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"The Road to Challenge X" on Automotive DesignLine
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Friday, 20 July 2007
Challenge X EXCLUSIVE: Ohio State University design team relies on Model-Based Design tools and determination in a four-year hybrid power train development effort
By Mike Arnett and the Ohio State University Challenge X team, July 13, 2007
The Road to Challenge X: Part 1 - the task and power train selection
Challenge X EXCLUSIVE: Ohio State University design team relies on Model-Based Design tools and determination in a four-year hybrid power train development effort
Challenge X is now a four-year competition among seventeen North American universities with the goal of re-engineering a 2005 Chevrolet Equinox for improved fuel economy and reduced emissions, while maintaining performance, utility, safety, and consumer acceptability. Primarily sponsored by the U.S. Department of Energy and General Motors, the program also attracted sponsorship from many other industry and government organizations. Teams achieve the goals of the competition by utilizing advanced hybrid power trains, novel control strategies, alternative fuels, lightweight materials, and innovative emissions control techniques.
Challenge X emphasizes the evolution of these technologies by following a progression that is representative of GM's Global Vehicle Development Process. Consequently, the first year of the competition (2004-05) stressed the validation of the chosen hybrid systems in a modeling and laboratory environment. The second year demonstrated engineered systems ready for implementation into the Equinox vehicle, as well as delivery of a road-worthy "mule," with primary focus on the power train. In year three, just concluded, teams demonstrated vehicles that meet the GM 99% Buy-Off criteria. This includes presenting a vehicle that would be considered 99% ready for production.
Challenge X vehicle development process: Year 1
During the first year of Challenge X, the university teams were required to develop, model, simulate, and analyze various hybrid-vehicle architectures and the actuators to construct them. From this analysis, the final architecture emerged. At this point, an extensive amount of modeling and simulation occurred to evaluate the feasibility of hypothetically meeting the Vehicle Technical Specifications as set by the competition.
The details of the architecture chosen by the Ohio State team will be shown later; however, this process involved the creation of a quasi-static and dynamic simulation tools (also to be discussed in detail later) for evaluating fuel economy, emissions, and drivability. These tools were created using The MathWorks software and the iterative process visualized in the figure below. By following this process and comparing simulation results to the limited amount of actual data supplied by the competition, the Ohio State team was able to create a Model-Based Design that yielded confident results.
The Year 1 hybrid-vehicle development process resulted in a power train control architecture.
Year 2
After the first year, each team received a stock 2005 Chevrolet Equinox from General Motors. The goal of this second year was to integrate the architecture developed in Year 1 onto the actual vehicle; moreover, each team was scored on the accuracy with which they predicted the Vehicle Technical Specifications (VTS) in year one, to the measured VTS with the completed prototype vehicle.
Throughout the year, the Ohio State team utilized the strategy as shown in the figure below. Using the various parameters determined in simulation in conjunction with Real-Time Workshop®, control strategy parameters were flashed to a target and fine-tuned on the vehicle to meet the requirements set by the team.
As lessons were learned from vehicle testing, the simulation tools developed in year one were also refined further increasing their accuracy. This was such that the Ohio State team won First Place honors for "Best Realization of VTS Targets" in the second year of the competition.
Year 3
Year 3 of the competition focused on refinement of the vehicle to meet the 99% Buy-Off standards as set by General Motors. Due to the nature of the required improvements, the Ohio State team created a number of other simulation tools with MathWorks products to implement features such as start-stop and traction control.
As can be seen in the graphic below, the team utilized their simulation tools to predict vehicle behavior and tuned control strategy parameters prior to the implementation on the vehicle. Once suitable results are obtained from these tools, the tuned parameters are uploaded to the vehicle using Real-Time Workshop. The process ends with vehicle testing and repeats with the evaluation of vehicle performance.
Year 4
The recently added fourth, and final, year of the competition begins in Fall 2007 and focus on further vehicle refinement, as well as public outreach regarding sustainable mobility and other goals of Challenge X.
The Ohio State University team chose to develop a series-parallel through-the-road diesel electric hybrid vehicle architecture [Ed. Note: The front and rear drives are not physically connected and are thus only linked "through the road"]. This architecture is illustrated below.
