Simulation and Optimization of the Energy ...

MARCH 25-28

Simulation and Optimization of the Energy Management of ITAN500 in the SUMO Traffic Model Environment D. Pacella, T. Donateo, D. Laforgia Dipartimento d‟Ingegneria Dell‟Innovazione Università del Salento, Via Monteroni, 73100, Lecce, Italy E-mail: [email protected] , [email protected]

Copyright © 2010 MC2D & MITI

Abstract: The paper describes the use of SUMO Traffic Model for the estimation of realistic driving cycles for ITAN500, a plug-in hybrid electric vehicle developed at the University of Salento. The energy performance (fuel consumption and energy battery) of the vehicle is estimated from the driving conditions (velocity, grade, state of the asphalt, etc.) obtained by SUMO according to different traffic scenarios. For each scenario, an optimization of energy performance has been executed with a multiobjective genetic algorithm by considering the parameters of the control strategy as design parameters. The energy flows in the ITAN vehicle were simulated with the help of on-purpose simulation programs developed in Matlab environment. The optimal control strategy for each scenario has been compared with that obtained with respect to the standard European urban driving cycle (ECE-15). Keywords: Hybrid vehicles, energy management, traffic simulation, optimization 1. Introduction ITAN500 is a plug-in hybrid electric vehicle (PHEV), i.e. its batteries that can be recharged either by running a gasoline engine or by connecting a plug to an electrical power source. Like HEVs, PHEVs allow efficiency improvements because they enable the engine to shut off rather than idle, recapture a portion of normally wasted braking energy, and permit engine downsizing to improve average in-use efficiency. However, thanks to the possibility to displace some energy not obtained by burning fuel in the vehicle‟s engine, they require fewer fill-ups at the gas station and have the advantage of home recharging. During their allelectric range (AER), plug-in hybrids use no fossil fuel but they can be considered zero emissions and high efficiency vehicles only if their batteries are recharged from renewable energy sources. Simpson [1] presented a comparison of the costs (vehicle purchase costs

and energy costs) and benefits (reduced petroleum consumption) of PHEVs relative to HEVs and conventional vehicles. On the basis of his model, Simpson found that PHEVs can reduce per-vehicle petroleum consumption. In particular, reductions higher than 45% in the petroleum consumption can be achieved using designs of PHEV20 or higher (i.e. vehicles containing enough useable energy stored in their battery to run more than 20 mi (32 km) on the UDDS cycle in all electric range). The study of Simpson [1] underlined that from the economic point of view, the PHEVs can become a competitive technology is the cost of petroleum will continue to increase and the cost of the batteries will decrease. The particular operating strategy employed in this kind of vehicles significantly influences the component attributes and the value of the PHEV technology [2] In order to minimize fuel consumption, to sustain battery charge and to reduce polluting

emissions, the supervisory controller of a PHEV should select the power split accurately at any time. The management of the ITAN vehicle [3]allows the driver to manually choose between a charge sustaining HEV and a full EV (charge depleting) operating mode. The power-train was optimized for the AER operating mode because the vehicle is designed to operate mainly in city's core and zero-emissions zones. In the present investigation, the optimization of the charge sustaining strategy is considered. This strategy will be selected when the vehicle is outside restricted zones to recharge the batteries or to overcome the speed limits in AER. Different charge sustaining strategies for series hybrid vehicles have been presented in literature [4][5][6]. They are usually optimized with respect to standard driving cycles (UDDS, EUDC, ecc.) but can fail when implemented on board due to the difference between real driving conditions and that used for the optimization. Control strategies that can adapt to the real driving conditions have been introduces in literature [5]. Their implementation requires the knowledge of the future driving profile (speed and related power demand) of the HEV. Some optimization techniques (the so called „autoadaptive‟ techniques) use the past driving conditions or a prediction-based future driving conditions evaluated by the past behaviour of the vehicle. Vehicular communications can contribute to the prediction of future driving profile of the HEV. In fact, a HEV could estimate its own driving cycle by exploiting messages received from other vehicles (Vehicle-to-Vehicle communication, V2V) or from the Intelligent Infrastructure (Vehicle-to-Infrastructure communication, V2I). The messages transmitted by a vehicle could carry status information such as position, speed, acceleration, etc., whereas those sent by the infrastructure could include the current status of traffic lights or the allowed emission limits. In particular, the future driving profile can be predicted by means of a traffic simulator like SUMO on the basis of the traffic conditions obtained by means of vehicular communications and radar systems. The driving conditions predicted by SUMO for a predetermined future time window will be used for an on board optimization of the control. As a preliminary investigation on these topics, the use of driving cycles predicted by SUMO for the optimization of the control strategy of the ITAN500 vehicle is presented here.

