Research ArticleEnergy Efficiency Oriented Design Method of PowerManagement Strategy for Range-Extended Electric Vehicles
Jiuyu Du, Jingfu Chen, Mingming Gao, and Jia Wang
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
Correspondence should be addressed to Jiuyu Du; [email protected]
Received 9 March 2016; Revised 3 June 2016; Accepted 6 June 2016
Academic Editor: Muhammad N. Akram
Copyright ยฉ 2016 Jiuyu Du et al.This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The energy efficiency of the range-extended electric bus (REEB) developed by Tsinghua University must be improved; currently,the energy management strategy is a charge-deplete-charge-sustain (CDCS) strategy, which exhibits low energy efficiency on thedemonstration model. To improve the energy efficiency and reduce the operating cost, a rule-based control strategy derived fromthe dynamic programming (DP) strategy is obtained for the Chinese urban bus driving cycle (CUBDC). This rule is extracted bythe power-split-ratio (PSR) from the simulation results of the dynamic powertrain model using the DP strategy. By establishing theREEB dynamic models in Matlab/Simulink, the control rule can be achieved, and the power characteristic of powertrain, energyefficiency, operating cost, and computing time are analyzed. The simulation results show that the performance of the rule-basedstrategy presented in this paper is similar to that of the DP strategy. The energy efficiency can be improved greatly compared withthat of the CDCS strategy, and the operating cost can also be reduced.
1. Introduction
Environmental concerns and increasing fuel cost have moti-vated manufacturers and governments to develop alternativetechnologies to replace conventional internal combustionengine (ICE) vehicles [1]. Recent works have shown thatvehicle electrification can reduce energy waste and minimizefuel consumption [2]. Nevertheless, battery electric vehicles(BEVs) still have a major drawback: energy storage. Formassive deployment of BEVs, the problems of driving range,charging time, lifetime, and higher upfront cost must besolved. Typically, a BEV stores energy in batteries that arebulky, heavy, and expensive. Due to this problem, withcurrent battery technology, it is very difficult to make ageneral purpose BEV that effectively competes with ICEvehicles [3].
A range-extended electric vehicle (REEV) provides aplatform to overcome the BEVโs drawbacks and reducefuel consumption and energy waste [4]. REEVs utilize asmaller size power battery than that of battery electricvehicle along with a small displacement range extender; thiscombination is an optimal solution to the economic and
energy storage issues of BEVs. In China, public buses areregarded as the priority concerning development of newenergy vehicles (NEVs). A type of range-extended electricbus has been developed by Tsinghua University (THU) andautomotive companies and demonstration operations havebeen performed in cities for several years. Presently, thefuel efficiency of the REEB is too low and urgently must beimproved. The energy efficiency is an important criterionto assess the performance of a range-extended electric bus,and the energy management strategy is a key influencingfactor on the energy consumption [5]. The present energymanagement strategy of the THU REEB is charge-deplete-charge-sustain (CDCS), which is established by engineeringexperience; as a result, the energy efficiency still has muchroom for improvement, and a new control strategy shouldbe developed. The energy management strategy of a hybridelectric powertrain can be classified into instantaneous strate-gies, global optimized strategies, or rule-based strategies[6]. The key to solving instantaneous optimization problemsis a reasonable objective function, such as the equivalentconsumption minimization strategy. Garcฤฑa et al. [7] andGeng et al. [8] utilized the equivalent fuel consumption
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 9203081, 9 pageshttp://dx.doi.org/10.1155/2016/9203081
2 Mathematical Problems in Engineering
minimization strategy to analyze the performances of a fuelcell electric vehicle and a plug-in electric vehicle, respectively.Sciarretta et al. [9] andBarsali et al. [10] applied the equivalentfuel consumption minimization strategy on series hybridelectric vehicles and parallel hybrid electric vehicles, respec-tively. By comparison, the instantaneous optimization is onlybased on current control step and real-time distributionstrategy; as a result, this strategy will affect performanceof hybrid powertrain greatly. Thus, a more optimal resultcan be obtained using global optimization [11]. The DPalgorithm is a widely used global optimization method,which is suitable for optimizing the control strategy whenthe driving cycle is known in advance. Kum et al. [12] andLin et al. [13] applied the DP strategy on a plug-in electricvehicle and a parallel hybrid electric vehicle, respectively,to achieve better fuel consumption and emissions. However,this type of strategy is difficult to apply on a vehicle dueto its heavy computational burden [14]. Rule-based strate-gies are mainly designed in accordance with engineeringexperiences and easy to be applied on vehicles. However,the optimal performance is difficult to achieve using rule-based strategies [15]. He et al. [16] presented several rule-based control strategies, such as the voltage control, cur-rent control, and brake regeneration control on fuel cellelectric bus by simulation. Wu et al. [17] devised a CDCSstrategy on the range-extended electric bus in the Harbinbus driving cycle. Gong et al. [18] and Schouten et al. [19]devised a fuzzy logic controller for a parallel hybrid electricbus. Gong et al. [18] developed a neural network controlstrategy for the energy management system based on the tripmodel.
