This document describes the design, implementation, and testing of an ultracapacitor-based auxiliary energy system (AES) installed in an electric vehicle (EV) to improve efficiency. The AES uses an ultracapacitor bank and buck-boost converter to provide power support to the EV's lead-acid battery pack. Two control strategies for the AES were developed and tested - one based on heuristics and the other using neural networks. Testing showed the AES could reduce costs if it extended the battery life by 50% or more, which is unlikely. The AES was also evaluated for a hypothetical fuel cell-powered EV, where it showed significant cost reductions compared to a fuel cell-only system, especially when using ultracapacitors.

1. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007 2147
Ultracapacitor-Based Auxiliary Energy System for an
Electric Vehicle: Implementation and Evaluation
Micah Ortúzar, Member, IEEE, Jorge Moreno, Member, IEEE, and Juan Dixon, Senior Member, IEEE
Abstract—In the search for better efficiency, an auxiliary energy
system (AES) for electric vehicles (EVs) was designed, imple-
mented, and tested. The system, which is composed of an ultra-
capacitor bank and a buck–boost converter, was installed in an
EV, which is powered by a lead-acid battery pack and a 54-kW
brushless dc motor. Two control strategies where developed: one
based on heuristics and the other based on an optimization model
using neural networks. These strategies were translated to algo-
rithms and implemented in a digital signal processor, and their
performance was evaluated in urban driving. The results were
incorporated to an economic evaluation of the system, which shows
that the reduction in costs would only justify the inclusion of
this type of system in a lead-acid battery-powered vehicle if the
battery life is extended by 50% or more, which is unlikely. The
same results were extrapolated to a case in which the lead-acid
batteries are replaced by a fuel cell. In this case, the costs of dif-
ferent power support systems were evaluated, such as ultracapac-
itors and high-specific-power lithium-based batteries. The results
showed a significant cost reduction when AES configurations are
included in contrast to a system powered by fuel cells only. Also,
the cost reduction was higher when using ultracapacitors for this
purpose.
Index Terms—Energy management, energy storage, road vehi-
cles, ultracapacitors.
I. INTRODUCTION
THE COMMERCIAL debut of hybrid vehicles [1], [2] is
proof of their improved efficiency and performance. The
word “hybrid” stands for the use of two or more energy storage
devices that combine three main characteristics: specific energy
(in watthours per kilogram) for driving range, specific power
(in watts per kilogram) for acceleration, and power reversal
capability for regenerative braking [3].
Most hybrid configurations use two energy storage devices:
one with high energy storage capability, called the “main energy
system” (MES), and the other with high power capability
and reversibility, called the “auxiliary energy system” (AES).
MES provides extended driving range, and AES provides good
acceleration and regenerative braking. Although a vehicle could
eventually run with MES only, the power and efficiency char-
Manuscript received March 30, 2005; revised August 10, 2005. This work
was supported by Conicyt under Project Fondecyt 1050683 and Millenium
Project P04-048-F.
The authors are with the Department of Electrical Engineering, Pontificia
Universidad Católica de Chile, Santiago 6904411, Chile (e-mail: jdixon@ing.
puc.cl; mortuzad@puc.cl).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2007.894713
acteristics will be poor. The MES–AES combination gives to
hybrids extended range, good power, high regenerative braking
capability, and better efficiency.
These two energy storage devices can be connected in differ-
ent ways, and cars that use them are classified into two main
categories: parallel hybrids and serial hybrids.
Parallel hybrids are vehicles powered from two independent
mechanical outputs working in parallel. These power units
may move the rear and front wheels independently or may be
combined in a special gearbox to move the same wheels. The
last case is shown in Fig. 1(a), and most commercial models,
like the Toyota Prius, use this configuration.
Serial hybrids are vehicles with only one mechanical out-
put (usually an electric motor), as shown in Fig. 1(b), which
receives power through static converters from two electrical
sources. In this case, any two sources that deliver electric power
could be used as long as they are compatible in terms of
electrical variables. This topology is the only one possible when
the energy sources are batteries or fuel cells, because they do
not produce mechanical power.