Architecture highlights include:
Downsized 1.9L GM diesel internal combustion engine (ICE) coupled to an Aisin six-speed automatic transmission to power the front axle
Kollmorgen belted starter alternator (BSA) coupled to the diesel engine providing start/stop [shutting the engine when the vehicle is not moving], torque assist, and battery charging capabilities
Ballard rear axle electric machine (EM) to drive the rear axle
Separate inverter for each electric machine
High-voltage Panasonic nickel-metal hydride battery pack serving both high-voltage and 12V system requirements
DC/DC converter to power the 12V system
Experience from previous competitions led the Ohio State Challenge X team to converge on this challenging vehicle architecture. In general, the selected architecture provides the flexibility of multiple driving modes that, with proper control, result in improved fuel economy, competitive performance, and enhanced drivability. Additionally, the intensive use of Model-Based Design makes it possible to execute exacting electric and hybrid launches [starting from a dead stop] and heavy accelerations, while managing axle slip in inclement weather and off-road situations.
Vehicle operating modes
All of the aforementioned attributes yield a modular and flexible power train that allows for a number of vehicle operating modes. The Ohio State team chose such a structure to take advantage of the increased flexibility provided by a multiple-mode system. The modes include: electric only (ZEV, zero emissions vehicle), full hybrid, and electronic all-wheel drive. To further increase fuel economy and reduce emissions, the team has developed and integrated engine start/stop into these modes. The team's control strategy utilizes the various vehicle operating modes to maximize the output of the team's chosen vehicle architecture. The table below summarizes the modes of operation of the OSU vehicle.
The Ohio State power train operates according to several discrete vehicle modes (see figure below). The appropriate operating mode is determined through an event-based control algorithm that is dependent on vehicle inputs and states (such as the accelerator and brake pedal positions, engine speed, and vehicle speed).
This curve shows the velocity profile and the various modes in which the hybrid-power train operates throughout an arbitrary driving cycle.
Several of the modes have simple control strategies based on the inherent benefits of that specific mode. For example, the Electric Launch mode uses the rear electric machine for a rapid and smooth acceleration from vehicle rest (it also provides creep and limp-home functions), while the Engine Start mode commands torque from the BSA to quickly accelerate the IC engine to a proper operating speed. However, the more complex Normal mode uses all three power train devices to meet the driver's power request while minimizing fuel consumption, maintaining battery SOC (state of charge), and assuring acceptable drivability.
Idle Mode
One of the advantages of the OSU hybrid architecture is the ability to significantly reduce engine idling, thus providing a potentially significant reduction in fuel consumption during urban driving. The engine is shut off during vehicle stop and vehicle coast down, as specified in the figure above, subject to vehicle and driving conditions. This mode requires that many accessories be run electrically at all times. Electric power is supplied via a DC/DC converter connected to the high voltage battery pack.
Electric Only Mode
When the vehicle is at rest and receives a torque request from the driver, it must respond quickly to maintain acceptable drivability. However, because the engine is off during Idle Mode and takes approximately one third of a second to start, using the engine for initial vehicle movement would result in a delay that is far too great for consumer acceptance. To eliminate this problem, Ohio State uses the rear electric transaxle to move the vehicle from rest up to a variable speed, which is determined by the available battery power, and other parameters such as engine coolant temperature.
The rear electric drive produces high torque at low speeds and has a response time that is comparable to or even slightly faster than the stock Equinox. As a result, the acceleration profiles of the stock Equinox and Ohio State's vehicle are quite similar. This feature provides much smoother and quieter launches compared to the stock Equinox.
Engine Start Mode
The Engine Start Mode is the transition between Electric Only Mode and either Normal or AWD (all-wheel drive) Mode, depending on road conditions.
When the electric tractive power is insufficient to meet the driver's power demand (or if the battery SOC becomes low), the BSA is used to start the engine smoothly and quickly. Once the engine is started, it is used to power the vehicle either alone or in conjunction with the BSA and rear electric drive.