2. ITAN500 ITAN500 (figure1) is a four-wheel vehicle prototype with a size comparable with that of a large scooter. The powertrain was chosen according to the following design goals: • small size with the traction at the front axle; • simple components to keep low the cost; • use of DC components; • possibility to run the engine at constant-speed; • possibility to recharge the battery from the three phase electricity network that allows for a faster and more efficient recharging.

Figure 1: ITAN 500, a micro plug-in hybrid vehicle

The scheme of the powertrain is shown in Figure 2. WHEEL

Figure 2: The ITAN 500 powertrain

The torque at the wheel is produced by an electric motor that is connected to the wheel through a constant transmission ratio. The

current to the motor is controlled by a chopper that regulates the current from the batteries and the generator. The generator is connected to the fuel converter (gasoline engine) through an inverter. The parameters of the control systems can be adjusted via software. The specification of the motor chosen for the ITAN vehicle is reported in table 1. Table 1 - Motor specification Type of motor Max power Max torque Mass

Separately excited DC machine 14 kW at 1200 rpm 127 Nm [0-750 rpm] 90 kg

A set of six lead acid batteries in series were considered in order to produce a nominal voltage of 72V required to feed the electric motor. The choice of lead acid batteries was due to the need of reducing the vehicle cost. However, other kind of batteries (es. NiMH energy storage systems) has been considered for the vehicle upgrade [7]. The main characteristic of the batteries chosen for the vehicle are shown in table 2 while the specifications of the engine are reported in table 3. Table 2 - Batteries specification Number of battery packs Nominal voltage (6 packs in series) Nominal Capacity Nominal specific energy (@12.5A) Nominal specific power density (@12.5A) Max current required from the motor Capacity @ 120 A Energy stored @ 120 A Efficiency @120A Pack size

6 72V 125 AhC10 37.5Wh/kg 31.48W/kg 120 A

upgrade and economic analysis [7], development of a speed control system for the engine by throttle valve regulation [8]. 3. The SUMO simulator SUMO (Simulation of Urban Mobility) is an open-source traffic simulation package developed since 2000 by the Institute of Transportation Research at the German Aerospace Center. Sumo is commonly used to study road safety solutions. SUMO is a microscopic and space-continuous simulation of vehicular traffic [9] where each vehicle has an own route and is simulated individually. In particular, the vehicles may follow the routes in random or predetermined mode and meet the road rules, the tool is capable to simulate different classes of vehicles (car or bus). The operator can assign to each vehicles class different rules (for example bus steps) and can set speed limits on every road stretch. This tool has been used in the present investigation to simulate the Ecotekne campus of the University of Salento (Figure 3) where the ITAN500 can move and be tested. The vehicle has been simulated to move together with other vehicles that, unlike ITAN500, can enter and exit the campus area. The number and the specification of the vehicle in terms of aerodynamics (frontal area AF and drag coefficient Cd), rolling coefficient (Cr), mass, maximum traction power (Pmax), brake power (Pbrake) and lenght are reported in table 4. Two different scenario named A and B are considered where the total number of vehicles is set equal to 200 and 20, respectively. The presence of a traffic light and different bus stop stations are also considered.

