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Analog Design Contest 2013 Project Report
INTELLIGENT BLINKER
Team Leader:
Team Members:
Advising Professor:
University:
Date:
Pietro Buccella
([email protected])
Camillo Stefanucci
([email protected])
Denis Sallin
( [email protected])
Naser Khosro Pour
([email protected])
Theodoros Kyriakidis ([email protected])
Maher Kayal
([email protected])
École Polytechnique Fédérale de Lausanne (Switzerland)
25.07.2013
Qty.
TI Part Number
Qty.
TI Part Number
1
1
2
BQ25504
TPS73001
TPD4E004
1
1
1
BQ24090
TCA6507
MSP430F5510
Project abstract: The rapid evolution of integrated technologies through last decade has enabled the realization of many portable devices. They are characterized by long lifetime operation,
very low power consumption and light weight. The world of bike is lately moving toward electronic
world. Bike shops have entire sections full of electronics accessories for bike, from cyclocomputers
to LED lights. With the exception of bicycle lights, intended to illuminate the rider path and especially to improve his visibility at night, a small number of electronic gadgets are made for improving
cyclists safety. For this reason we developed a bracelet-shaped wearable intelligent blinker.
The cyclist, with a simple arm gesture can give warning to the other vehicles about his intentions to turn. An accelerometer and a magnetometer are used to detect the cyclist motion.
Sensors data are processed by a microcontroller to switch on LEDs turn signal at the right time.
The bracelet is powered by a button Li-ion battery that is charged by a solar panel. Furthermore,
a USB charger is added to extend battery life in the most effective way. TI’s ultra-low power microcontroller in combination with TI’s nano-power energy harvester used in this work show an efficient
design of portable circuits powered by a small capacity battery.
TI Analog Design Contest 2013 Project Report
1
Introduction and Motivation
In many countries, the basic bike traffic code rules are already taught in primary school.
Such rules consist of simple arm gestures to indicate a left or right turn and even a stop.
Are they sufficient to indicate intentions to car drivers? Are such gestures still visible when
driving at night? The answers of these questions is the motivation of our project.
Actually, these hand signals remain often
unnoticed by car drivers which are used to the
classical car turn signals and brake lights. To
face this problem, car manufactures are adding
more and more electronics to cars for safety
purpose. Volvo Car Group announced the first
cyclist detection system that stops the car when
a forthcoming collision is predicted 1 .
As for cars, electronics can be used to improve safety on bike. To increase the effectiveness of arm gestures traffic code for bikes we
propose a wearable intelligent blinker (Fig. 1)2 .
Right Turn
Left Turn
The blinker will act as a directional indicator that
blinks according to the cyclist arm-motion. It will
Figure 1: Intelligent blinker concept
be easier for the cyclist to inform the intent to
turn!
This report is organized in 7 parts. The first part gives an overview of the proposed
architecture for the realization of the intelligent blinker. The resulting two main challenges
consist of the motion detection of the arm gestures and the power management of the
LEDs blinking for a portable device. The proposed solutions are detailed respectively in
the second and third part. As a proof of the concept, the realized prototype is described
in the fifth part followed by experimental results. Finally, conclusion and outlook are drawn
in the last sections.
2
System Architecture Overview
There is an increasing demand for battery-powered portable devices, especially for sensing and monitoring applications. These devices face the increasingly difficult challenge of
realizing multifunctional systems that offer at the same time extremely long lifetime and
energy autonomy. These are the main aspects that were taken into account during the
development of our project.
The system architecture of our project is presented in Fig. 2. The system has been
divided into three main blocks: power management, motion detection and debug interface.
Power management incorporates energy harvesting source, energy storage device
and electronic circuits for power management. A rechargeable battery has been selected
1
2
www.media.volvocars.com
Original background image from: http://www.nhtsa.gov/Bicycles
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TI Analog Design Contest 2013 Project Report
as the main power source. The bracelet-shaped blinker is for outdoor usage and here’s
why a solar panel has been added as the main energy source to recharge the battery. In
addition, the battery can be easily charged using a USB charger. For prototype version,
a low dropout regulator (LDO) has been used to power the circuit with a constant and
accurate voltage. In future prototypes, such LDO will be replaced by a step-down DC-DC
converter to improve energy efficiency.
The microcontroller (MCU) is the core component for signal processing part. It collects
and processes sensors data via I2 C interface to turn-on the LEDs by means of a LED
driver. A motion detection algorithm is implemented to detect the cyclist arm gestures that
is converted into a flashing light turn signal.