In the stock Equinox engine, a conventional starter that relies on numerous torque pulses turns over the engine. These pulses are what make the ignition of the stock Equinox engine (and other conventional engines) somewhat rough at start-up. In the OSU hybrid system, the belted starter alternator ramps the engine up to a desired speed following a predetermined speed profile. Fuel injection begins when the desired speed is reached. Use of the BSA as the engine starter results in superior start performance relative to the stock diesel engine starter, and a much faster start-up than the stock Equinox.
Normal Mode
Once the engine speed has reached a sufficient level, the torque request to the EM electric drive is gradually shifted toward the ICE. Such torque blending with the BSA allows for a smooth transition the between the "rear-wheel drive electric" and "front-wheel drive diesel" vehicle.
While in Normal Mode, the diesel engine is the primary actuator for vehicle movement. However, the torque split within the power train is subject to certain parameters that permit the EM to assist when necessary. A fuel minimization strategy is active in this mode along with additional controls that regulate the battery SOC and maintain acceptable vehicle drivability. The vehicle operates most frequently in Normal Mode, thus requiring a relatively more complex control strategy, discussed later.
Deceleration Mode
The Deceleration Mode is used to capture some of the energy typically lost during braking events. Instead of using only the brakes to stop the vehicle, as is done on the stock Equinox, the Ohio State vehicle uses the rear electric drive as a generator (regenerative braking), recouping some of the energy used to move the vehicle. This captured energy is transferred to the batteries to recharge them, making the Ohio State vehicle charge-sustaining.
AWD Mode
The AWD mode is active when the traction of the vehicle is compromised by adverse weather, off-road, or any other extreme conditions. Having the diesel engine to power the front axle and the large electric motor independently powering the rear axle requires a unique control algorithm to ensure maximum traction during all driving conditions.
This vehicle architecture adds the flexibility of controlling the individual axle, but at the same time adds the complexity of engaging the appropriate axle at the appropriate time. This mode must engage the front axle during rear axle instability (loss of traction in electric only mode) and also have the ability to engage the rear axle during front axle instability (loss of traction in Normal Mode). To accomplish this, the tire dynamics are evaluated to determine if the 'driving' axle is providing an adequate tractive force. During a time of inadequate traction, the other axle is engaged until the primary axle can deliver the necessary tractive force.
The Road to Challenge X: Part 2 - design and simulations
Challenge X EXCLUSIVE: Ohio State University design team relies on Model-Based Design tools and determination in a four-year hybrid power train development effort.
Part 1 of this feature discussed the four-year Challenge X tasks and selection of the hybrid power train architecture.
Model-based design and simulation
With the architecture and desired modes of operation defined, extensive modeling with The MathWorks tools began. The following series of simulators was developed by the Ohio State University Challenge X team.
Each simulator focuses on a different aspect of the design and optimization of the hybrid-electric vehicle (HEV) architecture
cX-SIM addresses fuel economy, acceleration performance, and emissions.
cX-Dyn focuses on the dynamics behavior of the driveline for drivability analysis.
cX-TRAC expands on the dynamic concepts within cX-Dyn to include tire dynamics for traction control development.
Start/stop modeling involves a more detailed and specific focus on the engine and belt dynamics found in cX-START.
Combining the results from each of these simulators allowed the Ohio State team to thoroughly evaluate a plethora of vehicle design and performance metrics prior to the integration on the physical system.
cX-SIM
Designing and tuning a hybrid-vehicle power train for maximum fuel-economy and minimum emissions encapsulates the primary motivation of the Challenge X competition. Ohio State used cX-SIM to effectively and efficiently accomplish this and evaluate basic performance specifications.
The above figure shows the main user-interface of cX-SIM. Four main subsystems elements compose this simulator. Starting from the left of the figure, the driver subsystem is essentially nothing more than a PI-controller (proportional integral) that compares an input desired vehicle speed, to actual vehicle speed and adjusts the accelerator and brake pedal positions accordingly.
HEV power train
The controller and models of each of the power train components reside within the HEV power train block in the user interface figure above. The figure below shows the contents of this subsystem.
