A Novel ECMS and Combined Cost Map Approach

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011 3557

A Novel ECMS and Combined Cost Map Approachfor High-Efficiency Series Hybrid Electric Vehicles

Volkan Sezer, Metin Gokasan, Member, IEEE, and Seta Bogosyan, Senior Member, IEEE

Abstract—The main aim in the control of a hybrid electricvehicle (HEV) is to decrease the fuel consumption and emissionswithout significant loss of driving performance. The performanceof the vehicle in terms of fuel economy and emissions is very muchdependent on the vehicle’s supervisory control strategy. In thispaper, the equivalent consumption minimization strategy (ECMS)is developed with a novel approach for the charge sustaining ofthe batteries to provide an overall improved optimization perfor-mance for series hybrid electric vehicles (SHEVs), considering theefficiencies of the internal combustion engine (ICE), generator,and battery. Another novelty is the development of a combinedmap, which simultaneously facilitates the optimization of the fuelconsumption and multiple emission components, unlike most paststudies that have concentrated on one component at a time. Afterthe derivation of the cost map, the algorithm is divided into twomain parts. The first part optimizes the engine–generator set(GENSET), and the second part determines how much power isneeded from the GENSET according to the ECMS. The algorithmis implemented using generic emission and fuel consumption mapsof an actual mid-sized series hybrid bus to reduce the desiredemissions. The hybrid electric vehicle in consideration is convertedfrom a conventional bus that is driven by an ICE. The performanceof the novel ECMS strategy is compared with the conventionalvehicle, as well as the SHEV version that is driven by an on–offstrategy. In addition to reduced fuel consumption, the resultsof this paper demonstrate a significant reduction of 14.58% inCO2 production with ECMS, whereas the on–off control strategyachieves only 6.47% reduction over the conventional vehicle.

Index Terms—Battery state of charge (SOC), emission,engine–generator set (GENSET), equivalent consumption mini-mization strategy (ECMS), fuel economy, internal combustion en-gine (ICE), optimization, series hybrid electric vehicles (SHEVs),supervisory control.

I. INTRODUCTION

HYBRID ELECTRIC VEHICLES (HEVs), which aremore viable options than conventional vehicles in terms

Manuscript received August 2, 2010; revised February 28, 2011 andJuly 1, 2011; accepted August 12, 2011. Date of publication September 1, 2011;date of current version October 20, 2011. This work was supported in part bythe California Energy Commission’s Energy Innovations Small Grant (EISG)under Transportation Grant 55785/08-01T, in collaboration with the Scientificand Technological Research Council of Turkey (TÜBITAK Evrena)-110E117.The review of this paper was coordinated by Mr. D. Diallo.

V. Sezer is with the Mechatronics Education and Research Center,Istanbul Technical University, Istanbul 34469, Turkey (e-mail:[email protected]).

M. Gokasan is with the Faculty of Electrical and Electronics Engineering,Istanbul Technical University, Istanbul 34469, Turkey (e-mail: [email protected]).

S. Bogosyan is with the Department of Electrical and Computer Engineering,the University of Alaska Fairbanks, Fairbanks, AK 99775 USA (e-mail:[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2011.2166981

of fuel consumption (FC) and emissions, have become verypopular in recent years. Improvements in HEV fuel economywith reduced emissions strongly depend on the supervisorycontrol strategies involved. A comparative study of some novelcontrol strategies can be found in [1]. A control algorithm thatappropriately manages the power distribution is a necessity forreduced emissions and FC in HEVs. To this aim, a new typeof equivalent consumption minimization strategy (ECMS) isdesigned in this paper for the series hybrid configuration.

Optimization algorithms in the literature are mostly designedfor parallel hybrid vehicles (PHEVS), and only a few algo-rithms exist for series hybrid electric vehicles (SHEVS). Someof these control strategies for SHEVs are given in [2]–[4],which adopt rule-based algorithms, dynamic programming,and the optimal control theory. Dynamic programming andglobal optimization algorithms are difficult to apply in realtime because of their heavy computation requirements. Thesealgorithms are generally used in offline simulations and infinding the global optimum solution to compare the real-timealgorithm performance.

ECMS is very popular in parallel hybrid electric vehicles(PHEV). Among the studies, we can cite [5]–[10]. These papersuse ECMS and its different versions such as maximizing overallefficiency strategy (MOES) for PHEVs to reduce the emissionsor FC [5]. Optimization approaches are more commonly soughtfor PHEVs mainly due to the operation principle of this config-uration, which is based on a power split between the engine andthe battery. However, ECMS can significantly benefit SHEVs,as demonstrated in [11] and [12], to optimize the power splitbetween the engine–generator set and the battery to minimizethe cost function.

ECMS uses the concept of negative or positive potentialcosts, depending on whether the electric power that is generatedby the engine–generator set (GENSET) is more or less thanthe requested mechanical power, and determines the optimumamount of the electric power that should be produced by theGENSET. Maintaining of the battery state of charge (SOC;charge sustaining) is very important in HEVs. Previous studieson ECMS [5]–[12] calculate the potential costs as a functionof the actual SOC to maintain the SOC around a rated value.If the actual SOC is less than the rated SOC, the potential costis penalized, resulting in a possible charging mode. Conversely,if the actual SOC is less than the rated value, the dischargingmode becomes a higher possibility. With this approach, thecharge can be sustained around its rated value; however, theoptimality is compromised to some extent.

