ArticleBattery Sizing for Electric Vehicles Based on RealDriving Patterns in ThailandBongkotchaporn Duangsrikaew 1, *, Jiravan Mongkoltanatas 2 , Chi-na Benyajati 2 ,Preecha Karin 3 and Katsunori Hanamura 41234*National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park,Thanon Phahonyothin, Tambon Khlong Nueng, Amphoe Khlong Luang, Phathum Thanni 12120, ThailandMTEC, National Science and Technology Development Agency (NSTDA), 114 Thailand Science Park,Thanon Phahonyothin, Tambon Khlong Nueng, Amphoe Khlong Luang, Phathum Thanni 12120, Thailand;[email protected] (J.M.); [email protected] (C.-n.B.)King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520,Thailand; [email protected] of Mechanical Engineering, Tokyo Institute of Technology, Tokyo 152-8550, Japan;[email protected]: [email protected]; Tel.: 66-8-64123751 Received: 5 April 2019; Accepted: 12 June 2019; Published: 15 June 2019Abstract: The rising population in suburban areas have led to an increasing demand for commuterbuses. Coupled with a desire to reduce pollution from the daily routine of traveling and transportation,electric vehicles have become more interesting as an alternative placement for internal combustionengine vehicles. However, in comparison to those conventional vehicles, electric vehicles have an issueof limited driving range. One of the main challenges in designing electric vehicles (EVs) is to estimatethe size and power of energy storage system, i.e., battery pack, for any specific application. Reliableinformation on energy consumption of vehicle of interest is therefore necessary for a successful EVimplementation in terms of both performance and cost. However, energy consumption usuallydepends on several factors such as traffic conditions, driving cycle, velocities, road topology, etc.This paper presents an energy consumption analysis of electric vehicle in three different route typesi.e., closed-area, inter-city, and local feeder operated by campus tram and shuttle bus. The drivingdata of NGV campus trams operating in a university located in suburban Bangkok and that of shuttlebuses operating between local areas and en route to the city were collected and the correspondingrepresentative driving cycles for each route were generated. The purpose of this study was to carryout a battery sizing based on the fulfilment of power requirements from the representative realdriving pattern in Thailand. The real driving cycle data i.e., velocity and vehicle global position werecollected through a GPS-based piece of equipment, VBOX. Three campus driving data types weregathered to achieve a suitable dimensioning of battery systems for electrified university public buses.Keywords: electric vehicle; energy consumption; driving cycle; battery sizing1. IntroductionDue to increasing demand for vehicles with lower consumption and emissions, the electric vehicleis one of the alternatives for efficient and clean energy solution [1,2]. The concentrations of PM10 , PM2.5 ,O3 , and N2 O in Bangkok, Thailand still exceed the standard level of national ambient air quality in2019. This information demonstrates that the Bangkok area is still facing an air pollution problem, witha major contributing factor being transport activity, especially by hazardous particulate matter (PM 2.5)from diesel vehicles’ exhaust emissions. The Pollution Control Department has created a master planfor the Air Quality Management for a 20-year period (2018–2037) including an impact prevention andWorld Electric Vehicle Journal 2019, 10, 43; doi:10.3390/wevj10020043www.mdpi.com/journal/wevj
World Electric Vehicle Journal 2019, 10, 432 of 15proactive prevention, which aim to reduce pollution by elevating the standards of exhaust for newvehicles, together with an improvement in fuel quality. This has led to a launch of “zero emission”regulations for new vehicles to promote the usage of electric vehicles and public transportation .The electrification of public transport vehicles could be carried out by utilizing different technologicalsolutions [4–6]. Many challenges facing electric vehicles such as limited range and speed, sparse ofelectric charging stations, long recharge time, etc. are related to an energy storage system design(energy and power), i.e., battery packs, for any specific application [6,7]. Several design approacheson battery sizing have been based on a real-world driving pattern [5,8]. Driving cycle is the series ofdata representing