BEV BMS Battery Consistency Bias Alarm Prediction

With Amazon S3, Amazon Lambda, Amazon SageMaker, Amazon DynamoDB and open-source Apache Superset, this solution offers machine learning developers and data scientists an entire tool chain for BMS Alert Prediction, including data store tool, data analysis tool, model process tool and data visualization tool.

Workflow in details:

1. OEM or BEV battery vendors upload battery data (captured with IoT, etc.) to S3 bucket.

2. SageMaker notebook instance obtain dataset from S3 bucket, which could be used for model training.

3. After model building, model training and model deployment, SageMaker outputs a runtime endpoint, which can be used to provide inference service.

Scenario 1:

    4. Connected vehicles upload battery data (batch data) to S3 bucket.

    5. S3 bucket create event invoke lambda function.

    6. Lambda function invokes the deployed SageMaker endpoint, perform inference on the uploaded batch data.

    7. Batch inference results are written into DymanoDB.

    8. Batch inference results are written into S3 for superset visualization.

Scenario 2:

    9. User invokes the prediction service via HTTP POST request.

    10. API Gateway routes the request to lambda function.

    11. Lambda function invokes the deployed SageMaker endpoint to perform inference with the post data as input.

    12. API single inference results are written into Dymanodb.

    13. API single inference results are written into S3 for superset visualization.

14. Superset is hosted in Fargate. Users can access inference results using SQL query.

15. Athena queries inference events from Glue Data Catalog.

16. The table defined in Glue Data Catalog describes the schema of inference events which are stored in S3 bucket.