This simulator employs torque-speed maps for the ICE (internal combustion engine (diesel)) and EM (electric machine) as this is a quasi-static simulator. Similarly, the torque converter model consists of polynomials and coefficients to determine output torque. The output from the torque converter is simply multiplied by the appropriate gear ratio to determine transmission output. In a similar fashion as the transmission, the output torque from the EM is manipulated by the gearbox ratio and sent to the rear brakes. Considering the brake pedal position and respective brake proportioning constant, the brakes subtract from the torque delivered by the transmission. Dividing by the wheel radius converts this torque into a force which acts on the vehicle as seen in the previous figure showing the user interface.
Vehicle
Aerodynamic forces, rolling resistance, and positive road grade reduce the magnitude of the force input to the vehicle from the power train. The "vehicle" subsystem contains each of these, as well as the mass factor. Dividing the resultant force by the vehicle mass results in vehicle acceleration. Integration of this value provides the vehicle speed feedback for both driver and power train. Thus, torque is the feed-forward term, and speed is the feedback term.
Exhaust aftertreatment
Similar to the power train actuators, the exhaust aftertreatment model uses emissions maps to predict the NOx, CO, and HC (hydrocarbon) emissions. Complex algebraic equations model the primary components of this custom system.
Assuming that all inertias within the driveline react infinitely fast (compared to the vehicle inertia) allows for cX-SIM to take a quasi-static approach to modeling. This constraint results in a computationally inexpensive tool for quickly and accurately assessing fuel economy, emissions, and acceleration.
In order to accurately evaluate drivability and other dynamic events during vehicle operation, the factors ignored during the creation of the aforementioned quasi-static simulator must be considered. Sharing the same interface of cX-SIM (see first figure, previous page), cX-Dyn only differs from the quasi-static counter part in the modeling of the power train. The figure below shows the power train of cX-Dyn.
View a full-size image
Most notably, the axles are modeled as torsional springs; moreover, the actuator maps and algebraic equations of the quasi-static model are replaced by dynamic relationships. These differences result in a far more computationally "expensive" simulator than cX-SIM, which enables drivability, shift transients, delays, and other dynamic events to be accurately evaluated.
In this dynamic simulator, both torque and speed are feed through the power train from the left (ICE) to the right (wheels). Once again, the torque from the power train creates a force that acts on the vehicle mass and this results in the vehicle speed feedback.
cX-TRAC
One of the requirements of the Challenge X competition requires the vehicle to include a traction control system for better stability and control during sub-par driving conditions. In order to develop this feature, the Ohio State team needed to create another tool. CX-Dyn models the behavior of the hybrid power train more accurately than cX-SIM by transferring both torque and speed through the power train; however, the wheel is modeled as a rigid disk that contacts the ground perfectly at all times.
In reality, the tire deforms under normal operation and the coefficient of friction between the tire and road can change creating a loss of tractive effort. In order to appropriately develop a control algorithm to mitigate this occurrence, the specifics of the tire/wheel must be included. Thus, cX-TRAC (below) includes these features.
As can be seen in the above figure, the dynamics of the wheel are included in a separate subsystem from the rest of the power train. Within this subsystem, wheel slip, contact patch length, and all forces and moments produced by the tire are calculated. This allows for the coefficient of friction between the tire and the road to change and yield a more realistic response from the vehicle.
cX-START
As stated in Part 1 of this series, the Ohio State vehicle has the ability to smoothly and quickly stop, or start, the engine to optimize fuel economy and reduce idle emissions. To develop and implement such a feature requires a more specific focus with regards to modeling than the previously mentioned simulation tools. CX-START only considers the dynamics of the engine, belted starter alternator, and related controllers. The figure below shows the main user interface of cX-START. Using this simulation tool, a control algorithm for effectively managing smooth starting and stopping of the engine, as well as torque blending, is created.
cX-DAQ and cX-Graphics
The Ohio State team leveraged the features of The MathWorks tools for data acquisition and analysis as well. By creating the graphical user interface of cX-DAQ (below), the team developers quickly and easily analyzed all of the data collected from the vehicle CAN bus.
Similarly, cX-Graphics (below) allowed the team to visualize every variable from any of the previously described simulators.