the speed of the vehicle versus time. It is important for a fleet to match routes tobattery technology to achieve maximum benefit. Hence, knowledge of driving cycles is important forimproving the electric vehicle performance and design purposes [7–9].Driving cycles have been developed by various organizations from many countries. They areused mainly for evaluation of performance, vehicle efficiency, energy consumption, and emission.The patterns and behaviors of driving characteristics differ depending on the area or city and country.It is therefore difficult to use one of the developed driving cycles for another city, even in the samecountry. As a result, this study focused on developing three different driving cycles of closed-area,inter-city, and local feeder by the collection of real-world data in specific areas. Designing driving cyclesis the abstraction and sublimation of a large amount of driving data. Although there are many methodsfor designing driving cycles, the cognition about its essential characteristic is not very clear .Many studies around the world [11,12] suggested that the driving cycle could be used for estimatingthe emission and analyzing fuel consumption for vehicles. A study by  also mentioned that pollutantemission and energy economy depend on vehicle characteristics, and actual driving data. In China,energy management strategies (EMS) for a plug-in hybrid electric vehicle (PHEV) were optimized bythe driving cycle model and dynamic programming (DP) algorithm . The relationships betweenthe energy consumption and vehicle velocity, acceleration, and roadway gradient were analysed [2,14].Energy consumption, vehicle scheduling, grid load profiles, and battery capacity were analysed byusing an existing bus network in the German city of Muenster regarding its electrification potentialwith fast charging battery buses . A practical methodology for constructing a representative drivingcycle must reflect the real-world driving conditions; however, there are several methodologies ofdriving cycle construction depending on the purpose of applying and the organization. The realdriving data is thus necessary to estimate the energy consumption for the best electric vehicle efficiency.Several design approaches on battery sizing have been based on a real-world driving pattern .Sizing battery methodology is based on the power requirements, including sustained speed tests andstochastic driving cycles.The relationships between different driving patterns and corresponding energy consumptionswere investigated in this paper. The main focus was on the comparison of minimum required batterysizing for each type of service route in the vicinity of Bangkok, Thailand. The aim was to provide aqualitative guideline for any interest party/stake-holder about the minimum amount/size of energystorage system needed for the service vehicles which would normally be required to operate in differenttypes of routes for efficient fleet management and planning including relevant charging facilities.This study presented the resulting route specific power and energy consumption analysis for the sizingbattery of closed-area, inter-city, and local feeder based on university bus driving cycles in Thailand.First, the driving data of university campus trams, university shuttle bus and university bus (SalayaLink) were collected and used as a reference. Then, the real driving cycle data, i.e., velocity and vehicleglobal position latitude and longitude, including road slope, were collected through a GPS-basedequipment, VBOX VB20SL3, Racelogic Ltd. (Buckingham, UK). Next, the calculated power and energyconsumptions were determined and used to design the suitable battery technologies and sizes.
World Electric Vehicle Journal 2019, 10, 433 of 152. Materials and W333 ofof15of 15152.1. Data CollectionTheonaaa operatingdatacollectedthroughGPS-basedThe analysisanalysis waswas basedbasedonVehiclereal-worldoperatingdata collectedcollected throughthrough aaa GPS-basedGPS-basedWorld ElectricJournal 2018, 9, x FORPEER REVIEWdata3 of setupareareshownshownininTableTableThe analysiswas detailsbased onofa datareal-worldoperatingdata arecollectedthrougha GPS-basedVBOX (VB20SL3, RacelogicLtd.). Thecollectionsetupshownin Table18.104.22.168.equipment, VBOX (VB20SL3, Racelogic Ltd.). The details of data collection setup are shown in Table1.Table 1. Summary of data collection processes: routes and outesroutesandandequipment.equipment.Table 