and Maccor 420012 battery cycling and testing hardware for
measuring the battery properties.
The model takes the initial SoC, OCP versus SoC, resistance versus SoC, concentration resistance, and plate
capacitance to emulate a battery. At each time step, based
on the SoC, it estimates OCP, and resistance. Using the
updated values, it calculates the values for the SoC after
the time step.
We build the battery model using a few batteries and validate the models against other batteries of the same type. The
validation results for one of the batteries are shown in Figure 8.
The results show that our model is accurate to 97.5%. We modeled 15 batteries in total: two of Type 4, two of Type 3, eight of
Type 2, and three more of other types (refer to Figure 1a).
We implement a simple software layer that takes the
input power requirement and splits it across a given number of batteries according to the power policies set by an OS.
The model and the SDB emulator are integrated into the OS
using 4800 lines of code across modules written in C#.
We focus on two hardware platforms: a tablet and a watch.
The tablet is a “2-in- 1” development device with Intel Core
i5 CPU, 4GB DRAM, 128GB SSD, and 12 inch display. The
watch is a Qualcomm Snapdragon 200 development board
with hardware similar to several smart-watches.devices are
instrumented to obtain fine grained ( 100 Hz) power-draw
measurements. The power-draw is then fed into the emulator to calculate the energy drawn from the batteries.
5. SDB APPLICATIONS
In this section, we describe two scenarios that benefit from
using SDB with heterogeneous batteries. We also show how
SDB policies can be customized for the different scenarios,
and demonstrate the benefits of integrating future workload
knowledge in the SDB system.
5. 1. Adopting flexible batteries
Flexibility and bendability are important structural prop-
erties for wearable devices, for example, a watch-strap
that is flexible and bendable tends to be easier to wear.
Coincidentally, there are a few emerging battery chemistries
that enable bendability. The bendability, unfortunately,
comes at the cost of other battery properties. Such batter-
ies use a solid (rubber-like) electrolyte in place of a tradi-
tional liquid (polymer) electrolyte. Unfortunately, the solid
(elastic) state of the electrolyte increases the resistance for
the Li-ions and therefore, such batteries have higher inter-
nal losses. Several prototype bendable batteries we tested
are excellent at handling low power workloads but often are
very inefficient for high power workloads.
SDB can enable a scenario where a small traditional
Li-ion battery in smart-watches is augmented with bend-
able batteries. This helps design better wearables that uti-
lize the strap space to increase capacity but are still able
to execute high power workloads like GPS tracking while
running and cycling. The reduction in the size of the rigid
Li-ion battery also allows for the design of a less bulky
The bendability of the battery in the strap is a boon, but
its low efficiency is a bane that has to be intelligently man-
aged to maximize effective battery life of the device. It is
important to preserve energy in the efficient battery for
times when the user is expected to perform power-intensive
tasks. For example, the user may exercise, run or bicycle dur-
ing certain times of the day, which all require high power.
Therefore, the SDB policies should preserve the efficient
battery for such times.
Since smart-watch usage will vary across users, we com-
pare two extreme parameter values to demonstrate the ben-
efits of SDB: One that minimizes instantaneous losses by
drawing appropriate amounts of power from both the bat-
teries and one that draws higher amounts of power from the
inefficient battery to conserve the efficient battery.
Figure 9 demonstrates the setting and the results.
We use a 200mAh Li-ion battery in combination with a
Figure 7. Battery simulator: (a) Battery modeled with four variables that are learned using experimentation: open circuit potential, internal
resistance, concentration resistance, and plate capacitance. This model allows us to conduct experiments in a scalable manner. (b) The open
circuit potential of a battery increases with the state of charge (amount of energy left) of the battery. (c) The internal resistance of a battery
decreases with the state of charge.
(a) (b) (c)
0 10 20 30 40 50
State of charge (%) State of charge (%)
100 0 10 20 30 40 50 60 70 80 90 100
Battery 1 Battery 2 Battery 3 Battery 4 Battery 5 Battery 1 Battery 2 Battery 3 Battery 4
Battery 8 Battery 7 Battery 6 Battery 5
0.2A Experiment 0.5A Experiment 0.7A Experiment
0.2A Model 0.5A Model
State of charge
0 0.2 0.4 0.6 0.8 1
Figure 8. Validating the model against battery testing hardware
reveals that our model is 97.5% accurate.