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International Journal of Automotive Technology, Vol. 13, No. 5, pp. 701−711 (2012) DOI 10.1007/s12239−012−0069−5

Copyright © 2012 KSAE/ 066−02 pISSN 1229−9138/ eISSN 1976-3832

SIMULATION OF A POWERTRAIN SYSTEM FOR THE DIESEL HYBRID ELECTRIC BUS B. SUH1), Y. H. CHANG2)*, S. B. HAN2) and Y. J. CHUNG3) 1)

Department of Mechanical & Aeronautical Engineering, University of California Davis, Davis, CA 95616, USA 2) Department of Mechanical & Automotive Engineering, Induk University, Seoul 139-749, Korea 3) Department of Automotive Engineering, Daegu Mirae College, Gyeongbuk 712-716, Korea (Received 22 October 2010; Revised 30 May 2011; Accepted 14 November 2011) ABSTRACT−The plug-in hybrid electric bus (HEB) is designed to overcome the vulnerable driving range and performance limitations of a purely electric vehicle (EV) and have an improved fuel economy and lower exhaust emissions than those of a conventional bus and convention HEBs. The control strategy of the plug-in parallel HEB’s complicated connected propulsion system is one of the most significant factors for achieving a higher fuel economy and lower exhaust emissions than those of the HEV. The proposed powertrain control strategy has flexibility in adapting to the battery’s state of charge (SOC), exhaust emissions, classified driving patterns, driving conditions, and engine temperature. Simulation is required to model hybrid powertrain systems and test and develop powertrain control strategies for the plug-in parallel HEB. This paper describes the simulation analysis tools, powertrain components’ models and modifications, simulation procedure, and simulation results. KEY WORDS : Plug-in hybrid bus (HEB), Electric vehicle (EV), State of charge (SOC), Internal combustion engine (ICE)

1. INTRODUCTION

electric vehicles have been successfully commercialized (Zhou and Hashimoto, 2004). Recently, there has been a growing interest in the hybridization of heavy-duty vehicles such as buses and trucks. Many cities are interested in the hybridization of diesel buses, which are responsible for atmospheric and noise pollution in the city (Plassat, 2004; Trigui et al., 2003; Wang et al., 2003). The objective of this research is to design the powertrain of a plug-in parallel hybrid electric bus (HEB) and develop a powertrain control strategy by using an optimization technique for the plug-in parallel HEB. To ensure a known level of optimality, the control strategy can be obtained by the instantaneous optimization technique. To overcome the delayed computational time of optimization, the powertrain control strategy is established by instantaneous optimization based on past information during a given period and is used to control the hybrid propulsion system and its components for a subsequent given period. Furthermore, an emission optimization that is flexibly adjusted according to the engine temperature, driving conditions, and hybrid mode is adopted into the powertrain control strategy optimization. The optimization searches for a point of compromise between fuel economy and emissions according to a specific emission standard. Finally, the proposed control strategy should be simulated to prove its validity using the analysis simulation tool ADVISOR (Markel and Wipke, 2001). In ADVISOR,

Atmospheric contamination by exhaust gases from automobiles threatens our health and nature, and the increased use of automobiles results in negative outcomes such as global warming. The transportation sector, rather than the industrial sector, has become the main cause of air pollution. Many of the medium- and heavy-duty vehicles using diesel fuels are discharging the same amount of exhaust emissions as vehicles using gasoline fuels. To reduce fuel consumption and air pollutants, many major automobile companies are facing a great turning point in technology that can overcome the limitations of the current conventional powertrain configurations of internal combustion engine (ICE). In recent decades, new vehicle powertrain systems and alternative energy sources have been proposed and developed (Matheson, 2003; He et al., 2006). The hybrid electric vehicle (HEV) has achieved recognition as an attractive and effective solution for reducing fuel consumption and exhaust emissions without compromising on-road performance. The HEV refers to a vehicle that obtains propulsion power from two or more types of energy sources, of which at least one is in the form of electric energy (Husain, 2003; Anatone and Cipollone, 2004; Chan and Chau, 2001). To date, many passenger and sport utility vehicle (SUV) hybrid *Corresponding author. e-mail: [email protected] 701

702

B. SUH, Y. H. CHANG, S. B. HAN and Y. J. CHUNG

the Simulink block diagrams of specific components are modified and rebuilt for new powertrain control strategies, including continuously variable transmission (CVT) gear shifting, hybrid mode selection, emission optimization application, driving pattern classification approach and revised CVT. A probabilistic neural network (PNN) is utilized to classify the driving pattern of the HEB as either urban and highway driving. The PTC activates the tradeoff optimization in the optimal control strategy depending on whether the amount of emissions is above the emission standard and the engine temperature is cold or hot.