1. Summary of data collection processes: routes and equipment.Typeof reaType ofClosed-ArearouteTramTramTramVehicles y LocalLocalFeederLocalFeederShuttle ayaLinkShuttleBusSalayaLinkShuttle Bus Salaya3535353535353535 0015.4124.614,21.10015.4124.614, 21.10021.10015.41176, 246,46,444 61.646,6,222 461.64VBOX 61.64Measurement Equipment(VB20SL3,Racelogic rementEquipmentAcquired DataSpeed(km/h), Latitude,Longitude,Time(s),Brake B20SL3,RacelogicRacelogicLtd.)Ltd.)The routessurveyedin thisstudycan Longitude,beseparatedintothree maintypes.TriggerOne of the fNumberofseatsNumberofseatsseatsNumberof seatsDDavg(km)Davgavg(km)(km)Davg (km)N(cycle)Ncycle(cycle)NcyclecycleN(cycle)cycle (cycle)Dtotal (km)DDtotal(km)Dtotaltotal(km)(km)Number of seatsClosed-Area2929292929Davg (km) 5.5575.5575.5575.557Ncycle (cycle)17171717Dtotal (km)94.47494.47494.47494.474service routes operated only in the university or “closed-area”. Additionally, another service offeredtransportation between the main and the branch campus located in the city or “inter-city”. The threemaintypes.OneoftheselectedThe routesroutessurveyedin thisthisstudycan bebecanseparatedinto threethreemaintypes.OneofOnethe ncampustothe nearestMassTransitSystemor“localThe routessurveyedthis tedfeeder”.Theuniversityoperation routesaredepicted in anotherserviceserviceofferedofferedservice routes operated only in the university or “closed-area”. Additionally, another service ndmainandthe branchcampuslocatedintheor “inter-city”.The ��localgroupgroupof mainMassTransitSystemor“localof services connected the main campus to the nearest main Mass Transit System or “local n inFigures1–3.(a)(b)(c)(d)Figure 1. Closed-area routes (a) Route1; (b) Route2; (c) Route 3; (d) Route 4.Commented [M3]: Please cin this manuscript(a)(a)(a)(b)(b)(b)(a)(c)(c)(c)(d)(d)(d) ) Route1; (b) Route2; (c) Route(d)3; (d) Route 4.Figure 1. Closed-area routes (a)Figure 2. Inter-City Routes: (a) Salaya-Wit ; (b) Wit-Salaya; (c) Salaya-Siriraj; (d) Siriraj-Salaya.Commented [M4]: Please cin this riraj-Salaya.
World Electric Vehicle Journal 2019, 10, 434 of 15Figure 2. Inter-City Routes: (a) Salaya-Wit; (b) Wit-Salaya; (c) Salaya-Siriraj; (d) Siriraj-Salaya.Figure 3. Local Feeder Routes: Salaya Link.2.2. Driving Cycle DevelopmentIn this study, the relation of driving cycle and energy consumption was a subject of interest fordesigning energy storage. First, the driving data of tram, shuttle bus, and Salaya Link were collectedfor closed-area route, inter-city route, and local feeder routes. Collected data were simulated in orderto generate the driving cycle pattern.2.2.1. Collected Driving Data CharacteristicsIn order to distinguish between each service route, the operating characteristics of each routetype were represented by the value of average velocity standard deviation (Vsd ), average speed(Vavg ), maximum velocity (Vmax ), and average time per one cycle. The operating characteristics from
World Electric Vehicle Journal 2019, 10, 435 of 15closed-area, inter-city, and local feeder of different service routes are shown in Tables 2–4. Tlimit is theaveraged time per cycle (s) that is an important factor for driving cycle generation process.Table 2. Closed-area characteristics per cycle for each route.Velocity (h:mm:ss)0:13:220:19:490:21:150:14:55Passengers (Person)MeanMax5913634304844Table 3. Inter-city characteristics per cycle for each route.Velocity :mm:ss)1:01:151:01:060:39:180:52:03Passengers (Person)MeanMax2763Table 4. Local feeder characteristics per cycle for each route.Velocity mit(h:mm:ss)1:21:55Passengers (Person)MeanMax22442.2.2. Microtrip Data SegmentationMicrotrip is a small portion of driving data that could be separated by periods of idle time.The process details for the driving data separation into microtrips can be illustrated as in Figure 4.From the previous study , the number of microtrips method (NM) was employed to separatedriving data to microtrips. However, it was found that the percentage error of distance and idle timefrom the NM method was not suitable for a speed range that exceeded 30 km/h. To solve this particularproblem, the time spent method (TM) was introduced for improving the obtained distance and idleerrors. The speed range limit time Trange of TM was calculated as Equation (1):Trange T Tlimit ,(1)where Trange is speed range limit time (s), T is time spent in microtrip in each speed ranges (%), andTlimit is the averaged time per cycle (s).