The requirements for an adequate AES are high efficiency,
high power density, and reversibility. The most mentioned can-
didates for this task are high specific-power batteries (Li-ion,
Ni-M-H) [4], ultracapacitors (widely researcher for other ap-
plications) [5], [6] and flywheels [7], of which the only com-
mercially available today are the first two. For the MES, the
most popular and proven device has been the internal combus-
tion engine. Nevertheless, there are other devices under study,
such as gas turbines, fuel cells [8], and high specific energy
batteries, which are suitable for serial hybrid configurations.
Recent developments in the areas of fuel cells and high specific
energy batteries (proton exchange membrane cells, Zinc-air
cells, ZEBRA batteries, etc.) suggests a good chance of having
a competitive alternative to internal combustion engines for
MES in the near future [9]. This means that a serial hybrid
configuration, not commercially available yet in passenger cars,
might be an interesting option to explore.
These facts motivated the development of a serial hybrid con-
figuration with ultracapacitors as AES [10] and with lead-acid
batteries as MES. The prototype was implemented in an elec-
tric vehicle (EV) transformed from a conventional Chevrolet
“LUV” truck (similar in weight and shape to a Chevrolet S-10)
with a brushless dc traction motor (32-kW nominal power and
53-kW maximum power) [11]. The MES is formed by a battery
pack of 26 lead-acid batteries connected in series (356 Vdc),
which are currently being changed by a single Sodium Nickel-
Chloride (ZEBRA) battery (371 Vdc). The AES was imple-
mented with a 20-F ultracapacitor bank and a buck–boost
0278-0046/$25.00 © 2007 IEEE

2. 2148 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007
Fig. 1. Power circuit of a typical (a) parallel hybrid and (b) serial hybrid vehicle.
Fig. 2. DC–DC converter schematic.
converter, with a nominal voltage of 300 Vdc, and a nominal
current of 200 Adc.
This paper presents the complete power converter design
and implementation, including digital signal processor (DSP)
control. Final tests were performed with the vehicle running on
lead-acid batteries only and powered by a hybrid configuration
of lead-acid batteries (MES) and ultracapacitors (AES). For
the hybrid configuration, the performance was analyzed using
two different control strategies. In addition, a chapter with
deducted economic implications complements the technical
evaluation for those cases and also for a hypothetical fuel cell
case, which is used as MES.
II. STATIC CONVERTER TOPOLOGY
The AES needs a static dc–dc converter, as shown in
Fig. 1(b). The dc–dc converter must be able to transfer energy
from the battery to the ultracapacitors and vice versa. It also
transfers energy from and to the ultracapacitors during accel-
eration and regenerative braking, respectively. Fig. 2 shows a
simplified schematic of the dc–dc converter and the two related
sources: the 20-F ultracapacitor (VU) and the battery pack
(VB). R2 includes the internal resistances of ultracapacitors
(ESR) and LS, and Rint is the battery internal resistance. The
positive and negative power terminals connect this system to
the electric drive system.
The main parts of the converter are the inductor LS, the
capacitor C, the insulated-gate bipolar transistors (IGBTs) T1
and T2, and the diodes D1 and D2. A 12 kHz, a fixed-frequency
pulsewidth modulation (PWM) is applied on either IGBT to
transfer energy back and forth. The converter has two operation
modes: Buck and Boost. Buck operation consists of transferring
energy from the Battery pack (or the power terminals) to the
ultracapacitors by triggering IGBT T2. Boost operation results
from triggering IGBT T1, and energy is transferred from the
Ultracapacitor to the Battery pack or power terminals. In either
case, the amount of current transferred, will depend on the Ul-
tracapacitor voltage, the system parameters (resistances, battery
voltage) and the duty cycle of the PWM applied. Under steady
state, and for mean current values during periods of several

3. ORTÚZAR et al.: ULTRACAPACITOR-BASED AUXILIARY ENERGY SYSTEM FOR AN ELECTRIC VEHICLE 2149
Fig. 3. Ripple current waveform through ultracapacitors.
milliseconds, the converter can be modeled as an ideal “dc–dc
transformer.”
