Predictive Maintenance for Electric Vehicles

Skills, Tech Stack, and Libraries

1. Skills: Predictive Analytics, Time-Series Forecasting, Feature Engineering, Data Visualization

2. Tech Stack: Python, SQL, AWS

3. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib

Approach

Objective:

I developed a predictive maintenance system to estimate the remaining useful life (RUL) of electric vehicle (EV) batteries and identify potential maintenance needs. The project aimed to reduce unexpected failures and optimize maintenance schedules for EV fleets.

Approach:

1. Data Collection and Preprocessing:

   • Collected battery telemetry data, including charge cycles, temperature readings, voltage, and current from IoT devices.

   • Cleaned and preprocessed the data using Pandas to handle missing values, remove noise, and align time-series data from multiple sensors.

2. Exploratory Data Analysis (EDA):

   • Analyzed trends in battery health and performance, identifying patterns correlating with degradation rates.

   • Visualized features such as state of charge (SOC) over time and temperature fluctuations using Matplotlib.

3. Feature Engineering:

   • Created features like depth of discharge (DoD), charge/discharge rates, and cumulative energy throughput to capture key battery performance indicators.

   • Generated rolling averages and lagged features to model temporal dependencies.

4. Model Development:

   • Built a regression model using Scikit-learn to predict RUL based on engineered features.

   • Trained an LSTM model using TensorFlow to capture sequential patterns and long-term dependencies in time-series data.

   • Evaluated models using metrics such as Mean Absolute Error (MAE) and R-squared (R²).

5. Deployment and Integration:

   • Deployed the best-performing model as an API using Flask, enabling real-time predictions based on incoming telemetry data.

   • Integrated the API with fleet management systems for continuous monitoring and maintenance alerts.

6. Visualization and Reporting:

   • Created dashboards to display battery health metrics, RUL predictions, and maintenance schedules for fleet managers.

     
     

Code Flow:

1. Import telemetry data using Pandas and preprocess it to clean and standardize sensor readings.

2. Engineer features such as DoD and lagged metrics to enhance model performance.

3. Train and validate regression and LSTM models to predict RUL.

4. Deploy the model via Flask for real-time integration and use visualization tools to display insights.

Results

The Predictive Maintenance System for Electric Vehicles delivered the following outcomes:

• Accurate Predictions: Achieved an MAE of 5% in RUL predictions, ensuring timely maintenance alerts.

• Reduced Downtime: Decreased unexpected battery failures by 30%, improving fleet reliability.

• Optimized Maintenance Schedules: Enabled proactive scheduling, reducing unnecessary maintenance by 20%.

• Enhanced Battery Lifespan: Provided actionable insights to EV operators, increasing battery life through optimized usage patterns.

This project demonstrated the effective use of machine learning in enhancing the operational efficiency and reliability of electric vehicle fleets.

Git Link

For more information and code, visit the Git link.