Update Data_Breakdown.md

Data_Breakdown.md

@@ -2,34 +2,36 @@
 
 py_packages/vis_buses/bus_1/!3J0018_ProfileData_20170920082828.csv
 
-    This is the first csv of bus 1, data from bus 1's first visit
+    This is the first CSV of bus 1, data from bus 1's first visit
 
     Each visit will have a data breakdown of the following:
 
 #### <Battery Current (A)>
 
-  Breaks down the amount of time (in seconds) that the battery was experiencing that value of current (in Amps). In bins of 10 Amps going from -350A to 350+A. This data was collected over 80985877 seconds which is 937.3 days worth of data. 
+  Breaks down the time (in seconds) that the battery was experiencing that value of current (in Amps). In bins of 10 Amps going from -350A to 350+A. This data was collected over 80985877 seconds which is 937.3 days worth of data. This parameter provides insights into the current flowing through the battery over time. By categorizing the data into bins of 10 Amps, ranging from -350A to 350+A, it allows for a detailed analysis of the distribution of current. Understanding the distribution helps in identifying trends, anomalies, and potential issues such as overcharging or underutilization.
 
 #### <Battery Voltage (V)>
-  Breaks down the amount of time (in seconds) that the battery spent at a corresponding Voltage (in Volts) . In bins of 10 Volts going from 450V to 750+V. This data was collected over 80985877 seconds which is 937.3 days worth of data. 
+  Breaks down the amount of time (in seconds) that the battery spent at a corresponding Voltage (in Volts). In bins of 10 Volts going from 450V to 750+V. This data was collected over 80985877 seconds which is 937.3 days worth of data. Monitoring the battery voltage is crucial for assessing its health and performance. With data categorized into bins of 10 Volts, ranging from 450V to 750+V, it provides a comprehensive view of voltage variations. Deviations from expected voltage levels can indicate issues like cell degradation, overcharging, or insufficient charging. 
 
 #### <Power (kW)>
-  Breaks down the amount of time (in seconds) that the battery spent at a corresponding Power (in kW) . In bins of 10 kW going from -200kW to 200+kW. This data was collected over 80985877 seconds which is 937.3 days worth of data. 
+  Breaks down the amount of time (in seconds) that the battery spent at a corresponding Power (in kW). In bins of 10 kW going from -200kW to 200+kW. This data was collected over 80985877 seconds which is 937.3 days worth of data. This parameter represents the power output or input of the battery system. By categorizing data into bins of 10 kW, ranging from -200kW to 200+kW, it helps in analyzing power usage patterns. Understanding power fluctuations is essential for optimizing energy efficiency and ensuring the system operates within safe limits. 
 
 The data is then broken down into modules 1 through 16
 Each module has 12 cells. 
 
 #### <Cell Voltages (V)>
-Breaks down the amount of time (in seconds) that each cell 1-12 of that module spent at a corresponding voltage (in Volts).  In bins of 10 kW going from 2V to 4+V. 
+Breaks down the amount of time (in seconds) that each cell 1-12 of that module spent at a corresponding voltage (in Volts).  In bins of 10 kW going from 2V to 4+V. Monitoring individual cell voltages is critical for ensuring uniformity and detecting abnormalities within the battery modules. Categorizing data into bins of 10 Volts for each of the 12 cells in a module provides detailed insights into cell performance. Deviations from expected voltage levels can indicate cell imbalance, degradation, or malfunction. 
 
 
 #### <Cell Balancer (on/off)>
-Shows the amount of time (in seconds) that the cells of the module were turned on and off to maintain even usage of each cell in the module.
+Shows the amount of time (in seconds) that the module's cells were turned on and off to maintain even usage of each cell in the module. This parameter indicates the operation of cell balancers within the module. Balancers are crucial for maintaining uniform cell usage and extending battery life. Monitoring the duration of balancer operation helps assess the effectiveness of cell balancing strategies and identify any issues with individual cells or the balancer system itself.
 
 #### <Module Temperatures (deg C)>
-Shows the time (in seconds) that the module spent at different temperatures (in degrees Celsius). In bins of 5C  going from -40C to 80+C. 
+Shows the time (in seconds) that the module spent at different temperatures (in degrees Celsius). In bins of 5C  going from -40C to 80+C. Temperature plays a significant role in battery performance and longevity. Monitoring module temperatures over time, categorized into bins of 5°C, provides insights into thermal management and potential overheating issues. Deviations from optimal temperature ranges can impact battery efficiency and lifespan. 
 
 #### <Module Voltages (V)>
-Breaks down the amount of time (in seconds) that the module spent at a corresponding Voltage (in Volts) . In bins of 2.4Volts going from 24V to 48+V.
-
+Breaks down the amount of time (in seconds) that the module spent at a corresponding Voltage (in Volts). In bins of 2.4Volts going from 24V to 48+V. Similar to battery voltage, monitoring module voltages help assess the overall health and performance of the battery system. Categorizing data into bins of 2.4 Volts provides detailed insights into voltage variations across modules. Deviations from expected voltage levels can indicate module imbalance or issues with the battery management system (bms).
+
+ By analyzing these parameters collectively, one can gain a comprehensive understanding of the battery system's behavior, performance, and health. Identifying trends, anomalies, and correlations within the data is crucial for optimizing battery operation, ensuring reliability, and minimizing the risk of failures. Additionally, historical data spanning over 937.3 days provides valuable insights into long-term trends and patterns, enabling proactive maintenance and optimization strategies.
+