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Design Impact » Optimization

Extreme Energy Efficiency and Robotic Redesign

Earlier this week a presented a conference paper that goes over some work I’ve done recently involving squeezing every bit of energy efficiency out of robotic systems using the natural dynamics of these systems. Any physical system tends to move or vibrate in a specific way (think resonant vibrations of a wine glass), and this is what I’m talking about when I say ‘natural dynamics’. It turns out that we can use natural dynamics to our advantage to reduce dramatically the energy needed to do certain tasks. In this paper the application was robotics, but the idea extends to other domains as well.

You can get all of the details from the conference paper or the MATLAB code, but I thought you might enjoy this video that explains some of the highlights of the paper and includes some animations of the different robot designs compared in the paper:

Posted: April 28th, 2012 | Filed under: Design, Energy, Modeling, Optimization, Publications | No Comments »

Part III: Fundamental Challenges Facing Existing ADAS Systems and Description of The Alternative

Sterling Anderson, a Ph.D. candidate at MIT, continues his discussion of semi-autonomous driver assistance systems in this post. If you haven’t read his previous posts (Part 1, Part 2) you might want to start with them.


In my last article, I described existing driver assistance systems and introduced the notion of threat assessment. Near the end of the post, I described how the choice of threat assessment metrics is critical to the performance of any advanced driver assistance or semi-autonomous system. This article explains how existing systems assess threat and describes two significant problems caused by their approach. It then describes our alternative approach to threat assessment and semi-autonomous control. My next and final post will then demonstrate our system’s performance in both simulated and experimental results.

Significant Challenges Facing the Current State-of-the-Art

The first challenge facing existing systems is their inability to accurately capture the threat inherent to a particular scenario in a meaningful way. Because assimilating multiple sources of threat into a single, actionable metric is difficult, the majority of ADAS systems in existence today consider only one hazard at a time and limit their assistance to one dimension or another. For example, adaptive cruise controllers focus on the closest obstacle within the ego vehicle’s line of sight (longitudinal dimension) and apply the brakes as necessary to avoid rear-end collisions. Lane keeping controllers focus instead on the lateral dimension, neglecting longitudinal vehicle dynamics and collision hazards in order to maintain a desired position and/or heading within the lane. Like the child playing checkers for the first time, the tactical, near-term focus of these systems often fails to consider the effect of current evasive actions on future threat scenarios. For example, Figure 1 illustrates the predictions of an emergency braking system (solid green), along with those of a lateral collision avoidance controller (dashed blue). Without considering Obstacle B or the maneuver required to straighten out after it has passed Obstacle C, the obstacle avoidance system might consider a pass to the left of Obstacle C preferable to an emergency braking maneuver. Such a decision could be disastrous if either 1) Vehicle A cannot straighten out in time to avoid running off the road or 2) Vehicle A cannot avoid an oncoming Vehicle B once it has passed Vehicle C.

Figure 1. Illustration of tactical avoidance maneuvers considered by existing driver assistance systems

Figure 1. Illustration of tactical avoidance maneuvers considered by existing driver assistance systems

The second major problem facing existing driver assistance systems is closely related to the first: the myopic focus of existing systems on a single source of threat requires that a vehicle be equipped with multiple safety systems. For example, comprehensive assistance might require that the same vehicle be equipped with warning devices to alert the driver, anti-lock brakes to prevent skidding, yaw stability control to prevent loss of control, adaptive cruise control to prevent rear-ending the vehicle ahead, lane-keeping control to prevent wandering out of one’s lane, etc. While each of these systems may perform adequately by itself, their combined output when placed together on the same vehicle can lead to vehicle performance that is suboptimal at best and unpredictable or inconsistent at worst.

For instance, imagine a vehicle traveling quickly along a curve in road (illustrated in Figure 2). Wanting to avoid the large truck that is passing you on the inside of the turn, you drift toward the outer edge of your lane. As the lane departure warning begins to sound (adding to your already-heightened apprehension), the lane assist system takes control of the steering wheel, turning it hard left. As you swerve left, the car begins to roll right, whereupon the rollover alarm lights begin to flash and the roll stability controller engages, turning you back toward the outer edge of the road and again engaging the lane keeping controller. You can imagine how this scenario might continue. The point is that when distinct systems with disparate goals try to control the same vehicle, conflicts in their warnings and their steering, acceleration, and braking commands are bound to result. Figure 3 shows a (simplistic) schematic of how multiple systems arranged on the same vehicle can sometimes interact.