Using these data analysis tools in parallel allowed for an efficient method to compare the results from simulation to actual results.
The Road to Challenge X: Part 3 - verification testing, validation, and control strategy
Challenge X EXCLUSIVE: Ohio State University design team relies on Model-Based Design tools and determination in a four-year hybrid power train development effort.
Part 1 of this feature discussed the four-year Challenge X tasks and selection of the hybrid power train architecture.
Part 2 detailed the power train Model-Based Design and simulation process used by the team.
Verification and validation
In order to ensure the simulation tools noted in Part 2 of this series were effective for Model-Based Design, verification and validation of the results obtained from these simulators was performed. The data shown below comes from the second year competition of Challenge X and individual experiments performed at the Center for Automotive Research (CAR) at The Ohio State University. At the present time, no verification of cX-TRAC has been accomplished as this simulator is still in the development stage.
cX-SIM
Using the data collected from competition, the acceleration performance and fuel economy predicted by cX-SIM is shown to yield a very reasonable match to reality.
Experimental testing at CAR led to the validation of the battery model used in cX-SIM as shown below. During an arbitrary driving cycle, voltage data from the battery is collected and then compared to the battery voltage reported by cX-SIM once the appropriate parameters are set.
In a similar fashion, the emissions model included in cX-SIM was validated and the results are shown below. The temperature of the lean-NOx trap and outlet NOx are of particular interest when using a diesel engine and are thus the primary metrics for validating the emissions predication of cX-SIM.
cX-Dyn
In a similar fashion to cX-SIM, the results of cX-Dyn are compared to results from actual vehicle testing for verification purposes. One of the competition events used accelerometers to evaluate drivability. Using the collected data from this event, the Ohio State team proved the accuracy of their dynamic simulator.
The plot below shows the vehicle speed as predicted by cX-Dyn (blue line) when the accelerator and brake pedal positions collected from the drive quality event are used as inputs. The actual vehicle speed collected from the same event is shown on the same plot as the red line.
The next plot shows the engine speed comparisons between the simulated results and acquired data from the event. Once again, a strong correlation between the prediction of cX-Dyn and reality was exhibited.
cX-START
Independent testing at CAR verified the accuracy of cX-START. The figure below shows the simulated engine speed data during an engine stop event as the green line. Included in this plot, the actual engine speed during an engine stop closely matches the behavior of the simulation. Intake manifold pressure is shown as the red line for reference purposes.
The plot below shows the simulated and collected engine speeds during an engine start. Once again, the simulation proved to be a useful and accurate tool for predicting engine start and stop behavior.
Control strategy development and implementation
A set of conditions determined by the control strategy enables the Normal Mode. A fuel minimization strategy is active in this mode along with additional controls that regulate the battery state of charge (SOC) and maintain acceptable vehicle drivability.
Regulation of the battery SOC is an inherent feature of the fuel minimization strategy. However, additional controls are occasionally required to achieve firmer constraints on battery SOC. Drivability control is mostly achieved by means of a torque demand constraint imposed on the fuel minimization strategy. Avoiding other conditions such as pedal sensitivity, response delays, and mode transition disturbances require additional controls.
The details of these control strategies follow:
Adaptive consumption control
The Ohio State team utilized an adaptive version of the Equivalent Consumption Minimization Strategy (ECMS) to optimize the power split among energy converters. ECMS operates based on the principle that all energy consumed by the vehicle ultimately comes from the fuel tank, so that energy extracted from, or put into, the battery equates to an equivalent fuel usage or savings, respectively. The goal of ECMS is to find (through static optimization) the set of power train component torque inputs that, at a given vehicle state, minimizes the instantaneous equivalent fuel usage:
Subject to the following constraints:
Further, power flow to and from the electric machines (EMs) affects the SOC of the battery. This also has several limitations:
Another restriction arises from the responsibility to continuously meet the driver's instantaneous power request:
The equivalent fuel consumption terms for the electric machines can be expressed as:
ECMS has an inherent weighting factor (denoted as S in (1)) that determines the equivalency between the cost of electric power and that of the chemical fuel. The fuel equivalency factor is highly correlated with the battery SOC, and its optimal value is also driving cycle-dependent. This relationship is depicted below, showing that a different choice of the equivalence factor results in measurable changes in fuel economy for different driving cycles.
