DOI: 10.5061/dryad.g1jwstqr4
title: Battery discharge characteristics for IEEE 802.15.4 based radio load profile

description: This dataset reports the discharge profiles of 4 battery chemistries under
 IEEE 802.15.4 radio load profiles. The batteries were independently
 subjected to a five-step method to record the discharge characteristics.
 These discharge currents, their effect on battery capacity, and surface
 temperature may affect the overall battery lifetime, which was the major
 aim to discover. The following batteries were used in these experiements
 Tag Manufacturer Model Battery Chemistry Capacity (mAh) Nominal Voltage
 C-Rate Batt1 Powerizer MH-AAA1000APZ  Nickel-Metal Hydride
 (Ni-mh) 1000 1.2 V 1C Batt2 Data Power Technology DTP603450 
 Polymer Lithium-Ion (LiPo) 1000 3.7V 1C Batt3 Panasonic
 UF553443ZU  Lithium-Ion (Li-ion) 1000 3.6V 1C Batt4 Energizer
 LR-6  Alkaline (Zinc, Magnesium Dioxide) Variable, load dependent
 1.5V 2C The five step methodlogy proceeded as: Pre-conditioning Tests
 Relaxation Tests Battery Capacity Test OCV Vs SOC Test Battery Surface
 Temperature Tests The parameters were recorded at every 1 minute interval
 from a fully charged to a fully discharged state.
description: The batteries were placed in a climate chamber to keep the
 temperatures constant. A data logging and buffering circuit was designed
 that recorded the battery voltages during the discharge cycles. Each
 battery was independently subjected to IEEE 802.15.4 based radio current
 profiles and therefore recorded accordingly. A total of 12 observations
 were made to record the discharge characterisitics of the batteries. In
 addition, 4 set of observations were made to record the effect of current
 on battery surface temperature.  The battery relaxation
 time is important in identifying the optimum charge capacity of the
 battery. Therefore, the batteries were fully charged before being
 discharged at three separate SOC levels (corresponding to 90%, 50%, and
 10%, respectively) after a 24-hour rest period. During this relaxation
 time, voltages were measured with a one-minute resolution and were
 compared to OCV measurements after 24 hours. The battery was considered
 quasi-stabilized at this stage. The OCV error between stabilized battery
 states can be computed from Equation below.  
description: The files are sorted, filtered and can be used as it is for
 future research work. It is important to consider that the discharge
 profiles may change with the increase or decrease in battery capacity,
 under ambient effects (such as temperature and humidity) and also with the
 relaxation period. Therefore, it is recommended to use this dataset to
 report only the battery chemistries as described above. In addition, it is
 very important to provide a minimum rest period of 4 hours to each
 battery, after a full charge cycle.  The following
 folder and file structure is made available: 
 <strong>Battery Discharge
 Characteristics </strong>(Contains individual battery discharge
 parameters for the above mentione battery capacities)
 <strong>Battery Surface
 Temperatures </strong>(Contains ambient temperature, as well as
 readings from two PT100 sensors mounted on top of each battery)
 <strong>Normalized Discharged characistics for
 plotting </strong>(Presents a template to selected weighted
 average-based normalized values for plots) 
 <strong>Relaxation Time Calculation</strong> (The OCV and
 relaxation time values are computed and the percentage error is
 calculated) <strong>Code
 Files</strong> This data set provides 3 code
 files that will help to quickly plot and visualize the dataset. The code
 files are written in Python and can be described as 
 <strong>dataprocessor.py </strong>imports the raw
 data, splits it into columns and removes unwanted noise and null
 values 
 <strong>smoothing_function.py</strong> utilizes the linear
 interpolation technique to estimate missing values between data points.
 This helps to estimate missing values as well as improves the smoothness
 of the curves. 
 <strong>battery_discharge_plots.py </strong>imports, splits
 and visualizes various battery discharge characteristics. This code is
 helpful in quickly visualizing the provided dataset.