Figure 2. Illustration of a passing maneuver (see text for narrative/context)

Figure 2. Illustration of a passing maneuver (see text for narrative/context)

Figure 2: Illustration showing how using multiple heterogeneous driver assistance systems in the same vehicle can have detrimental consequences.

Figure 3: Illustration showing how using multiple heterogeneous driver assistance systems in the same vehicle can have detrimental consequences.

The Alternative: An Integrated, Semi-Autonomous Active Safety Framework

To summarize the above, existing active safety systems are both incomplete in their assessment of threat and ineffective at sharing control with the human driver. This is where we come in.

When I began working with Karl Iagnemma and Steve Peters at MIT’s Robotic Mobility Group a couple of years ago, we began exploring a promising new idea for semi-autonomous control. We based our system on the simple observation that most human drivers tend to operate within a field of safe travel as opposed to along a predetermined path. Thus, instead of starting with a simple avoidance path, we chose to define a corridor through the environment that avoids obstacles, road edges, and other hazards. Then rather than selectively replace the driver when s/he strayed from the automation-desired path, we chose to gradually blend the driver’s inputs with the controller’s – giving the driver free reign while s/he remained within the safe corridor and only intervening as the likelihood of his or her leaving that safe corridor or losing control, increased.

The framework we developed (illustrated in Figure 4) storyboards as follows:

As a human driver navigates the vehicle, forward-looking sensors detect road edges, identify and terrain features (slopes, holes, etc.), and localize obstacles. Based on this information, a safe corridor that avoids these hazards is defined. A mathematical model of the vehicle is then forward-simulated to determine the safest or most stable path through the corridor given the vehicle’s current state (position, velocity, roll angle, etc.) and the current state of the environment. Because this optimal trajectory can be considered the best maneuver that can possibly be performed given the current circumstance, it is then used to assess the (best case) threat posed to the vehicle. In other words, no matter how skilled the human driver is, s/he will be unable to perform any better than the “best case” avoidance maneuver. As this “best case” maneuver becomes more dangerous, so does any maneuver that the human might attempt. In low-threat scenarios, the human retains full control of the vehicle (K=0 for you sharp-eyed readers). As threat increases owing, for example, to the driver’s failing to make adequate preparations to avoid an obstacle, so does the level of control authority given to the autonomous controller (gain K in the Figure 4 below). In extreme scenarios, when the avoidance maneuver required to keep the vehicle safe becomes so severe that only an optimal maneuver can be expected to pull it off, gain K becomes one and the system effectively acts as an autonomous controller until threat is reduced to human-manageable levels.

Figure 3: Block diagram illustrating basic framework operation

Figure 4: Block diagram illustrating basic framework operation

Figure 5 illustrates what the predicted avoidance maneuver might look when the vehicle is at position 1 (low threat, full human control), as well as how that prediction might appear when the vehicle reaches position 2 (high threat, nearly-autonomous operation).

Figure 5. Illustration of how corridor boundaries affect the optimal trajectory prediction and how the threat assessment based on that prediction affects the control allocation (level of autonomy)

Figure 5. Illustration of how corridor boundaries affect the optimal trajectory prediction and how the threat assessment based on that prediction affects the control allocation (level of autonomy)

The video below illustrates the semi-autonomous controller in action. Corridor constraints are shown in black and green, with the vehicle’s trail in blue and the predicted trajectory in red. In this simulation, the human driver fails to see or respond to a hazard. As the system’s prediction begins to predict a more and more severe avoidance maneuver, it gradually asserts only enough control necessary to avoid the hazard before giving control back to the human driver.

To conclude, the system we’ve developed combines the environment’s many hazards into a single, safe corridor and moderates the driver’s steering and braking inputs as necessary to keep the vehicle within that corridor. In low threat scenarios, the driver maintains full control of the vehicle. As threat increases, the controller shares control with the human driver to ensure that the vehicle does not leave the safe corridor. By inherently considering multiple sources of threat in a single, unified framework, this approach provides a significant advantage over existing driver assistance systems.

In my next post, I’ll show how this framework performed in the over 800 experimental trials with 8 different human drivers on a Jaguar S-Type. Stay tuned.

Posted: February 2nd, 2011 | Filed under: Design, Optimization, Transportation | 2 Comments »

Design for Energy Efficiency at ASME iDETC 2011

Last year I announced a conference session very relevant to the theme of Design Impact. We received several submissions, and this year we are soliciting articles again on the topic of Design for Energy Efficiency. If you are working on designing something that helps reduce energy consumption while maintaining or increasing performance or value delivered, please consider submitting a paper describing your work to this year’s ASME iDETC conference. It will be held in Washington D.C. this year from August 28th through 31st.