The abovementioned optimal control strategies are allglobal optimal resolution approaches using theDP algorithm,which are difficult to implement on-board because of thecomputational burden. In contrast, the rule-based controlstrategy is easy to apply in real-time.This paper combines theadvantages of the DP strategy and the rule-based strategy andpresents a rule-based strategy derived from theDP strategy toreduce the energy consumption for THU REEB.
2. Modeling and Simulation
2.1. Powertrain Configuration and Operational Modes. Theschematic diagramof electric powertrain is shown in Figure 1.The range extender acts as on-board generating deviceconsisting of an engine, a generator, and a rectifier. Accordingto the demand of the REEB under different driving cycles, thebattery and the range extender provide power to the tractionmotor to drive vehicle either jointly or separately [17].
2.2. Range Extender Model. The dynamic characteristics ofthe engine and the generatormodels are ignored to reduce thecomputational burden of the DP strategy. Instead, the brakespecific fuel consumption (BSFC) map, which is indexed byengine speed and torque, is employed for the fuel consump-tion calculation, as shown in Figure 2. The same method isadopted for the generator via the efficiency map, as shown inFigure 3. Bothmaps of the engine and generator are generatedusing the data from the bench tests.
Given that generator is mechanically coupled to theoutput shaft of the engine, the generator and engine are atthe same working points. The ideal fuel economy curve ofthe range extender is obtained using the method describedin Chen et al. [20]. The fuel consumption map and idealoperation region of the range extender derived from theengine and generator are shown in Figure 4.
2.3. Power Battery Model. Because the RC battery model istoo complex for use in the DP process, the battery modelis simplified as an equivalent circuit with a voltage sourceand resistance, as shown in Figure 5. In the simplification,a current response faster than 1 s is ignored due to the stepsize of the simplified backward-in-time simulation model.The thermal effect on the battery dynamics is also ignoredby assuming the battery temperature does not undergomajorchanges during vehicle testing.Thebatterymodel is as follows[21]:
SOC = โ
๐ผbat๐bat
,
SOC = โ๐SOC
๐OCV (SOC) โโ๐
2
OCV (SOC) โ 4 (๐ int (SOC) + ๐
๐ก) (๐motor (๐) โ ๐re (๐))
2 (๐ int (SOC) + ๐
๐ก) ๐bat
,
(1)
where ๐ผbat is the battery current, ๐bat is the battery capacity,๐SOC is the Coulomb efficiency, ๐OCV is the open circuitvoltage of the battery, and ๐ int and ๐
๐กare the internal
resistance and thermal resistance, respectively. ๐ int and๐OCVare functions of the SOC. ๐motor and ๐re are the output powerof the range extender and the input power of the motor,respectively.