The strategy that is used in the aforementioned studies is tosustain the SOC of the battery by penalizing the battery charge

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3558 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011

when the SOC is high and encouraging the battery chargewhen the SOC is low. This approach means that, if the SOCis very low, the battery is forced to be charged, and if the SOCis very high, the battery is forced to be discharged, althoughthese behaviors are inefficient in terms of the cost function.Therefore, SOC deviation is a factor of cost function in previousstudies. This paper uses ECMS for the SHEV configurationand, to further improve the optimization performance, proposesa new approach for the charge sustaining of the batteries.

One of the main contributions of this paper is to achievefurther minimization of emissions and FC by eliminating theeffect of “SOC deviation” from the cost function but by usingthe actual SOC values as an optimization constraint. Hence,in the proposed approach, the SOC deviation is not a penaltyfunction that forces the actual SOC to its rated value. In theclassical approach found in most previous literature, the SOCdeviation is a part of the penalty function; hence, when theSOC is very high, it is very expensive to charge the battery, andwhen the SOC is very low, it is very expensive to discharge thebattery. Because our main aim in this paper is the minimizationof FC and some selected emission components, intuitively, itmakes better sense not to include SOC deviation into the costfunction, but in this case, it is essential that SOC is taken intoaccount as an optimization constraint to avoid SOC drainingor overcharging. In this algorithm, as shown in Section IVand in Fig. 8, the upper and lower limits are determined forSOC, which is similar to the common approach in the on–offstrategies, as shown in [13]–[16]. However, in the proposedapproach, these upper/lower SOC limits are not used to start orstop the GENSET but, instead, to determine the search space forthe optimized power of the GENSET (Pgenset), as explained inSection IV. When the SOC touches the upper or lower limit, thealgorithm will narrow down the search space for the Pgenset,as a result of which the battery will be forced to charge ordischarge. More specifically, when the lower limit is hit, thesearch is conducted between the required power from te vehicledriver (Preq) and the highest limit of the GENSET power(Pgenset), and conversely, when the upper limit is reached, thePgenset is searched between the lowest limit of the GENSETpower (Pgenset) and the required power Preq . These searchregions are given in Fig. 8. Once the rated SOC level has beenreached, the algorithm is relaxed for the search, yielding a moreflexible and efficient performance.

As another contribution of this paper and different from theexisting supervisory control studies in the literature [5]–[12],which individually use multiple cost maps, a combined costmap is derived from individual cost maps [CO2, CO, NOX,total hydrocarbon (THC), and FC] for the minimization ofthe desired components with less computational burden, andyet with more of the targeted emission components and fueleconomy simultaneously taken into consideration. This ap-proach also avoids the increase in certain components whiledecreasing the targeted component. The combined cost map,which contains the characteristics of several different costmaps, is prepared before the minimization algorithm and is aweighted combination of different cost maps, such as CO2,CO, NOX, THC, and FC. Because the performance directlydepends on the weight coefficients, we can set the ratio of the

coefficients according to which components are desired to beminimized.

Finally, this paper also provides an optimized control strategyfor the GENSET to determine the operating point in termsof speed and torque. This calculation has not explicitly beenpresented in previous ECMS studies.

This paper is organized as follows. After the introduction inSection I, the combined cost map is derived using some normal-ization and weighting calculations. After the derivation of thiscost map, the GENSET is separately optimized. The derivedcost map and efficiency map of the electric motor are used forGENSET optimization. After the optimization of the GENSET,ECMS is developed with a new SOC-sustaining strategy asthe supervisory controller. ECMS determines how much powershould be requested from the optimized GENSET for cost min-imization. Finally, a case study for ECMS is done at the end ofthis paper using actual emission and FC map data. Comparisonsof this new algorithm are made with the “on–off control” [4]and “conventional mode” of the vehicle at the end of this paper.

II. DERIVATION OF COST MAP

In this section, the derivation of a cost map for an internalcombustion engine (ICE) using the specific emission and FCmaps is given prior to the derivation of ECMS. The commonapproach that is taken in previous studies is to derive costmaps either for the FC or for the emission component. Theproposed new map structure in this paper is a combinationof emission components and FC for reduced computationalburden. However, because FC and emission maps have differentscales, the derivation of the cost map is done by normalizingand weighting. The combined cost map can be viewed asprecomputation to reduce the real-time computation of severaltable lookup operations.

The ICE cost function is the weighted combination of CO,CO2, NOX, THC, and FC maps. The actual cost maps used areillustrated in Fig. 1. All the maps are given in terms of gramsper kilowatt hour, and as shown in the figures, they all havedifferent characteristics. For example the [2000 r/min–300 Nm]operating point is optimum for the NOX map, whereas it is notoptimum for FC and CO production, as shown in Fig. 1.