Abstracts are due by February 11th, and draft papers are due by February 18th. Click here to begin the submission process, and select DAC-7, which is part of the 37th Design Automation Conference (DAC). Articles will be reviewed before acceptance, and authors of accepted papers will have an opportunity to revise their submission after receiving feedback. If you have any questions or suggestions regarding the session or conference, please feel free to contact me, or post your ideas to the comments section below. Read below for more details.

Design engineers have the opportunity to improve quality of life and sustainability simultaneously through better design. One of the most significant areas engineering design has an an impact on is energy use. In addition to reducing consumption, we need to develop and put into service products and systems that use energy more efficiently. By using advanced design techniques, such as design optimization, incorporating more efficient technology, or simplifying systems and processes, engineers can help propel us toward energy sustainability.

Here is a description of the session from the conference website:

Design for Energy Efficiency: DAC-7

The ASME Design Automation Committee invites papers focused on design theory, innovation, or methods that enhance energy efficiency of energy consuming products or systems. Analytical design techniques that reduce energy consumption while maintaining or improving performance are of particular interest. Sample topics of interest include but are not limited to the following:

  • Using optimization to improve energy efficiency
  • Reducing energy consumption through process analysis and redesign
  • Energy recovery and reuse
  • Advanced/intelligent/alternative transportation systems
  • Novel control techniques that reduce energy consumption
  • Efficient energy storage
  • Challenges in transitioning to more efficient technologies
  • Economics of energy efficient technology
  • Energy savings through system simplification

Posted: January 14th, 2011 | Filed under: Design, Energy, Optimization, Sustainability | No Comments »

Design for Energy Efficiency at ASME DETC 2010

A central theme of Design Impact is how design engineers can improve quality of life and sustainability simultaneously through better design. Design engineers make decisions about how things work and how they are made, and these decisions have profound impact on our society. One of the most significant areas engineering design has an an impact on is energy use. In addition to reducing consumption, we need to develop and put into service products and systems that use energy more efficiently. By using advanced design techniques, such as design optimization, incorporating more efficient technology, or simplifying systems and processes, engineers can help propel us toward energy sustainability. It’s important to recognize that efficiency alone won’t solve our energy challenges. Without incentive to consume less, energy consumption may not go down. Motorists, for example, tend to drive more miles as fuel efficiency rises. We need policy changes that stimulate energy conservation, which in turn will drive demand for energy efficient products and improved engineering design.

To provide a forum to discuss recent advances in energy efficiency research, I’m organizing a new session (DAC-9) at 2010 ASME iDETC, an engineering design conference organized by the American Society of Mechanical Engineers. The conference will be held August 15-18, 2010 in Montreal. The topic of the session I’m organizing is Design for Energy Efficiency, and I’m hoping to get the word out early about this session to stimulate interest in the topic and encourage strong participation. If you are working on any projects that involve improving energy efficiency through design, please consider sharing what you have learned by contributing to this session. Draft papers are due by January 29th, 2010. If you have any questions or suggestions regarding the session or conference, please feel free to contact me, or post your ideas to the comments section below. Here is a description of the session from the conference website:

Design for Energy Efficiency: DAC-9

The ASME Design Automation Committee invites papers focused on design theory, innovation, or methods that enhance energy efficiency of energy consuming products or systems. Analytical design techniques that reduce energy consumption while maintaining or improving performance are of particular interest. Sample topics of interest include but are not limited to the following:

  • Using optimization to improve energy efficiency
  • Reducing energy consumption through process analysis and redesign
  • Energy recovery and reuse
  • Advanced/intelligent/alternative transportation systems
  • Novel control techniques that reduce energy consumption
  • Efficient energy storage
  • Challenges in transitioning to more efficient technologies
  • Economics of energy efficient technology
  • Energy savings through system simplification

Posted: October 13th, 2009 | Filed under: Design, Education, Energy, Optimization, Sustainability | No Comments »

Chapter 18: The Great Disruption, and the Case for Design Optimization

Thomas Friedman, the author of Hot, Flat, and Crowded, has invited readers to contribute ideas for a final chapter for the second version of the book. He wants to hear our thoughts on how we might ‘grow people’s living standards in a more sustainable and regenerative way’. (If you haven’t yet read HFC, I highly recommend it.) Here is my response to Friedman’s invitation:

In Hot, Flat, and Crowded you discuss the importance of ’smarter’ design; by changing how things are built, how they work, and are retired, we can reduce energy consumption and environmental impact dramatically, as well as improve quality of life and national security. I believe better design is at the core of a green revolution, and we need increased efforts to help others solidify mental links between design improvements and a vision for a sustainable future. In addition to helping citizens deepen their appreciation for the role of design, we must address this issue on two other fronts: public policy and engineering expertise. We need the right policy and incentives to set the stage for a transition to sustainability, as well as the technical expertise to implement the transition rapidly. I would like to address the latter issue.