An external data memory has been used for the debug purpose in the initial prototype.
During the initial design phase, sensor data were stored in the external memory during a
bike tour to record arm gestures. Afterwards, memory data were downloaded to a PC via
the USB interface and processed with MatlabTM to test the motion detection algorithm.
POWER MANAGEMENT
MOTION DETECTION
Sensors
USB Battery
Charger
OR
VCC
I2C
Energy VBAT
Storage
LDO
VCC
Microcontroller
Energy
Harvesting
I2C
Data
Memory
Solar Energy
Source
I2C
LED Driver
LED
USB DEBUG INTERFACE
USB
PC - Matlab TM
Figure 2: System level block diagram
3
Motion Detection
For motion detection an inertial measurement unit (IMU) is usually required. This electronic module contains several sensors used to measure the position and the velocity of
an object in the space. Different analog signals are generated from the sensors depending on the number of degrees-of-freedom (DoF). These signals are amplified, converted
in digital form with an ADC and outputted to the microcontroller for further signal processing. In this work we are interested in the determination of the three attitude angles with
respect to the coordinates system axes: the pitch, the roll and the yaw. In the following we
describe the choice of sensors, the proposed algorithm for arm-motion detection and the
MCU specifications.
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TI Analog Design Contest 2013 Project Report
3.1
Proposed Algorithm
For the targeted application we have to detect the arm movement when the cyclist
get acc and mag
interrupt
measurements
wants to turn. This threshold detection
strongly relaxes the requirement for sensors
since the exact dynamical characterization of
Low-Pass filter
the wrist (for a bracelet implementation) is
not required.
The turn gesture forces the wrist to move
from a position parallel to the bike course, to
Use acc to retrieve
Use acc to retrieve
a position almost perpendicular to that. The
Pitch angle
Roll angle
basic sensor needed to measure an angular position with respect to an absolute refstore
erence is the magnetometer. However, this
store Yaw angle
Use mag to retrieve
is not sufficient since the wrist can be tilted
values
Yaw angle
with respect to the sensor reference. This
implies also the knowledge of the relative angular attitude of the wrist that can be easily
measured by means of an accelerometer. As
π/2 diff in Yaw
angle
a result we have chosen for our application a
6-DoF IMU with a 3-axes magnetometer and
detected
a 3-axes accelerometer. To implement the
tilt compensation a single frame attitude estitrigger LED blink
interrupt
mation is proposed based on the Factored
Quaternion Algorithm (FQA3 ). An overview
Figure 3: Algorithm overview
of the algorithm is shown in Fig. 3.
First we estimate from the accelerometer
the pitch and roll angles and the corresponding vector rotation quaternions are computed.
Then, these two angles are used to tilt-compensate the yaw angle measured by the magnetometer. This is done interpreting the quaternion as a rotation matrix. Notice that the
main advantage of FQA is that it always tries to avoid trigonometric functions. All inter√
mediate results are obtained using only +, −, ·, / and operations saving computational
power and time.
About signal processing, new measurements are taken by the sensors using a timed
interrupt triggered every ts = 0.01s. Before feeding the raw measurements into the algorithm, a preprocessing step is done. The sensors raw data can be written as:
a = atr − g + ab + av + an
m = mearth + mb + mv + mn
(1)
where a is the accelerometer reading, m the magnetometer reading, atr the translational
acceleration, g the gravitational acceleration, mearth the geomagnetic field, ab , mb the measurement drift and an , mn the measurement noise.
For the algorithm it should ideally hold a = −g and m = mearth , thus a preprocessing
3
X. Yun, E. R. Bachmann, and R. B. McGhee, IEEE Trans. Instr. Meas., 57, 3, 638-650, (2008)
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TI Analog Design Contest 2013 Project Report
step aims to reduce the influence of the other components. For this, the signal is low-pass
(LP) filtered, using a Butteworth IIR filter with a normalized cutoff frequency of ωc = 0.2.
Empirical studies showed that this LP-filtering greatly improves the quality of the signals
to be fed into the algorithm. In the above ab and av are considered to be negligible after
the LP filtering. However, the translational acceleration is still present in the final signal.
This reduces the effectiveness of the algorithm when the target experiences heavy motion.
For this study, it is considered atr = 0, for simplicity purposes. A possible solution to this
could be the spectral characterization of atr , and the design of a bandpass (BP) filter, for
high-frequency noise components an , and low-frequency atr components.
