Guidance for Electric Vehicle Battery Health Prediction on AWS
Overview
This Guidance demonstrates how to use historical battery health data with artificial intelligence and machine learning (AI/ML) algorithms to improve the accuracy of battery State of Health (SoH) and Remaining Useful Life (RUL) estimations. Currently, these estimations largely rely on a static formula-based approach, which can provide near-term battery health information. Using this Guidance, automotive original equipment manufacturers (OEMs) can predict battery SoH and RUL into the future with easy-to-train AI/ML models built using historical data stored in the Cloud.
Predictions of battery health will help OEMs and EV owners proactively plan for battery replacement, and most importantly, can be used to move battery into a new life and promote the overall circular economy of a battery. OEMs can retrain these models at regular intervals using incoming battery health status data, and monitor the battery fleet health using out-of-the box dashboards. Along with information such as driving trends, charge and discharge behaviors, OEMs can also use this Guidance to provide EV owners with recommendations on how to slow SoH decline and extend the battery lifespan.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
Operational Excellence
Amazon CloudWatch provides centralized logging with metrics and alarms to raise alerts for operational anomalies. CloudWatch is also used to monitor model drift. You may want to establish a drift threshold, above which the ml pipeline will be triggered to retrain the model. This helps continuously improve battery health predictions.
Security
Forecast uses TLS with AWS certificates to encrypt any data sent to other AWS services. Forecast endpoints support only secure connections over HTTPS.
Data at-rest is encrypted using server-side encryption with Amazon S3 managed keys (SSE-S3).
Reliability
This Guidance follows an event-driven architecture with loosely coupled services, making it easy to isolate behaviors and therefore increase resilience and agility. It uses managed serverless services, such as Amazon EventBridge and Lambda , to communicate between loosely coupled services.
Performance Efficiency
Services selected for this Guidance are all purpose-built. For example, Forecast is a purpose-built time series forecasting service based on machine learning. EventBridge is a purpose-built serverless service to connect loosely coupled components using events.
Cost Optimization
This Guidance follows an event-driven architecture with AWS services that are fully managed, such as Lambda , AWS Glue , and Amazon S3 . These services autoscale according to the workload demand. As a result, you only pay for what you use.
Sustainability
The Amazon S3 Intelligent-Tiering storage class is designed to automatically move data to the most sustainable access tier in Amazon S3 .
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
Disclaimer
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