To realize a green revolution, we can’t settle for products that are ‘good enough’, or green technology that evolves slowly. Instead, we must seek to develop the very best, most efficient designs, and do so quickly. Instead of taking small steps each year with slightly more efficient cars, slightly better wind turbines, let’s make giant leaps! We need the backing of citizens, the support of policy makers, and boldness from engineers and engineering educators to advance our ability to create sustainable systems and products. Researchers have developed impressive new engineering design methods the last few decades that can help us create products and systems that use less energy and other resources, while making leaps forward in performance. Some of these methods are mature and proven, but unfortunately are not yet used widely by engineers. First, let’s have a look at the conventional design process.

Suppose we were designing a car to be very energy efficient, but still performs well at a reasonable cost. Using a conventional design process, engineers would generate design ideas, test these candidate designs, propose new designs, and iterate until they converge on a design that meets (or comes close to) design targets. In the past, engineers relied heavily on expensive physical prototypes for testing. More firms now use computer models that predict how something will perform without having to build it. While this saves time and money, design refinements often are still made by engineers based on test results, experience, and expertise. Managing all these often conflicting design decisions is often overwhelming, particularly as products evolve and become more complicated; engineers stop when they find a design that meets basic requirements, instead of pursuing the best possible, or optimal, design.

One prominent method developed by researchers is design optimization. Other readers have also described optimization as an important solution; I hope to strengthen this position and clarify the link between optimization and engineering design. When using design optimization, engineers work to minimize or maximize some important aspect of a product, in addition to seeking to meet design requirements. In the car example, we might seek to maximize fuel economy, while meeting acceleration, handling, comfort, cost, safety, and other constraints. Framing a design problem in this way allows engineers to use computer models and powerful optimization algorithms together to help generate the best possible design. In this process design candidates to be tested are chosen analytically using mathematical techniques, reducing the number of tests and time to market. It can help engineers learn what is really achievable, opening our eyes to new possibilities. Design optimization also accelerates design evolution by enabling engineers to make more substantial design changes between product generations, instead of just small perturbations of the last version (as is usually the case now).

The design optimization approach is actually a pretty natural fit for how engineers already go about designing things; using formal design optimization is an enhancement that produces better results in less time, and leverages investments many firms have already made in computer modeling. It’s not a push-button solution; it automates some aspects of design, but requires engineering expertise and experience to implement successfully. (In the parlance of The World is Flat, design optimization is a high-level, ‘icing’ activity). Awareness is perhaps the biggest hindrance to the adoption of design optimization. It needs to be taught in undergraduate (not just graduate) engineering courses, as well as in industry training programs.

In summary, design engineers make a lot of important decisions that have tremendous impact on our world. Moving beyond status quo design processes can help engineers deliver sustainable products and systems while improving living standards; these changes in engineering design are essential to a successful green revolution. Right now there is a lot of low-hanging fruit; there are many opportunities to improve our world through better design. Design optimization can help us put new technology into production faster, as well as refine systems that use existing technology. This can help us bring energy efficient designs into production more quickly, and accelerate the transition to renewable energy systems. We have the technical tools, but we need the societal impetus to put them to broad use.

James T. Allison, Ph.D.
http://www.design-impact.org

Posted: September 24th, 2009 | Filed under: Design, Education, Energy, Optimization, Sustainability | 1 Comment »

Streamlined Water Distribution Systems, Engineering Design, and Optimization

Water and energy are scarce resources, the conservation of which is becoming increasingly important. Researchers at Wayne State University, lead by civil engineering department chair Carol Miller, are developing a computer-controlled approach for operating the Detroit water system. According to the Chicago Tribune, Miller hopes to reduce energy consumption for the system by shifting from manual control of system pumps, to automatic control. The water system is so large that these improvements stand to deliver significant energy savings. In addition, Miller estimates this will save ‘10 million tons of greenhouse gases and other pollutants per year’.