During boost operation, steady-state voltages and currents
are, respectively, described by [12]
V C =
VS
(1 − δ)
(1)
Ib ≈
(VU/(1−δ)−VB)
(Rint+R2/(1−δ)2) VU/(1 − δ) − VB ≥ 0
0 VU/(1 − δ) − VB 0
(2)
where
VU ultracapacitor voltage;
VB battery voltage;
Rint battery’s internal resistance;
R2 LS resistance plus ultracapacitor ESR;
δ duty cycle of the PWM applied (0 δ 1).
For simplicity, these equations do not take into account the
diode and IGBT voltage drop effect. As can be seen from
(1) and (2), Boost operation can be modeled as a one-way-
conducting dc transformer, where the transformer ratio, as seen
from the ultracapacitor side, is 1/(1 − δ).
The following equations describe steady-state voltages and
currents during Buck operation (energy transferred from battery
to ultracapacitor):
V s = δ · V c (3)
Ib ≈
− (VB·δ−VU)
(R2+Rint·δ2) (VB · δ − VU) ≥ 0
0 (VB · δ − VU) 0
. (4)
As in the previous case, the Buck operation may be modeled
as a one-way-conducting dc transformer, where the transformer
ratio seen from the battery side is δ.
Equations (2) and (4) do not take into account the ripple
component of currents. Equations (1)–(4) will help understand
the converter behavior under different conditions and will help
elaborate an adequate control strategy.
III. BUCK–BOOST CONVERTER DESIGN
AND IMPLEMENTATION
A careful design is required for the implementation of the
static converter. Some of the most important tasks where the
design of the inductor LS and the water-cooled heatsink. Other
key components were the flat conductors within the static
converter, their semiconductors, dc capacitor, and snubbers.
A. Design of Inductance LS
The inductance LS allows transient energy storage during the
operation of the dc–dc converter. Its design is also related with
the current ripple amplitude, which is one of the variables that
has to be minimized, because it produces undesirable electro-
magnetic interference, mechanical vibrations, and losses due
to current induction on the surrounding conducting material.
Fig. 3 shows a typical steady-state current waveform for a Buck
operation.
As is well known, the commutation frequency f and the
inductance LS are the two design configurable values that,
together with the fixed battery voltage, will determine the max-
imum ripple amplitude [12]. The frequency was set to a value
of 12 kHz (considered low enough to keep commutation losses
low). Therefore, in order to keep a maximum ripple amplitude
value of 5 A (2.5% nominal current), the LS inductance value
had to be of at least 1.4 mH (considering a nominal battery
voltage of 350 V). On the other hand, its weight, volume, and
series resistance had to be as small as possible.
In order to minimize the skin effect at high frequencies,
a laminated conductor (0.5 mm thick and 12 cm wide) was
selected, and because of its better conductivity-to-weight ra-
tio (better than copper), aluminum was the chosen material.
The achieved inductance was of 1.6 mH with resulting total
resistance of 37 mΩ. The inductance is capable of transferring
currents up to 200 A for several seconds without considerable
heating.
The resulting inductance weighted 22 kg, which is adequate
considering the currents it must withstand. Fig. 4 shows the
design and constructed prototype of this element.
B. Design of Heatsink for Power Transistors
Another important design element is the heatsink, which
is needed to evacuate energy from the conduction and com-
mutation losses of the semiconductors. The thermal resis-
tance had to be low enough to maintain the semiconductor
junction temperature below the maximum allowed of 150◦
[13] while delivering maximum power. Several air-cooled and
water-cooled heatsinks were considered, but in most cases, the

4. 2150 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007
Fig. 4. Inductance LS: Schematic and photograph of the constructed prototype.
Fig. 5. Schematic and photograph of the heatsink prototype.
thermal resistance was higher than the maximum allowed of
0.05 ◦
C/W (according to the thermal model) [13]. Besides, the
big size did not adjust to the design constraints. Hence, a water-
cooled heatsink was designed and constructed from aluminum
material. It consisted of two complementary parts with several
parallel channels of equal length and width to ensure equal dis-
tribution of water flow. The designed heatsink, shown in Fig. 5,
has a theoretical thermal resistance calculated at 0.01 ◦
C/W
[14], which is much lower than the required 0.05 ◦
C/W.
C. Other Components
The semiconductors that were used were IGBTs from
POWEREX’s Intellimod line, with incorporated gating circuits.