The FC and emission maps have different scales and henceshould be normalized as

Cni(ω, T ) =Ci(ω, T )

max [Ci(ω, T )](1)

where Ci(ω, T ) is the original cost map, and Cni(ω, T ) is thenormalized cost map (values are between 0 and 1).

After the derivation of the normalized the cost map, theweighting process is done by the following equation:

Cf (ω, T ) =k1Cn1 + k2Cn2 + · · · + klCnl

k1 + k2 · · · + kl(2)

wherel number of maps that are used;ki weight coefficient for the ith normalized cost

map;Cf (ω, T ) final cost map, which is normalized and

weighted.

SEZER et al.: NOVEL ECMS AND COMBINED COST MAP APPROACH FOR HIGH-EFFICIENCY SERIES HEVS 3559

Fig. 1. Cost map of the FC, CO, CO2, NOX, and THC components.

The ki coefficients depend on the components that are de-manded to be minimized and the amount of this demand.

The formulation in (2) can be thought for our examples as

Cf (ω, T )

=k1CCO+k2CCO2+k3CFC +k4CNOX +k5CTHC

k1+k2+k3+k4+k5. (3)

Some different examples of weighted and normalized finalmaps for different weight coefficients are illustrated in Fig. 2.The figure on the left is created for both FC and CO2 reduction,whereas the figure on the right is a more mixed map, in whichCO reduction is the main aim, and the FC, CO2, and THCreduction follow CO, respectively.

III. CONTROL STRATEGY

The proposed control strategy is divided into the followingtwo main parts:

1) the determination of the operating point of the GENSETin terms of speed and torque to produce the desired power,using the efficiency map of the generator and the cost mapof the ICE;

2) the determination of how much power should be pro-duced by the GENSET.

In the first stage, the operating points of the GENSET arecalculated in terms of speed and torque for each desired powervalue, which will be discussed in Section III in further detail.This calculation is done with the use of the previously derivedcombined cost map and the generator efficiency map. Theseoperating points are saved in a map with their costs for furtheruse in the algorithm.

In the second stage, the optimization of the desired GENSETelectric power is performed using the map calculated in thefirst stage. In this part, ECMS considers the effect of batteryefficiency and the potential costs of charging and dischargingthe batteries. This approach splits the search space into twoparts: the first part increases the SOC, whereas the other partdecreases the SOC. Normally and whenever possible, the searchis performed in the whole operating space of the GENSET.However, this space may be restricted to sustain the charge,depending on the actual SOC. To this aim, the search area ofthe optimization is dynamically divided into two parts. If thebattery SOC is between the upper and lower limits, the wholearea is used for the search, which means full optimization.However, if the SOC has reached one of the limit values,only one section of the whole area is used for search. Thiscase sustains the SOC between the predetermined limits witha realistic optimization. Charging or discharging may result in


Fig. 2. Normalized and weighted cost map for different weight coefficients.

a more efficient outcome under full optimization over a longperiod of time due to some torque request of the driver orother states of the vehicle. However, if the full optimization isimplemented without considering the upper and lower limits,SOC sustaining would be impossible. This case is the main roleof using the upper and lower limits.

IV. GENSET OPTIMIZATION

ECMS calculates the amount of power that should be pro-duced by the GENSET to meet the power demand of thedriver or the traction motor. The optimization strategy takes intoaccount whether any portion of the demanded power is met bythe batteries. If the GENSET power is lower than the driver’spower demand, the future replacement cost of this power iscalculated. Conversely, if some portion of the power could bepumped into the batteries, i.e., if the GENSET power is higherthan the driver’s power demand, the cost amount that will besaved in the future is also calculated.

To realize this approach, the minimum cost operating pointsof the ICE for each electric power output value of the GENSETshould be calculated.

A. GENSET Operating-Point Determination

The GENSET operating-point map given in Fig. 3 depicts therequired electric power from the GENSET (Pgenset) as inputs

Fig. 3. GENSET optimization map inputs and outputs.

Fig. 4. Generator efficiency map.

Fig. 5. Generator torque boundaries.

and the optimum operating torque (ToptICE), speed (ωoptICE),and cost as outputs.

For this optimization, all possible combinations of revolutionper minute and torque for each Pgenset value are calculated,considering the efficiency map of the generator. Because thetorque value of the ICE is not the torque value for elec-tric power generation, the ICE torque must be multiplied bythe generator efficiency, given in Fig. 4, to calculate howmuch electric power (Pgenset) needs to be produced. Thisfactor is the key of GENSET optimization. A search algo-rithm calculates the appropriate revolution per minute–torquevalues (ToptICE , ωoptICE), which yields the minimum costfor each Pgenset value. The cost value of this revolution perminute–torque combination is also calculated and saved in the


Fig. 6. GENSET optimum operating points and corresponding cost values for (k1 = 0, k2 = 0.5, k3 = 0.5, k4 = 0, and k5 = 0).

Fig. 7. GENSET optimum operating points and corresponding cost values for (k1 = 0.4, k2 = 0.2, k3 = 0.3, k4 = 0, and k5 = 0.1).

same table. During the search process, the maximum variationof the torque and speed values between adjacent power valuesis limited to track these operating points in real time, as shownin Figs. 6 and 7.

