Development a new power management strategy for power split hybrid electric vehicles

Development a new power management strategy for power split hybrid electric vehicles

Transportation Research Part D 37 (2015) 79–96 Contents lists available at ScienceDirect Transportation Research Part D journal homepage: www.elsevi...

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Transportation Research Part D 37 (2015) 79–96

Contents lists available at ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Development a new power management strategy for power split hybrid electric vehicles Morteza Montazeri-Gh, Mehdi Mahmoodi-k ⇑ Systems Simulation and Control Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, P.O. Box 16846-13114, Iran

a r t i c l e

i n f o

Article history: Available online 16 May 2015 Keywords: Power-split hybrid vehicle Energy management Fuzzy logic controller State of charge Pollution emissions and fuel consumption

a b s t r a c t Reduction of greenhouse gas emission and fuel consumption as one of the main goals of automotive industry leading to the development hybrid vehicles. The objective of this paper is to investigate the energy management system and control strategies effect on fuel consumption, air pollution and performance of hybrid vehicles in various driving cycles. In order to simulate the hybrid vehicle, the combined feedback–feedforward architecture of the power-split hybrid electric vehicle based on Toyota Prius configuration is modeled, together with necessary dynamic features of subsystem or components in ADVISOR. Multi input fuzzy logic controller developed for energy management controller to improve the fuel economy of a power-split hybrid electric vehicle with contrast to conventional Toyota Prius Hybrid rule-based controller. Then, effects of battery’s initial state of charge, driving cycles and road grade investigated on hybrid vehicle performance to evaluate fuel consumption and pollution emissions. The simulation results represent the effectiveness and applicability of the proposed control strategy. Also, results indicate that proposed controller is reduced fuel consumption in real and modal driving cycles about 21% and 6% respectively. Ó 2015 Elsevier Ltd. All rights reserved.

Introduction Nowadays due to the transportation sector has been one of the top contributors in increasing air pollution and fuel consumption, a significant interest in hybrid electric vehicle (HEV) has arisen globally to reduce fuel consumption and pollution emissions. According to the U.S. Department of Energy (USDE), about 15% of the total fuel energy is consumed to run a car and its other accessories. The main concern in vehicle emissions is CO2 which is related to fuel consumption linearly (Chau and Chan, 2007). In order to overcome these problems, HEVs incorporates two power drives including an internal combustion engine (ICE) and an electric motor (EM) which results in optimal energy management. HEV’s reduce Green House Gas (GHG) emissions, and displace petroleum energy by utilizing both powertrains to increase vehicle efficiency. The conventional vehicle engine is typically sized to meet high power demands, while the hybrid electric vehicle powertrain is sized Abbreviations: HEV, hybrid electric vehicle; USDE, U.S. Department of Energy; ICE, internal combustion engine; EM, electric motor; FLC, Fuzzy Logic Controller; FCHV, fuel cell hybrid vehicle; SOC, state of charge; MPC, Model Predictive Control; PHEV, plug-in hybrid electric vehicle; NEDC, New European Driving Cycle; GHG, Green House Gas; THS, Toyota Hybrid System; EVT, electronically variable transmission; DP, dynamic programming; EMS, energy management strategy; QP, quadratic programming; MG, motor/generator; UDDS, Urban Dynamometer Driving Schedule; FTP-75, Federal Test Procedure; Teh-car, Tehran Car. ⇑ Corresponding author. E-mail address: [email protected] (M. Mahmoodi-k). http://dx.doi.org/10.1016/j.trd.2015.04.024 1361-9209/Ó 2015 Elsevier Ltd. All rights reserved.

