Evaluation of powertrain solutions for Future Tactical Truck Vehicle Systems

Transcription

1 Evaluation of powertrain solutions for Future Tactical Truck Vehicle Systems Pierluigi Pisu a, Codrin-Gruie Cantemir a, Nicholas Dembski a,, Giorgio Rizzoni a, Lorenzo Serrao *a, John R. Josephson b,c, James Russell c, a Center for Automotive Research, The Ohio State University 930 Kinnear Road, Columbus, OH b Computer Science and Engineering Department, The Ohio State University 2015 Neil Avenue, Columbus, OH c Aetion Technologies LLC, 1275 Kinnear Road, Columbus, OH ABSTRACT The article presents the results of a large scale design space exploration for the hybridization of two off-road vehicles, part of the Future Tactical Truck System (FTTS) family: Maneuver Sustainment Vehicle (MSV) and Utility Vehicle (UV). Series hybrid architectures are examined. The objective of the paper is to illustrate a novel design methodology that allows for the choice of the optimal values of several vehicle parameters. The methodology consists in an extensive design space exploration, which involves running a large number of computer simulations with systematically varied vehicle design parameters, where each variant is paced through several different mission profiles, and multiple attributes of performance are measured. The resulting designs are filtered to choose the design tradeoffs that better satisfy the performance and fuel economy requirements. At the end, few promising vehicle configuration designs will be selected that will need additional detailed investigation including neglected metrics like ride and drivability. Several powertrain architectures have been simulated. The design parameters include the number of axles in the vehicle (2 or 3), the number of electric motors per axle (1 or 2), the type of internal combustion engine, the type and quantity of energy storage system devices (batteries, electrochemical capacitors or both together). An energy management control strategy has also been developed to provide efficiency and performance. The control parameters are tunable and have been included into the design space exploration. The results show that the internal combustion engine and the energy storage system devices are extremely important for the vehicle performance. Keywords: military truck, hybrid powertrain, design space exploration, simulation, optimization. 1. INTRODUCTION The Future Tactical Truck System is a new family of military vehicles. The Center for Automotive Research of the Ohio State University has been involved in the conceptual design and the preliminary evaluation of architectures capable to satisfy the required targets of two of the vehicles in the family. The first one is the Maneuver Sustainment Vehicle (MSV), a medium-duty truck (18-20 tons) mainly used for transportation and support; the second vehicle is the Utility Vehicle (UV), smaller and lighter than the MSV (7-8 tons), used for crew displacement and light duty transportation. According to the TACOM specifications, both vehicles must be capable of generate a large amount of electrical power for stationary use, thus avoiding the necessity to carrying an external gas-powered electric generator. * serrao.4@osu.edu, phone +1 (614)

2 These requirements can be satisfied using series hybrid electric powertrain architectures; the article shows the approach used to define the many architecture options and evaluate them by use of a simulation model, developed in the past years at the Ohio State University. 2. VEHICLE MODEL 2.1. Overview The optimization is performed using a vehicle model developed at the Center for Automotive Research of the Ohio State University. A detailed description can be found in the paper by Pisu et al. [1]. The model is based on quasi-static energy analysis, as proposed by Rizzoni et al. [2, 3, 4]. The road load (i.e. required power) is the input and the fuel consumption is the output. The operating conditions of the powertrain components are determined in order to follow the drive cycle, and the physical limitations of each powertrain component (for example, min./max. power or speed) are taken into account. Figure 1: Information flow in the vehicle model The vehicle model is composed by a driving cycle block, a vehicle dynamics or load block, and a powertrain block. The driving cycle block supplies the inputs for the simulation, i.e. velocity profile, grade profile, and terrain information. The vehicle loads block uses these inputs to determine the actual forces and speeds which the powertrain must develop as well as the actual profile which the vehicle can meet. The powertrain block incorporates the latest technology among vehicle design options, including ultracapacitor and Ni-MH battery packs as well as a variety of generator, engine and traction motor configurations. Energy Flow I.C. Engine Road/ Wheels Electric Drive Generator DC Bus Road/ Wheels Electric Drive Energy Storage Systems (Batteries, SuperCapacitors) Figure 2: Energy flow in a series hybrid electric vehicle