A generic generator efficiency map is illustrated in Fig. 4.The green dashed lines show the rated operating limits of thegenerator. The rated operating limit indicates that it is notrecommended to operate the motor outside this limit for a longtime period. According to Fig. 4, the most efficient regionis around the [2500 r/min–200 Nm] operating point, and themaximum efficiency is 93% in this region.

We should be careful when searching for the GENSEToptimization. Searching should be done in the intersection areaof boundary torque values of the ICE and the generator. Thesimulated ICE and generator torque–speed characteristics areshown in Fig. 5. Note that the generator torque restricts thesearch area.

The generator efficiency is considered when calculating thegenerated GENSET electric power. Consequently, the operatingpoints on the graphics do not precisely occur on the minimumcost value contours of the cost maps, as shown in Figs. 6 and 7.The optimum GENSET operating points are also illustratedin these diagrams, with the normalized cost values of theseoperating points given in Figs. 6 and 7, respectively. Thegenerator torque restricts the operating points, as illustrated inthe following figures. Each operating point corresponds to theoutput power of the GENSET between the minimum (10 kW)and the maximum (104 kW) power values, with intervals of500 W. There are no calculations performed beyond thatinterval.

It is possible to use these different cost maps in the samevehicle. The optimization results of these maps can be switchedonline while the car moves based on the existing conditions.

For example, in a city with high air pollution, the emissionreduction cost map can be used, whereas in a cleaner environ-ment, the FC cost map can be used. The switching can be donethrough the use of Global Positioning System (GPS) signals,which will provide information on the location of the vehicle.This information may be combined with other sensor data todetermine the emission ratio of the air.

V. OPTIMIZATION OF THE REQUESTED GENSET POWER

EQUIVALENT CONSUMPTION MINIMIZATION STRATEGY

The main aim of ECMS is to calculate the optimum powerthat will be produced by the GENSET to meet the powerdemand of the driver. Potential costs of the charge and dischargepower of battery system should be considered in this opti-mization. Previous studies calculate these potential costs as afunction of the actual SOC to maintain the charge sustaining, asaforementioned. However, these potential costs are independentof the actual SOC. Here, we have a more realistic solution forboth optimization and charge sustaining.

A. New Charge-Sustaining Method

Normally, the search for the optimal solution can be per-formed between the lower limit Pgensetmin and the upper limitPgensetmax of the GENSET. To maintain the charge sustaining,the GENSET operation is divided into the following modes:

• the SOC-increasing mode;• the SOC-decreasing mode;• the free operation mode.

Fig. 8 illustrates the usage of these three modes according toa sample SOC pattern.


Fig. 8. Sample SOC pattern with algorithm modes.

For the operation area of the battery system, the upper andlower limits are determined for the SOC, i.e., (SOChl) and(SOCll), respectively. The battery most efficiently operatesin between these limits. If the battery SOC touches the upperlimit (SOChl), the SOC-decreasing operation is implementeduntil the SOC attains the average value of SOChl and SOCll.SOC decreasing is performed by searching the GENSET power(Pgenset) between the lowest limit of the GENSET power(Pgenset) and the required power from the vehicle driver(Preq). Similarly, if the SOC touches the lower limit (SOCll),the SOC-increasing operation is implemented until the SOCagain reaches the average value. SOC increasing is performedby searching the GENSET power (Pgenset) between the Preq

value and the highest limit of the GENSET power (Pgenset).After reaching the average SOC value, the free operation modebecomes active. In this mode, the GENSET is free to charge ordischarge the batteries according to the optimization process.This means that the whole operational area of the GENSETcan be used from its minimum Pgenset to maximum output

power Pgenset for optimization. The free operation mode canbe considered the combination of the SOC-increasing and SOC-decreasing modes. The flowchart of this new charge-sustainingalgorithm is illustrated in Fig. 9.

With this charge-sustaining strategy, the optimization area isrestricted only if the SOC reaches its limits, whereas the wholeoptimization area is in use, as long as the SOC remains betweenthese limits in a driving cycle.

Each of the SOC-increasing and SOC-decreasing modes hasan instant and potential cost for the vehicle. Before explainingthe main two modes of the algorithm, the main equation thatsummarizes the power flow between the battery, GENSET, andtraction motor is given by

Preq = Pgenset + Pbat (4)

where Preq is the required power for traction motor, Pgenset

is the output electric power of the GENSET, and Pbat is thebattery power.

B. SOC-Decreasing Mode Operation

When the SOC-decreasing mode is active according to theactual SOC value, the electric power that is produced by the

Fig. 9. New SOC-sustaining algorithm.

GENSET should be lower than the required power. This condi-tion is achieved by searching in the GENSET range betweenthe minimum power (Pgenset) in the GENSET power rangeand the required power (Preq). Therefore, an additional batterypower will be used to supply the requested power from thetraction motor, which is shown in (2). For every operating pointof the GENSET, we have an instant cost Cost(PELgenset)and a potential cost Costpot. The instant cost comes from theoperating point of the GENSET, and the potential cost comesfrom the battery usage, which has to be regained in the futureoperations. Equation (5) shows the potential cost calculation,considering the battery efficiencies

Costpot = Cost

(Preq − Pgenset

ηbatηbat+

). (5)

Here, ηbat is the instant battery efficiency, and ηbat+ is theaverage battery efficiency in charge condition.