2. ANALYSIS TOOLS FOR THE SIMULATION Various computer simulation tools and methods are used to design and develop an automotive powertrain and simulate and evaluate the powertrain control strategies of the vehicle. The simulation tools’ results include the fuel economy, exhaust emissions, and driving performance of the vehicle models. These results are then used to evaluate the vehicle models and control strategies. Computer simulation methods can be classified as either forward- or backwardlooking. This method can simulate the dynamics of a system. As shown in Figure 1, the powertrain controller (PTC) provides control commands for each powertrain component based on input signals, such as the braking and acceleration pedal angles, from a driver model that corresponds to the reference signal of the driving cycle and vehicle speed. Each powertrain component receiving orders from the PTC produces the output values representing physical reactions of the dynamic model. These output values allow for the real-time control systems of the vehicle to be analyzed and developed with proper modeling of the control systems and component behavior. The PNGV Systems Analysis Toolkit (PSAT) is a forward-looking simulation tool that has been used to test the powertrain controllers of the hybrid electric vehicles of UC Davis’ HEV center. PSAT is useful for sizing components and estimating the fuel economy and emissions of conventional and alternative vehicles, such as hybrid electric vehicles and fuel cell vehicles. The backward-looking simulation tool provides faster results. As shown in, at each time step of the simulation, the necessary torque and speed of the wheel are

first calculated to achieve the vehicle speed required by the driving cycle model; then, the requested torque and speed are propagated back to the powertrain controller (PTC). The PTC computes the effective torque and speed of the engine and then distributes the available torque and speed to the engine and electric motor models. In other words, the input demands of a powertrain system are computed from the pre-calculated outputs. ADVISOR, which was developed by NREL, is a representative simulation tool of backward-looking simulation programs. ADVISOR was built based on Simulink and MATLAB data, as well as script files. The Simulink block diagram and MATLAB files can be modified. represents a schematic based on the Simulink block diagram of ADVISOR. In hybrid electric vehicle research, ADVISOR has been utilized to design an initial prototype model and determine an effective powertrain configuration and components corresponding with customer’s request. The PSAT is useful for testing the control programming of each powertrain component and the powertrain controller before the real controller is implemented in the hybrid electric vehicle. As the actual control system, the PAST decides the system outputs based on the commanded inputs and feedback process. The PAST requests a considerable time to calculate the fuel economy and emissions of the plug-in HEB, which are operated for average of 5~8 hours. ADVISOR is used to design and simulate the model of the plug-in parallel hybrid electric bus. Although ADVISOR may be unsuitable for simulating and developing each component’s real-time control system, the backward-looking simulation method is practical for testing the powertrain optimization and calculating the fuel economy and emissions of conventional and hybrid electric vehicles, such as a plug-in parallel HEB. ADVISOR compares the plug-in HEB to a conventional bus and a plug-in HEB in which a rule-based control strategy is implemented. In addition to the research, the transmission model is modified in accordance with the CVT, and the powertrain controller (PTC) is replaced with a new PTC model.

3. COMPONENT MODELS IN ADVISOR This section describes the component philosophies for the plug-in parallel HEB. Although many component models

Figure 1. Schematic of the forward-looking simulation for a vehicle.