Finally, calibration is always required to adjust offsets and conversion-gain differences
on the three axes. Partial self-calibration is already available in commercial sensors. Additional compensation (e.g. hard-iron) procedures are available, and have been easily
implemented 4 .
3.2
Signal Processing Hardware
Since the sensors are always on, the ultra-low power compact solution LSM303DLHC
from STMicroelectronics has been selected that allows power consumptions as low as
110µA and integrates both accelerometer and magnetometer.
An important parameter for the power optimization is the sampling frequency. Since
the movement to be detected is in the order of hundreds of milliseconds, 30 Hz data rate
for sensors operation has been selected. The chosen data rate allows to reduce measurements errors with average values and to decrease noise. After preliminary estimation, 12
bits resolution with typical ranges of ±2g for acceleration and ±1.3G for magnetic field are
sufficient to accurately detect the arm movement.
Regarding the MCU for the implementation of the algorithm of section 3.1, this work
uses the MSP430F5510. Also for the choice of the MCU, the most important requirement
is the low power consumption. This is a big advantage for the MSP430, since it is a
very low-power MCU with lots of different configurable low-power modes. Moreover, the
MCU must be able to perform effectively the calculations that are needed in the signal
processing algorithm. The fact that a hardware multiplier is present in MSP430F5510 is
an advantage. Finally, the availability of the MCU in relatively small packages is useful
since the final design should be compact in order to be wearable.
4
Power Management
Our bracelet-shaped intelligent blinker has been designed to be an autonomous portable
device; therefore a rechargeable battery has been used as the main power source that
is charged by solar energy harvesting. In this chapter the power management has been
analyzed at system level to select an appropriate rechargeable battery together with an
energy harvesting solution to guarantee autonomous operation of the intelligent blinker. A
detailed analysis of the power consumption of each part is presented.
4
T. Ozyagcilar, Freescale Application Note 4246, (2013)
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TI Analog Design Contest 2013 Project Report
4.1
System Level Power Management
Many energy harvesting methods exist to convert the collected energy into electrical energy from different sources, including piezoelectric, electrostatic, thermoelectric, photovoltaic and electromagnetic sources. Among the different solution we examined piezoelectric and photovoltaic to be the most appropriate energy harvesting solutions for our
project. The piezoelectric energy source was finally discarded, because it was difficult to
find a trade-off between piezoelectric sensor size and resonance bandwidth along with
bike vibration spectrum 5 .
The photovoltaic energy solution instead has been retained. However, as energy harvested from solar cells is intermittent and the maximum power that it can provide may be
much less than the required peak power during circuit operation, a rechargeable battery
is used to store the harvested energy.
In this project yellow power LEDs are used as
turn signal with a on/off blinking rate of ∼1 Hz. As for
cars, such LEDs should not dazzle other people and
should have high visibility even in direct sunlight.
VCC
The LEDs parameters taken into consideration
include light intensity versus the viewing angle and
current/voltage specifications. To increase visibilRP
ity and reduce the number of LEDs (max 6) on
the bracelet, we selected a SMD power LED with
a viewing angle of 120 ◦ and a luminous intensity
VF
varying from 360 to 720 mcd at IF = 20mA. TI’s TCA6507
TCA6507 LED driver has been used to turn on and
off the LEDs, controlling at the same time their intenVOL
sity. The low voltage operating condition along with
the pulse-width modulation (PWM) driving feature
makes TCA6507 device ideal for our project to further reduce the overall power consumption. A 25Ω
Figure 4: TCA6507 output driver
resistor RP (Fig. 4) has been used to limit the LED
maximum intensity current to 20mA (VCC=2.5V).
Regarding the supply voltage, all circuits can operate at 2.5V. Energy storage device
should be selected according to the required voltage level and power throughput. The
battery should provide enough voltage and peak discharge current even when it is close to
fully discharged state. Meanwhile it should have enough capacity to guarantee continuous
operation of the device, even when it is not charged for a long time. LIR2450 of Multicomp
has been used as the target battery. This battery has a nominal voltage of 3.6V, a typical
capacity of 120mAh and a peak discharge current of 240mA that is more than the peak
current consumption of the system. The battery voltage reaches an end-of-charge voltage
(VEOC) of 4.2V when it is fully charged and drops to 2.75V when it is fully discharged.