35 Ah 2160 Wh 82% 345*170*285mm

Table 3 - Engine characteristics Number of cylinders Displacement Compression ratio Max. power Max torque Fuel consumption

1 404 cm3 8.3 9.9kW @ 3600rpm 28.4Nm @ 2500 rpm 310g/kWh

The basic power-train of ITAN 500 has been designed at the CREA research center in 2006 and then it has been the subject of further investigations including the optimization of the control strategy [3], experimental tests, battery

Figure 3: University of Salento campus traffic pattern

Table 4 – Traffic scenarios for SUMO simulations of the Ecotekne university campus Vehicle AF·Cd [m2] Cr Mass [kg] Pmax [kW] Pbrake [kW] Length [m] Number (scenario A) Number (scenario B)

ITAN 1.5 0.013 750 10 30 2 1 1

SUV 1.2 0.017 2000 155.0 465.0 5 9 1

FULL SIZE 0.7 0.013 1500 111.5 345.0 5 50 4

4. Energy management strategy The energy management strategy includes an initial Charge Depleting (CD) mode where the battery only is used until a threshold value of battery SOC is reached (SOCCD). Then, the vehicle can be run in three different modes. Mode 1: The power to the motor is supplied only by the generator/engine group; Mode 2: Only battery is used to supply power; Mode 3: The engine is used both to charge battery and to supply power to the motor; Note that a fourth mode could be implemented where the engine and the battery are used together to feed the motor. However, the current power-train of ITAN doesn‟t allow this possibility. The transition from one mode to the other is regulated by the battery SOC, the load power and the minimum time at which the engine must stay ON or OFF. The energy management strategy is illustrated in figure 4 and has been obtained by adapting the control strategy developed by Paladini et al. for a Fuel Cell Hybrid Vehicle [4].

COMPACT 0.6 0.012 1000 77.0 231.0 4 80 8

LIGHT WEIGHT 0.4 0.008 750 57.0 171.0 3 40 4

BUS 4.2 0.015 20000 200.0 600.0 9 20 2

If the operative point is in Area 1 (i.e. power is negative) batteries are charged by regenerative braking; When the power request is very low ( SOCmin), the engine is used only when the power request is higher than a threshold value named PICEmin. Otherwise, only batteries are used and the engine is off. This choice allows the engine to be run in its best efficiency zone. In Area 6 (PLOAD > PICEmin and SOC< SOCmin) the engine can charge batteries if the sum of PLOAD and the power required for a correct battery charge is lower than PICE,max, (mode 3). Otherwise, PLOAD is supplied by the ICE but the batteries are not charged (mode 1). If (PLOAD < PICEmin and SOC< SOCmin) the operative point is in a critical area because both ICE and battery are not used in their optimal operating conditions. The choice between ICE (mode 1) and Battery (mode 2) is implemented in this way: if SOC  K  PLOAD , the ICE is used (mode1), otherwise the battery is used (mode2). The optimal values of K, SOCmin, PICE,min and SOCCD were selected by a multi-objective genetic algorithm while the energy flows were simulated with the help of on-purpose simulation programs (VPR) developed in Matlab environment. 5. The VPR model

Figure 4: Energy management strategy

The Energy Strategy defines different operative areas. In the charge depleting area (SOC>SOCCD -Area 3) only batteries are used.

An on-purpose model of the ITAN powertrain has been developed in the Matlab environment to simulate the energy flows according to the control strategy described above and the driving conditions given by SUMO. The outputs of the model are the evolution of fuel consumption and battery SOC along the driving cycle (see figure 5). Starting from the

velocity speed and grade traces given by SUMO, the vehicle power request is calculated by considering aerodynamic force, grade force, inertial contribution and rolling force.

An example of vehicle power request trace is shown in figure 5 together with other VPR output described below.