3 The vehicle architecture is shown in Figure 2. It is a series hybrid electric vehicle, in which the internal combustion engine (ICE) is not directly linked to the wheels, but it drives an electrical generator producing electric current. The electric power needed by the traction machines may come directly from the generator, from the electric storage system (ESS, batteries and/or super capacitors), or from both sources together. An important part of the model is the supervisory control strategy that determines the power split between the different sources; a strategy developed by some at the Ohio State University [5-9] have been implemented Backward simulator The option typically chosen when developing a simulator is to create what we define a forward simulator, in which the information flows as shown in Figure 3 and Figure 4. The desired speed (from the cycle inputs) is compared to the actual vehicle speed, and braking or throttle commands are generated in order to follow the imposed vehicle profile. This driver command is an input to the engine and the rest of the powertrain components, which ultimately produce a tractive force. Finally, the force is applied to the vehicle dynamics model, where the acceleration is determined taking into account the road load information. Drive & load cycles Desired speed Cycle inputs Driver Brake Throttle Force Vehicle Dynamics Actual Speed Actual Speed Figure 3: Information flow in a forward simulator Force/torque information propagates from prime mover (through wheels) to vehicle dynamics Driver command Frc Spd Wheel Axle Transmission Torque Converter Engine Speed information propagates from vehicle dynamics (through wheels) to prime mover FUEL Figure 4: Basic information flow within the powertrain block of a forward simulator Another approach that can be followed is to build a so-called backwards simulator, which has the structure shown in Figure 5 and Figure 6. Unlike the forward simulator described earlier, in this case no driver is necessary, since the desired speed is a direct input to the simulator, while the engine torque and fuel consumption are outputs. The simulator determines the net tractive force to be applied based on the velocity, payload, and grade profiles, along with the vehicle characteristics. Based on this information, the torque that the traction motors should apply is calculated, and then the torque/speed characteristics of the various powertrain components are taken into account in order to determine the engine operating conditions and, finally, the fuel consumption. Figure 5 shows the basic information flow in a backward simulator. The vehicle loads block uses the basic cycle information to determine the force and speed required from the powertrain, which can be schematically represented as in Figure 6.

4 Drive & load cycles Cycle inputs Vehicle loads Force Speed Figure 5: Basic information flow in a backward simulator Frc Spd Wheel Information (both speed and torque/force) propagates from vehicle loads to prime mover Axle Transmission Torque Converter Engine FUEL Figure 6: Information flow within the powertrain block in a backward simulator Both the forward and backward simulation approaches have their relative strengths and weaknesses. Fuel economy simulations are typically conducted over predetermined driving cycles, and therefore using a backward simulator ensures that each different simulation exactly follows this profile. By contrast, a forward simulator will generally not exactly follow the trace, and a small error between the actual and the desired signal will generally exist. Proper tuning of the driver block can reduce the differences, at the price of some extra time and effort, whereas the backward version keeps the error at zero without any effort. However, backward simulator cannot easily capture the powertrain limits. There is no guarantee that a given vehicle/powertain will actually be able to meet the desired trace. In a backward simulator, however, it must be must assumed that the trace can be met, and information about the actual limits does not become available until the end of the calculations, when the operating conditions of the prime movers are evaluated. A forward simulator takes these limits into account, since the force information originates at the prime mover (where it can be limited to the component maximum), and then is transmitted to the vehicle (allowing other constraints to be imposed). Similarly forward simulators are more suited to acceleration tests, by simply forcing the driver to give a full throttle command. A basic backward looking approach has been adopted here, in order to give a very clean energy breakdown. However, given the need to provide an indication of powertrain limits (i.e., to show what actual cycle might result when a underpowered system is driven on an aggressive cycle) and perform acceleration measures, a hybrid simulation approach (with both a backward and a forward facing path) is implemented. This approach can provide the advantages of each, at the expense of increased complexity and run time. Drive & load cycles Cycle inputs Vehicle loads Saturate velocity if needed Desired Speed Max Force Actual Speed Force Get maximum force Energy Use Figure 7: Information flow in the simulator developed forward and backward facing paths