Finally, TPS73001 LDO with adjustable output voltage has been used to provide the 2.5V
supply voltage.
5
E. Minazara, D. Vasic, F. Costa, Proceedings of ICREPQ, (2008)
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TI Analog Design Contest 2013 Project Report
4.2
Energy Harvester Circuit
After selecting the right battery, the next step is finding an appropriate energy harvesting
solution (Fig. 5). The BQ25504 has been used for harvesting solar energy. This integrated
circuit is a state-of-the-art intelligent nano-power management solution from TI that is well
suited for our project.
Solar Panel
BAT
+5V
Solar
Battery
2
1
Vbat
Header 2
VSS
D12
pwr
2
1
+5V
2
1
Header 2
VSS
LDO
VBUS
D Schottky
Header 2
VSS
U5
Vbat_LDO
VCC
1
BOOST charger
2
VSS
L1
Solar_cell
3
Inductor
22uH
Chvr
Cap Semi
4.7uF
VRDIV
VRDIV
Rov2
Res3
6.8M
D15
D Schottky
VRDIV
Rov3
Res3
DNP
Ruv2
Res3
6.8M
7
8
LBST
VIN_DC
VSTOR
VOC_SAMP
VBAT
VREF_SAMP
VSS
OT_PROG
AVSS
VBAT_OV
VBAT_OK
VRDIV
OK_PROG
VBAT_UV
OK_HYST
BQ25504
VSS
Rov1
Res3
4.7M
VSS
Ruv1
Res3
5.6M
4
C4
Cap Semi
10nF
ON/OFF SWITCH
C5
Cap Semi
15pF
switch 3
R14
Res3
30.1k
Vbat_LDO
2
Vbat
1
SW-SPDT
VSS
16
15
14
VSS
Vbat_boost
13
12
Cfltr
Cap Semi
100nF
USB Charger
Cstor
Cap Semi
4.7uF
VSS
VSS
11
R30
Vbat_OK_boost
10
Res3
0
VSS
9
VBUS
U3
D16
1
C3
Cap Semi
1uF
D Schottky
D17
VBUS_boost
Rok1
Res3
3.3M
D Schottky
VSS
/PG
R3
Res3
1K
R4
Res3
2k
Rok2
Res3
5.6M
2
3
4
5
IN
OUT
ISET
TS
VSS
/CHG
PRETERM
ISET2
/PG
BQ24090
EP
6
VBUS
VSS
NR
R13
Res3
51k
NC
10
Vbat_usb
9
TS
8
/CHG
7
ISET2
6
R9
Res3
1K
TS
R7
Res3
1K
ISET2
R10
Res3
1K
R8
Res3
1K
R6
Res3
1.5k
R5
Res3
1.5k
D1
LED2
/PG
D2
LED2
/CHG
11
5
VSS
EP
4
Cref
Cap Semi
10nF
EN
6
5
TPS73001
17
3
VSS
FB
VSS
2
Roc1
Res3
10M
OUT
GND
U1
1
VSS
Roc2
Res3
2.2M
IN
VSS
Rok3
Res3
560K
VRDIV
VSS
VSS
Figure 5: Power management circuit
The BQ25504 has an ultra-low quiescent current and nominally transfers up to an
average of 100 mA of input current. According to the datasheet, this circuit has the highest
efficiency, when the input voltage is not less than 1V. By applying 1V-2V as Vin, more than
80% efficiency is achievable for 10mA-100mA input current.
The BQ25504 implements a programmable maximum power point tracking (MPPT)
sampling network. Since the ratio between the open-circuit voltage (Voc) and the maximum power point voltage (Vmpp) of the solar cell is almost fixed for different illumination conditions (see next section), the MPPT sampling ratio was set between 0.7-0.8
by programming the external resistive divider. For successfully extracting energy from
the source, three different threshold voltages were programmed using external resistors,
namely the under voltage (UV) threshold, battery good threshold (VBAT OK) and over
voltage (OV) threshold.
As the solar energy harvesting source is intermittent, USB Li-ion charger BQ24090
from TI has been used as a backup solution to charge the battery through a USB port.
4.3
Solar Energy Harvesting
SMLD121H04L from IXOLAR has been used as the photovoltaic (PV) module in our
project. This monocrystalline cell has 22% efficiency and can be used in both indoor
and outdoor applications. The main electrical specifications can be seen in Fig. 6.