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to enable engine start/stops, regenerate during braking. In order to make more significant reductions in pollution emissions and petroleum energy usage vehicle architectures also need to be considered. There are many different hybrid configurations currently proposed by vehicle manufacturers (Fan, 2007; He et al., 2012; Zhang, 2011, Liu, 2013; Murphey et al., 2013), most configurations can be categorized into three hybrid systems: Series, Parallel and Power-split. In this paper, power-split hybrid vehicle is considered, which it combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine as shown in Fig. 1. With the potential for achieving higher fuel economy, power-split HEV has been seen as one of the hybrid powertrain architecture to improve fuel economy when their power-management algorithms are properly designed. Most of the attention has been given to designing energy management control systems in power-split HEVs which is responsible for selecting operating points of the subsystems to achieve better vehicle fuel efficiency (He et al., 2012; Zhang, 2011; Liu, 2013; Murphey et al., 2013; Zheng and Mi, 2009). The power-split hybrid configuration can switch between the parallel and series which, according to driving condition the high efficiency range of each one is selected. Depending on the situation, both power sources (electrical and mechanical paths) can also be used simultaneously to achieve the maximum power output efficiency. The biggest advantage of the planetary gear mechanisms is that high rotational ratios can be produced by using small number of relatively small dimensioned gear systems. Also the input and output shafts are coaxial so the mechanism is extremely well assembled (Liu, 2007; Macor and Rossetti, 2013). Other advantages are eliminated radial loads, working silently and the facility of using planets in steps. Power split powertrains can be divided into single (Toyota Hybrid System (THS)) and multi-mode systems (Allison Hybrid System) which uses two or more planetary gears and has two electronically variable transmission (EVT) modes. Trade-off between multi-mode powertrain complexity and fuel consumption has been performed in Kim et al., 2010 to provide a detailed review of the benefits and drawbacks of the single and two-mode systems, the three-mode and four-mode vehicles. It was found that the multi-mode system has more fuel economy advantage during the high-speed cycle due to the relatively higher system efficiency. In this paper due to complexity, high cost and additional weight of multi-mode systems, single mode configuration of Toyota Prius has been considered. One of the most significant factors in the performance of hybrid vehicles are control strategies, which play an important role in improving energy management of HEVs. Different strategies has been used in previous studies which mainly are classified into rule based and optimization approaches (Tie and Tan, 2013). Majority of the proposed solutions for the power management control logic can be classified under two types: rule-based approach and optimization-based approach. Rule-based control strategies consist of deterministic and fuzzy logic rule-based methods, while optimization-based approaches typically utilized global optimization when determining the control strategy (Salmasi, 2007). In Zahabi et al. (2014) the effect of different factors on fuel efficiency including road driving conditions (link type, city size), temperature, speed, cold-starts and eco-driving training is compared for HEVs and conventional gasoline vehicles. Results demonstrated that winter time significantly increased the fuel consumption of HEVs. Delprat et al. (2004) and Mansour and Clodic (2012) proposed a global optimal strategy based on dynamic programming (DP) methods for parallel HEV and parallel-series HEV, respectively. An overview of the controllers was given in Wirasingha and Emadi (2011), and an analysis on which strategy is more suitable to maximize HEV performance in different drive cycle conditions was provided. They presented a new classification for HEV control strategies based on the operation of the vehicle and verified through simulation results. Kim et al. (2009) proposed nonlinear model predictive control algorithm based on DP procedure for the vehicle control system to maximize fuel economy while satisfying constraints on battery state of charge, relative position and vehicle performance. One of the disadvantages of this control is approximation/transformation that may not be applicable in complicated drive train system. Power-split hybrid vehicles have two degrees of freedom in the

Fig. 1. Power-split hybrid vehicle modeling.

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control strategy. So, it’s difficult to apply the DP algorithm in these kinds of vehicles and it is probably cannot converge to exact optimal solution. Also, it is not suitable for real-time control (Tie and Tan, 2013). Previous studies (Jalil et al., 2002; Montazeri et al., 2006; Baumann, 1997; Kono, 1998; Martínez et al., 2013, 2012), already indicated that Fuzzy Logic Controller (FLC) is very suitable for HEV control. It is an appropriate method for realizing an optimal tradeoff between the efficiencies of all components of the HEV. A FLC with Mamdani implication is used in this paper, which is in the group of rule-based controllers. The FLC of the hybrid powertrain does not rely on a precise mathematic model and has preferably robust properties. Wu et al. (2012) presented a fuzzy energy management strategy (EMS) based on driving cycle recognition to improve the fuel economy of a parallel hybrid electric vehicle by considering the effect of the driving cycle on the EMS. Ning et al. (2010) constructed a comprehensive simulation model for the fuel cell hybrid vehicle (FCHV) power train in parallel with a power control strategy that uses a logic threshold approach implemented with a hybrid control unit. The proposed optimal configuration model has six possible operating modes and power flows between each subsystem according to the particular conditions identified by the system operation. It provides the capability of achieving the requested drive power while also meeting the vehicle driving schedule and recovery needs of the state of charge (SOC) battery, with lower fuel consumption levels. In Xiong et al. (2009), two separated controllers using fuzzy logic called Mode Decision and Parallel-driving Energy Management has been employed to fulfill switching between the series mode and the parallel mode as well as the instantaneous power distribution. By considering the effect of the driving cycle on the EMS, a fuzzy EMS based on driving cycle recognition has been proposed to improve the fuel economy of a parallel hybrid electric vehicle. The EMS was composed of driving cycle recognition and a fuzzy torque distribution controller, which optimized simultaneously by using particle swarm optimization (Wu et al., 2012). A Model Predictive Control (MPC) strategy is developed to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. The proposed algorithm is causal and has the potential for real-time implementation (Borhan et al., 2009). Chen et al. (2014) proposed an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) introduce. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. In this paper multi input FLC developed for energy management of power-split configuration, which is adaptive to various driving cycles. Then, the efficiency of proposed controller in terms of fuel consumption and pollution emissions compared with rule-based controller of Toyota Prius power-split hybrid vehicle. Vehicle model In order to develop longitudinal vehicle dynamics for power-split hybrid vehicle configuration, a force balancing along the vehicle longitudinal axis yields:

m

du Te 1 ¼  qAf C d u2  mg sin h  lmg cos h dt Rtire 2

ð1Þ

where m, u and Te are vehicle mass, longitudinal velocity and engine torque respectively. Af and Rtire are frontal area of the vehicle and the wheel radius, l is the friction coefficient, Cd and q are the drag coefficient and air density, h is the road grade and g is gravitational acceleration. In the next step, powertrain and planetary gear sets dynamics are extracted for power-split hybrid vehicle to achieve its state space form. Dynamic modeling of power-split hybrid vehicles Power-split hybrid vehicle implements planetary gear set instead of transmission to link the engine with the final drive. Fig. 2 shows a powertrain design example of the single-mode power-split hybrid system. A single planetary gear set serves as a power-split device that transfers the engine power to the vehicle through a mechanical (parallel) and an electrical (series) paths. The engine power through the mechanical path goes directly to the final drive of the vehicle. The rest of the engine power goes to the motor/generator 1 (MG1), where it is transformed into electricity. This power is then either stored in the battery or send to the motor/generator 2 (MG2) by a controlled power bus. Planetary gear is the main part of power-split powertrain system. As shown in Fig. 2 it consists of the three basic components: Sun gear, Planet carrier, Ring. The overall gear ratio of a simple planetary gear set can be reliably calculated using the following two equations, representing the sun–planet and planet–ring kinematic interactions respectively:

Rs xs þ Rp xp  ðRs þ Rp Þxc ¼ 0

ð2Þ

Rr xr  Rp xp  ðRr  Rp Þxc ¼ 0

ð3Þ

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Fig. 2. Planetary gear set operation mechanism (http://www.carbibles.com/images/planetarygearset.jpg).

where R and x are radius and rotational speeds of gears respectively. Indexes r, s, p and c represent ring, sun, planet and carrier gears. The fundamental formula of the planetary gear train with a rotating carrier is obtained by recognizing that this formula remains true if the angular velocities of the sun, planet and annular gears are computed relative to the carrier angular velocity. This becomes,

Rr xs  xc ¼ Rs xc  xr

ð4Þ

Each of the speed ratios available to a simple planetary gear train can be obtained by using clutches to hold and release the carrier, sun or annular gears as needed. The vehicle longitudinal components includes engine, motor, generator, power-split, final drive and wheels. In the power-split system, planetary gear sets in a way that, the engine is connected to planet carrier as an input. Generator and motor are linked with sun and ring gears respectively. Also, the ring gear connected to the final drive is considered as the output. It is assumed that all powertrain components are rigid and the power loss in the final transmission can be neglected with respect to other sources of power losses. So, the equation of motion for components described as:

Ie ae ¼ T e  FðRs þ Rr Þ Im am ¼ T m þ FRr 

ð5Þ

Td þ Tb N

ð6Þ

Ig ag ¼ T g þ FRs

ð7Þ

where I and T are the inertia and torque. Indexes e, m and g represent engine, motor and generator respectively. Rs and Rr are the radius of the sun and ring gears. N, F and a indicate final drive ratio, interaction force between different parts of power-split and the rotational acceleration, Td and Tb are the drive shaft and brake torques respectively. Fig. 3 shows a planetary gear and its equivalent lever diagram (Zhang et al., 2012). The three nodes in the lever diagram represent the ring gear, carrier, and sun gear of the planetary gear, and each node can be connected to one or more powertrain elements. By using the lever diagram presented, system equations of motion for planetary gear can be derived when neglecting the inertias of the mass of the pinion gears, are given by

Ir ar ¼ FRr  T r

ð8Þ

Fig. 3. Lever diagram of planetary gear set.

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Fig. 4. Schematic of FLC construction.

(a) Membership function for driver torque

(c) Membership function for speed

(b) Membership function for SOC

(d) Surface of Membership functions

Fig. 5. Membership function for FLC in braking mode.

Ic ac ¼ T c  FRr  FRS Is as ¼ FRS  T S

ð9Þ ð10Þ

where Tr, Ts and Tc are the torques on the ring gear shaft, the sun gear shaft, and the carrier shaft, respectively, and Ir, Is, and Ic are the corresponding inertia. In lever diagram method for describing state space of planetary gear set elements, one of the motor/generator (MG1) units interconnect with the sun gear. The governing equation is

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(a) Membership function for driver torque

(c) Membership function for speed

(b) Membership function for SOC

(d) Surface of Membership functions

Fig. 6. Membership function for FLC in traction torque.

IMG1 aMG1 ¼ T S þ T MG1

ð11Þ

where TMG1, aMG1 and IMG1 are the MG1 torque, rotational acceleration, and inertia, respectively. Combining Eqs. (10) and (11) resulted in

ðIMG1 þ IS ÞaMG1 ¼ FRS þ T MG1

ð12Þ

Equation of motion for the carrier gear part, which connected to engine, is given by

Ie ae ¼ T e  T C

ð13Þ

where Te, ae and Ie are the engine torque, rotational acceleration, and inertia, respectively. Substituting Eq. (9) in Eq. (13), yields

ðIe þ IC Þae ¼ T e  FðRS þ Rr Þ

ð14Þ

The third dynamic equation is related to annual gear which is connected to final drive and wheels, which includes vehicle longitudinal dynamics. So, the governing equation for the ring gear shaft with the consideration of Eq. (1) becomes,

Ir þ IMG2 þ

R2tire N2

! m ar ¼ ðT MG2 þ FRr Þ 

  x 2 1 1 r T b þ mgfr Rtire þ qAC d R3tire N 2 N

ð15Þ

where 0.5qAfCd presents the aerodynamic drag resistance, fr is the rolling resistance coefficient, IMG2 is the inertia of the motor, Rtire is the tire radius, Tb is the brake torque applied by the friction brake system, and TMG2 is the motor torque.