5 Figure 7 shows the basic information flow in the simulator developed for this application. Again, the drive and load cycles blocks generates the inputs. Given the desired speed, the maximum force that can be exerted is determined in the powertrain block, and fed back into the vehicle loads block. Based on the maximum available force, the vehicle loads block can now check if the desired speed can in fact be achieved. This is done by determining the net road loads which the desired profile requires, and comparing it to the available force. If the trace cannot be met, the speed must be saturated, so that the road loads required by the new trace match the available force. After having checked (and possibly saturated) the speed and determined the road loads at the actual speed, the speed and force information can be passed back to the powertrain. The powertrain then uses these inputs to determine the net energy (using a backward facing simulation path). 3. DESIGN SPACE EXPLORATION The large-scale design-space exploration can be efficiently done by converting the vehicle simulator into C-code and compiling it into executables. The advantage is that executables run much faster and they can be called by a Seeker software [10]. This opens the door to massive design space explorations, with the Seeker coordinating many computers simulating different vehicle configurations in parallel and returning the results. As illustrated in Fig. 9, the Seeker-Filter-Viewer architecture for design-space exploration has three synergistic components [11]: Seeker - generates design alternatives and evaluates them according to multiple criteria, Filter - selects from the generated alternatives a Pareto-optimal subset, i.e. the set of non dominated alternatives. A decision candidate A dominates another candidate B if A is better or equal to B in every criterion and is better than B in at least one criterion. Any candidate that is dominated by another candidate is discarded. The survivors comprise the Pareto subset. No member of the Pareto subset dominates any other element in the subset (thus, deciding among alternatives in the Pareto set is always a matter of tradeoffs). Viewer - enables a decision-maker to perform visual trade-off analyses of the elements of the Pareto set and possibly narrow to a subset for further exploration. The Seeker can use any number of networked computers to evaluate designs in parallel. Thus, it makes it possible to consider very large numbers of design alternatives. Figure 8: Seeker-Filter-Viewer architecture The ability to explore such large spaces makes it possible for the decision maker to move away from the current dominant practice of exploring variations around a set of known good designs, and towards exploring all regions of a decision space, looking for design nuggets. As illustrated in Figure 9, the Seeker sits at the center of multiple loops, determining the tunable parameters and receiving the final metrics from another module which calls the simulator and post processor via the appropriate executable calls. As mentioned, the Filter selects the Pareto-optimal subset from the set of generated designs. In practice, in most domains of application the Filter is able to eliminate most alternatives, most of the time. Moreover as the size of the design space increases, the efficiency in eliminating alternatives increases. This kind of efficiency is extremely important for practical applications of the technology: a decision-maker can have the confidence of having explored large spaces, while needing to study in detail an extremely small percentage of the decision alternatives.