6
TI Analog Design Contest 2013 Project Report
In order to evaluate the deliverable power, the PV module has been modeled using
the well-known PV cell equivalent circuit and it was finally simulated with TINA tool in
combination with BQ25504 battery charger to check the overall system functionality.
90
80
PV Module Power [mW]
70
89.2 mW
Direct sun (100klx−120klx)
Bright (50klx−100klx)
Cloudy (10klx−50klx)
Rainy (1klx−10klx)
IPH
D1
RP
RS
60
50
41.9 mW
40
30
16.0 mW
20
7.7 mW
10
0
0
0.5
1
1.5
PV Module Voltage [V]
2
2.5
3
Figure 6: Deliverable power of our PV module under different illumination levels
When illumination level is only 10% of direct sun condition, the PV module can still
provide 7.7 mW. In this situation, BQ25504 has more than 80% efficiency and the output
power is approximately 6.1mW that is used to charge the battery. In order to evaluate
autonomous operation of the system in this situation, we have analyzed the power consumption of the whole system and the results have been presented in Table 1.
The nominal voltage of the battery (3.6V) has been considered to calculate the average
power consumption.
Components
Power
(Active)
Power
(Idle)
Power
(Avg.)
MCU (8 MHz)
7.2 mW
0.3 mW
1.0 mW
Sensors
1.6 mW
4 µW
1.6 mW
Energy Harvester + LDO
0.6mW
6 µW
0.6 mW
LEDs driver + 6 LEDs
54 mW
10 µW
1.8 mW
Total Power (10s turn time each 5 min.)
5.0 mW
Table 1: Power consumption of different components
As a result, the total power consumption of the system reaches to 5 mW and the
system can continue its autonomous operation even at 10% of direct sun illumination.
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TI Analog Design Contest 2013 Project Report
5
Prototype Implementation
The realized prototype PCB (Fig. 7) is a two-layer board
designed using Altium Designer. Its dimensions are
70mm x 75mm and it has power planes for GND and
2.5V regulated VCC that supplies MCU, sensor, LED
driver, and debug EEPROM memory. In order to limit
PCB size and component cost, emphasis was put on
the use of SMD components whenever possible.
The PCB contains all the required debug features
in addition to the necessary components (including
TPD4E004 for ESD protection) and can be easily connected to a PC through the MSP-FET430UIF debugging interface. A generic connector is used for the battery, such that different types can be used. The same
concept is applied to the solar cell connection.
6
Figure 7: Prototype PCB
Experimental results
Yaw before [degree]
The functionality of the BQ25504 with 4 PV cells in series (84 mW measured at full sunlight), the USB charger and the LIR2450 was successfully checked. MSP430F5510 was
programmed by Code Composer Studio in order to be able to communicate via I2 C with
sensors, LED driver and EEPROM memory. In Fig. 8 the results after the signal processing of raw sensors data for different arm movements are reported. When the arm is
moving left or right, the threshold for the yaw angle is reached turning on the LEDs. The up
(and down) movement is indeed filtered out by the tilt-compensation algorithm. However,
this allows to successfully retain lateral movements with wrist rotation.
Arm up
Arm left
100
Blinking
Threshold
0
−100
Arm right
0
10
20
30
40
Yaw after [degree]
time [s]
LED on
100
Arm right 50
(rotated wrist)
LED on
LED off
60
70
LED on
Blinking
Threshold
0
−100
0
10
20
30
40
50
60
70
time [s]
Figure 8: Measured yaw angle before (up) and after (down) processing
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TI Analog Design Contest 2013 Project Report
7
Conclusion
This project shows how a solar energy source in combination with TI power management
circuits, sensors and TI microprocessor can be used to power a bracelet-shaped wearable
intelligent blinker for bike safety application. The system benefits from the fractional open
circuit MPPT approach implemented in BQ25504 circuit to track the maximum power point
voltage of the PV module and to extract the maximum power for a 5 mW portable device.
We showed also that MSP430F5510 with its low-power modes is an optimal choice to
process sensors data and to implement FQA algorithm for motion detection.
Moreover this project gave us the possibility to work together as a team. We got more
familiar with TI product and development tools and we will continue promoting TI product
for developing student bachelor and master projects.
8
Future plans
As future developments we have identified two main targets. The first target is developing
a compact prototype. In this case the PCB should be reduced to the minimal elements
including the power management circuitry, the microprocessor and the sensors. Solar
panels and the rechargeable battery can be easily assembled as shown in Fig. 9. Thin
films Li-ion batteries and flexible amorphous PV modules can be eventually used.