The electrical connections between the capacitor C into the
converter and the semiconductors had to have the least parasitic
inductance to avoid overvoltage conditions during commuta-
tions. Therefore, they were constructed from laminated copper,
which reduces the parasitic inductance of conductors [13]. R–C
snubbers were also incorporated to absorb excess energy from
parasitic inductances during commutations.
A picture of the constructed Buck–Boost converter is shown
in Fig. 6. Here, the main components, such as capacitor, snub-
bers, semiconductors, and water-cooled heatsink, are displayed.
D. Integration to Vehicle
For integration of the AES, several hardware and software
protections were incorporated to the design. Fuses were in-
stalled for protection against failure. A diode was also in-
corporated in parallel to the fuse that connects the converter
and batteries. This was done because a fuse breakdown during
Boost operation could result in a dangerous voltage rise in the
converter capacitor, which could possibly produce capacitor
Fig. 6. Photograph of the constructed static converter (without the cover)
showing main components.
blast and/or energy discharge through arc. Anyhow, the men-
tioned fuse is monitored by the control processor to disable
operation when failure occurs. Fig. 7 shows the power circuit
schematic with the AES installed.
The 132 ultracapacitors were packed in five groups, con-
nected in series, and installed in the vehicle. Dissipative equal-
izing devices were also installed on each ultracapacitor unit.
Fig. 8 shows pictures of the vehicle, the installed converter, and
ultracapacitors.
IV. CONTROL SYSTEM AND ENERGY
MANAGEMENT ALGORITHMS
The control of AES, such as data monitoring, current control,
and energy management algorithm, is performed by a DSP

5. ORTÚZAR et al.: ULTRACAPACITOR-BASED AUXILIARY ENERGY SYSTEM FOR AN ELECTRIC VEHICLE 2151
Fig. 7. Resulting power circuit in EV.
Fig. 8. Installation of the ultracapacitor system. (a) EV. (b) Buck–boost
converter. (c) Inductance LS and ultracapacitors in the back. (d) One ultra-
capacitor box.
TMS320f241 and its associated circuitry. This unit acquires
all relevant signals, processes data, and generates the PWM
signals to commutate IGBTs in the dc–dc converter. In addi-
tion, a communication protocol with a personal computer was
implemented in this processor to perform real-time monitoring
of relevant data [15].
Two energy management algorithms were implemented to
verify their influence in system behavior and overall efficiency:
a heuristic-based algorithm and an optimized algorithm.
A. Heuristic Algorithm
The first algorithm, which is shown in Fig. 9, is based on
heuristics and follows two main rules [16]. First, a rule of
energy balance that ensures long-term convergence of capacitor
state of charge. Second, a rule that consists of limiting the
battery current to predefined maximum positive and negative
values, which limits its power to values slightly above the mean
power consumption.
The first rule states that the energy content in the ultracapaci-
tors must have an inverse relation to vehicle speed. Thus, when
the vehicle runs at low speeds, energy is reserved to accelerate.
On the other hand, when the vehicle runs at high speeds, the
space to store energy from braking is made available. This is
achieved by injecting or extracting current from the ultraca-
pacitors to reach a predefined state-of-charge reference, which
depends on the vehicle speed.
The second rule consists of limiting the current extracted
from (or injected to) the battery pack. Currents outside those
limits will come from the AES through the ultracapacitors.
These current limits automatically change when the battery is
fully charged, which avoids overvoltages during regenerative
braking.
The two aforementioned rules are complementary because
when the vehicle accelerates, current must be drawn from
capacitors to maintain energy balance, which usually coincides
with a large amount of current being consumed by the traction
drive.
B. Optimized Algorithm
The second algorithm, which is shown in Fig. 10, was devel-
oped with optimization tools. It obtains from a neural network
(NN) the learned value of the most efficient current from the
AES at all times [17].