problem, the time spent method (TM) was introduced for improving the obtained distance and idleerrors. The speed range limit time (T) of TM was calculated as Equation (1):𝑇where𝑇 Vehicleis speedrangeWorldElectricJournal 2019,10,limit43 time (s),𝑇 𝑇 𝑇,(1)𝑇 is time spent in microtrip in each speed ranges (%),6 andof 15is the averaged time per cycle (s).Figure 4. MicrotripMicrotrip separation process.2.2.3. DrivingDriving CycleCycle ConstructionConstruction2.2.3.Each routeroute surveyedsurveyed inin thisthis studystudy hadhad aa differentdifferent operatingoperating patternpattern becausebecause ofof thethe naturenature ofof dy,operations such as T-junction, washboard road, pedestrian crossing, traffic light, etc. In this study,driving cyclecycle patternspatterns werewere constructedconstructed byby usingusing thethe speedspeed rangerange limitlimit timetime (𝑇(Trange )) calculatedasdrivingcalculated asEquation (1). The sum of error including weight factor use for the main criterion constructed drivingEquation (1). The sum of error including weight factor use for the main criterion constructed drivingcycle must be within 15%. The procedure of driving cycle construction is shown graphically in Figure 5.cyclemust be within 15%. The procedure of driving cycle construction is shown graphically in Figure22.214.171.124. Generated Driving CycleIn the previous section, a driving cycle in each service route was formed by a random selectionof microtrips from all collected driving data. The example of generated driving cycle is illustratedin Figure 6. In this study, ten candidates of such driving cycles were generated for each route.The percentage of the error between, time, distance, and idle were considered in order to select the bestrepresentative driving cycle. The sum error of the time of generated driving cycle can accepted in 15%.
WorldWorld ElectricElectric VehicleVehicle JournalJournal 2019,2018, 10,9, x43FOR PEER REVIEWof 151567 ofFigure 5. Driving cycle construction procedure flow chart.Figure 5. Driving cycle construction procedure flow chart.The generated driving cycle was employed to calculate the errors between generated time of each2.2.4.CyclespeedGeneratedrange (T genDriving) and Trange. The resulting error from each speed range, i.e., E1 , E2 , . . . , E8 , was thefunction of discrepancy between T gen and Trange including weight factor by time spent of microtrip forIn the previous section, a driving cycle in each service route was formed by a random selectionof microtrips from all collected driving data. The example of generated driving cycle is illustrated inFigure 6. In this study, ten candidates of such driving cycles were generated for each route. Thepercentage of the error between, time, distance, and idle were considered in order to select the best
World Electric Vehicle Journal 2019, 10, 438 of 15each speed range presented in the database. The equation of the errors calculation in each speed rangeis given by:Trange T gen(2) Wan bn 100 (%),En Trangewhere En is the error of the speed range n-th, n 1, 2, . . . , 8, Wa b is weight factor for each speed range0–10 km/h, . . . , and 70–80 km/h, respectively. In other words, the contributions to the total error (E)were due to the percentage of number of microtrip in each speed range for each route. Finally, the sumof error including weight factor was determined as follows:X8World Electric Vehicle Journal 2018,E 9, x FOR PEER E1REVIEW E2 E3i 1 E4 E5 E6 E7 E8 ,7 of(3)15representativedrivingcycle.The sum error of the time of generated driving cycle can accepted inwhereE is the totalof error(%).15%.Figure 6. The example of the generated of driving cycle.Figure 6. The example of the generated of driving cycle.2.3. Energy Consumption CalculationThe generated driving cycle was employed to calculate the errors between generated time of2.3.1.TractionEnergyCalculationeach speedrange(𝑇 Consumption) and 𝑇. Theresulting error from each speed range, i.e ,.E1, E2, , E8, wasthe functionofdiscrepancybetween𝑇and 𝑇 from includingweighttheoryfactorof bytimedynamics.spent ofThe traction energy consumption was calculatedthe inIn this study, the electric power was assumed to be equal to the power to produce a tractive force