The real cost (Costreal) is the sum of the potential andinstant costs, i.e.,

Costreal = Cost(Pgenset) + Costpot. (6)

The operation point that minimizes the real cost is theoperation point of the GENSET (Pgenset) during the SOC-decreasing mode. The 1-D optimization problem is formulatedas follows:

minPgenset∈[Pgenset,Preq]

Costreal. (7)

The flowchart of the search algorithm of the GENSET forSOC decreasing is illustrated in Fig. 10. The cost values aretaken from the map, which was illustrated in Fig. 3 during thecalculations. ∆P is the search step of the algorithm.

C. SOC-Increasing Mode Optimization

When the battery SOC needs to be increased, the GENSETshould produce an electric power more than the driver demand


Fig. 10. SOC-decreasing-mode searching algorithm.

power (Preq). This is achieved by performing a search betweenthe required power (Preq) and the maximum power (Pgenset)in the GENSET power range, as a result of which some of theelectric power that is produced by the GENSET is stored in thebattery. This is also a result of (4). In this condition, again, thereis also an instant cost and a potential cost (Costpot); however,in this case, the potential cost comes from the stored power inthe battery, which will be used as traction power in the future.We have

Costpot = Cost ((Pgenset − Preq)ηbatηbat−) (8)

where ηbat is the instant battery efficiency, and ηbat− is theaverage battery efficiency in discharge condition.

The real cost is the difference of the potential and instantcosts, i.e.,

Costreal = Cost(Preq) − Costpot. (9)

The operation point that minimizes the real cost is the oper-ation point of the GENSET during the SOC increasing mode.The 1-D optimization problem is formulated as follows:

minPgenset∈[Preq,Pgenset]

Costreal. (10)

The flowchart of the GENSET search algorithm for the SOC-decreasing mode is illustrated in Fig. 11. ∆P is the search stepof the algorithm. The cost values are taken from the map, whichis illustrated in Fig. 3 during the calculations.

VI. CASE STUDY

This case study demonstrates the improved performance ofthe novel ECMS algorithm over the performance of the on–offstrategy running on the same vehicle, as well as the conven-

Fig. 11. SOC-increasing-mode searching algorithm.

Fig. 12. Longitudinal vehicle model.

tional strategy of the nonhybrid vehicle. The comparison isperformed using an actual mid-sized hybrid bus that is modeledby single-track vehicle dynamics equations.

The basic description of the simulation model has beendemonstrated in Fig. 12. Basically, the longitudinal vehiclemodel is described by the following equation:

m · a = Fxfront + Fxrear − Raero − Rxfront

− Rxrear − mg sin(θ) (11)

where m is the vehicle mass, a is the acceleration along thex-axis, Fxfront and Fxrear are the longitudinal tire forces com-puted using the Pacejka tire model, Raero is the aerodynamicdrag force, Rxfront and Rxrear are the rolling resistances, andθ is the inclination of the road.

Critical vehicle parameters that are used for simulations areillustrated in Table I.

Further information about modeling a hybrid vehicle can befound in [17]–[19]. In this paper, the reduction of the CO2emissions and FC is aimed as an example, resulting in (k1 =0, k2 = 0.5, k3 = 0.5, k4 = 0, k5 = 0) in (3). The optimized


TABLE IVEHICLE PARAMETERS

Fig. 13. Simulink model snapshot for simulations.

Fig. 14. SHEV architecture.

GENSET operating points of this cost map are illustratedin Fig. 11.

The ICE of the vehicle is the ISBe4 160B model of a112-kW diesel engine from the Cummins Engine Company. Itsspecific data, such as the torque–speed curve, emission, andefficiency maps, are used for performance comparisons. Thepermanent-magnet brushless dc (PMDC) electric motor andgenerator are UQM PowerPhase 150. The simulated batterypack is a KOKAM SLBP-60460330. Ninety-eight of these cellsare serially connected for the battery pack. The electric motor,generator, and battery pack components are modeled using theactual data provided by the manufacturers. A Simulink snapshotof the simulation model is given in Fig. 13, whereas Fig. 14

Fig. 15. KOKAM SLBP-60460330 battery efficiency map.

depicts the SHEV vehicle that is taken into account for thesimulations.

As aforementioned, we have three modes in ECMS. In thefree operation mode, which is illustrated in Fig. 8 as a free area,both the SOC-increasing and SOC-decreasing optimizations areperformed. The minimum of Costopt+ and Costopt− values isselected to determine the operating point of ICE in terms ofrevolution per minute and torque (ωICE , TICE). By means ofthis, the whole operating area of the GENSET is scanned in thefree operation mode.