The second goal is indeed to use additional sensors for non-invasive health monitoring applications. One of the most common applications is measuring oxygen level and
heart rate by pulsoximetry that is also known as photoplethysmography (PPG). We plan
to integrate an existing reflective PPG prototype we developed based on SLAA274B TI
application note in the intellingent blinker and to take advantage of the accelerometer for
motion tolerant operation using active noise cancellation approach.
Finally, USB port for battery charging allows the connection of the MCU to the computer. This will be used for a user-configurable programming of the LED patterns and/or
the access to sensor data and eventual add-on memory storage.
Figure 9: Bracelet-shaped wearable intelligent blinker compact prototype
9
TI Analog Design Contest 2013 Project Report
Bill of materials
Description
Designator
LibRef
Quantity
Value
Crystal Oscillator
LDO
Boost Converter (backup)
ESD protection
LED Driver
SPDT Subminiature Toggle Switch,
Right Angle Mounting, Vertical
Actuation
Switch
Y3
U5
U2
U6a, U6b
U10
XTAL
TPS73001
TPS61070
TPD4E004
TCA6507
1
1
1
2
1
switch
SW-SPDT
1
S1, S2, S3, S4, S5
SW-PB
5
Resistor
R11, R12, R20, R28, R29, R37, R39, R40, R41, R42, Roc1
Res3
11
10K
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
R13
R14
R21, R22, R23
R24, R25
R26
R27
R2a, R30, R31, R32, R33, R35
Res3
Res3
Res3
Res3
Res3
Res3
Res3
1
1
3
2
1
1
6
51k
30.1k
50
27
1.4K
100
0
Resistor
R3, R7, R8, R9, R10, R15, R16, R17, R18, R19, R43, R44, R45, R46, Rshield1, Rshield2
Res3
16
1K
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
Resistor
MCU
Accelerometer + Magnetometer
Typical RED, GREEN, YELLOW,
AMBER GaAs LED
Typical RED, GREEN, YELLOW,
AMBER GaAs LED
Inductor
Header, 12-Pin, Dual row
Header, 7-Pin, Dual row
Header, 2-Pin
R4
R5, R6
R73
Roc2
Rok1
Rok2, Ruv1
Rok3
Rov1
Rov2, Ruv2
U7
U8
Res3
Res3
Res3
Res3
Res3
Res3
Res3
Res3
Res3
MSP430F5540IRGZ
LSM303DLHC
1
2
1
1
1
2
1
1
2
1
1
2k
1.5k
1M
2.2K
3.3K
5.6K
560
4.7K
6.8K
D1, D2, D6, D7, D8, D9
LED2
6
D3, D4, D5
LED2
3
Inductor
Header 12X2
Header 7X2
Header 2
FT232RL
D Schottky
1
1
1
3
1
6
22uH
Schottky Diode
L1
P2
P1
Battery, pwr, Solar
FTDI1
D10, D11, D12, D15, D16, D17
Capacitor (Semiconductor SIM Model)
C11, C19
Cap Semi
2
10uF
Capacitor (Semiconductor SIM Model)
C14a, C14b, Cdec1, Cdec2
Cap Semi
4
100pF
Capacitor (Semiconductor SIM Model)
C17, C18
Cap Semi
2
10pF
Capacitor (Semiconductor SIM Model)
C20
Cap Semi
1
470nF
Capacitor (Semiconductor SIM Model)
C3
Cap Semi
1
1uF
Capacitor (Semiconductor SIM Model)
C4, Cref
Cap Semi
2
10nF
Capacitor (Semiconductor SIM Model)
C5
Cap Semi
1
15pF
Capacitor (Semiconductor SIM Model)
C6, C7, C10, C12, C13, C21, C22, C23, C24, Cfltr
Cap Semi
10
100nF
Capacitor (Semiconductor SIM Model)
C8, C15, C16
Cap Semi
3
220nF
Capacitor (Semiconductor SIM Model)
C9, Chvr, Cstor
Cap Semi
3
4.7uF
Capacitor
Boost Converter
USB Charger
dual-SPDT Switch
1024K I2C Serial EEPROM
Mini USB Type B
C108, C109
U1
U3
U4
U9
J1, J2
Cap
BQ25504
BQ24090
ADG884
24FC1025
1-353576-1
2
1
1
1
1
2
10pF
10