The NN was trained using various sets of data, in each of
which the most efficient AES current for a given load current
was calculated. The most efficient AES currents are determined
using optimal control techniques, where the “path” or load
current is known. However, different AES currents will result
in different battery currents, and therefore, the most efficient set
of currents must be found, maintaining border conditions such
as ultracapacitor state of charge. A model of the battery and the
AES (ultracapacitors plus buck–boost converter) was used to
determine the efficiency of these devices working at different
conditions. As a result of this training, the network acquires
the “knowledge” necessary to determine the most efficient AES
current under different conditions. The optimality will depend
on how many sets of data (or the driving conditions) are used to
train the network. The evaluation process and results obtained
are analyzed in the next sections.
V. URBAN OPERATION TESTS
Once all supporting parts of the system (power and control)
were tested and ready, the evaluation process was prepared. The
goal was to determine and quantify the improvements in vehicle
performance due to the use of the AES. This assessment allows
seeing the technical and economic contribution of this kind of
equipment to pure EVs and the possible application to hybrid
vehicles.
The variables to be measured where the available power in-
crease (in kilowatts) and the energy efficiency increase (in kilo-
meters per kilowatthour) due to the use of the AES. Therefore,
a protocol to measure these variables was established.
As the AES has been conceived for urban driving, a 14-km
urban route was established for testing. The circuit had slow
and fast driving portions with stops every 100 or 200 m. The
stops were introduced to simulate congested urban driving
conditions.

6. 2152 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007
Fig. 9. Schematic of the heuristic control algorithm.
Fig. 10. Schematic of the optimal control algorithm using NNs.
During the tests, a monitoring system stored currents and
voltages from batteries and ultracapacitors, and vehicle speed.
Several tests were performed, computing the total energy
used in four different conditions: 1) without regeneration;
2) with battery-only regeneration; 3) with AES using the
heuristic-based algorithm; and 4) with AES using the opti-
mized algorithm. The results of these tests are summarized
in Table I.

7. ORTÚZAR et al.: ULTRACAPACITOR-BASED AUXILIARY ENERGY SYSTEM FOR AN ELECTRIC VEHICLE 2153
TABLE I
URBAN TEST RESULTS SUMMARY
The results shown in Table I demonstrate that a measur-
able improvement is achieved with the use of AES in battery
powered EVs. Even better, the use of optimization tools in the
energy management algorithm allows getting superior results
when compared to those obtained using an algorithm based on
heuristics.
Without the AES, the intensive cycling of batteries wears
them down and produces important energy loses. By contrast,
the AES maintained the battery current under 35 A and over
−5 A, avoiding high loses and battery deterioration.
Fig. 11(a) shows that, without support from the AES, the
battery current and load current are obviously the same. The
battery voltage drops below 300 V when load currents are
higher than 80 A. By contrast, with AES and similar currents,
the battery voltage drops only to 315 V, as shown in Fig. 11(b).
On the other hand, the same figures show huge regenerative bat-
tery currents without the AES (higher than −80 A). With AES,
regenerative currents go almost entirely into the ultracapacitor,
increasing the energy efficiency of the system and protecting
batteries from damage.
VI. ECONOMIC EVALUATION
An AES produces an improvement in maximum power ca-
pability, vehicle efficiency (in kilometers per kilowatthour),
and autonomy. These facts would probably extend the battery
life due to reduction in maximum power demanded. They will
also improve performance and produce cost-reduction benefits,
which may be measured to calculate the cost–benefit relation
of the AES inclusion in future EV configurations. Therefore, a
cost–benefit analysis is presented, including only cost-related
benefits generated by this equipment. The analysis was made in
terms of total mean costs (in US dollars per kilometer) of an EV
powered with lead-acid batteries and compared to the costs of
the same vehicle using an ultracapacitor-based AES, as the one
described on this paper.
To obtain the total mean cost (in US dollars per kilometer) of
a vehicle, all present and future costs are calculated and added
using a discount rate, which represents the uncertainty of future
costs throughout time. The result represents the present value
(PV) of all costs, which may also be expressed as a monthly
payment throughout the vehicle’s lifetime using an interest
rate representing the cost of capital. This payment divided by
the amount of kilometers per month will represent the total
mean cost (in US dollars per kilometer) of the vehicle. The
analysis will be performed over a lifetime of 12 years, which
corresponds to 240 000 km.
