Figure 3: AutoML System Architecture
The AutoML system includes a meta-learning component that leverages cleaned historical information for training models. The ML framework is responsible for automatically choosing data processor and feature preprocessor algorithms and selecting the models. In deep learning (DL), this model selection is typically replaced by Neural Architecture Search (NAS) that performs additional post-processing by combining multiple models together, as shown in Figure 3.
Model Deployment: On the platform, a deployment space acts as a registry to manage the assets associated with a model deployment. The deployment assets may include a serialized model, scoring script, schema of the inference dataset, and more. This model can be accessed via REST endpoint to send the test date and get the model predictions.
Leveraging the AutoML approach for solution development results in reduced time and effort. The system takes care of data preprocessing, partitioning, feature engineering, hyper-parameter tuning and more, running multiple ML algorithms in auto-pilot mode to churn the best fit model and deliver the most optimal performance while predicting EV battery health with 96-97% accuracy.
Leveraging the Solution for More Than EV Battery Health Prediction
While battery health prediction is a horizontal capability, this solution can be augmented with other technologies and be leveraged for other uses like customer support, inventory optimization, and product quality.
a) Enhanced Customer Service
For instance, the solution can predict electric vehicle battery health and display the status on the vehicle’s dashboard. If the battery needs servicing, this information can be easily integrated with geographic information systems to help the driver locate the nearest charging station or service center. If the driver fails to immediately address an alert, s/he is advised on the next best action using a voice-driven chatbot (via mobile phone or head unit) to avoid the hassle of delayed support. If the DIY approach fails via chat support, the chatbot could then spot the nearest service center and initiate human assistance by creating a ticket in the system. The dealer could then look at the ticket details for vehicle location, spare parts required, etc. and automatically dispatch a service engineer to remedy the breakdown issue.
b) Inventory Optimization
Orchestrated from the central server, it is possible to send notifications/alerts to EV dealers such as the predicted/reported electric vehicle battery health issues for EVs within the dealer’s geographic area. Based on these alerts, the dealer can anticipate service volumes and optimize its EV parts inventory accordingly.
c) Product Quality Improvement
By conducting a periodic review of all predicted malfunctions, the solution can decipher defect trends in electric vehicle battery health and performance and provide meaningful inputs for battery health and quality improvements at the production level. R&D teams can inspect the defect trends and determine the root causes, over time improving the overall quality of the electric vehicle battery.
The adoption of AI/ML for predicting EV battery health is gathering momentum. With a data-driven model that addresses the entire EV battery lifecycle across different charging and loading cycles, battery health forecasting can be much more reliable. Platforms leveraging AutoML help fast-track development cycles by automating the routine data-science activities without compromising prediction accuracy. Wipro’s EV battery health prediction solution can be further leveraged to realize multiple uses including customer experience, dealer inventory management, product quality improvement, and more.