SIMULATION OF A POWERTRAIN SYSTEM FOR THE DIESEL HYBRID ELECTRIC BUS

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Figure 2. Schematic of the backward-looking simulation for a vehicle. of the plug-in HEB are not modified, the model block diagrams of the transmission and powertrain controllers are modified in this study to apply new powertrain control strategies, a revised CVT and a new CVT gear shifting strategy. All of the plug-in HEB’s component models are initialized by the corresponding initial files. Each model block adopts the data, such as the engine fuel and engine emission maps, motor efficiency, motor input power and battery internal resistance that are loaded from component data m-files and placed in the MATLAB workspace. The data and data file parameters are adjusted based on the actual powertrain component data. 3.1. Engine The engine is expressed by a fuel converter in ADVISOR. The inputs to the engine block include the engine on/off command, required torque, and required rotational speed. The engine block produces outputs, including the cumulative fuel consumption and exhaust emissions (NOx, HC, CO, and PM), based on the required torque and speed determined by the powertrain controller (PTC). The engine block sends the torque and speed achieved at the engine to the powertrain controller. The efficiency of the engine is calculated using the fuel flow rate, the power for a given speed and torque, and the lower heating value of the fuel, as shown in Equation (1). fc_power_kW*1000 fc_efficiency = ------------------------------------------------------------- , fc_fuel_flow*fc_fuel_lhw

the internal motor losses. The subsystem of the motor controller logic interface limits the input power of the motor based on an allowed motor power that can be computed by the motor’s maximum current and the battery’s bus voltage under the current battery’s SOC. The input power of the electric motor is sent to the battery model block as the required battery power. 3.3. Battery The battery model block receives the input power of the motor that must be supplied from the battery pack as a block input. The outputs of the battery pack block include the battery power available to the electric motor and the battery’s SOC. One subsystem contains lookup tables that output the open circuit voltage, discharging internal resistance and charging internal resistance as a function of the SOC and temperatures. The terminal voltage and current of the battery are calculated at another subsystem containing the corresponding equations. A subsystem for the SOC algorithm computes the battery’s SOC using Equation (2). Max.cap.battery – Used cap.battery SOC = -----------------------------------------------------------------------------------------Max.cap.battery

(2)

The battery’s SOC determines the engine engagement strategy and control strategy mode in the energy management of the PTC.

(1)

where fc_efficiency = Efficiency of the engine fc_power_kW = Engine power (kW) for the corresponding torque and speed fc_fuel_flow = Engine flow rate (g/s) for the corresponding torque and speed fc_fuel_lhv = Lower heating value of the fuel (J/g) 3.2. Electric Motor The inputs to the electric motor block include the torque and speed required from the electric motor. The required torque and speed are used as inputs for the lookup table containing the motor input power map as an output matrix. The lookup table calculates the actual input power that the motor can provide by considering the required power and

3.4. Transmission – CVT A modified gearbox model for the CVT is implemented in ADVISOR. The CVT model block diagram is based on a CVT dynamic equation shown in Equation (3), which represents the relationship between the input and output torques. This equation is different from a typical transmission equation in terms of the gear ratio rate. The output torque of a conventional transmission is the input torque multiplied by the gear ratio. Existing CVT models also ignore the influence of the CVT’s gear ratio rate. The CVT dynamic equation for output torque is as follows: TCVT_output = R ⋅ TE + M – r ⋅ ω CVT_input ⋅ IDS – TLoss

where

(3)

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R = Transmission gear ratio r = Transmission gear ratio rate TCVT-input = Input torque of the CVT (Engine torque + Motor torque) TCVT-output = Output torque of the CVT TLOSS = Output torque of the CVT IE+M = Sum of the engine inertia and the motor inertia IDS = Inertia of driving shaft In this study, the torque in terms of the gear ratio rate is added to the existing gearbox model block for the CVT in ADVISOR. This is the first attempt at adding this variable to the gearbox model in ADVISOR. 3.5. CVT Gear Shifting Control Strategy The vehicle’s transmission makes the engine operate within the most efficient operating zone. Figure 3 represents the typical gear shifting strategy of the discrete transmission used for conventional vehicles and HEVs. As shown in Figure 3, the gear ratio point of the discrete transmission moves along a constant power line, but the point steers past the engine’s ideal operation line (IOL), which connects the most efficient operating points of a propulsion system, such as an engine, at each possible speed. As shown in Figure 3, the discrete transmission’s gear ratio may not lead the engine to operate on its IOL, depicted by a red line. The discrete transmission’s gear ratios will be shifted within the blue highlighted zone near the engine’s IOL. The gear shifting strategy of the continuous variable transmission (CVT) placed in the plugin parallel hybrid electric bus is similar to that of the discrete transmission. However, the CVT’s gear ratio can continuously change along the constant power line in Figure 3 so that the engine’s operation point settles precisely on the IOL. The fundamental gear shifting control strategy of the HEV makes use of the IOL on the engine’s efficiency map to determine the optimum gear ratio corresponding to the commanded propulsion power. The engine’s IOL is mainly used for shifting the gear from the hybrid and engine-only modes of the HEV. When the HEV operates in the electricmotor-only mode, the gear ratio is controlled by the electric motor’s IOL. In the generator mode of regenerative braking, the transmission’s controller employs the generator/motor’s