The costs considered are: the vehicle’s chassis, power train,
and accessories1
; original and replacement batteries throughout
lifetime2
; AES (ultracapacitors3
+ static converter4
); cost of
spent energy5
; annual maintenance6
; and the residual value7
at the end of the period analyzed. It is assumed that the costs
of components and maintenance will not change throughout
the lifecycle. Electric energy prices are supposed to follow a
projected trend,5
and possible deviations from this trend are not
considered.
The base case is the test vehicle already described in Section I
(Chevrolet “LUV”). This case is compared to the same vehicle
with the AES, which is installed for peak power support. The
AES is composed of a 20-F ultracapacitor bank, a buck–boost
converter, and an energy control system. For the evaluated alter-
native, two different assumptions were tested for the increase in
battery life: first, a rather optimistic 50% extra life, and second,
a more realistic 20% extra life.
Table II shows all of these costs and the corresponding PV
and total average cost for each case analyzed. The total average
costs, calculated for each case, show that the AES convenience
is relative to the battery life extension it produces. If batteries
last 50% longer (an optimistic scenario) with the AES installed,
the total mean cost is almost the same as the cost of the vehicle
working on batteries only. However, the advantage of better
vehicle performance in terms of power and efficiency will
increase customer satisfaction. On the other hand, if batteries
last 20% more cycles (a more realistic scenario), then the total
average costs are 8.3% higher for the vehicle equipped with
1Arbitrary estimated value of vehicle without energy source of US $8000. It
represents an approximate cost of structure, accessories, and drive train.
2Cost of batteries for 12 years of operation based on $150/kWh. On the
batteries-only configuration (base case), the battery life is 40 000 km. On the
“50%+” case, the battery life is 60 000 km. For the “20%+” case, the battery
life is 48 000 km.
3Projected cost of ultracapacitors is US $30 per unit (2700 F, 2.5 V each).
4Arbitrary estimated cost of static converter is US $1200.
5Cost of energy is based on projections by Energy Information
Administration.
6Cost of maintenance is an arbitrary estimated value of US $400/year.
7The residual value represents the approximate price for a 12-year-old
vehicle in good conditions or its parts.

8. 2154 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007
Fig. 11. System currents and battery voltage for comparison. (a) With the AES disabled. (b) With the AES enabled.
the AES. Hence, in terms of costs only, the system described
in this paper would only justify its inclusion in a lead-acid
battery-equipped vehicle if the battery life extension were equal
or higher than 50%. If the customer satisfaction factor were
included in the analysis, then the battery life extension may not
need to reach a 50% life extension to be cost effective.
Currently, a new state-of-the-art Sodium/Nickel-Chloride
(ZEBRA) battery is being installed in the Chevrolet “LUV”
EV. This battery increases the range of the vehicle to around
150 km per charge and weights only 240 kg. With a cycle life
of around 1600 cycles, only one ZEBRA battery (with a total
cost of around US $12 000) could cover the entire 12 years
of operation period. As this battery weighs less than half the
weight of a previous lead-acid battery pack, a better efficiency
(in kilometers per kilowatthour) should also be expected. The
situation is being evaluated to determine cost–benefit results
when compared with the previous lead-acid hybrid EV.
Another interesting scenario was analyzed, which is cur-
rently being explored by automakers: the implementation of
fuel-cell-powered vehicles. For this analysis, three power con-
figurations were evaluated: the vehicle powered by fuel cells
only, fuel cells plus Li-ion batteries, and fuel cells plus ultra-
capacitors. The efficiency for a fuel cell vehicle (running on
gas hydrogen from electrolysis) from the power grid to the
wheels can be derived from the composition of typical efficien-
cies of all conversion processes involved: electrolysis (72%),
fuel cell (54%), and electric drive train (89%), with a total
integrated efficiency of 34%. The energy efficiency achieved
by the wheel-to-road conversion was assumed at 8 km/kWh,
which is equivalent to running 300 mi with 60 kWh coming out
of the wheels [18]. An extra 18% efficiency improvement was
considered for the fuel cell plus batteries configuration (because
of regeneration savings), and a 24% of extra improvement
for the configuration running on fuel cells plus ultracapacitors

9. ORTÚZAR et al.: ULTRACAPACITOR-BASED AUXILIARY ENERGY SYSTEM FOR AN ELECTRIC VEHICLE 2155
TABLE II
TOTAL MEAN COSTS COMPARISON WITH BATTERIES AS MAIN ENERGY SOURCE
TABLE III
TOTAL MEAN COSTS COMPARISON WITH FUEL CELL AS MAIN ENERGY SOURCE
(because of better specific power than Li-ion batteries). These
numbers were obtained from experience acquired while testing
the AES with and without regeneration.