6 Seeker Inputs (tunable parameters) Final Outputs (Metrics) Simulation Raw outputs Post processing Figure 9: Seeker-based simulation and evaluation management The Viewer is used to interactively explore the surviving design alternatives, and to visualize the trade-offs between pairs of evaluation criteria. The surviving alternatives are presented as a set of scatter plots, one plot for each pair of criteria for which the user wishes to examine the trade-off behavior. The user can select interesting subsets in one plot, perhaps seeing that, in a certain region, for a small decrease in one evaluation criterion, substantial improvement is possible in the other criterion. The user then might select the candidates in that region; perhaps seeing that there are enough candidates with properties in the desirable ends of the scales, so that the ones with less desirable values can be discarded. It is important to note that this kind of decision cannot be made a priori; it is the actual distribution of candidates that makes it possible for the user to make choices. The displays in the Viewer are linked; candidates selected in one plot are simultaneously highlighted in other plots so that a user can immediately see how the selected alternatives fare on other criteria. Subsets may be also selected by structural constraints. The decision maker may narrow to selected regions and study the candidates in greater detail, back out, and then narrow to another region, and so on. Thus, the Viewer is useful for aiding understanding of the design space, as well as enabling narrowing the choice to favorable candidate designs, or down-selecting to a single best design. 4. SIMULATIONS AND RESULTS The tools presented in the previous sections allow for the selection of the optimal vehicle configurations; in fact, while the Pareto front can be obtained using the automated process described, the selection of the vehicle configuration requires the user to directly make some decisions. In particular, one must decide the acceptable values of each performance metric, taking into account the specific application; then choose a compromise between several contrasting characteristics, using engineering knowledge, in order to achieve the best configuration. The description of the selection process and an analysis of the results are provided in this section MSV The architecture choices include number of axles, number of motor per axle, size and type of the internal combustion engine and energy storage systems (batteries, electrochemical capacitors or both). Engines The power required from the vehicle engine is in the order of HP. Three alternatives were considered, using available experimental maps: two conventional, heavy duty Diesel engines, and a set of two identical, smaller engines. A simple control strategy makes them running at identical conditions, switching one of them off when not needed. Number of axles Two possibilities were taken into account: a traditional 2-axles vehicle, and a 3-axles vehicle. Number of motors per axle The use of a series hybrid electric powertrain allows the designer to decide whether the power is provided to the wheels using a single electric motor per axle and a set of transmission shafts and differential gearings, or using as many motors as wheels, thus eliminating the need for a mechanical power transmission inside the vehicle body. The details about the advantages and disadvantages of each choice are beyond the objective of the present investigation, which only focuses on evaluation of fuel consumption and overall performance. Energy storage systems All hybrid electric vehicles commercially available make use of Ni-MH batteries as storage medium for electrical energy, because of their good characteristics of energy density (W/kg). However, batteries have a limited power density, due to the limitation of current output. Conversely, other storage devices, such as electrochemical capacitors (also called supercapacitors ), have high power density but low energy density, and can deliver very high current for a limited time. Using the two energy storage systems together allows for both high energy and high power density, the drawback being increased cost, mass and possibly a more complicated energy management

7 strategy. Supercapacitors allow for high performance over a limited time and for higher capacity of power recovery during impulsive braking, while batteries offer a sizeable energy buffer which helps exploiting the advantages of the hybrid powertrain in terms of fuel economy. The simulator allows for the use of batteries only, capacitors only or both sources, and for several sizes; some control-related parameters, such as state of charge limits, are tunable as well. Table 1 summarizes the possible vehicle architectures that have been considered. Table 1: MSV architectures Architecture: Engines: Cummins 300 HP CAT 275 HP 2x VM 138 HP 2 axles 1 motor per axle batt caps both batt caps both batt caps both 2 motors per axle batt caps both batt caps both batt caps both 3 axles 1 motor per axle batt caps both batt caps both batt caps both 2 motors per axle batt caps both batt caps both batt caps both 4.2. UV The UV is smaller and requires much less power than the MSV; to preserve modularity in the simulator (but also in the actual vehicles, in order to achieve some scale economy), the UV and MSV share the powertrain components, such as batteries, electric machines. Engines Given the lower power required, in this case the engine used is always a single VM 138 HP. Number of axles and motors per axle The architecture choice in this case is not possible: this vehicle will have 2 axles and 1 motor per axle. Energy storage systems The energy storage system is a battery pack; the high autonomy required for the vehicle requires a total capacity of at least 2000 Ah, corresponding to 4 strings of 86 batteries. In this case, it has been decided not to add super capacitors, in order to limit the total weight Driving cycles For a comparison of the various possible configurations, a set of four driving cycles and three acceleration tests has been set up, in order to evaluate fuel consumption and acceleration performance in different conditions. The driving cycles are time histories of vehicle velocity, representative of the vehicle s mission profile and actual performance capabilities. As explained before, the simulator uses this information to calculate the maximum force required at the wheels and then the power required from the powertrain, taking into account all the losses across the powertrain through the combined efficiency of the single components. Table 2 compares the driving cycles giving the basic information on each of them Metrics A few metrics have been defined to characterize a vehicle configuration after the seven simulations described above; some of them are obtained from the driving cycles, others from the acceleration tests. Performance metrics Three tests are used to evaluate performance: full power acceleration on straight road, with 3% or 60% grade, and repeated acceleration (10 times from 0 to 30 mph, braking between successive accelerations). The outputs are the following: - Top speed (in mph) on 3% grade mph time on 3% grade - Ability to move on 60% grade - Total time to complete the dash acceleration test