Figure 3. Transmission gear ratio for the discrete transmission.

IOL as the gear shifting strategy criterion. 3.6. Powertrain Control Strategy In ADVISOR, an original CVT gear-shifting strategy is replaced by a modified CVT gear-shifting strategy based on the optimization results. According to the initial plug-in parallel HEB simulation, in the charge-sustaining mode, the control strategy for the existing CVT could not maintain the low SOC of the battery at high-speed driving conditions on the highway. A new gear-shifting strategy was applied to determine the gear ratio of the CVT and resolve the chargesustaining problem. The new CVT contains a subsystem with lookup tables to determine the input speed of the CVT as a function of power and speed. The CVT’s gear ratio is computed as the input speed of the CVT divided by its output speed. The lookup table parameters can be adjusted based on the results of the tradeoff optimization between efficiency and exhaust emissions. The total torque and speed required to propel the vehicle meet at the engine torque and speed block in the PTC through the clutch block. The control strategy accomplished by the optimization process determines the optimal engine torque as a function of the required power and the powertrain’s rotational speed. If the battery’s SOC decreases, the internal combustion engine should produce more power than required to propel the vehicle. The existing powertrain controller uses a linear function to compute an additional charging power regardless of the control strategy mode. In other words, the original PTC commands an additional power requirement to charge the battery in the charge-depleting hybrid mode. In this study, this method is not suitable with the intention of the charge-depleting control strategy for the plug-in HEB. Additional charge power subsystems consist of city and highway modes. According to the battery’s SOC, the outputs of the corresponding lookup table are selected through the multi-port switch block. As an example, during urban driving of a vehicle at a SOC of 20%, the corresponding lookup table outputs the additional charging powers shown in Figure 4. Through such an energy management, the engine power is determined to supply the power required to maintain a specific defined battery’s SOC in the charge-sustaining hybrid mode. Energy optimization was used to compute the block parameters of the lookup table. 3.7. Vehicle The vehicle model for the plug-in HEB is modified based on the BC211 M transit bus, which is the latest transit bus manufactured by the Daewoo Bus Corporation in Korea. It is being used as a prototype of the hybrid electric bus series recently developed by Hyundai Heavy Industries (HHI) and Enova systems. The vehicle model contains the dynamic equation of vehicle motion shown in Equation (4).


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Table 1. Specifications of the driving cycle.

Figure 4. Additional charge power in a battery with a SOC of less than 20%.

dVveh Ftrac = Froll + Faero + Fgrade + mveh ⋅ ----------dt

8666 (sec)

Distance

78.56 (m)

Maximum speed

107.70 (km/hr)

Average speed

32.62 (km/hr)

Maximum acceleration

2.68 (m/s2)

Maximum deceleration

- 2.64 (m/s2)

Average acceleration

0.45 (m/s2)

Average deceleration

-0.52 (m/s2)

Idle time

1774 (sec)

