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AWS Solutions Library

Guidance for Electric Vehicle Battery Health Prediction on AWS

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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.

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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.

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.

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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).

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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.

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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. 

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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.

Read the Cost Optimization whitepaper 

The Amazon S3 Intelligent-Tiering storage class is designed to automatically move data to the most sustainable access tier in Amazon S3 .

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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. 

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.