8 - Time needed for the slowest 0-30mph acceleration during the dash test Fuel consumption metrics The four driving cycles are used to evaluate the equivalent fuel consumption (i.e., the actual fuel consumption corrected with the net RESS energy balance over the cycle) Once all the simulations were ran, the tool described in Section 3 was applied to extract the Pareto front containing the non-dominated configurations. Then, the choice of the optimal configurations is made by selecting the vehicles that achieve the desired values of performance metrics. Obviously, there is not a single optimal configuration: it depends on the desired characteristics of the vehicle. For this reason, two configurations were selected: one to obtain the best fuel economy, and one with the best performances. Starting from the full design space content, the configurations that do not attain the required value of some performance index are eliminated; the procedure is repeated focusing on other performance indices, until only one configuration remains, which will be the final choice. The order in which the different indices are considered is essential and must correspond to their relative importance: the most important parameter is the first aspect to be considered and the less important is the last. Table 2: Driving cycles Fuel Consumption evaluation Performance evaluation Driving cycle Description Basic characteristics 1 Urban driving cycle, recorded in Columbus, OH. Stop-and-go cycle, always on road, with a maximum speed of about 20 mph. It lets evaluate the fuel consumption when the power request is small and the vehicle is running at idle for a conspicuous amount of time. 2 Urban driving cycle Same as before, with higher average speed % grade 60% grade Dash Cycle based on data recorded on actual vehicles Cycle based on data recorded on actual vehicles Acceleration test on a paved road with a 3% grade. Acceleration test on a paved road with a 60% grade. Repeated acceleration test It is composed by 6 parts, corresponding to 6 different soil types, ranging from asphalt and concrete to mild and heavy off-road. It's based on statistical considerations and can be considered representative of the typical mission profile for the vehicle. Similar to cycle 3, with different proportions of on-road and off-road sections. Used to measure the maximum speed and the time to 30 mph. In this test the capability of motion on extreme grades is evaluated. A sequence of 10 accelerations 0-30 mph on level road, to evaluate the maximum performance and the capability of energy storage and recovery Choice of the best configuration MSV To select the configuration with the best performance, the following criteria have been used (in order of decreasing importance): - maximum speed and acceleration time on 3% grade - acceleration time on dash test - powertrain mass - fuel consumption The process of choice can be done using the viewer tool described in Section 3, which allows the user to plot any of the input or output variables against any other, and visualize each of the configurations as a dot in a x-y graph. In Figure 10.a, the maximum speed is plotted against the acceleration time (0-30 mph); not surprisingly, the distribution of the dots indicates that the configurations with high speed also offer low acceleration time.