Number of stops

135

(4)

where the tractive force (Ftrac) refers to a reaction force from the ground to the tire of the vehicle. The tractive force should overcome the road load, which is the sum of the rolling resistance force (Froll), aerodynamic force (Faero), and gravitational force (Fgrad) at the steady-state speed driving condition. If an increase in the speed of the vehicle is demanded, the tractive force must overcome the force required to accelerate the vehicle in addition to the road load. 3.8. Driving Cycle A new driving cycle is created for the simulation of the plug-in parallel hybrid electric bus, as shown in Figure 5. The new driving cycle is used to simulate the proposed powertrain control strategy established by the optimization processes. The new driving cycle combines the properties of the urban and highway driving cycles and contains the Manhattan driving cycle, heavy-duty Urban Dynamometer Driving Schedule (UDDS), and Orange County driving cycle. In addition, the New York City Cycle (NYCC), West Virginia University (WVU) city driving cycle, WVU suburban driving cycle, WVU interstate driving schedule, Highway Fuel Economy Test (HWFET) driving cycle, and US06 highway driving cycle are also included. The driving cycle specifications represented in 5 are shown in Table 1. The driving cycle shows a variety of characteristics for urban, suburban, and highway driving cycles.

Figure 5. Driving cycle for the simulation.

Time

4. INPUT PARAMETERS OF POWERTRAIN COMPONENTS AND VEHICLES This section describes the input parameters of the powertrain components and vehicles used for the simulation; these parameters are shown in Table 2.

5. SIMULATION RESULTS The following sections consist of four simulations named Simulation (1), (2), (3), and (4). Simulation (1) is performed to observe the impact of the plug-in hybrid electric powertrain system. In Simulation (2), the powertrain control strategy by optimal control process is compared with the rule-based control strategy. Simulation (3) tests a powertrain control approach in which a specific control strategy is selected corresponding to the driving location recognized by driving pattern classification. Finally, Simulation (4) determines the correlation between fuel economy and exhaust emissions and the way in which exhaust emissions can be controlled while compromising the fuel economy of the vehicle when the tradeoff optimization is applied to the optimal control strategy. 5.1. Simulation (1) Simulation (1) compares the fuel economies and exhaust emissions of plug-in and non-plug-in parallel HEBs to justify the selection of the plug-in hybrid system. A conventional bus is also simulated as a comparison with the two hybrid electric buses. The input parameters for the conventional bus and the pug-in parallel HEB are represented in Table 3. According to Table 3, without considering the consumption of electric energy from the battery pack, the plug-in parallel HEB produced the best fuel economy and exhausted the lowest tailpipe emissions compared to the conventional bus and the non plug-in parallel HEB. For the hybridization of heavy-duty buses, the plug-in hybrid system is expected to further improve the merits of hybrid

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Table 2. Input parameters of the modeled buses. Plug-in parallel Conventional bus hybrid electric bus Vehicle Model

BC211M

BC211M

Simulated mass

11,943 (Kg)

12,374 (kg)

Tire rolling resistance coeff.

0.01

0.01

6.60 (m2)

6.60 (m2)

0.4

0.4

1014 (mm)

1014 (mm)

DL08

-

Front area Aerodynamic drag coeff. Diameter of tire Engine Model Weight

815 (kg)

256 (kg)

Maximum power

280 (kW)

130 (kW)

12 (L)

3.3 (L)

Peak power

N/A

180 (kW)

Continuous power

N/A

90 (kW)

Motor drive

N/A

DC-to-AC inverter

Voltage

N/A

356 (V)

Capacity

N/A

240 (Ah)

Energy

N/A

85.4 (kWh)

Energy density

N/A

123 (Wh/kg)

Mass

N/A

693 (kg)

Displacement Motor/Generator

Battery pack

Transmission Type

Manual

CVT (High torque GCI chain)

Weight

246 (kg)

121 (kg)

electric vehicles. Fiugre 6 shows the battery SOC history of the plug-in and non-plug-in HEBs and represents the different battery management strategies between the plugin and non-plug-in hybrid systems. 5.2. Simulation (2) Simulation (2) verifies the impact of the powertrain control strategy achieved based on the optimization. Simulation (2) presents and compares the fuel economy and exhaust emissions of plug-in and non-plug-in parallel HEBs using the rule-based powertrain control strategy considering the emission optimization of the tradeoff optimization. The optimal control strategy used for simulations in this section is based on the energy flow optimization without considering the emission optimization of the tradeoff

Figure 6. Battery SOC history of non-plug-in and plug-in HEB.