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(a) Membership function for driver torque

(b) Membership function for SOC

(c) Membership function for speed

(d) Surface of Membership functions

Fig. 7. Membership function for FLC in traction speed.

Table 1 Fuel consumption and pollution emission in various driving cycle for different control strategy. Driving cycle

UDDS (12 km)

Controller

Rule-based

FLC

Rule-based

FLC

Rule-based

FLC

Rule-based

FLC

Fuel (L/100 km) HC (g/km) CO/100 (g/km) NOx (g/km)

4.9 0.704 0.791 0.152

3.8 0.688 0.787 0.142

5 0.542 0.615 0.135

4.1 0.535 0.609 0.134

5.1 0.784 0.772 0.108

4.8 0.815 0.806 0.092

5.5 0.703 0.645 0.080

4.3 0.701 0.626 0.076

2

3 Ie þ I C ae 6 a 7 6 6 r 7 6 0 6 7¼ 6 4 aMG1 5 6 4 0 2

F

FTP (17.8 km)

0 R2tire N2

Rr þ RS

m þ IMG2 þ Ir 0 Rr

0

Rr þ RS

0

Rr

IMG1 þ IS RS

RS 0

NEDC (10.9 km)

Teh-car (13 km)

31 2 7 7 7 7 5

3 Te 7 6T 1 6 MG2  N ½T b þ F rr þ F D  7 7 6 5 4 T MG1

ð16Þ

0

The internal force can be eliminated by solving Eq. (16) and dynamic equation of rotational speed of power train system achieved. Control of power-split HEVs In the control of power-split HEVs, two-level hierarchical control architecture is commonly used (Mensing, 2010). In the low level control feedback controllers are designed to ensure that components will operate at specified points, which specifies the optimum operation of the system to result in the minimum losses in the entire drive train. On the higher level an

86

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0.7

Optimized Fuzzy Prius-Rullbased

100

Optimized Fuzzy Prius-Rulebased

0.65

ess_soc_hist

fc_brake_trq

80 60 40 20

0.6

0.55

0 0.5 -20 -40

0

200

400

600

800

1000

0.45

1200

0

200

400

600

800

1000

time (s)

time (s)

(a) Fuel convertor brake torque

(b) Battery state of charge

100

1200

0.12

Optimized Fuzzy Prius-Rulebased

Optimized Fuzzy Prius-Rulebased

emissions (grams/km)

fc_trq_out_a

0.1

50

0

0.08

0.06

0.04

0.02

0

-50 0

400

600

1000

1200

200

400

600

(c) Fuel convertor output torque

(d) HC emission

300

Torque (Nm)

0.9 0.85 0.8 0.75

-200 0.65 0.6

0.75 0.8 0.85

0.85 0.8 0.8 0.85

-100

0.7

0.60.65

200

0.9

0

100 0.9

-100 -200

0.85 0.8 0.75 0.7

max cont. motoring torque max motoring torque max cont. gen. torque max gen. torque actual operating points

0.65 0.6

-300

2000

3000

4000

5000

6000

0.9 0.8 0.8

0.85

0

0.7

-300

1000

1200

400

max cont. motoring torque max motoring torque max cont. gen. torque max gen. torque actual operating points

0.6 0.65 0.85 0.8 0.75 0.7

1000

Motor/Controller Operation PRIUS J PN 30-kW permanent magnet motor/controller

J

0

800

time (s)

100

-400

0

time (s)

200

Torque (Nm)

800

Motor/Controller Operation PRIUS PN 30-kW permanent magnet motor/controller

400 300

200

-400

0

1000

2000

3000

4000

5000

6000

Speed (rpm)

Speed (rpm)

(e) Electric motor operation (FLC)

(f) Electric motor operation (Rule-based)

Fig. 8. Comparison simulation results (SOC, HC emission, fuel convertor and electric motor performance) over NEDC by FLC and Prius Rule-based controller.

87

M. Montazeri-Gh, M. Mahmoodi-k / Transportation Research Part D 37 (2015) 79–96 0.7 100

Optimized Fuzzy Prius-Rulebased

Optimized Fuzzy Prius-Rulebased

80

0.65

ess_soc_hist

fc_brake_trq

60

40

20

0.6

0.55

0 0.5 -20

-40

0

500

1000

1500

2000

0.45

2500

0

500

1000

1500

time (s)

(a) Fuel convertor brake torque

(b) Battery state of charge

100

Optimized Fuzzy Prius-Rulebased

2500

0.18 Optimized Fuzzy Prius-Rulebased

emissions (grams/km)

0.16

fc_trq_out_a

2000

time (s)

50

0

0.14 0.12 0.1 0.08 0.06 0.04 0.02

-50

0 0

500

1000

1500

2000

2500

0

500

1000

1500

2000

2500

time (s)