The battery charging efficiencies ๐chg and dischargingefficiencies ๐dis are calculated using [15]
๐dis =๐OCV โ IRdis
๐OCV=
1
2
(1 + โ1 โ
4๐ dis๐bat๐
2
OCV) ,
๐motor (๐) โ ๐re (๐) โฅ 0,
๐chg =
๐OCV๐OCV โ IRchg
=
2
(1 + โ1 โ 4๐ chg๐bat/๐2
OCV),
๐motor (๐) โ ๐re (๐) < 0,
(2)
Mathematical Problems in Engineering 3
Mechanical joint joint
Electrical joint
Engine Generator RectifierPower battery
Traction motor controller
Traction motor
Mechanical joint
Transmission and main reducer
Electrical
Electrical joint
Range extender
Figure 1: Electric powertrain configuration of the THU REEB.
217
217
217
217
222
222
227
227
227
232232
232
237237
237
242
242 242
242
247
247
247247252 252258 258263 263268 268273 273
278278
283283
288
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303
308313
318323
328333
338343
348353
358363
368373
378383
388394
399404
409414
419424 424429 429434 434439
444449
454459
464469
474479
484489
494499
504509514 519524 530535 540545 550555 560565 570575 580585590595600605610615
1000 1500 2000 2500 3000 3500 4000Engine speed (rpm)
50
100
150
200
Engi
ne to
rque
(Nm
)
5kw
10kw15kw
20kw25kw
30kw35kw
40kw45kw50kw
55kw
Figure 2: Engine BSFC map.
0.4130.425 0.4380.451 0.4630.476 0.4890.502 0.5140.527 0.540.552 0.5650.578 0.590.6030.6160.628 0.641
0.6540.666
0.679 0.6920.705 0.7170.73 0.7430.755
0.768
0.781
0.793
0.806
0.819
0.831
0.844
0.857
0.8690.869
0.869
0.882
0.882
0.882
0.882
0.895
0.895
0.895
0.895
0.908
0.908
0.908
0.908
0.908
1400 1600 1800 2000 2200 2400 2600 2800 3000 3200Generator speed (rpm)
50
100
150
200
Gen
erat
or to
rque
(Nm
)
5kw10kw
15kw20kw
25kw30kw 35kw
40kw45kw50kw 55kw
Figure 3: Generator efficiency map.
11.1
22.2
33.3
44.4
55.5
66.6
77.7
88.8
99.9
111
122
133
144
155
167
178
189
200
211
222
233
233
244255255
266
278
289
300
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333
344355
366377 389400
411422
433 433444 444455 455466 466477 477489 489500 500511 511522 522533 533544 544555 555566 566577 577588 588 600611 622633 644655 666677 688699 711722 733744 755766 777788 799810 822833 844855 866877888
1500 2000 2500 3000Range extender speed (rpm)
50
100
150
200
Rang
e ext
ende
r tor
que (
Nm
)
5kw
10kw15kw
20kw25kw
30kw35kw
40kw45kw50kw
55kw
Figure 4: Range extender fuel consumption map.
ibRtRint
Uocv
+
โ
Figure 5: Equivalent circuit of simplified battery model.
where ๐ dis and ๐ chg are the discharging and charging resis-tances, respectively.
2.4. Powertrain Modeling. Targeting the fuel consumption, abackward simulation model is established based on the DPstrategy.The resistance force of powertrain is expressed as thefollowing state equation:
๐น
๐ก= ๐น
๐+ ๐น
๐ค+ ๐น
๐+ ๐น
๐, (3)
where ๐น๐is the acceleration resistance, ๐น
๐คis the aerodynamic
drag force, ๐น๐is the rolling resistance, and ๐น
๐is the slope
resistance, and they are determined as follows:
๐น
๐= ๐ (๐
๐+ ๐
๐) ๐ cos๐ผ,
๐น
๐ค=
1
2
๐ถ
๐๐ด๐V2,
๐น
๐= (๐
๐+ ๐
๐) ๐ sin๐ผ,
๐น
๐= ๐ฟ (๐
๐+ ๐
๐)
๐V๐๐ก
,
(4)
4 Mathematical Problems in Engineering
Table 1: Parameters of range-extended electric bus.