Figure 5: Example of VPR results

Note that during deceleration the power request is negative which means that the braking energy can be recovered and stored in the batteries. (see the SOC trace of figure 5). According to the energy management strategy described above, the engine is run only when the battery SOC is low and the vehicle power request is high. The engine is assumed to be run always at the speed of 3000 rpm and its efficiency is simulated with a second order polynomial of the requested torque with a maximum value of 0.26. The overall electric efficiency between the chopper and the wheels is set constant and equal to 0.65 for the present investigation. 6. Driving cycles For the optimization, six driving cycles were considered. The first cycle was obtained by running the ITAN500 vehicle in the SUMO environment (scenario A) over a total time of about 3 hours corresponding to 46.9 km. The

speed profile of this cycle (cycle#1) is shown in figure 6. The second, third and forth cycle are obtained by dividing the cycle#1 in three parts, each of about 1 hour (cycle#2, cycle#3, cycle#4). Cycle#5 (see fig. 6) is the standard European urban cycle ECE-15 repeated sequentially until obtaining the same duration of cycle#1. Cycle#6 is obtained in the same way as cycle#1 but considering the scenario B and is also reported in figure 6. The specifications of the cycles are reported in the table 5 together with the initial state of battery charge considered for the optimization. Note that for cycles #3 and #4 the initial SOC was set to the final SOC obtained with the optimal solution of the previous cycle (cycles #2 and #3, respectively) as described in the following paragraphs. For each cycle a complete optimization process has been performed. For the sake of brevity only the results for cycle#1 are analyzed in details.

consumption and battery usage with a multiobjective approach. Table 6 – Design variables Variable SOCCD (%) SOCMIN (%) K PICE,min [W]

Min 60 20 0 500

Max 80 60 50 6000

Step 1.33 2.66 0.2 130

A random distribution of 25 experiments was considered as initial population. The genetic algorithm was run for 50 generations. The results obtained for Cycle #1 are shown in figure 7. Note that, it is possible to obtain a minimum fuel consumption of 0.2 l/100km by using only the 20% of the battery charge. On the other hand, it is possible to achieve a perfect charge sustainment (overall battery usage equal to 0) with a fuel consumption of about 1.8 l/100km. In this case the battery is continuously discharged and recharged during the cycle but the final state of the charge is perfectly equal to the initial value (80%). Accepting that the battery can be discharged up to 65% (battery usage equal to 15%), the corresponding fuel consumption is 0.6 l/100 km. The battery usage of 15% was used also to compare the results obtained with the other cycles.

Figure 6: Speed profile of cycles #1 , #5 and #6 Table 5 – Cycles specification Cycle

Total time

Distance

#1 #2 #3 #4 #5 #6

10000 s 3600s 3551s 2800s 10000s 10800s

46.9km 13.77km 20.5km 12.6km 47.15km 74.3km

Initial SOC of Batteries 80% 80% 75% 70% 80% 80%

7. Analysis of the results A Optimization of the energy management for cycle #1 The optimization of the control strategy was performed with the MOGA-II [10] algorithm in the ModeFrontier environment [11]. The minimum and maximum values and the steps of variation of the design variables are reported in table 6. The goal of the optimization was the reduction of both fuel

Figure 7: Results of the optimization (cycle#1)

To underline the influence of each design parameter on battery usage and fuel consumption, the main effect analysis tool available in the ModeFrontier environment has been used. The results are shown in figure 8 and 9 respectively.

compared with batteries (PICE,mi) has a low effect, probably because of the large use of batteries with respect to the engine. Summing up, the best fuel consumption for cycle#1 is obtained with the highest values of K , SOCCD, SOCmin but, of course, this correspond to the higher usage of batteries. The quantitative value of the effects is shown in figures 10 and 11 as a comparison with the results of the other cycles. B Effect of driving cycle specification

Figure 9: Effect of design parameters on fuel consumption for cycle#1

From this analysis, the threshold value SOCCD can be said to be the most important design value for both battery usage and fuel consumption. By choosing lower values of SOCCD the battery usage is increased while fuel consumption is decreased. In fact, lower values of SOCCD correspond to a larger use of the charge depleting mode with respect to the charge sustaining. For cycle #1, the K and SOCmin variables has the same effect as SOCCD, but with less importance. The minimum power at which engine is preferred