(c) Fuel convertor output torque

(d) HC emission

Motor/Controller Operation PRIUS PN 30-kW permanent magnet motor/controller

Motor/Controller Operation PRIUS PN 30-kW permanent magnet motor/controller

J

300

J

400

400 0.60.65

200

0.7

300

0.75 0.8

200

max cont. motoring torque max motoring torque max cont. gen. torque max gen. torque actual operating points

0.6 0.65 0.85 0.8 0.75 0.7

Torque (Nm)

Torque (Nm)

0.85

100 0.9 0.85 0.8 0.8 0.9

0 -100 -200

max cont. motoring torque max motoring torque max cont. gen. torque max gen. torque actual operating points

0.85 0.8 0.75 0.7 0.65 0.6

-300 -400

0.9

100

0.85 0.8 0.8 0.85

0

0.9

-100

0.85 0.8 0.75

-200 0.65 0.6

0.7

-300 -400

0

1000

2000

3000

4000

5000

6000

0

1000

2000

3000

4000

5000

Speed (rpm)

Speed (rpm)

(e) Electric motor operation (Rule-based)

(f) Electric motor operation (FLC)

6000

Fig. 9. Comparison simulation results (SOC, HC emission, fuel convertor and electric motor performance) over FTP by FLC and Prius Rule-based controller.

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100

fc_brake_trq

80 60 40 20 0 -20 -40

0

200

400

600

800

1000 1200

1400 1600 1800

time (s) Fig. 10. Fuel convertor brake torque in Teh-car.

energy management strategy is implemented that coordinates the sub-systems to satisfy certain performance targets (e.g., fuel economy). It must determine the desired output to be generated by the sub-systems and send these output signals to the corresponding sub-systems. In order to achieve the competition goals a supervisory controller was developed to safely control the power-split hybrid powertrain. The supervisory controller was designed to interpret driver commands and control the interaction between powertrain components. In all modes of operation, the control system must effectively control the speeds and torques of the engine, motor and generator, maintain a desired SOC and safely distribute the braking power between the electrical (regenerative) and mechanical brake systems while braking. For a power-split HEV, the possible hybrid states include, engine only propulsion, motor only propulsion, hybrid propulsion, engine-battery charging, and regenerative braking. In order to evaluate the benefits of power-split configurations from the standpoint of fuel consumption and GHG emissions, an HEV control strategy based on FLC has been developed. Given the desired driver torque, a vehicle speed and a battery SOC, the optimal battery power for maximizing the total system efficiency can be obtained using the FLC method. The fuzzy logic technique as shown in Fig. 4 has been applied to identify the battery power for a given driver torque request, speed and the battery SOC. The proposed fuzzy rule system here, has three inputs. One input is the battery SOC, defined by three membership functions (High, Medium and Low). The others are driver torque and speed request, defined by using three membership functions (High, Medium and Low). The output is engine or generator torques, which can be defined using five membership functions (Positive High, High, Medium, Positive Low and Low). The fuzzy rules can be established based on related heuristic knowledge. The basic control strategy is that the electric motor supplements additional power when required. Additionally, the motor is also exclusively used when the vehicle accelerate from standstill and also at low speed. Also, if the battery SOC reaches its lower threshold, the engine provides additional power to recharge the battery by regenerative braking. In order

2.5 Optimized Fuzzy Prius-rulebased

fc_fuel_rate

2

1.5

1

0.5

0 0

200

400

600

800

1000

1200

1400 1600 1800

time (s) Fig. 11. Fuel convertor fuel consumption rate in Teh-car.

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M. Montazeri-Gh, M. Mahmoodi-k / Transportation Research Part D 37 (2015) 79–96 Table 2 Energy usage (kJ) in power and regenerative mode of Teh-car driving cycle. Multi input FLC and (rule-based) controllers Power mode

Fuel convertor Generator Energy storage Motor controller Final drive Aux. loads Aero. + rolling

Regenerative mode

In

Out

Loss

Eff.

18,256 (23,420) 585 (846) 2481 (1545) 2859 (2507) 5100 (5110) 1258

6104 (6131) 489 (605) 2890 (2282) 2440 (2083) 5100 (5110)

12,151 (17,289) 97 (241) 590 (333) 419 (424) 0 1258 800 + 1621

0.33 0.83 0.83 0.85 1

In (0.26) (0.72) (0.83) (0.83)

Out

Loss

Eff.

0 (495) 1069 2578 (1933) 1096

2183 (1642) 1096

394 (291) 0

0.85 (0.85) 1

0.9 SoC=0.7, Optimized Fuzzy SoC=0.9, Optimized Fuzzy SoC=0.9, Prius-Rulebased Soc=0.7, Prius-Rulebased

0.85

ess_soc_hist

0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0

200

400

600

800

1000 1200 1400 1600 1800

time (s) Fig. 12. Effects of initial SOC and controller on SOC variation.