This is one of many engineering systems that can be viewed as a design optimization problem, and I would like to use water distribution system improvement as an example to explain what design optimization is.

In engineering design we have lots of decisions to make, decisions like what materials to use, the size of components in our system, or how parts of our system should work together. In mathematical optimization, we seek to minimize or maximize something by choosing the right values for some set of variables. Design optimization links engineering design with mathematical optimization in a way that helps us identify what design decisions will lead to the best possible engineering design.

How can we frame the operation of a water distribution system as a design optimization problem? We have three tasks to make this happen:

  1. Identify Design Decisions: each design problem has some degree of design flexibility. That is, designers are free to make decisions about certain aspects of their system. In new systems that are being designed from the ground up, there is a lot more design freedom (more decisions to make). If a system must use some already developed components, then some design decisions are already made, reducing design freedom. In the case of the water distribution system, there is even less design freedom, since the physical system already exists. In any case, designers need to identify what aspects of a system they have control over; these aspects are the design variables. One set of specific values for the design variables represents one system design alternative. In the water distribution problem here, the design variables are quantities that define how each pump in the system should be controlled.
  2. Specify Design Objective: We need to have some way of comparing design alternatives and evaluating which designs are better. A design objective, or objective function, is a system property that we can measure, and that reflects the usefulness of a particular design. The design objective drives the design process, and is a critical choice in product development. Whether or not design optimization is used formally, product designers choose a design objective, or at least set priorities for their product (influenced by the market segment they are targeting). For example, in automotive design, Porsche engineers have performance as a design objective, while Aptera engineers consider energy efficiency paramount. The resulting designs reflect the difference in design objective. In the water distribution system problem, we are seeking to minimize energy consumption.
  3. List Design Constraints: Engineering design is full of tradeoffs; that is, if we seek to optimize one thing, something else is bound to get worse. We can’t simply focus on the design objective alone and expect to develop a usable system. In the automotive example, some constraints include safety, size, range, and cost. In addition, by choosing energy efficiency over performance for a design objective, Aptera engineers still need to meet some minimal performance constraints. Who would want to buy a car so slow that it’s not driveable in traffic, even if it could acheive 500 mpge? If we sought to minimize energy consumption in the water distribution system problem without considering any constraints, we might arrive at a solution that says we simply should never turn on any pumps. We need to impose a constraint to make this work: require that water delivery needs are met.

The design optimization approach to engineering design involves minimizing or maximizing some design objective, while meeting a set of constraints, by varing something you have control over. This way of presenting an engineering design problem is actually pretty natural. Some engineers may be using the design optimization process informally, even if they are not aware of it. Design can be viewed as the process of finding the set of design variable values that satisfies the design constraints and optimizes the design objective. To summarize the water distribution system design optimization problem, we are trying to find an automated pump control policy that minimizes energy consumption, while ensuring water delivery needs are met.

Now that we have presented our design problem as an optimization problem, how do we actually solve it? In some cases, engineers could build physical prototypes and use a trial and error approach to search for the optimal design. This could get very expensive. A little more sophisticated approach might employ systematic testing and statistical models. This still requires expensive physical protypes. Would this even be practical for the water distribution system needs? Would engineers be allowed to try out new (untested) pump control ideas, risking water delivery failures for such a large metropolitan area? It sounds like we need some way to test design alternatives without actually having to test them in real life. This is where physics-based modeling and computer simulations come into play. Researchers and engineers have developed computer models for all sorts of systems that allow designers to test out ideas in a virtual world. These models help predict how a system design will behave, without actually having to build it. The software and computers are far from free, and the models are not 100% accurate, but they are accurate enough to help make design decisions, and allow designers to test out far more design alternatives than are possible with physical prototypes. If you would like to learn more about computer modeling, you can read through an ongoing series of articles on modeling.

If a system can be modeled using a computer simulation, then engineers can use optimization algorithms to solve the design optimization problem described above. These algorithms are computer programs that very intelligently choose what designs to test (using computer simulations) so that we can find the optimal design quickly. Using design optimization can help engineers develop better products in shorter time periods. Using optimization to develop better water distribution systems has actually been going on for several years. A full issue of the journal Engineering Optimization was devoted to this topic (you can read an overview of the issue here). In many of these articles, the engineers have additional design flexibility; they are not just looking at changing how the system is operated, but also at how the physical system is designed.