The accuracy of the ECMS optimization strongly depends onthe ∆P step value. If it is not possible to do the calculationsin real time on the actual vehicle, these calculations can bedone offline, as was the case in this paper. One example of suchtables is illustrated as follows for CO2 reduction and FC (k1 =0, k2 = 0.5, k3 = 0.5, k4 = 0, k5 = 0). These maps show thePoptgenset− and Poptgenset+ values and the corresponding costvalues Costopt− and Costopt+. These values are calculated foreach requested power value. The efficiency of the battery is afunction of battery SOC; therefore, the SOC is also representedas an axis of the graphics. Battery current limitations are alsoconsidered in the algorithm, and the battery current is heldbetween −150 and 350 A. The parameters ηbat+ and ηbat−can be added as an axis to the graphs, but this approach isnot critical, because adding these parameters to the table willalso increase the data amount. These average battery efficiencyvalues are taken as 0.97 in these calculations according to thebattery efficiency map illustrated in Fig. 15.

Fig. 16(a) and (c) show the optimum power request valuesfrom the GENSET in the SOC-decreasing and SOC-increasingregions. Fig. 16(b) and (d) illustrates the cost values of theserequested power values. Fig. 16(b) and (d) is combined inFig. 17 to illustrate the benefit of this new approach.

Inspecting Fig. 17, we can clearly see the benefit of thisnew approach. In this figure, we see the normalized cost mapof the GENSET for both the charge and discharge conditions.Although the charge and discharge costs are both sensitive tothe SOC change, they do not demonstrate similar sensitivity.This condition is to be expected, because the search boundariesfor charging and discharging are different for every requestedpower value. The figure illustrates that both charging and


Fig. 16. Optimum power request and corresponding cost values for SOC-increasing and SOC-decreasing regions. (a) Optimum power request from the GENSETin the SOC-decreasing region. (b) Cost of Poptgenset− in the SOC-decreasing region. (c) Optimum power request from the GENSET in the SOC-increasing region.(d) Cost of Poptgenset+ in the SOC-increasing region.

Fig. 17. Cost of Poptgenset− and Poptgenset+.

discharging can be efficient at different times. By using this newSOC-sustaining technique, we have a free area, as illustrated inFig. 8, which means that the most appropriate of the charge anddischarge optimization processes can be selected here, whereas

Fig. 18. ECE inner city part (four times).

in the classical approach, a penalty function is always takeninto account for charge sustaining, and this approach restrictsthe operational freedom of the controller.

The comparisons are performed for the following three dif-ferent control strategies to better analyze and highlight thebenefits of the new approach:

• conventional vehicle (nonhybrid);• on–off strategy;• ECMS.


Fig. 19. Simulation results for a conventional vehicle. (a) ICE operating points. (b) Production (in grams) and FC (in grams).

The drive cycle that was used in the simulations is thecombination of four ECE cycles, as illustrated in Fig. 18.Because the algorithm is independent of the drive cycle, asimilar performance is expected for other cycles. The drivecycle determines the speed versus time profile of the vehicle.A driver model with a proportional–integral (PI) controller isalso designed for the simulations.

A. Conventional Vehicle

The simulations were started with the ICE alone, which isalso known as the conventional strategy. This strategy involvesno optimization, and all of the required torque is provided bythe ICE, with the mechanical brakes used for negative torquerequest.

The operating points of the ICE are shown in Fig. 19(a). Itis shown that operating points are scattered on the cost mapwithout any optimization. The corresponding CO2 productionand FC of the engine are given in Fig. 19(b). During four ECEinner city cycles, 2514 g of CO2 is produced, and 769.5 g offuel is consumed, as illustrated in Fig. 19(b).

B. On–Off Strategy

Next, the tests are performed for the on–off strategy. Thisapproach is the commonly applied method in SHEVs. Thestrategy is based on the operation of the ICE at a constantoperating point (revolution per minute and torque) until thebattery SOC reaches a predetermined upper limit. Then, theengine stops until the SOC reaches a predetermined lower limit.The flowchart of this basic operation is illustrated in Fig. 20.

The ICE operating point (Topt, ωopt) is selected as1950 r/min and 400 Nm, as illustrated by the red circle inFig. 21(a). This point is the optimum for the reduced CO2production and FC (k1 = 0, k2 = 0.5, k3 = 0.5, k4 = 0, k5 =0), as illustrated in Fig. 21(a).

The variation of the SOC is illustrated in Fig. 21(b). Theupper limit of the SOC is selected as 0.704, and the lowerlimit is selected as 0.696. As expected, the SOC profile is set ina triangular shape. The observed deviations from triangularityare caused by regenerative braking and engine stop. Fig. 21(c)depicts the CO2 production and FC during the cycle. Duringthe four ECE inner city cycles, 2324 g of CO2 is produced, and689.1 g of fuel is consumed, as illustrated in Fig. 21(c).

Fig. 20. Flowchart of the on–off mode.

C. ECMS With the Classical SOC-Sustaining Strategy

In this section, to provide a comparison benchmark for theproposed strategy, ECMS is implemented with the classicalSOC-sustaining method. The SOC deviation is a factor of costfunction in the classical approach.

The operating points of ICE, SOC variation, CO2 production,and FC graphics during the ECMS simulations for the classicalstrategy are shown in Fig. 22.