0.14 Optimized Fuzzy, SoC=0.7 Optimized Fuzzy, SoC=0.9 Prius-Rulebased, SoC=0.9 Prius-Rulebased SoC=0.7

emissions (grams/km)

0.12 0.1 0.08 0.06 0.04 0.02 0

0

500

1000

1500

2000

time (s) Fig. 13. Effects of initial SOC and controller on HC emission.

to design the membership functions, fuzzy logic rules and surfaces for braking, traction torque and speed modes are illustrated in Figs. 5–7 respectively. The engine only starts when the vehicle has passed a certain speed or load to ensure vehicle performance during high load condition to achieve desirable gradability and longitudinal performance. Regenerative braking is commanded whenever the torque is less than zero across the vehicle speed range and the battery SOC range. This is necessary to ensure that the full amount of regenerative power is stored during braking. The proposed MFs are developed based on the limits of vehicle performance and battery’s SOC. The proposed FLC selects proper IC engine torque based on driver’s required torque, vehicle speed and battery SOC. So, FLC sets power-split configuration to determine the amount of torque split between mechanical and electrical paths.

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Table 3 Initial SOC effects on Teh-car cycle. Initial SOC

0.7

Controller

Rule-based

FLC

0.9 Rule-based

FLC

Fuel (L/100 km) HC (g/km) CO/100 (g/km) NOx (g/km)

5.5 0.703 0.645 0.08

4.3 0.701 0.626 0.076

5.1 0.606 0.857 0.079

4.1 0.596 0.689 0.069

Fuel Converter Operation Prius jpn 1.5L (43kW) from FA model and ANL test data

Fuel Covertor Operation- optimized controller Prius JPN 1.5 L (43KW) from FA model and ANL test data

120

120

100 80

36.5

60

30.5

40 26.5

22.5

20 0 -20 -40 1000

36.5

2000

3000

4000

5000

34.5

60

30.5

40 26.5

22.5

20 0

max torque curve gc max torque curve output shaft op. pts(includes inertia & accessories)

0

37.5

80

34.5

Torque (Nm)

Torque (Nm)

100

37.5

max torque curve gc max torque curve output shaft op. pts(includes inertia & accessories)

-20 -40 6000

0

1000

2000

3000

4000

Speed (rpm)

Speed (rpm)

(a) Prius Rule-based controller

(b) Multi input FLC

5000

6000

Fig. 14. Fuel convertor operation comparison for FLC and Prius Rule-based controller (initial SOC = 0.7).

Fuel Converter Operation-optimized controller Prius j pn 1.5L (43kW) from FA model and ANL test data

Fuel Converter Operation Prius j pn 1.5L (43kW) from FA model and ANL test data 120

120 100 80 60

37.5 36.5

80

36.5 34.5

Torque (Nm)

Torque (Nm)

100

37.5

30.5

40 26.5

22.5

20

34.5

60

30.5

40 26.5

22.5

20 0

0 max torque curve gc max torque curve output shaft op. pts(includes inertia & accessories)

-20 -40 0

1000

2000

3000

4000

5000

max torque curve gc max torque curve output shaft op. pts(includes inertia & accessories)

-20 -40 6000

0

1000

2000

3000

4000

Speed (rpm)

Speed (rpm)

(a) Prius Rule-based controller

(b) Multi input FLC

5000

6000

Fig. 15. Fuel convertor operation comparison for FLC and Prius Rule-based controller (initial SOC = 0.9).

The relation between the input and the output variables can be clearly related in the surface plots as shown in Figs. 5–7. In this paper, the target is to minimize the fuel consumption with the ending SOC of 55% while minimizing pollution emission by limiting engine operation points. The rules are designed in such a way to guarantee the efficient operation of motor and maintain the battery SOC in its optimal zone.

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0.7 Opimized Fuzzy- 1.5% grade Optimized Fuzzy- No slpe Prius Rule-based- 1.5% grade Prius Rule-based- No slope

ess_soc_hist

0.65

0.6

0.55

0.5

0.45 0

200

400

600

800

1000 1200 1400 1600 1800

time (s) Fig. 16. Effects of road grade and controller on SOC variation.

Results The proposed power management strategy is implemented in the ADVISOR environment. The assumed vehicle is Prius hybrid vehicle with power-split configuration. Actually, ADVISOR does not predict the actual emissions to the same level of accuracy as its energy use and fuel economy predictions. However, its NOx prediction is quite close to the actual value, and its values are generally of the same order of magnitude of the actual values (Senger, 1998; Johnson et al., 2000; Holmén, 2010). Some simplifications are assumed in ADVISOR may cause errors. Some of them are listed below: Combustion chamber geometry, Chemical reactions, fuel properties, speed variation of transmission components and air/fuel ratio variation are not considered in more detail in ADVISOR. Therefore, CO and HC results in ADVISOR are used only to evaluate the effectiveness of proposed control strategy and comparison the developed model with original one. With the transmission models and controller described in the previous section, the vehicle was simulated on modal and real world standard drive cycles: the Urban Dynamometer Driving Schedule (UDDS); The Federal Test Procedure (FTP-75); The New European Driving Cycle (NEDC); A more aggressive newly published real world Tehran Car driving (Teh-car) cycle Montazeri-Gh and Mahmoodi-k, 2015. Simulation results using the proposed FLC strategy and a conventional rule based control strategy are provided in Table 1. These simulations are done for ten repetitions of four different driving cycles. The average fuel consumption and pollution emissions for these cycles is used for comparison. As it can be seen, the proposed strategy improves the fuel efficiency about 20% and 5.7% on real and modal driving cycles respectively. Also, the overall amount of pollution emissions (HC, CO and NOx) reduces by using multi input FLC. In order to the evaluation of energy management strategy controller effects on power-split hybrid vehicle, variation of SOC, Motor operation, hydrocarbon emissions, brake and output torque of fuel convertor are compared for developed multi input FLC and Prius default Rule-based controller in modal (NEDC) and real world (FTP, UDDS and Teh-car) driving cycles.

