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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.
Step 1
Battery health data is ingested through the Connected Mobility Platform.
Step 2
Streaming data ingested through the Connected Mobility Platform is stored in Amazon Timestream for near real-time monitoring. Amazon Simple Storage Service (Amazon S3) is used to store historical battery data. Amazon DynamoDB stores the state of each phase of the machine learning (ML) pipeline, including pre-processing, model generation and prediction, post-processing, and drift monitoring.
Step 3
When new data becomes available, AWS Lambda is initiated to start a pre-processing job in AWS Glue using the uploaded processing plugin script to transform battery health data into the desired format.
Step 4
Processed datasets are imported into Amazon Forecast, which uses AutoML to automatically choose the most optimal data processing and ML algorithm to build. It then trains and makes predictions on battery state of health (SoH) and remaining useful life (RUL).
Step 5
Predictions made using Forecast are exported into Amazon S3 after getting processed using AWS Glue. This data is used to serve data through a web interface for Original Equipment Manufacturer (OEM) and the vehicle dashboard for connected vehicles.
Step 6
Exported forecasts in Amazon S3 are also used to monitor drift. You can set a drift threshold, for example 10%, above which the forecast pipeline gets triggered to retrain the model.
Step 7
You can view battery health status and predictions through the Connected Mobility Platform.
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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.
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.