Fig. 22(b) demonstrates the SOC variation. Here, the classi-cal SOC-sustaining method is used without high and low limitsas illustrated in Fig. 8. The GENSET power is calculated withthe ECMS formulation that was given in the previous sectionsand a penalty function that penalizes the SOC deviation fromits rated value. The rated value of the SOC is selected as 0.7.

Fig. 22(c) depicts the CO2 production and FC of the vehicleduring the cycle, respectively. During the four ECE inner citycycles, 2230 g of CO2 is produced, and 679.6 g of fuel isconsumed, as illustrated in Fig. 22(c).

D. ECMS With the New SOC-Sustaining Strategy

Finally, ECMS with the new SOC-sustaining algorithm isimplemented to better evaluate the benefits of this new op-timization approach. This approach does not consider SOCdeviation, as illustrated in Section IV.

The operating points of ICE, SOC variation, CO2 production,and FC graphics during the ECMS simulations are shownin Fig. 23.


Fig. 21. Simulation results for the on–off control. (a) ICE operating point. (b) SOC variation. (c) CO2 production (in grams) and FC (in grams).

Fig. 22. Simulation results for ECMS with the classical SOC-sustaining strategy. (a) ICE operating points. (b) SOC variation. (c) CO2 production (in grams)and FC (in grams).

The operating points of the cost map for CO2 pro-duction and FC (k1 = 0, k2 = 0.5, k3 = 0.5, k4 = 0, k5 = 0)were shown in Fig. 6. In Fig. 23(a), it is shown thatthe operating points of the ICE are almost the same as

in Fig. 6. The differences between these two figures aremainly due to the performed interpolation, because theoptimized map in Fig. 6 was calculated for a 500-Wgrid.


Fig. 23. Simulation results for ECMS with the new SOC-sustaining strategy. (a) ICE operating points. (b) SOC variation. (c) CO2 production (in grams)and FC (in grams).

TABLE IICOMPARISON OF THE CO2, CO, AND NOX PRODUCTION AND FC FOR THE CONVENTIONAL, ON–OFF, AND ECMS MODES WITH

THE CLASSICAL AND NEW SOC-SUSTAINING METHOD IN THE ECE INNER CITY DRIVING CYCLE

Fig. 23(b) demonstrates the SOC variation. The upper limitof the SOC is selected as 0.704, and the lower limit is againselected as 0.696. The ECMS results for this case demonstratethe tendency of the SOC to increase. Regenerative braking isalso effective on this SOC profile. For example, some SOCvalues are higher than 0.704 due to regenerative braking nearthe SOC value of 0.704. Fig. 23(c) shows the CO2 productionand FC of the vehicle during the cycle, respectively. During thefour ECE inner city cycles, 2184 g of CO2 is produced, and659.5 g of fuel is consumed, as illustrated in Fig. 23(c).

E. Results

Table II demonstrates the CO2–CO–NOX–THC productionand FC results for the conventional method, on–off strategy, Fig. 24. Japanese 10–15-mode driving cycle.


TABLE IIICOMPARISON OF THE CO2, CO, AND NOX PRODUCTION AND FC FOR THE CONVENTIONAL, ON–OFF, AND ECMS MODES WITH

THE CLASSICAL AND NEW SOC-SUSTAINING METHOD IN THE JAPANESE 10–15-MODE DRIVING CYCLE

and ECMS in units of gram per kilometer. The difference ofthe initial and final SOC is also considered while calculatingthe CO2 production and FC values of the on–off and ECMSmodes.

Because the main weighted components were the CO2 andFC for the final cost map, a significant reduction in FC andCO2 production is obtained with the novel ECMS strategycompared with the on–off strategy. The THC and NOX pro-duction is not affected by our algorithm as much as the CO2and FC rates. Moreover, the NOX production increased be-cause of its different map characteristics compared with theCO2 and FC cost maps; however, NOX and THC could alsobe reduced along with the other emissions by changing theweight coefficients in the derivation of the cost map. If NOXand THC are also weighted along with other components,their reduction ratio will increase, whereas the CO2 and FCreduction ratios decrease. CO reduction is obtained near theCO2 and FC reduction rates due to its similar cost map withthe CO2 and FC cost maps. Finally, ECMS with the newSOC-sustaining method is generally better than the classicalSOC-sustaining method reductions. Only CO production ap-pears to be better in the classical SOC-sustaining method,but this case is not a concern, because our main aim was todecrease CO2 and FC according to the cost map that we haveused.

To show that the algorithm is independent of the drivingcycle, simulations are performed with another drive cycle. The10–15-mode cycle, which is illustrated in Fig. 24, is currentlyused in Japan for emission certification and fuel economy forlight-duty vehicles.

Table III demonstrates the CO2, CO, and NOX productionand FC for the conventional, on–off, and ECMS modes withthe classical and new SOC-sustaining methods in the Japanese10–15-mode driving cycle. Results are similar with Table II,which demonstrates the same results in the ECE inner citydriving cycles. This case shows that our algorithm is indepen-dent of the driving cycle.