Bus
Curb mass/kg 13400Passenger mass/kg 2760Frontal area/A/m2 7.83
๐ถ
๐0.75
๐ 0.0076 + 0.00056V๐/m 0.512๐
06.2
๐
๐2.18
Motor
Rated power/kW 100Peak power/kW 180
Maximum torque/Nโ m 860Maximum speed/r/min 4500
Voltage/V 300โผ450
Engine Displacement/L 1.9Maximum output power/kW 82 (4000 r/min)
Generator and generator controller Rated power/kW 50Rated torque/Nโ m 220
Power battery Capacity/Ah 180Voltage/V 350โผ460
where the parameter ๐ is air density,๐ถ๐is air drag coefficient,
๐ด is the frontal area of the vehicle, ๐ is the rolling resistancecoefficient, and ๐ผ is the climbing slope, V is the vehiclespeed (km/h), ๐ฟ is the conversion coefficient of the rotatingcomponents mass, ๐
๐is the curb mass of range-extended
electric bus, and๐
๐is the mass of passengers.
The driving power of powertrain is expressed as follows:
๐
๐ก= ๐
๐+ ๐
๐ค+ ๐
๐+ ๐
๐= ๐น
๐กโ V,
๐
๐ก=
(๐
๐+ ๐
๐) ๐๐V
3600
+
(๐
๐+ ๐
๐) ๐ sin๐ผV
3600
+
๐ถ
๐๐ดV3
76140
+
๐ฟ (๐
๐+ ๐
๐) V
3600
๐V๐๐ก
.
(5)
The demand power of the motor ๐motor is provided by batteryand the range extender together or separately as
๐motor =๐
๐ก+ 3.6๐aux
3.6๐
๐โ ๐ep โ ๐
๐กโ ๐fd
= ๐re + ๐bat, (6)
๐motor =๐น
๐กโ ๐
๐
๐โ ๐
0โ ๐
๐กโ ๐fd
, (7)
where ๐น
๐กis vehicle driving force, ๐aux is the demand power
of the electrical accessories, ๐bat is the battery power, ๐
๐
is the efficiency of the traction motor, ๐๐กis the efficiency
of transmission, ๐ep is efficiency of the power electroniccomponents, and ๐fd is the efficiency of the final drive.
The parameters of the range-extended electric bus areshown in Table 1.
0
10
20
30
40
50
60
v (km
/h)
200 400 600 800 1000 1200 14000Time (s)
Figure 6: Chinese typical urban bus driving cycle.
3. Driving Cycle
As the driving cycle is an important factor influencingthe energy consumption of electric vehicles, the simulationof the range-extended electric bus is conducted based onthe Chinese urban bus driving cycle (CUBDC) shown inFigure 6. Based on the statistical results of the main runninglines of the electric buses in typical cities of China, the dailytraveled range is approximately 200 km. Therefore, given thelarge battery capacity of the range-extended electric bus andthe one-charge-per-day operationmode, the driving cycle forthe simulation is 35 CUBDCs, which spans approximately200 km.
The DP strategy is suitable for the optimizing energymanagement system only if the driving cycle is known inadvance. The application scope of the preset rule, which isderived from the DP strategy based on CUBDC, should bedefined. It is assumed that if the driving cycles are similarto CUDBC, then the abovementioned preset rule by DPcan be applied directly. The eigenvalues that can distinguish
Mathematical Problems in Engineering 5
different types of driving cycles should be selected as appli-cable conditions of the control strategy for different drivingcycles. In the research of Montazeri-Gh and Fotouhi [22],the idle rate and the average acceleration of driving cyclescan distinguish different types of driving cycles effectively.Therefore, the control strategies in this paper are suitable forthe driving cycles with nomore than 5%difference in terms ofthe idle rate (39.19%) and the average acceleration (1.12m/s2)of CUBDC.
4. Energy Management Strategy
4.1. Overview of the DP Strategy. DP is a mathematicalmethod to design optimal energy management controllers ofhybrid powertrain [23], so it serves as benchmark tool withwhich other energy management strategies are compared.