9 The selected elements represent all vehicle configurations able to reach top speed higher than 60 mph and to accelerate from 0 to 30 mph in less than 10 s. These configurations are retained, while all the others are eliminated. The successive elimination is graphically represented in Figure 10.b, where two outputs from the dash acceleration test (see Table 2) are represented: the total time to complete the 10-acceleration test, and the time needed for the slowest single acceleration. The dots represent now the configurations surviving the first elimination process; the faster elements are selected while the other are discarded. (Note that each of the points may actually represent more vehicles achieving the same result 1 ). Then, the process is repeated using the powertrain mass as discriminating parameter: as can be seen in Figure 11.a, the total mass of all powertrain components can be quite large (up to 3000 kg, due to the fact that batteries and capacitors are heavy), but the choice is limited to configurations with a powertrain mass of less than 1500 kg. Finally, the fuel consumption is used to select the optimal vehicle, as shown in Figure 11.b. The resulting configurations are listed in Table 4. The same procedure can be repeated to select the vehicle with optimal fuel economy; the order of the metrics to be considered is different, and it is shown in Table 3; the results are listed in Table 5. By comparing the results in Table 4 and Table 5, it can be noted how the Cummins engine (300 HP), being more powerful, is chosen for high performance vehicles, while the Caterpillar engine (275 HP), on the other hand, is more efficient and is found in all the configurations with best fuel economy. The energy storage devices are large for both configurations. Four strings of batteries are used to achieve good performance, thanks to the large amount of energy that can be stored, while the economy configurations use super capacitors, thanks to their superior capability in terms of peak power (which allows to regenerate an higher amount of energy during braking). In both cases, the two-axles configurations are chosen, because of the lower amount of losses with respect to the threeaxle solution; however, it appears that two electric motors per each axle offer higher fuel efficiency, because of the different operating conditions of the machines. The scaling factors of the electric machines do not appear to be important, since they assume various values in the selected configurations, but a difference can be seen between performance and economy configurations: the former tend to have a smaller generator and various motor sizes, while the latter have bigger generator and smaller (more efficient) traction machines. Finally, the mass is different between the two sets of configurations; this is due to the different combinations of scaling parameters and powertrain components, but also to the different restrictions applied while choosing. UV The same procedure has been applied to the choice of the best UV configurations (Table 6). The results, presented in Table 7 and Table 8, in this case show less differences between the best performance and the best economy vehicle. This is partially due to a reduced number of parameters to be chosen: the architecture in this case is fixed, and the design space exploration was applied in order to select scaling and control parameters, such as the size of the electric machines, and the size and allowed range of use of the batteries (i.e., minimum and maximum state of charge allowed by the control strategy). From the results, it appears that smaller electric motors and generator are optimizing both performance and economy, while a larger use of battery (in the sense of number of batteries and allowed range of operation) benefits the economy of the vehicle, allowing for a larger amount of energy regeneration and in general for a more efficient use of the engine; however, this results in a heavier vehicle. 1 The values of the various variables shown in all the plots generated by the viewer are rounded to a certain resolution, because it would not make sense to distinguish between configurations whose performance difference is lower than the simulation accuracy. For example, the acceleration times are rounded to 0.5 seconds, which means that configurations achieving 6.4 or 6.8 s are considered the same and therefore such small difference does not lead to the elimination of the slightly worse vehicle.

10 (a) (b) Figure 10: (a) Maximum speed vs. acceleration time on 3% grade road. The configurations with the best performance are in evidence. (b) Outputs from a dash acceleration test are used to eliminate the slower vehicles. (a) (b) Figure 11: (a) Histogram of the powertrain mass. The lightest solutions are selected. (b) Equivalent fuel consumption on two driving cycles (corresponding to cycles 3 and 4 in Table 2). Table 3: process of choice for MSV Step 1: Step 2: Step 3: Step 4: Performance speed on 3% grade > 60 mph time to 30mph on 3% grade < 10 s Total time, dash test < 180 s Longest acceleration, dash test < 6.5 s mass < 1500 kg Fuel consumption < 5.5 gph / 7 gph (in objective / threshold cycles) Economy (minimal performance requirements): Speed on 3% grade > 50 mph Total time for dash accelerations < 200 s Step 2: Fuel consumption < 4.9 gph / 6.3 gph (in objective / threshold cycles) mass < 2150 kg only 1 string of capacitors (this means a vehicle with a lower cost)

11 # of Axles # Motors per Axle Engine Type Table 4: MSV performance configurations Gen Rated Scale EM Rated Scale RESS Type Battery # Parallel Cap # Parallel Mass (kg) 2 1 Cummins 300 HP Batt only Cummins 300 HP Batt only Cummins 300 HP Batt only Cummins 300 HP Batt only Cummins 300 HP Batt only Cummins 300 HP Batt only # of Axles # Motors per Axle Engine Type Table 5: MSV economy configurations Gen Rated Scale EM Rated Scale RESS Type Battery # Parallel Cap # Parallel Mass (kg) 2 2 Cat 275 HP Caps+Batt Cat 275 HP Caps+Batt Cat 275 HP Caps+Batt Cat 275 HP Caps+Batt Table 6: Process of choice for UV Step 1: Step 2: Step 3: Step 4: Performance time to 50mph on 3% grade < 8.5 s total dash time < 145 s Fuel consumption (in both objective and threshold cycles) < 4 gph mass < 1270 kg Economy Fuel consumption < 4 gph (in both objective and threshold cycles) Fuel consumption (CMH1 cycle) < 1.3 gph performance on 3% grade: speed > 65 mph time to 30mph < 3.5 s - mass < 1200 kg Gen Rated Scale Table 7: UV performance configurations EM Rated Scale Batt SOE abs max Batt SOE abs min Battery # Parallel mass (kg) Gen Rated Scale Table 8: UV economy configurations EM Rated Scale Batt SOE abs max Batt SOE abs min Battery # Parallel mass (kg)