andeachspeedinvolvingrange is givenby: component and auxiliary system, and regenerative brake were ignored.theenergythe mainThe tractive force is described by the following equation: 𝑊 100 (%),𝐸 (2)F R Rr Rcl ,(4)2, , 8, 𝑊is weight factor for each speedwhere 𝐸 is the error of the speed range n-th,a n 1,range 0–10 km/h, , and 70–80 km/h, respectively. In other words, the contributions to the total errorwhere F is tractive force (N), Ra is the aerodynamic resistance (N), Rr is the rolling resistance (N), and Rcl(E) were due to the percentage of number of microtrip in each speed range for each route. Finally,is grade resistance (N). Ra , Rr , and Rcl were calculated when a tram was traveling at constant velocity:the sum of error including weight factor was determined as follows:ρ𝐸 𝐸𝐸Ra 𝐸 Cd 𝐸Av2 ,𝐸𝐸𝐸𝐸,(5)(3)2where E is the total of error (%).Rr fr mgcosθ,(6)2.3. Energy Consumption CalculationRcl mgsinθ,(7)ρF CalculationCd Av2 fr mgcosθ mgsinθ,(8)2.3.1. Traction Energy Consumption23consumptionwas calculated from the fundamentaltheory of vehiclewhereThev istractionvelocityenergy(m/s2 ), Cd is coefficient of drag, ρ is air density (kg/m ), A is frontal area of the32 ), m is aamassdynamics.electric powerwasg assumedbe equal to theto producetractivevehicle(m In), thisfr is study,rollingtheresistanceconstant,is gravitytoacceleration(g power9.81 auxiliarysystem,andregenerativebrakevehicle (kg), θ is the road grade (degree), and F is the Tractive force (N). Finally, the tractive force (F) iswere ignored.The(8)tractiveforce is describedfollowingfoundin Equationby combiningEquationby(5),theEquation(6),equation:and Equation (7). To calculate energyconsumption, the power for vehicle traveling𝐹 𝑅at velocity𝑅𝑅 (v), was required. Required power could be(4)determined from the relationship between F and v in Equation (9):where 𝐹 is tractive force (N), 𝑅 is the aerodynamic resistance (N), 𝑅 is the rolling resistance (N),and 𝑅 is grade resistance (N). 𝑅 , 𝑅 , andP 𝑅were calculated when a tram was traveling(9)at F·v,constant velocity:𝑅 𝐶𝐴𝑣 ,(5)𝑅 𝑓 𝑚𝑔𝑐𝑜𝑠𝜃,(6)𝑅 𝑚𝑔𝑠𝑖𝑛𝜃,(7)
World Electric Vehicle Journal 2018, 9, x FOR PEER REVIEW8 of 15energythe 10,powerWorldElectricconsumption,Vehicle Journal 2019,43 for vehicle traveling at velocity (v) was required. Required power9 of 15could be determined from the relationship between F and v in Equation (9):𝑃 𝐹 and𝑣, v is velocity (m/s2 ). In this study, the traction(9)where P power (Watt) is, F is the tractive force(N),energyconsumptionwas calculatedusing geometricof 9-meterandwhere𝑃 power (Watt)is, 𝐹 is thebytractiveforce (N), parametersand 𝑣 is velocity(m/s2EV). Inbusthisprototypestudy, thetractionenergyconsumptionwasotherconstantsas shownin Table5. calculated by using geometric parameters of 9-meter EV busprototype and other constants as shown in Table 5.Table 5. Parameters for energy consumption calculation.Table 5. Parameters for energy consumption calculation.General Characteristics of Vehicle (Medium-Sized Bus)GeneralParametersCharacteristics of Vehicle (Medium-SizedBus)ValueParametersCurb weight (kg)9000 Value7.5Vehiclefrontal(kg)area (m2 )Curbweight90000.0015 7.5VehicleRollingfrontalResistancearea (m2)Dragcoefficient0.7 0.0015RollingResistance0.114Air Density (kg/m3 )Drag coefficient0.79.8Gravity Acceleration (m/s2 )Air Density (kg/m3)0.114Gravity Acceleration (m/s2)9.8The values from Table 5 were used for calculating power in Equation (9). The transmissionTheofvaluesfrom drivetrainTable 5 werefor calculatingpowerinstudy.EquationThe transmissionefficiencythe electricwasusedassumedto be 100%in thisThe(9).controlvariables wereefficiencythe electric inter-city,drivetrainandwaslocalassumedto routesbe 100%in thiscyclesstudy.generatedThe riablesdescribedwerein thevelocity.The closed-area,and tedas describedinprevioussectionwere used inter-city,as a calculationinputfor eachvehicle weightwas a reachserviceroute.Avehicleweightwasaof the curb weight and the maximum passenger weight recorded in each university service ghtrecordedineachuniversityserviceMATLAB Simulink (version, Manufacturer, City, US State abbrev. if applicable, Country) was used toroutes.theMATLABSimulink(version,City,US State abbrev.if applicable,Country)wascalculatemaximumpowerand