The state equation for DP strategy in the discrete form isgiven as follows:
๏ฟฝ๏ฟฝ (๐ + 1) = ๐ (๐ฅ (๐) , ๐ข (๐)) . (8)
In the horizon (๐ก0, ๐ก
๐), the state variables of the range-
extended electric bus powertrain include the battery SOCandthe vehicle speed. As the vehicle speed can be determinedfrom the driving cycle, the state variable is ๐ฅ = SOC.According to the optimal objective of minimum equivalentfuel consumption, the output power of range extender isregarded as the control variable, ๐ข = ๐re. The state equation issubject to the following constraints:
๐bus,max (๐OCV โ ๐bus,max)
๐ chgโค ๐bat
โค
๐bus,min (๐OCV โ ๐bus,min)
๐ dis,
0 โค ๐re โค ๐re,max,
SOC๐ฟโค SOC โค SOC
๐ป,
๐
๐,min โค ๐
๐(๐ก) โค ๐
๐,max,
(9)
where ๐bus,max, ๐bus,min, and ๐OCV are maximum voltage,minimum voltage, and open circuit voltage and ๐ chg and ๐ disare charging resistance and discharging resistance of tractionbattery, respectively. ๐re,max is the maximum power of therange extender. SOC
๐ปand SOC
๐ฟrepresent the maximum
and minimum values of the SOC, respectively. ๐
๐is the
traction motor torque; ๐๐,min and ๐
๐,max represent the maxi-mum torque and the minimum torque of the traction motor,respectively.
The key of the DP strategy is to present a reasonablecost function. In this paper, the electricity consumption isconverted to the equivalent fuel consumption, so minimumfuel consumption is regarded as the only optimal objective.The cost function ๐ฝ is given as follows:
๐ฝ =
๐โ1
โ
๐=0
{๐ถre (๐) + ๐
๐(๐) โ ๐ถbat (๐)} , (10)
0 50 100 150 2000
5
10
15
20
PSR
The dead band
Pmotor (kW)
Figure 7: Distribution of the PSR points using the DP strategy.
where ๐ถre is the fuel consumption of the range extender, ๐ถbatis the equivalent fuel consumption of the battery, and ๐
๐is
the coefficient of SOC boundary value. These quantities arecalculated as follows:
๐ถre = ๐eng๐๐ฮ๐ก,
๐ถbat = ๐bat (๐dis๐chg)โsgn(๐bat) ๐ถre,avg
๐re,avgฮ๐ก,
๐
๐= 1 โ
2๐ (SOC โ 0.5 (SOC๐ป
โ SOC๐ฟ))
(SOC๐ป
โ SOC๐ฟ)
,
(11)
where ๐eng is the output power of the engine, ๐๐ is the specificfuel consumption, ๐ถre,avg is the average fuel consumption ofthe range extender, ๐re,avg is the average output power of therange extender, and ๐ is the balance coefficient required tomaintain the SOC within the reasonable range [24].
4.2. PSR Rule-Based Strategy. To develop the control rulederived from DP strategy, the parameter of power-split-ratio (PSR) is proposed indicating the ratio between rangeextender and motor. PSR is defined as follows:
PSR =
๐re๐motor
. (12)
To minimize the energy consumption using the DP strategy,the range-extended electric bus is modeled and simulatedunder CUBDC. The PSR points extracted from simulationunder DP strategy are shown in Figure 7.
In Figure 7, when the ๐motor is close to 0 kW, the PSRvalues are distributed dispersedly in the range of 0 to 20.The consumptions of the fuel and battery energy are takeninto consideration simultaneously under the DP strategyto optimize the energy management strategy of the hybridpowertrain. The range extender charges the battery whenthe ๐motor is under low power condition. To find the PSRregular pattern, a dead band was defined as the red boxshown in Figure 7. The PSR points in the red rectangle areextracted, and the corresponding range extender power andmotor power can be obtained, as shown in Figure 8. It can beseen that the range extender powers in the dead band are nomore than 0.2 kW, so they can be ignored for developing thePSR rule-based (PSR-RB) strategy.
The PSR-RB strategy is formulated based on the PSRpoints outside of the dead band, which can be fitted by the
6 Mathematical Problems in Engineering
Time (s)
0
0.05
0.1
0.15
0.2
Pow
er (k
W)
Range extender powerMotor power
0 1 2 3 4 5
ร104
Figure 8: Power feature of the PSR points in the dead band.