FC [l/100km]

SOCCD (%)

SOCMIN (%)

K

PICE,MIN [kW]

Table 7– Optimal energy use for each cycle

 SOC [%]

In these figures the horizontal axis is formed by the low and high level of each design variable. The vertical axis represents the mean of the response variable (battery usage or fuel consumption) for each level of the design factor. For each level of each design variable the mean value, the standard error and the standard deviation are shown. A design factor is important for a response variable if it leads to a significant change in the mean value going from the “-“ settings of the factor to the “+”setting of the factor.

Cycle

Figure 8: Effect of design parameters on battery usage

As mentioned before, the battery usage of 15% was used to compare the results obtained with the different cycles. Thus, for cycle 1# and 5#, the minimum fuel consumption corresponding to a battery usage of about 15% was found from the corresponding Pareto front and reported in table 7. For cycles 2#, 3# and 4# the Pareto solution corresponding to a battery usage of about 5% was selected (table 7). In this way the overall battery usage of the three cycles performed sequentially is the same as cycle #1 and #5.

#1 #2 #3 #4 #5 #6

14.5 5.0 5.5 4.5 15.2 15.8

0.6 0.4 0.8 0.5 1.1 1.2

65.3 74.7 69.3 65.3 66.6 64.0

20.0 20.0 20.5 46.7 57.3 22.7

50 12.5 46.3 2.2 22 42.9

6.0 5.1 4.6 4.2 5.5 6.0

The results of table 7 shown how the optimal parameters of the control strategy strongly vary according to the cycle used for the optimization. Note that the threshold value between Charge Depleting and Charge Sustaining mode SOCCD is always in the central area of its interval of variation (between 60 and 80%). This is due to the choice of accepting a battery usage of 15%. SOCCD is in fact, the key parameter to move on the Pareto front between the solution corresponding to the minimum fuel consumption and that giving the minimum battery usage. As a confirmation, s, the plots of figure 10 and 11 compare the effect size of design variables on battery usage and fuel consumption for all the investigated cycles. Note that the sign of

FC [l/100km]

SOCCD (%)

SOCMIN (%)

13.2 15.2 13.1

0.7 1.1 1.33

66.6

57.3

22

PICE,MIN [kW]

 SOC [%]

#1 #5 #6

K

Cycle

the SOCCD effect is always the same but the magnitude is quite different. The effect of the other parameters is completely different both in sign and magnitude among the different cycles. Moreover, the effect size changes also by considering different portions of the same cycle (see cycles #2, #3 and #4 compared with the global cycle #1).

5.5

8. Conclusions

Figure 10: Effect of cycle on battery usage

Figure 11: Effect of cycle on fuel consumption

Another way to underline these results could be the simulation of cycle #1 and #6 with the optimal parameters found for cycle #5. A common procedure for the optimization of control strategy is, in fact, to consider the standard driving cycles and then to apply the results to the real vehicle. The results of this analysis are reported in table 8. Note that fuel consumption of cycle #1 increases from 0.6 to 0.7 l/km but the energy usage decreases from 15.5 to 13.2. Thus, the results cannot be compared. The same conclusion can be drawn for cycle #6. Table 8– Energy use for each cycle with the design values optimized for cycle#5