12 5. CONCLUSION The process of concept design for a hybrid vehicle has been presented, describing the simulation model and the optimization method using design space exploration. The method described here allows designers to acquire the feeling for the impact and the side effects of a design choice, showing the relative importance of many factors playing a role in complex systems, like hybrid electric vehicles. In general, the proposed design space exploration is an effective, easy and intuitive method of multi-objective optimization. It shows, however, a remarkable downside: requiring the evaluation of all possible combinations, the number of parameters to be considered or the number of their possible values are limited by the available computing resources. When dealing with very complex systems, this can become a significant limitation. One possibility to overcome this problem, which will be the object of future research, is the use of methods more computationally efficient, such as genetic algorithms. 6. BIBLIGRAPHY 1. P. Pisu et al., Modeling and Design of Heavy Duty Hybrid Electric Vehicles, IMECE , Proc. of IMECE2005, Orlando, USA, G. Rizzoni, L. Guzzella, and B. Baumann, Unified modeling of hybrid electric vehicle drivetrains, IEEE/ASME Transactions on Mechatronics, September G. Rizzoni et al., Modeling, Simulation, and Concept Design for Hybrid-Electric Medium-Size Military Trucks, Proceedings of SPIE -- Volume Enabling Technologies for Simulation Science IX, Dawn A. Trevisani, Alex F. Sisti, Editors, May 2005, pp G. Rizzoni, Y. Guezennec, A. Brahma, X. Wei and T. Miller, VP-SIM: A Unified Approach to Energy and Power Flow Modeling Simulation and Analysis of Hybrid Vehicles, SAE Paper , Proc Future Car Congress, Arlington, Va, USA, April G. Paganelli, G. Ercole, A. Brahma, Y. Guezennec, and G. Rizzoni, General Supervisory Control Policy for the Energy Optimization of Charge-Sustaining Hybrid Electric Vehicles, JSAE Review, Vol. 22, No. 4, pp , Brahma, A., Guezennec, Y., G and Rizzoni, G. Optimal Energy Management in Series Hybrid Electric Vehicles, Proc American Control Conference, Vol. 1, pp , Chicago, IL, Peer reviewed, June Brahma, A., Guezennec, Y. G., Paganelli, G., Rizzoni, G. and Yurkovich, S., A Hardware- and Architecture- Independent Supervisory Control Strategy for Hybrid-Electric Drivetrains, 4th Stuttgart International Symposium on Motor Vehicles and Combustion Engines, Stuttgart, Germany, Invited, February Dembski, N., Guezennec, Y., and Soliman, A., Analysis and Experimental Refinement of Real-World Driving Cycles, SAE Paper , 2002 SAE International Congress, Detroit, MI, March Pisu, P., Musardo, C., Staccia, B., and Rizzoni, G., A Comparative Study of Supervisory Control Strategies for Hybrid Electric Vehicles. IMECE 2004, November 2004, Anaheim, CA. 10. J. Josephson, B. Chandrasekaran, M. Carroll, N. Iyer, B. Wasacz, G. Rizzoni, Q. Li, and D. Erb, An Architecture for Exploring Large Design Spaces, Proc. of National Conference of the American Association for Artificial Intelligence, 1998, Madison, Wisconsin, USA. 11. Visualization Environment developed by Aetion Technologies LLC ( in collaboration with the Ohio State University (