0 20 40 60 80 100 120 140 160 180
The fitting curve
012345
PSR
Pmotor (kW)
Figure 9: Fitting curve according to the PSR points outside of thedead band.
curve shown in Figure 9. The PSR values along red curve areregarded as the power-split rule between the range extenderand the battery.
5. Model Verification
5.1. Analysis on System Performance. To verify the validityof the aforementioned control strategies, the range-extendedelectric bus is simulated to analyze the energy efficiency usingthe DP and PSR-RB strategies on the basis of the CUBDC.Moreover, for comparison to the present strategy of the THUREEB, the CDCS strategy is conducted in the simulationmodel. When the SOC value decreases to 0.3, the rangeextender is forced to charge the battery sometimes in thecharge sustaining stage [25]. Variation curves of the batterySOC with the three types of control strategies considered areshown in Figure 10.ThePSR-RB strategy is found to be similarto the DP strategy in terms of the SOC variation. However,the computational efficiency is improved significantly for thePSR-RB strategy compared with the DP strategy.The PSR-RBstrategy is more responsive, with almost no time delay, and itcan be used in real-time as the CDCS strategy to control therange-extended electric bus.
Figure 11 shows the relationship among the motor power,the range extender, and the battery power for the differentcontrol strategies. The power features of the PSR-RB strategyare almost in line with those of the DP strategy. Given thedriving cycle for the simulation spans approximately 200 km,the battery consumes most of the electricity to supplementthe range extenderwhen the demandpower of the powertrainexceeds the maximum output power (50 kW) of the range
Time (s)10 2 3 4 5
ร104
DP strategyPSR-RB strategyCDCS strategy
0.2
0.4
0.6
0.8
1
SOC
Figure 10: SOC decrease curves using the DP, PSR-RB, and CDCSstrategies.
โ20โ10
0
10
20
30
40
50 60
70
80
90
100
110
120
130
140
150
160
170
180
190 200
DP strategyPSR-RB strategy
010203040506070
Rang
e ext
ende
r pow
er (k
W)
0 20 40 60 80 100 120 140โ20Battery power (kW)
Motor power
Figure 11: Power features of the components based on the differentstrategies.
extender. If the demand power of powertrain is lower than50 kW, then the range extender serves as the main energysource, and the battery charges and discharges over a smallrange (โ10 kW to 10 kW) repeatedly to achieve the optimalpowertrain efficiency. This process is inherently differentcompared to the CDCS strategy, which is widely used inengineering practice.
5.2. Energy Consumption and Operating Cost. The electricityand fuel consumption of the REEB powertrain are shownin Table 2. The fuel consumption is improved by 8% usingthe DP and PSR-RB strategies compared with the use of theCDCS strategy. For comparison of the energy savings, the fueland electrical consumptions are converted to MJ-equivalentvalues [26]. The PSR-RB strategy has a similar energy savingeffect as that of the DP strategy, which saves approximately7% relative to the CDCS strategy.