theManufacturer,traction energyconsumptionby usingthe workflowas onenergyconsumptionbyusingtheworkflowin Figure 7.as depicted in Figure 7.Figure 7. MATLAB Simulink for energy consumption calculation.Figure 7. MATLAB Simulink for energy consumption calculation.2.3.2. EV Main Components and Auxiliary System Energy Consumption sandauxiliarysystemhavea significanteffect to the overall parametersofcomponentssystemThe main components and auxiliary system could have a significant effect toandthe auxiliaryoverall energyforconsumptionenergy consumptionof electriccalculation.vehicles. Table 6 shows the parameters of components and auxiliary systemfor energy consumption calculation.Table 6. Parameters of components and auxiliary system for energy consumption calculation.ComponentsLoad (kW)Pneumatic pumpAir conditionDC water cooling pumpSteering pump and controllerAccessory load2.2100.061.50.5
World Electric Vehicle Journal 2019, 10, 4310 of 15The constant value of total load for main components and auxiliary system used in this study was14.26 kW. The EV main component and auxiliary system energy consumption (Ec ) were calculated asEquation (10):TEc Pc total ,(10)3600where Ec is energy consumption of EV main components and auxiliary system (kWh), Pc is EV maincomponents and auxiliary system load (kW), and Ttotal is time of representative driving cycle (s).2.3.3. Total Energy ConsumptionThe total energy consumption rate (Etotal ) is sum of traction energy consumption (Ed ) and mainEV components and auxiliary system (Ec ) divided by distance per cycle of each route. The energyconsumption rate (kWh/km) is shown in Equation (11):Etotal Ed Ec,D(11)where Etotal is energy consumption rate based on driving cycle and EV main components and auxiliarysystem (kWh/km), Ed is energy consumption based on driving cycle (kWh), Ec is energy consumption ofEV main components and auxiliary system (kWh), and D is representative driving cycle distance (km).2.4. Battery SizingOne significant parameter of electric vehicles is installed battery energy in watt-hours (Wh)because of its high installation cost and lower energy density compared to gasoline. The tractionenergy consumption rate and minimal required power that were obtained in the previous section wereused as the main parameters to design the battery. The minimal required size of the battery is based ondaily power usage, charging strategy and feeder design. Operation distance per day (Dtotal ) of the timespent method was calculated as Equation (12):Dtotal Ncycle Davg ,(12)where Dtotal is operation distance per day (km), Ncycle is number of driving cycle per day (cycles), andDavg is average distance per cycle (km).For sizing the battery, assuming daily charge, total energy consumption rate (Etotal ) and operationdistance per day (Dtotal ) were used to calculate minimal required energy from battery sizing per day ofeach route as Equation (13):Erequired Etotal Dtotal ,(13)where Erequired is energy consumption for battery sizing (kWh), Etotal is sum of traction energyconsumption rate and EV main components and auxiliary system (kWh/km), and Dtotal is operationdistance per day (km).3. Results and Discussion3.1. Driving CycleThe time spent for the microtrip in each speed range (Trange ) was used to generate the drivingcycle which is the sum of time error (E) of each speed range that must be within 15% as explained inSection 2.2. Close-area, inter-city, and local feeder had significantly different characteristics, close-areaspeed range was 0–40 km/h and inter-city and local feeder speed range was 0–100 km/h. The resultingdriving cycles were then constructed as explained in Section 2.2.3. Ten candidates of generated drivingcycle were chosen by the lowest sum of error which had taken into account the weight factor asexplained in Section 2.2.4. The representative driving cycles for each route are shown in Figures 8–10.
Several design approaches on battery sizing have been based on a real-world driving pattern . Sizing battery methodology is based on the power requirements, including sustained speed tests and stochastic driving cycles. The relationships between di erent driving patterns and corresponding energy consumptions were investigated in this paper.