In the present investigation the SUMO simulation tool has been used to provide realistic driving cycles for the optimization of energy management of ITAN 500. The energy management strategy described in the paper considers a charge depleting mode and a charge sustaining mode. The shift between the two modes is defined by a threshold value of the battery state of the charge, named SOCCD. For all the cycles considered in the investigation, the optimal value of this parameter is in the central area of its interval of variation (between 60 and 80%). This is due to the choice of setting a battery usage of 15% for the comparison of the results. SOCCD is in fact, the key parameter to move on the Pareto front between the solution corresponding to the minimum fuel consumption and that giving the minimum battery usage. The engine is run at the constant speed of 3000, and it is preferable used when the power request is very high and its efficiency is better. The importance to know the actual driving profile of a hybrid vehicle in order to fully exploit its potential in terms of fuel consumption reduction has been underlined in the following ways. 1) Optimal values of the design variables for each cycle were found with the application of a multi-objective genetic algorithm. These values strongly changes from cycle to cycle showing that the common procedure of optimizing with respect to standard cycles is not satisfactory. 2) A statistic analysis of the effect of each design parameter on the battery usage and fuel consumption was also performed for each cycle. This analysis revealed that, except for the SOCCD threshold, it is not possible to drawn direct conclusions on the correlation between each control strategy variable and the energy performance (fuel consumption and battery usage).

In the present investigation, the actual driving conditions of ITAN500 were obtained by simulating the movement of the vehicle in the Ecotekne campus with two different traffic scenarios. The optimization was performed a posteriori once obtained the driving profiles. In future, the energy flows of the vehicle will be monitored and optimized in real time by installing the SUMO simulation tool on the vehicle. Vehicular communications and radar systems will be used to obtain information about the traffic conditions with a predetermined temporal frequency. At each time interval, this information will be used to set the initial conditions of the SUMO simulation that will be performed on board. The driving conditions predicted by SUMO will be used to choose the optimal values of the energy management strategy for the next time window. References [1]

A. Simpson, “Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle Technology”, 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition (EVS-22) Yokohama, Japan October 23–28, 2006;

[2] Jeffrey Gonder and Tony Markel, “Energy Management Strategies for PlugIn Hybrid Electric Vehicles”, SAE Technical paper 2007-01-0290, 2007; [3] T.Donateo, F.Zecca and D.Laforgia, “Experiences on Hybrid Electric Vehicles”, 62° ATI National Congress, University of Salerno – Italy, 11-14 September 2007 [4]

V. Paladini, T. Donateo, A. de Risi, D. Laforgia, “Control Strategy Optimization of a Fuel-Cell Electric Vehicle”, ASME EFC05 Technical Conference, 2005, 1416 December 2005 Rome, Italy , in printing on Journal of Fuel Cell Science ;

[5]

Pisu, P., Musardo, C., Staccia, B., Rizzoni, G., A Comparative Study of Supervisory Control Strategies for Hybrid Electric Vehicles, IMECE 2004, 13-19 November 2004, Anaheim, CA.

[6] Farzad Rajaei Salmasi, “Control Strategies for Hybrid Electric Vehicles, Evolution, Classification, Comparison, and Future Trends,” IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 56, NO. 5, SEPTEMBER 2007. [7] T.Donateo, S.Campilongo and F.Zecca , “Il Progetto ITAN500 – Ottimizzazione Energetica ed Economica”, ATA Journal Vol.62 July August 2009. pp. 42-51 (in Italian)

[8] M. Schirinzi, F.Adamo, T.Donateo, D. Laforgia, “Modellazione Dinamica e Controllo di un motore a benzina orientato alla regolazione del regime di rotazione per la trazione ibrida”, 64° ATI National Congress, Montesilvano Pescara - Italy, 811 September 2009, (in Italian) [9] E. Brockfeld, R. Kühne, P. Wagner,"Calibration and Validation of Microscopic Traffic Flow Models". Transportation Research Board, Nr. TRB2004-001743, TRB Annual Meeting (CD-Rom), January 2004, Washington, USA, 2004 [10] Silvia Poles, Enrico Rigoni and Tea Robic. MOGA-II Performance on Noisy Optimization Problems,, Proceedings of the International Conference on Bioinspired Optimization Methods and their Applications, BIOMA 2004, pp. 51--62, Jožef Stefan Institute, Ljubljana, Slovenia, October 2004 [11] ESTECO s.r.l. - modeFRONTIER user manual - www.esteco.com.