Skip to main content
Dryad logo

Battery discharge characteristics for IEEE 802.15.4 based radio load profile

Citation

Ali, Omer; Ishak, Mohamad Khairi; Ai Ooi, Chia; Bhatti, Muhammad Kamran Liaquat (2021), Battery discharge characteristics for IEEE 802.15.4 based radio load profile, Dryad, Dataset, https://doi.org/10.5061/dryad.g1jwstqr4

Abstract

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:

  1. Pre-conditioning Tests
  2. Relaxation Tests
  3. Battery Capacity Test
  4. OCV Vs SOC Test
  5. Battery Surface Temperature Tests

The parameters were recorded at every 1 minute interval from a fully charged to a fully discharged state.

Methods

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.

 

Usage Notes

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:

  1. Battery Discharge Characteristics (Contains individual battery discharge parameters for the above mentione battery capacities)
  2. Battery Surface Temperatures (Contains ambient temperature, as well as readings from two PT100 sensors mounted on top of each battery)
  3. Normalized Discharged characistics for plotting (Presents a template to selected weighted average-based normalized values for plots)
  4. Relaxation Time Calculation (The OCV and relaxation time values are computed and the percentage error is calculated)

Code Files

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

  1. dataprocessor.py imports the raw data, splits it into columns and removes unwanted noise and null values
  2. smoothing_function.py 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.
  3. battery_discharge_plots.py imports, splits and visualizes various battery discharge characteristics. This code is helpful in quickly visualizing the provided dataset. 

Funding

Universiti Sains Malaysia, Award: 1001/PELECT/8014049