Table 3. Fuel economy and exhaust emissions from simulation (1). Conventional Non-plug-in Plug-in Bus parallel HEB parallel HEB Fuel economy (mpg)

5.75

7.16

13.02

HC (g/mi)

1.620

0.151

0.127

CO (g/mi)

2.399

0.157

0.122

NOx (g/mi)

7.702

10.123

5.477

PM (g/mi)

0.017

0.017

0.01

SOC (%)

N/A

70% → 68% 100% → 28%

Table 4. Fuel economy and exhaust emissions from simulation (2). rule-based control strategy

Optimal control strategy

Fuel economy (mpg)

13.02

14.51

▲ + 11.4 %

HC (g/mi)

0.127

0.075

▼ – 40.9 %

CO (g/mi)

0.122

0.073

▼ – 40.2 %

NOx (g/mi)

5.477

6.266

▲ + 14.4 %

PM (g/mi)

0.01

0.01

0%

optimization. According to Table 4, the optimal control strategy improved the fuel economy of the plug-in parallel HEB by 11.4% compared with the rule-based control strategy. The optimal control strategy also reduced the plug-in HEB’s exhaust emissions compared with the rule-based control strategy, except for NOx emissions. In particular, HC and CO emissions were reduced by up to 40.0% and 40.2%, respectively, in the plug-in HEB using the optimal CS. However, NOx emissions were 14.4% higher for the optimal CS compared with the rule-based CS. For the plug-in HEB employing the optimal control strategy, the engine was operated within the most efficient


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operation zone more frequently than when the rule-based control strategy was employed. The engine operation in the most fuel efficient range can increase NOx exhaust emissions because the greatest NOx production zone is similar to the engine’s most fuel efficient area.

Table 6. Comparison of the exhaust emissions during the initial driving cycle.

FE (mpg)

30.31

30.68

▲ + 1.22 %

5.3. Simulation (3) Simulation (3) validates the performance of the adaptive control strategy concept that implements the corresponding control strategy according to the driving location of the vehicle. The PTC collects the driving location information for the previous 5 minutes recognized by the PNN and uses the recognized driving pattern to select the next control strategy corresponding to the driving location. In Table 5, the first plug-in HEB model adopts a control strategy optimized for city driving with a driving cycle combining the city and highway driving cycles, as depicted in Figure 5. The second model uses the optimal control strategy for highway driving conditions regardless of the driving location of the vehicle. The last model alternately implements both of these control concepts depending on the driving pattern of vehicle. The third model, which employs the control concepts adapting to the recognized driving pattern, obtained higher fuel economies than the other plug-in HEBs that used a fixed control strategy regardless of the driving pattern. Although impressive effects could not be obtained by the adaptive control strategy based on driving pattern classification, the possibility could be extended to the electric energy management of the battery pack.

HC (g/mi)

0.158

0.087

▼ – 44.9 %

CO (g/mi)

0.133

0.059

▼ – 55.6 %

NOx (g/mi)

2.509

2.112

▼ – 15.8 %

PM (g/mi)

0.005

0.005

0%

Electric energy consumption (kWh)

10.45

10.59

▲ + 1.34 %

5.4. Simulation (4) Simulation (4) ascertains the effects of the tradeoff optimization for emissions on the performances of the plug-in HEB with optimal control strategies based on optimization described previously. The tradeoff optimization between the thermal efficiency and emissions of the engine is applied to the optimal powertrain control strategy for the plug-in parallel HEB depending on the amount of exhaust emissions in each section. As mentioned previously, the tradeoff optimization is represented by the no-emission and emission optimizations. Table 5. Fuel economy and exhaust emissions for simulation (3). City only

+ HighHighway only City way

Fuel economy (mpg)

14.45

14.41

14.51

HC (g/mi)

0.075

0.075

0.075

CO (g/mi)

0.073

0.074

0.073

NOx (g/mi)

6.297

6.278

6.266

PM (g/mi)

