15-09-2014, 02:04 PM
Regenerative Braking System
RBS.pdf (Size: 248.41 KB / Downloads: 66)
Abstract
When riding a bicycle, a great amount of kinetic energy is lost when braking, making
start up fairly strenuous. The goal of our project was to develop a product that stores the
energy which is normally lost during braking, and reuses it to help propel the rider when
starting. This was accomplished with a spring and cone system whose parameters were
optimized based on engineering, consumer preference, and manufacturing models. The
resulting product is one which is practical and potentially very profitable in the market
place. A spring (of tension 22,100 N/m) is stretched (at most 37cm) by a wire which
wraps around a cone (of 15 cm large diameter and 2 cm small diameter), while braking.
A clutch is then released and the cone drives the bike’s gears to assist the rider while
starting. The product weighs 14 lbs, will cost $87, and will return 85% of the rider’s
stopping energy when starting up again.
The Product Design Problem
Bicycles have been the heart of human transportation since the dawn of its creation.
Many advances have been made to make the bike more desirable and friendly for the
millions of users throughout the world. In many countries throughout Western Europe, a
very large number of professionals use bicycles to commute to work in their business
suits with their briefcases. It is our goal to design a device that can make their commute
an easily traveled one. The Regenerative Braking System (RBS) is a device that can do
so by reducing the overall energy the day to day business commuter is required to use.
Product Development Process
Many decisions need to be made in order to produce the most desirable and affordable
product to make the highest profit and most unique device. The flow chart in figure 1
shows how our product fits into the product development process. There are three
distinct phases: the Concept Phase, the Design Phase, and the Production Phase. During
the Concept Phase, we defined the problem of losing energy while braking on a bicycle.
We then conceptualized different ways of using that energy with different regenerative
braking systems. Through research and customer surveys, we entered the Design Phase
knowing consumer preferences. We generated designs based on known preferences,
constraints, and parameters. We then made a CAD drawing of our design. We analyzed
our model from the viewpoint of the consumer and manufacturer and did a profit analysis
of the optimal designs. After reviewing our results, we hypothesized how we would enter
the Production Phase. Because this product would be produced in bulk, we took into
account the price of machinery, storage, labor, etc. After all of these costs were
accounted for, we analyzed potential profit again to make sure we would still make
money. Initial results indicate that we would eventually make a profit if this product
were actually placed in the market.
Optimization Model and Solutions
Two steps were done in order to complete the design optimization model. The first thing
that needed to be done is to find what the optimal stopping distance must be before we
can determine what the shortest spring length should be. This device is only using the
rear brake to slow the bike to a stop. As the bike begins to slow, the weight is transferred
to the front tire, therefore the normal force on the rear tire is reduced, producing less
stopping force than. The excel model in figure 2 shows the maximum stopping force and
therefore the minimal stopping distance. This force is then extracted from the model and
inputted into the optimization model for the minimal spring length. Now knowing the
maximum stopping force, we can calculate the stopping torque and use the solver to find
the minimal spring length as shown in figure
Interpretation of Results
The solver found that the minimum spring compression length is 0.35 meters, and by
using a common rule of total spring length is 1.5 times the compression length the total
spring length is 0.52 meters or almost 21 inches. The spring constant was chosen to be in
a reasonable range of 25000 N/M, but the spring length seems to converge around the
same optimal length as k goes higher. The only active constraint that is present is the
final radius of the cone. This active constraint is expected because the smaller the final
radius is the less the spring will be compressed and with the number of times the wheels
rotate
ANALYTICAL TARGET CASCADING
We have so far discussed how to optimize the RBS from three points of view:
engineering, manufacturing, and customer. The engineer attempted to minimize the
amount of spring deflection (x) for a given value of spring stiffness constant (k) based on
a minimum stopping distance (D), which was derived from a physical description of the
system. The manufacturer considered the effect of k and x on three design characteristics:
cost to manufacture, weight of the product, and the capacity of the product to return
energy to the rider. The customers were presented with surveys and asked for their
preferences of characteristics for the product based on the weight, capacity, and retail
price of the product.
Conclusion
The overall goal was to design the Regenerative Braking System while keeping the
engineering, producer and customer models in check. The key design decision was based
on the spring length and the spring constant. The reason why this feature was used more
than all of the other features are because the other features would not have as much effect
on the complete system. By changing the size and spring constant, desirable price,
weight and capacity can be realized.
We used a survey to find out how the price, weight and capacity were scaled. Much was
learned on how to and not to conduct a survey. A preliminary survey should have been
conducted to determine a realistic value of variables. Also many of choices were not
close enough together to get a reasonable cut off value. Therefore the data that was
produced using conjoint analysis was most likely not as accurate as it could have been.
There are some limitations to our model. For the sake of simplicity, the spring was
modeled with the length and the spring constant rather than wire thickness, stress, strain
and all the other complex analysis that would make the solver take too long to process.
By getting a rough idea of what the ranges can be, simple experimentation can be done to
prove or disprove this assumption.
Future work would consist of a redesign of the spring model to see exactly how much
data we may be missing with the assumption that we made with how price, weight and
capacity vary with spring length and spring constant. Despite all the assumptions, we
still have realized that this product can be very marketable and that the demand is
extremely large which means this is a viable design that will yield a high return on an
investment.