The energy flow diagram of the PSR-RB strategy andthe CDCS strategy is presented in Figure 12. For a distancetraveled of 200 km, the strongest difference between thesestrategies is the charge energy of the battery, which is fromthe range extender. Under the CDCS strategy, the rangeextender produces 507.36MJ of electrical energy to chargethe battery. However, only 38.78MJ is produced by the rangeextender in the PSR-RB strategy. In addition, there is a slight
Mathematical Problems in Engineering 7
Engine
Generator and rectifier
Battery
Charge
Transmission and main reducer
Efuel
2430.9MJ
๐eng, avg
๐chg, avg = 93.8%
Eb
= 95.2%
Emotor, input = 1064.1MJ
Emotor, output = 933.52MJ
๐gen, avg90.50%
Edem = 891.7MJ
๐T = 95.52%
Eloss, tran = 41.82MJ
๐m, avg = 87.73%
237.99MJ
213.02MJ
36.53%
Eloss
Egrid
, b
13.81MJ
Eloss, eng = 1475.19MJ
Eloss, gen = 90.81MJEg = 864.9MJEb, g = 38.78MJ
Eloss, m = 130.58MJ
Eeng = 955.71MJ
Traction motor
Axle
๐dis, avg
(a) PSR-RB strategy
Engine
Battery
Charge
Efuel
2653.3MJ
๐chg, avg = 93.6%
Eb
๐dis, avg = 95.7%
Emotor, input = 1070.0MJ
Emotor, output = 933.52MJ
๐gen, avg90.99%
Edem = 891.7MJ
๐T = 95.52%
Eloss, tran = 41.82MJ
๐m, avg = 87.72%
649.75MJ
188MJ
๐eng, avg
38.42%
Eloss
Egrid
, b
45.60MJ
Eloss, eng = 1633.9MJ
Eloss, gen = 91.8 MJEg = 927.6MJEb, g = 507.36MJ
Eloss, m = 130.58MJ
Eeng = 1019.4MJ
Generator and rectifier
Transmission and main reducer
Traction motor
Axle
(b) CDCS strategy
Figure 12: The energy flow diagram of the REEB powertrain for different strategies.
Table 2: Energy consumption comparison.
DP PSR-RB CDCSElectricity consumption/kWh 59.25 59.17 52.22Fuel consumption/L 61.12 61.39 67.01Fuel saving rate compared with the CDCS strategy/% 8.7 8.4 โEnergy saving rate compared with the CDCS strategy/% 7.3 6.9 โOperating cost/RMB per day 356.92 358.38 386.83
difference in the engine energy efficiency between the twostrategies because the range extender operates at the optimalworking points in the CS mode, which improves the engineenergy efficiency of entire CDCS mode. Therefore, the PSR-RB strategy balances the energy loss between engine energyefficiency and charging the battery via the range extender.
The operating cost is also analyzed. Because the REEB ischarged at night, the off-peak electricity price is used for thecalculation. The operating cost of the PSR-RB strategy is inagreement with that of the DP strategy; however, comparedwith the CDCS strategy, the operating cost savings of morethan 28 RMB per day are achieved, according to the currentelectricity and fuel prices in typical cities of China. For thetraveled distance (1,000,000 km) of urban buses during thelifetime of the buses [27], the PSR-RB strategy can achievemore than 140,000 RMB in savings compared with the CDCSstrategy.
6. Conclusion
To present a new type of energy management strategy thatcan greatly improve the energy efficiency, reduce operatingcost, and meet the real-time range application requirementfor the THU REEB, a rule-based control strategy with powersplitting characteristic (PSR-RB), which is derived from theDP global control optimal strategy, was investigated. Thesimulations were conducted to analyze the power charac-teristic of powertrain, energy efficiency, operating cost, andcomputing time by different strategies under the China urbanbus driving cycle.
The PSR-RB strategy was found to achieve lower fuel andenergy consumption compared with those of the CDCS solu-tion. An inherent difference between the proposed strategyand the CDCS strategy was demonstrated in this research.Compared with the CDCS strategy, the fuel savings rate
8 Mathematical Problems in Engineering
and the energy savings rate can reach approximately 8.4%and 6.9%, respectively, because PSR-RB strategy balancesthe energy loss between the engine energy efficiency andcharging the battery via the range extender to improve thepowertrain energy efficiency.
The cost-effectiveness was also demonstrated to beimproved by using the PSR-RB strategy. Comparing to theCDCS strategy, the operating cost can be reduced by over140,000 RMB during the lifetime of the vehicle, according tothe current electricity and fuel prices in typical Chinese cities.
Based on the aforementioned simulation results, therange-extended electric vehicle using the PSR-RB strategyexhibits excellent performance, in terms of both energyefficiency and real-time ability; thus, the PSR-RB strategy is afeasible on-board energy management strategy for use in therange-extended electric bus designed by THU.
Competing Interests
The authors declare that they have no competing interests.
Acknowledgments
This study is sponsored by new energy vehicles strategicprojects of Chinese Academy of Engineering (2015-XZ-36-03-03).
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