0.01

0.01

0.01

No-emission optimization

Emission optimization

All of the proposed control strategy approaches are utilized in this simulation. The simulation consists of the two stages. The first stage is to analyze the emissions exhausted at the initial driving cycle, in which the engine temperature is cold. The second simulation analyzes the fuel economy and exhaust emissions of the plug-in HEB for the entire driving cycle, where the interrelation between fuel economy and exhaust emissions is analyzed and the exhaust emission tendencies are analyzed according to the driving cycle and conditions. Furthermore, each stage simulates and compares the plug-in HEB using the optimal control strategy with noemission optimization and using the emission optimization of the tradeoff optimization. 5.5. Emissions and Fuel Economy during the Initial Driving Cycle Distribution charts of the emissions discharged from the plug-in HEB without considering the emission optimization of the tradeoff optimization are shown on the left side of These figures are used to analyze the emissions discharged by early operations of the cold temperature engine and observe the initial exhaust emission tendencies. As shown in the left side of Figure 7, more HC and CO emissions are produced during the early driving sections than the later sections. The coolant temperature of the engine is cold during the early driving sections. The HC emissions during the first six sections exceeded the emission target. Although CO emissions were significantly higher during the cold temperature of the initial engine operation than in the later cycles, they remained within the bounds of the emission standards. In contrast, fewer NOx emissions were produced when the engine was cold than when it was hot. The results show that cold temperature tends to be more effective in reducing NOx emissions than hot temperature, in contrast to other exhaust emissions. HC emissions exceed the target line for 1440 seconds of the initial engine operation, so the optimal control strategy implemented the emission optimization of tradeoff optimization at the cold temperatures. The PTC controlled the emission discharged from the cold engine. For the no-

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Figure 7. Analysis of the emissions during the initial driving cycle with a cold engine. emission and emission optimizations, the fuel economy and exhaust emissions of the plug-in HEB are shown and compared in. The powertrain controller with emissions of the tradeoff optimization for 20 minutes reduced HC and CO emissions by 44.9 % and 55.6 %, respectively. NOx emissions are modestly reduced, and PM emissions remained the same regardless of whether the emission optimization was implemented. Most of the emissions tended to decrease but to different extents. The fuel economy of the control strategy adopting emission optimization was better than that with no-emission optimization, but the amount of electric energy consumed increased by 1.34%. The electric motor allowed the engine to be operated within the lowemission zone, during which the required power exceeds the specific maximum power defined by the tradeoff optimization. 5.6. Fuel Economy and Emissions for the Entire Driving Cycle Simulations of the plug-in parallel HEB produced and

compared fuel economy and exhaust emissions detailed in for two powertrain control strategies over the entire driving cycle, which is approximately 5 hours long. Figure 8 and 9 show the engine operation points described by the two control strategies. Finally, the distribution charts of each emission (HC, CO, NOx and PM) are presented in Figure 10 for each section. The figures represent how the emission optimization of the tradeoff optimization regulates the vehicle’s exhaust emissions. In Table 4, as the emission optimization of the tradeoff optimization was implemented in the optimal control strategy at the corresponding sections to control the powertrain, NOx and HC emissions decreased by up to 14.2 % and 9.25 %, respectively, when reducing the fuel economy by 3.45 %. However, CO and PM emissions increased by up to 0.41 % and 10.0%, respectively. Figure 8 shows the engine operation points of the powertrain with no-emission optimization, and Figure 9 represents the engine points that are operated while the PTC implements the optimal control strategy along with the emission optimization. Both of these figures show how


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Table 7. Comparison of fuel economy and emissions for the simulation. No-emission optimization

Figure 8. Engine operation points with no-emission optimization.

Figure 9. Engine operation points with emission optimization.

the control strategy by tradeoff optimization was implemented in the powertrain control strategy. Compared with the engine operation points in Figure 8 with the noemission optimization, the engine operation points in Figure 9 are distributed within the highest thermal efficiency range and the lowest torque range to decrease the emissions. The left distribution charts of Figure 10 show the emission distributions of each section considering the noemission optimization of the tradeoff optimization. The right distribution charts show the emission distributions as the PTC implemented the emission optimization. For HC emissions, the amount of emissions is exhausted at the early driving section and exceeds the emission standard. For the same sections, CO emissions were discharged in a large amount, but the level stayed below the emission standard. Therefore, the tradeoff optimization for HC emissions at the cold engine temperature was applied for the powertrain control strategy. Consequently, HC

Emission optimization

Fuel economy (mpg)

