In our previous blog, we started exploring the key health indicators for assessing the State of Health (SoH) of electric motorbike batteries. We focused on voltage-based features and demonstrated how they can reveal vital information about battery health. In this continuation, we’ll delve into additional health indicators that are crucial for a comprehensive understanding of battery diagnostics.
Current Based Features
Just like voltage, the current flowing through a battery during charging and discharging provides significant insights into its health. Current measurements can help identify issues such as internal resistance changes and cell degradation. Let’s explore some important current-based features:
1.Charge/Discharge Current Rates:
The rate at which a battery is charged or discharged (C-rate) can impact its longevity. Higher C-rates can accelerate degradation.
# Calculate C-rate for charging and discharging
df['C_rate'] = df['Current(A)'] / df['Nominal Capacity(Ah)']2. Current Slope
Analyzing the slope of the current during different phases of charging/discharging can reveal changes in internal resistance.
# Calculate the slope of current during charging
df['Current Slope'] = df.groupby('Cycle number')['Current(A)'].transform(lambda x: np.polyfit(x.index, x, 1)[0])3. Current Peaks
Identifying peaks in current can help in detecting abnormal charging/discharging events that could be harmful to the battery.
# Identify peaks in current
current_peaks = df.groupby('Cycle number')['Current(A)'].max()Temperature Based Features
The most critical stress factor for LIB degradation is temperature; all reaction rates, parasitic and nonparasitic, are related to temperature. It, therefore, affects all other stress factors. The most common technique to model degradation due to temperature is using Arrhenius law. Due to the complex nature of LIB degradation, it is challenging to give a rule of thumb figure as the aging rate will always depend on multiple correlated factors. However, a common rule of thumb considered in many studies while modelling is that the aging rate doubles for every 10 ◦C increase in temperature. It is important to check the sanity of this thumb rule in different scenario. For this reason, the testing conditions, especially temperature, should be as close as possible to the operating conditions of the application, as extremely high temperatures result in different aging mechanisms and, therefore, different aging behaviors.
1.Operating Temperature Range
Monitoring the temperature range during operation helps in ensuring the battery is within safe limits.
# Calculate operating temperature range
df['Temp Range'] = df['Max Temp(C)'] - df['Min Temp(C)']2. Average Temperature
The average temperature during charging/discharging cycles can indicate thermal management efficiency.
# Calculate average temperature
df['Avg Temp(C)'] = df.groupby('Cycle number')['Temperature(C)'].transform('mean')Conclusion
In this blog, we've expanded on the health indicators for assessing battery health by exploring current and temperature-based features. These parameters, in conjunction with voltage-based features discussed in Part I, provide a more comprehensive picture of battery degradation. It's crucial to remember that not all health indicators will be equally important for predicting battery health. The optimal set of features will depend on factors such as battery chemistry, operating conditions, and the specific degradation mechanisms at play. Feature selection techniques, such as correlation analysis, principal component analysis (PCA), or recursive feature elimination (RFE), can help identify the most relevant features for building accurate predictive models.
The insights gained from analyzing health indicators have significant implications for electric vehicle users and manufacturers. By monitoring these parameters, we can: tailor charging strategies to minimize battery degradation based on factors like temperature, state of charge, and current rate, implement early warning systems, detect anomalies or signs of impending failure, allowing for proactive maintenance or replacement.
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