The costs in Table III clearly show how fuel cells represent
an important percentage in the cost of structure. Therefore, a
small fuel cell drastically reduces the mean costs, which can be
seen in the case of combination with batteries or ultracapacitors.
The use of a much smaller fuel cell (20 kW in hybrid cases)
is compensated by power support from batteries or ultraca-
pacitors during peak power demand. However, this cannot be
sustained for a long time due to the limited amount of energy
stored in these devices. Hence, the use of hybrid configurations
would limit the amount of continuous time allowed to drive
at maximum power, which makes it unsuitable for sustained
high speeds or hill climbing. Nevertheless, these configurations
could still perform more than well in urban conditions and
even in highways at reasonable speeds (assuming good aero-
dynamics), and the mean cost is drastically reduced in 33.6%
and 31.8% for combinations with ultracapacitors and batteries,
respectively.
VII. CONCLUSION
An AES based on ultracapacitors and dc–dc converter has
been designed, implemented, and evaluated. The buck–boost
topology selected for dc–dc converter design has behaved ad-
equately, achieving in very small equipment a satisfactory ther-
mal control, low parasitic inductances, and low current-ripple
amplitude. The AES was installed in a Chevrolet LUV pick-up
truck powered by lead-acid batteries. A control and monitoring
system was implemented on a DSP from Texas Instruments.
Two different control algorithms were implemented in the
control module. The first one, based on heuristics, establishes
an inverse relation between the energy stored in capacitors and
the vehicle’s kinetic energy. The second one uses an NN, which
has been offline trained to imitate the numeric solutions of an
optimization model. A series of tests were performed with and
without AES using both algorithms. The results were evaluated
from an economic approach, which showed that a battery life
increase of about 50% was required to compensate for the AES
costs. Similar analyses were performed for a hypothetic fuel-
cell-powered hybrid vehicle under three scenarios: 1) without

10. 2156 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 4, AUGUST 2007
the AES (just a bigger fuel cell to cope with peak power
demand); 2) using an ultracapacitor-based AES; and 3) using
a Li-ion battery-based AES. Results showed that the case
without AES was notoriously more expensive than the other
two alternatives. On the other hand, the ultracapacitor-based
AES showed to be the least expensive combination.
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Micah Ortúzar (S’02–M’04) received the degree in
electrical engineering and the Ph.D. degree from the
Pontificia Universidad Católica de Chile, Santiago,
Chile, in 2002 and 2005, respectively.
He is currently with the Pontificia Universi-
dad Católica de Chile. He has worked on active
power filters, multilevel inverters, dc–dc converters,
ultracapacitor control, and electric vehicles research
projects. He is currently working on improve-
ments of an electric vehicle prototype using high-
temperature traction batteries.
Jorge Moreno (S’04–M’06) received the degree in
electrical engineering and the M.Sc. degree from the
Pontificia Universidad Católica de Chile, Santiago,
Chile, in 2004.
He was an Intern in the SPIN Department, l’Ecole
Nationale Supérieure des Mines de Saint Etienne,
Saint-Étienne, France. He has worked on automatic
control, power electronics, and electric vehicles re-
search projects. He is currently a Consultant Engi-
neer at the Pontificia Universidad Católica de Chile.
Juan Dixon (M’90–SM’95) received the degree in
electrical engineering from the Universidad de Chile,
Santiago, Chile, in 1977, and the Master of Engineer-
ing and Ph.D. degrees from McGill University, Mon-
treal, QC, Canada, in 1986 and 1988, respectively.
From 1977 to 1979, he was with the National
Railways Company (Ferrocarriles del Estado). Since
1979, he has been a Professor at the Pontificia
Universidad Católica de Chile, Santiago. His main
interest research activities are active power fil-
ters, high-power rectifiers, multilevel converters, and
electric vehicle traction systems.