14.51

14.01

▼ -3.45 %

HC (g/mi)

0.0746

0.0677

▼ – 9.25 %

CO (g/mi)

0.0726

0.0729

▲ + 0.41 %

NOx (g/mi)

6.2656

5.3755

▼ – 14.2 %

PM (g/mi)

0.01

0.011

▲ + 10.0 %

SOC (%)

100 → 25.62

100 → 24.05

59.5

61.06

Electric energy consumption (kWh)

+ 2.62 %

emissions were reduced to the emission standard level at this interval. High amounts of NOx and PM emissions were emitted between sections 29 and 36, wherein the highway is located, as shown in the left figures of Figure 10. By the characteristics of the charge-depleting hybrid control strategy, the engine is operated more during highway driving than urban area driving; thus, exhaust emissions increase in the interval. NOx emissions exceeded the emission standard, leading the tradeoff optimization to increase the NOx emission weight. Consequently, the amount of NOx emissions decreased below the emission standard after this point. So far, the emission distributions have been analyzed for the interval of the charge-depleting mode. After section 62, the exhaust emissions for the charge-sustaining hybrid mode were distributed as depicted in Figure 10. In the charge-sustaining hybrid mode sections, emissions are produced because the engine is used with higher frequency, and more power is demanded to the engine. In the chargesustaining hybrid mode, it is harder to control the exhaust emissions than in the charge-depleting hybrid mode. For example, comparing the NOx emissions in sections 28 through 36 during the charge-depleting hybrid mode and those in charge-sustaining hybrid mode, there are some differences between the decrement amounts of exhaust emission. In the charge-depleting mode, it is easier to control the exhaust emissions due to the electric motor with sufficient battery pack electricity.

6. CONCLUSION The simulation results allowed for the following analysis and conclusions to be made: Through Simulation (1), the superiority of the plug-in hybrid electric vehicle over conventional vehicles and nonplug-in HEVs was proven due to increased electricity consumption. Although this research did not consider well-

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Figure 10. Exhaust emissions section by section for the entire driving cycle. Table 8. Summary of the fuel economy and emissions of the plug-in HEB. Optimal CS Rule- with no-emission based CS optimization

Optimal CS with emission optimization

FE (mpg)

13.02

14.51

14.01

HC (g/mi)

0.127

0.0746

0.0677

CO (g/mi)

0.122

0.0726

0.0729

NOx (g/mi)

5.477

6.2656

5.3755

PM (g/mi)

0.01

0.01

0.011

to-wheel emissions and energy consumption, the electricity is generated by a variety of energy sources, unlike the internal combustion engine, which uses fuels from petroleum. Thus, it is difficult to determine the well-towheel emissions and energy consumption for electricity. If the electricity is generated by renewable energy sources, such as solar, wind, water, and geothermal energy, the wellto-wheel emissions and energy consumption are close to zero. Thus, the use of electricity generated from renewable sources maximizes the benefits of the plug-in HEV. Further research is required to develop energy sustainability technology and increase the use of renewable energy.


In Simulation (3), the powertrain control strategy that adapted to the classified driving pattern and location did not provide noticeable improvements over the fixed control strategy. However, another predictive control strategy for the effective energy management of the battery can be developed and will be described in future work. The performance results of Simulations (2) and (4) can be used to analyze the impact of the optimal control strategy and the emission optimization of the tradeoff optimization on the plug-in HEB compared to the existing rule-based control strategy. According to Simulation 2, compared to the plug-in HEB with rule-based control strategy, implementing the optimal control strategy with no-emission optimization significantly improved the fuel economy and reduced tailpipe exhaust emissions but produced more NOx emissions. In Simulation (4), however, the optimal control strategy implementing the emission optimization further decreased NOx emissions and other emissions but reduced the fuel economy. To control the initial emissions from a cold engine, the PTC employed an emission optimization during the cold engine temperature sections until the engine warms up. The emission optimization was based on the cold temperature emission maps. As a result, effective optimization and an electric motor can regulate the exhaust emissions and reduce the fuel consumption until the engine warms up. ACKNOWLEDGMENTS−This work was supported by the Induk University. Also, the data were provided by University of California Davis.

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