Modal driving cycle (NEDC) Fig. 8 illustrates the simulation results for NEDC driving cycle. Proposed FLC reduces SOC variation, which can improve the battery’s life. Furthermore, as shown in Fig. 8e and f, with applying FLC the share of motor power, in the overall power of vehicle increases and engine power restored as a regenerative energy, which results in fuel consumption and GHG emission reduction over NEDC driving cycle.

Table 4 Effects of controller on fuel consumption and pollution emissions in 1.5% grade of Teh-car. Controller

Prius

FLC

FC (L/100 km) HC (g/km) CO/100 (g/km) NOx (g/km)

5.5 (6.9) 0.703 (0.729) 0.645 (0.682) 0.08 (0.113)

4.3 (5.3) 0.701 (0.730) 0.626 (0.677) 0.076 (0.111)

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0

5000

10000

15000

Fig. 17. Energy usage in power mode with FLC.

Energy Usage (Power Mode) (kJ) Gearbox Torque Coupling Torque Converter Clutch Fuel Converter Rolling Aux Load Braking Aero Motor/Controller Wheel/Axle Energy Storage Generator Final Drive

0

0.5

1

1.5

2

2.5

x 10

4

Fig. 18. Energy usage in power mode with Rule-based.

Real world driving cycle (FTP) In order to investigate the effects of FLC and default Prius Rule-based controller on real world driving conditions, simulation performed on FTP driving cycle. Simulation results include SOC, Motor operation, hydrocarbon emissions, brake and output torque of fuel convertor are compared for multi input FLC and Prius default Rule-based controller in FTP driving cycle in Fig. 9. Results indicate that proposed controller in real world driving cycles increase life time of battery by reducing SOC variation during cycle. Also, by increasing motor power ratio and shifting the operation point to optimal conditions as shown in motor plots, reduces fuel consumption and pollution emissions. In other words, the proposed controller is adaptive to various driving conditions. However, compared to modal driving cycles (NEDC), in the real world driving conditions (FTP and UDDS), controller performance is more effective than Prius conventional Rule-based one. Real world driving cycle (Teh-car) In the next stage, the control strategies performance of power-split HEV in newly developed real world cycle in Tehran city called ‘‘Teh-car’’, has been evaluated. Brake torque and fuel rate of fuel convertor for FLC and Rule-based controllers are illustrated in Figs. 10 and 11 respectively. Results indicate that due to The-car driving cycle aggressive nature, in the beginning of the Teh-car driving cycle, FLC controller needs more engine power. With the advancement in the cycle fuel rate in FLC mode reduced, whilst the overall fuel rate decreases more than Rule-based one. Furthermore, in order to analysis the powertrain efficiency at all stages for components, the power flow in both cases, power and regenerative modes, has been compared with multi input FLC and conventional Rule-based control strategies over Teh-car driving cycle in Table 2. Results indicate that, power losses reduce significantly and fuel convertor efficiency increases about 20% by applying multi input FLC in power mode. Also, other components efficiency (generator, energy storage and motor controller) are

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Optimized Fuzzy, SOC=0.9 Prius-Rulebased, SOC=0.7

ess_soc_hist

0.8

Prius-Rulebased, SOC=0.9

0.75 0.7 0.65 0.6 0.55 0.5 0.45

0

1000

2000

3000

4000

5000

6000

7000

time (s) Fig. 19. SOC variation in 4-Teh-car.

Table 5 Effects of controller and initial SOC on fuel consumption and emissions in 4Teh-car. Initial SOC

0.7

Controller

Rule-based

FLC

0.9 Rule-based

FLC

FC (L/100 km) HC (g/km) CO/100 (g/km) NOx (g/km)

5.5 0.703 0.645 0.08

4.3 0.700 0.626 0.076

5.1 0.606 0.857 0.079

4.1 0.596 0.689 0.069

improved in both cases (power and regenerative modes) while, longitudinal forces (performance) are the same, for both of multi input FLC and rule-based controllers.

Sensitivity analysis In this section, the robustness and sensitivity of the control system has been investigated through the changes in the initial SOC of battery and road grade over Teh-car driving cycle.