VII. CONCLUSION

In this paper, new approaches have been developed forthe optimized operation of SHEVs in terms of improved fuel

economy and reduced emissions. The major contributions canbe listed as follows.

• A novel cost map concept has been presented, whichallows for the simultaneous optimization of a wide rangeof emission components (e.g., CO2, CO, NOX, THC, andFC). This approach is different from the commonly usedmultiple-cost-map approach, because it involves a singlecost map, which combines various cost maps related to thedesired performance.

• GENSET operating-point determination, the calculation ofwhich has not explicitly been presented in previous ECMSstudies, has also been presented. The determination of thetorque and speed values for a better tracking performanceis also a novelty.

• Finally, the ECMS performance was improved with anovel SOC-sustaining approach, resulting in a widersearch space and, hence, an improved optimization per-formance in the reduction of emissions and FC.

Using actual data from a mid-sized bus, the developed al-gorithm was compared to the on–off strategy for the samevehicle and a conventional vehicle. The results demonstrate14.26% reduction in CO2 production and 15.38% reduction inFC with ECMS, whereas the on–off controller achieves only9.06% reduction in CO2 production and 11.09% reduction inFC. This case is because ECMS considers the efficiency of theelectric path inside the potential cost. The on–off strategy, onthe other hand, does not care about how much current is drawnwhile charging/discharging batteries. Hence, it is expected thatthe advantage of the proposed ECMS will be even more evidentif less efficient batteries are used instead of Li-Ion batteries.One future direction can be using more than one type of thesecombined cost maps in the same vehicle and switching betweenthe optimization results according to the pollution conditions ofthe road and environment. The THC and NOX reduction ratiosare not satisfactory as much as CO2 and FC due to the selectionof weight coefficients in a cost map. Different combinations ofthese coefficients and their effects on all components’ reductionratio values can be analyzed in the future.

In this paper, the generator torque limits the search area ofthe ICE, as illustrated in Fig. 10. Using generators with highertorque capacity or using an appropriate gear ratio between the


ICE and the generator will provide an opportunity to search thewhole area of the ICE map. This approach may also improvethe ECMS performance for future studies.

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Volkan Sezer received the B.Sc. degree in electron-ics and telecommunications engineering from YildizTechnical University, Istanbul, Turkey, in 2005 andthe M.Sc. degree in mechatronics engineering fromIstanbul Technical University in 2008. He is cur-rently working toward the Ph.D. degree in controland automation engineering with the Faculty of Elec-trical and Electronics Engineering, Istanbul Techni-cal University.

Since 2009, he has been with the MechatronicsEducation and Research Center. His research inter-

ests include the control of hybrid electric vehicles, energy efficiency, thedesign of solar cars, active safety in road vehicles, semiautonomous vehicles,autonomous ground vehicles, trajectory planning, obstacle/collision avoidance,and real-time programming.

Metin Gokasan (M’09) received the B.Sc., M.Sc.,and Ph.D. degrees in electrical and control engi-neering from Istanbul Technical University, Istanbul,Turkey, in 1980, 1982, and 1990, respectively.

Between 2003 and 2006, he was a Visiting Scholarwith the University of Alaska, Fairbanks, where heconducted research and worked on several projectsinvolving the control of hybrid electric vehicles andsensorless control of induction motors. He is cur-rently a Professor with the Faculty of Electrical andElectronics Engineering, Istanbul Technical Univer-

sity, where he is also an acting Department Chair of Control Engineering. Hisresearch interests are the control of electrical machinery, power electronics andelectrical drives, and the control of hybrid electric vehicles and mechatronicssystems. He has authored two books and over 80 journal and conferencepublications

Dr. Gokasan is a member of the IEEE Industrial Electronics Society (IES)and the Technical Committee on Education in Engineering and IndustrialTechnologies of the IEEE IES.

Seta Bogosyan (M’92–SM’05) received the B.Sc.,M.Sc., and Ph.D. degrees in electrical and con-trol engineering from Istanbul Technical University,Istanbul, Turkey, in 1981, 1983, and 1991, respec-tively. She conducted her Ph.D. research with theCenter for Robotics, University of California, SantaBarbara.

Between 1987 and 1991, she was a Researcherand Lecturer with the Center for Robotics, Universityof California, Santa Barbara. For the last decade,she has been an Associate Professor with Istanbul

Technical University. She is currently a Faculty Member with the Department ofElectrical and Computer Engineering, University of Alaska, Fairbanks. She isthe author or coauthor of more than 100 journal papers, conference proceedings,and several book chapters. She is an Associate Editor for the InternationalJournal of Intelligent Automation and Soft Computing (Autosoft). Her researchinterests include motion control, high-efficiency control of hybrid electricvehicles, teleoperation/bilateral control systems, and applications of nonlinearcontrol/estimation techniques to electromechanical systems in general.

Dr. Bogosyan is an Associate Editor for the IEEE TRANSACTIONS

ON INDUSTRIAL ELECTRONICS and the IEEE INDUSTRIAL ELECTRONICS

SOCIETY MAGAZINE.

Documents

A Novel ECMS and Combined Cost Map Approach