Design optimization and modeling are topics that I will revisit. These are important tools that could be used to transform how engineering design is done, and enable engineers to create systems that use much less energy, while meeting or exceeding our performance expectations. It’s my hope that more engineers adopt design optimization and use it to improve sustainability and quality of life, and that more people can become aware of design and design optimization, their impact on how we live, and the role they can play in our shift to a sustainable path.

Posted: August 2nd, 2009 | Filed under: Design, Energy, Modeling, Optimization | 5 Comments »

Wicked Fast Electric Vehicles on (Pareto) Curves

By now most of you have probably heard about the Tesla Roadster, Fisker Karma, and other high performance electric cars that demonstrate we can make spectacular gains in energy efficiency AND enjoy amazing performance by designing cars in a new way. Improving efficiency and performance simultaneously is an impressive feat. These are competing objectives, that is, improving one objective normally involves degrading the other. We can design a car that is either high performance or highly efficient, but not both. We can visualize this kind of design tradeoff using a tradeoff curve usually called a Pareto curve or efficient frontier. The drawing below is a conceptual illustration of a Pareto curve in automotive design, showing the tradeoff between performance and efficiency.

pareto1

We would like to maximize both performance (one aspect of performance is acceleration) and energy/fuel efficiency. Ideally we would like a design that is in the upper right corner of the plot above. Unfortunately, when a design tradeoff exists, this is not physically possible. We can’t focus on both performance and efficiency because they are competing objectives. If we focus on performance in vehicle design, we might end up with something like a Porsche 911 Turbo, which has a blistering fast 0-60 mph time as low as 3.2 seconds. Unfortunately this car doesn’t get great fuel economy. If we want to improve fuel economy we will need to sacrifice performance, that is, we will need to trade some performance for fuel efficiency (perhaps by reducing engine size, using smaller tires, reducing mass, etc.). If we focus on just fuel efficiency we might end up with something like a Geo Metro. The Pareto curve connecting these two points on the plot above represents what designs in between the Porsche and Geo are physically realizable. It’s not possible to create a vehicle to the upper right of the curve. Something on the interior of the curve is not Pareto optimal, meaning that it’s possible to improve both objectives simultaneously. Designs on the interior of this curve are to be avoided. Advanced design techniques, such as simulation and design optimization, can help engineers ensure that their designs are on the Pareto curve. It is up to the engineers and market analysts to determine where on the curve their product should be.

What if we changes the rules of vehicle design? What if instead of assuming powertrains had to include a conventional gasoline engine linked to a manual or automatic transmission, we allowed battery electric powertrains? The previously impenetrable Pareto curve shifts to the upper right if we can escape the inefficiencies of gasoline engines. New technology, and new ways of designing things, can push the Pareto curve to a new and better location, as shown in the diagram below. We can improve both performance and efficiency by introducing new technology. This is what’s going on with the Tesla and Fisker. The Aptera 2e places more emphasis on energy efficiency than performance, and solar cars are the ultimate in energy efficiency. The Prius uses a power split hybrid electric powertrain. It’s an improvement in efficiency over conventional powertrains, but it can’t compare in efficiency to pure electrics like the Aptera (that’s why it is a design on the interior of the Pareto curve). In fact, although my crude diagram doesn’t really depict this, the powerful Tesla gets better energy efficiency than the Prius.

pareto2

The Tesla might be a fast EV, but have a look at the X1 Wrightspeed. It’s wicked fast. See where it’s positioned on the Pareto curve? The X1 is an Ariel Atom retrofitted with an all-electric powertrain created by AC Propulsion, makers of the eBox. Here is the X1 smoking both a Ferrari and a Porsche:

Now that’s what pushing out the Pareto curve looks like! Here is another race between the X1 and a Lamborghini, and then with a NASCAR racer:

The above analysis is admittedly simplified. The diagrams are conceptual and do not represent actual performance and efficiency numbers (if they did the solar car point would be way off to the right of your computer screen). In addition, there are many other competing objectives that need to be considered in vehicle design, such as range, safety, durability, utility, cost, and total lifecycle environmental impact. Nevertheless, Pareto curves are a helpful tool for visualizing and understanding design tradeoffs.

What emerging technologies do you think will expand the current Pareto curve for vehicle design (or other products)? Can you think of some additional tradeoffs important to vehicle design that I haven’t listed here? If we want to look at three, four, or more competing objectives, how do you think we can visualize the tradeoff relationships between them?

Posted: May 29th, 2009 | Filed under: Design, Optimization, Transportation | 3 Comments »