Initial SOC effects Additionally, in order to investigate the initial SOC effects on HEV’s performance, hydrocarbon emissions and SOC variation over Teh-car cycle for initial SOC of 0.7 and 0.9 are shown in Figs. 12 and 13 respectively. Also, fuel consumption and pollution emission with different initial SOC are compared in Table 3 for FLC and Rule-based controller modes. The effects of SOC on HEV performance showed that, a higher initial values of SOC, leads to reduction of pollution emissions and fuel consumption. Also, the multi input FLC with the 90% of SOC represents optimal condition of HEV’s performance. Based on the fuel convertor plot in Figs. 14 and 15, it is clarified that 90% initial SOC in both controllers (FLC and Rule-based) reduces amount of engine operation points and shifted them to optimal conditions with more efficiency, which results in reducing fuel consumption and pollution emissions. It is obvious that combination of multi input FLC with 90% initial SOC requests minimum torque of the engine and regenerate the extra torque more efficiently, which maximizes the powertrain efficiency. Whilst, Rule-based with 70% SOC, needs more torque of engine, which lead to increasing fuel consumption and GHG emission. As shown in Fig. 15, multi input FLC with power-split configuration minimize on-road emissions by minimizing the use of ICE during driving cycle.

Effects of road grade In order to enhance the impacts of the developed controller under more severe driving conditions, simulation results for fuel consumption and pollution emissions are compared (in Fig. 16 and Table 4) to HEV according 1.5% of road grade in Teh-car driving cycle. Also, energy usage in power mode compared for multi input FLC and Rule-based controller over Teh-car driving cycle with 1.5% road grade in Figs. 17 and 18.

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Motor/Controller Operation PRIUS PN 30-kW permanent magnet motor/controller

J

J

400

400 300

0.7

0.60.65

300

200

0.9 0.8 0.8

0.85

0.9

-100 max cont. motoring torque 0.85 0.8 0.75 0.7

Torque (Nm)

Torque (Nm)

0.85

100

-200

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0.65 0.6

0.75 0.8

200

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-200

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max gen. torque

actual operating points

-400

actual operating points

-400 0

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2000

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max cont. gen. torque

-300

0.7

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0.75 0.8

6000

0

1000

2000

3000

4000

Speed (rpm)

Speed (rpm)

(a) Prius Rule-based controller

(b) Multi input FLC

5000

6000

Fig. 20. Electric motor operation.

Fuel Converter Operation

Fuel Converter Operation Prius pn 1.5L (43kW) from FA model and ANL test data

Prius pn 1.5L (43kW) from FA model and ANL test data

j

j

200

200

max torque curve

max torque curve

gc max torque curve

gc max torque curve

150

150

output shaft

output shaft op. pts(includes inertia & accessories)

100

Torque (Nm)

Torque (Nm)

op. pts(includes inertia & accessories) 37.5 36.5 34.5

50

30.5

100

37.5 36.5 34.5

50

30.5

26.5

26.5 22.5

22.5

0

0

-50 0

1000

2000

3000

4000

5000

6000

-50

0

1000

2000

3000

4000

Speed (rpm)

Speed (rpm)

(a) Prius Rule-based controller

(b) Multi input FLC

5000

6000

Fig. 21. Engine operation.

As compared in Figs. 17 and 18, fuel convertor is the main source of energy usage in power train components. Also, it is obvious that using FLC, reduces fuel convertor energy consumption about 32% in contrast to conventional Prius Rule-based one, which can effectively reduce fuel consumption and emission during driving cycle.

Iteration of Teh-car driving cycle (4 sequential Teh-car) Finally, in order to realize driving conditions for a usual day trip in Tehran city (40–60 km), simulation results for both proposed controllers has been compared over four iteration of Teh-car driving cycle (52 km). Fig. 19 depicts the initial SOC impact on energy storage system charge and discharging in 4-Teh-car driving cycle. Also, fuel consumption and emissions are presented in Table 5. Engine and motor operation maps by multi-input FLC and Rule-based controller are illustrated in Figs. 20 and 21 respectively. It shows that engine and motor operate in their optimal conditions by using FLC controller. Moreover, it is obvious that in FLC controller mode, engine operation point decrease and power ratio shifted to more motor power (electrical path).

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Conclusion In this paper, power-split hybrid vehicle modeling and planetary gear sets kinematics has been proposed. Then, an optimal energy management control strategy based on multi input FLC developed for power-split hybrid vehicle to reduce emissions and improve fuel economy. A comparative study of power-spilt HEV control strategy with multi input FLC and conventional rule-based one is discussed. This strategy achieves about ten percent increase in fuel and emissions efficiency in most drive cycles over a rule based control strategy. The simulation results in the cycle of road conditions show that the proposed energy management strategy can effectively reduce pollutants emission and fuel consumption between the reasonable distribution in the ICE and motor power. Also, investigation of various driving cycles effects on power-split HEV performance, fuel consumption and emission clarified that proposed power-split model and energy management control strategy are adaptive to different driving conditions. However, results indicate that proposed multi input FLC is more effective for real world driving cycles (Teh-car and FTP) than modal driving cycles (NEDC). Finally, initial SOC variation and road grade effects has been investigated in terms of energy usage and emissions. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.trd. 2015.04.024. References Baumann, B.M., 1997. Intelligent Control Strategies for Hybrid Vehicles Using Neural Networks and Fuzzy Logic. 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