-DC converter are applied to the system. The road load power of the simulation model is used as an input for tractive power substituted by the power supply and the electric load of the experimental setup. Thus, the battery, super-capacitor, and enginegenerator are controlled by the proposed power distribution algorithm, and the distributed results are compared to the simulation results. Figs. 9e11 show the simulation results of the full-scale vehicle model and the experimental results of the reduced-scale system. As shown in Fig. 9(a), the vehicle is well tracked to a speed reference and speed error is within ±2 km/h. The power distribution in the DC-link side is well balanced through the proposed power control algorithm. Therefore, the vehicle can travel the driving cycle through the powertrain with the designed capacity and dynamics of each component. Fig. 9(b) shows the experimental result of the reduced-scale system with the input of a scaled load power. The traction power (load power) is scaled as 1/126 of the required load power from the simulation result. By applying the same rate, the maximum power of the engine, battery and super-capacitor are scaled as 1.42 kW, 634 W, and 1.5 kW respectively. In order to make both the simulation and experimental system equivalent in terms of the per-unit, the impedance of the battery and super-capacitor in the reducedscale system is adjusted by applying Ohm's law and the definition of the per-unit of Eqs. (20) and (21).
Pbase ¼ Vbase $Ibase ; Pbase
sd
¼ Pbase =SD;
Zbase ¼ Vbase =Ibase Ri
pu
¼ Ri =Zbase
(20) (21)
In this case, since the battery base voltage (Vbase) of the reducedscale system is 44 V, Ibase and Pbase are calculated from Eq. (20). Therefore, the battery per-unit resistance of the reduced-scale system is calculated from Eq. (21) using the measured internal resistance of the battery denoted as Ri. In the same manner, the perunit resistance of the full-scale simulation system is calculated and to make the per-unit resistance same, the extra resistance is connected in series with the battery in the reduced-scale system. Then,
the added resistance is 0.3385 U. On the condition of the scaled load power, assuming that the vehicle travels the same driving cycle, the power sources of the engine, battery, and super-capacitor are stably distributed within the pre-determined boundary conditions of each power source through the proposed power control method. Fig. 10 shows the power distribution results that are represented as the per-unit values which are normalized based on each engine maximum power of both simulation and experimental results. Fig. 11 shows the per-unit voltage of the battery and supercapacitor from simulation and experiment results. From both results, we find that the per-unit power of each power source is almost equally distributed during the driving cycle. The per-unit voltages of the battery and the super-capacitor are almost same and the ESS is operated within the stable region as shown in Table 7. Thus, the validity of the designed powertrain and the control algorithm are verified before manufacturing the vehicle system. 5. Conclusion In this approach, the component-sizing method using linear programming (LP) and the power distribution algorithm for the series-hybrid electric vehicle are investigated. The proposed methods are verified through simulation and reduced-scale experiment. Although the SHEV has the structural type equipped with hybrid energy storage, the proposed component-sizing method can be used for other structural types as well. For example, an optimal structure and component capacity such as the fuel cell, battery and super-capacitor of the FCEV can be designed by applying the proposed method. The proposed reduced-scale system approach can also be utilized in hardware-in-the-loop (HILS) applications, especially in battery HILS. References [1] Chan CC. The state of the art of electric and hybrid vehicles. IEEE Journals Mag 2002;90(2):247e75. [2] Han J, Park Y, Kim D. Optimal adaptation of equivalent factor of equivalent consumption strategy for fuel cell hybrid electrical vehicles under active state inequality constraints. J Power Sources 2014;267:491e502. [3] Chopra S, Bauer P. Driving range extension of EV with on-road contactless power transfer-a case study. IEEE Trans Ind Electron 2013;60(1):329e38. Y. Enhanced fuel cell hybrid electric [4] Maalej K, Kelouwani S, Agbossou K, Dube
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