The Ohio State team used an adaptive algorithm that recognizes the past driving pattern in a short time window and modifies the equivalence factor appropriately. This algorithm computes statistical measures of past driving conditions (such as mean and peak vehicle velocity) and matches the estimated driving pattern to the optimal equivalence factor for that driving pattern. A slight increase in computational complexity brings significant fuel economy improvement when compared to a non-adaptive version of the ECMS.
To accelerate the computation of a relatively complex algorithm, the ECMS minimization problem of (1) is solved off-line and look-up tables are then generated. For the Normal Mode, the input/output relationships are described by 6-dimensional maps of actuator torques as a function of engine speed, rear electric machine speed, accelerator pedal position, engaged gear, battery SOC, and the equivalence factor.
In order to have an accurate control of the battery SOC, a reliable estimate of the SOC is needed. The Ohio State team has designed an adaptive SOC estimation strategy that is shown schematically here.
This routine uses experimental models of the NiMH battery pack along with direct current integration to estimate the battery SOC. The concept behind this adaptive algorithm is to use a weighted average of two estimates of the battery SOC. One estimate is obtained by current integration and the other (much less frequently) by using the battery voltage at rest. This algorithm can be expressed in mathematical form as:
At key-on, the SOC estimate is initialized. Once a sufficiently long resting period has elapsed, the SOC estimate is corrected using experimentally determined charging-discharging maps, which are based on battery temperature and voltage measurements. An adaptive weighting factor, w, is modified in real-time according to the slope of the battery maps at the current operating conditions. This weighting factor renders the estimation routine more stable and reliable for a wide range of battery operating conditions.
Proper tuning of the equivalence factor results in a charge-sustaining control strategy over most driving cycles; however, it is beneficial to employ stricter boundary constraints into the control strategy to absolutely guarantee a desirable battery state-of charge. To achieve this, the controller switches to a modified strategy to charge or discharge the battery more aggressively near the firm SOC boundaries by adjusting the torque commands to both electric machines.
Conclusion
Through modeling and experimentation, the Ohio State Challenge x team predicted that is hybrid vehicle outperforms the stock Chevy Equinox by having a higher fuel economy and a smoother vehicle start. The team made these predictions using models developed and refined over the first three years of the Challenge X competition. These models have been verified using experimental data. As additional components are being integrated into the vehicle, the team is further refining these models using in-vehicle experimental data.
Mike Arnett is the Ohio State University Challenge X team leader. He can be contacted at
Read more in these exclusive first-hand accounts from other Challenge X student design teams
Design tools spur fuel cell development year round
Student engineers develop and test a hybrid power train: Part 1 - the model
Student engineers develop and test a hybrid power train: Part 2 - the controller
References
1. The Energy Information Administration of the U.S. Department of Energy, visited 11/28/2005
2. OSU Challenge X Team, "Validation of Design and Vehicle Capabilities," Ohio State University, 2007
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4. S. Midlam-Mohler. "Modeling, Control, and Diagnosis of a Diesel Lean NOx Trap Catalyst," Ohio State University, 2005
5. Paganelli, G., et al. "General Supervisory Control Policy for the Energy Optimization of Charge-Sustaining Hybrid Electric Vehicles." Journal of SAE of Japan. Vol. 22. 2001
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8. S. Midlam-Mohler, Y. Guezennec, G. Rizzoni, S. Haas, H. Berner, M. Bargende, "Mixed-Mode HCCI with External Mixture Preparation" FISITA 2004 World Automotive Congress, Barcelona, Spain, May 23-27 2004
9. Title 13, California Code Regulations, Section 1968.2,(OBDII), draft released February 17, 2006
10. Title 13, California Code of Regulations, Section 1968.5
11. Paul Baltusis, On Board Vehicle Diagnostics, SAE2004-21-0009
12. Ford Motor Company, 2006 MY OBD System Operation Summary for Hybrid Electric Vehicles, March 24, 2005
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