Building an EV Battery Monitoring solution with AWS IoT FleetWise (Part 2)

git clone https://github.com/aws-samples/aws-iot-fleetwise-evbatterymonitoring

aws ec2 create-key-pair --key-name fleetwiseblogec2key | \
jq -r .KeyMaterial > fleetwiseblogec2key.pem

chmod 0600 fleetwiseblogec2key.pem

Please change the directory:

cd aws-iot-fleetwise-evbatterymonitoring

Please deploy the stack.

aws cloudformation deploy \
--template-file simulatedvehicle/ec2simulation/template.yaml \
--stack-name vehiclesimulation --disable-rollback \
--parameter-overrides Ec2KeyPair=fleetwiseblogec2key IoTCoreRegion=$AWS_REGION \
--capabilities "CAPABILITY_NAMED_IAM"

cd ~/aws-iot-fleetwise-evbatterymonitoring/cloud
./prepare_templates.sh
cd cli-inputs

aws iam create-role --role-name AWSIoTFleetWiseServiceRole\
    --assume-role-policy-document file://1_setup/trustpol.json

aws iam create-policy --policy-name AWSIoTFleetwiseIAMUserPolicy \
                      --policy-document file://1_setup/policy.json

aws iam attach-role-policy --cli-input-json file://1_setup/policy_attach.json

Please review the AWS IAM role policy to learn more about the necessary permissions.

Create signal catalog

The signal catalog is a definition of standardized vehicle signals. To create a new signal catalog, please run the following command:

aws iotfleetwise create-signal-catalog\
    --cli-input-json file://2_signal_catalog/create-signal-catalog.json

You can learn about the structure of the signal catalog by reviewing the input file create-signal-catalog.json. Please refer to the CreateSignalCatalog API for details.

Create vehicle model manifest

Let’s review the concept of a vehicle model in AWS IoT FleetWise. The purpose of vehicle model is to enforce consistent data structure across multiple vehicles of the same type, so that you can process data from fleets of vehicles. With AWS IoT FleetWise, we use the term “signal” referring to the vehicle data.

For example, a fleet operator may have a fleet of two vehicles types – EVs and electric scooters. Both EVs and scooters may generate some common data (also called “vehicle signals”). An example for that is “current vehicle speed”. However, the EVs may generate signals which are not applicable for the scooters, for example tire pressure. To differentiate between data schemas for EVs and electric scooter, the fleet operator can create two vehicle models in AWS IoT FleetWise.

The model manifest formally describes the schema of the data to be collected from a specific type of vehicle. Please review the model manifest examples in files vehicle-model1.json and vehicle-model2.json .

Please note that a newly created model manifest must be activated before further use. Once activated, no further changes of the model manifest are possible. To create and activate model manifests for two vehicle models, please run the following commands.

# Model 1
aws iotfleetwise create-model-manifest\
    --cli-input-json file://3_model_manifest/vehicle-model1.json
aws iotfleetwise update-model-manifest --status ACTIVE --name blog-modelmanifest-01
# Model 2
aws iotfleetwise create-model-manifest\
    --cli-input-json file://3_model_manifest/vehicle-model2.json
aws iotfleetwise update-model-manifest --status ACTIVE --name blog-modelmanifest-02

Please refer to the CreateModelManifest documentation for details.

Please note that while there is overlap between the signals for both models, some signals are only available for the specific model. For example, “EVBatterySample.BMS.Relay01.Status” is only available for the first model, not the second model. This is to demonstrate how you can model heterogeneous vehicle fleets while providing consistent access to the common signals.

Create decoder manifest

The decoder manifest describes the decoding rules for signals of a particular vehicle. To create and activate decoder manifests, please run the following commands:

# Decoder manifest 1
aws iotfleetwise create-decoder-manifest\
    --cli-input-json file://4_decoder_manifest/decoder-manifest1.json
aws iotfleetwise update-decoder-manifest --status ACTIVE --name blog-decodermanifest-01
# Decoder manifest 2
aws iotfleetwise create-decoder-manifest\
    --cli-input-json file://4_decoder_manifest/decoder-manifest2.json
aws iotfleetwise update-decoder-manifest --status ACTIVE --name blog-decodermanifest-02

You can learn about the model manifest by reviewing the input files decoder-manifest1.json and decoder-manifest2.json.

Please note that encoding for the signal “EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a” is different between the first and the second decoder manifest. This is to demonstrate how AWS IoT FleetWise creates an abstraction of decoding from the vehicle model. This allows you to handle different encodings while providing consistent access to vehicle signals.

Please review CreateDecoderManifest API for details. Please note that you can also use the ImportDecoderManifest API to create a decoder manifest using a DBC file.

Create a vehicle

The vehicle is an instance of a vehicle model, representing a physical vehicle. To create vehicles, please run the following commands:

aws iotfleetwise create-vehicle --cli-input-json file://5_vehicle/vehicle01.json
aws iotfleetwise create-vehicle --cli-input-json file://5_vehicle/vehicle02.json

You can learn about the vehicle by reviewing the input files vehicle01.json and vehicle02.json . Please review the CreateVehicle API for details.

Create a fleet and associate vehicles with the fleet

With AWS IoT FleetWise, you can target a campaign either towards a specific vehicle or a fleet, i.e. a set of vehicles. To create an example fleet and associate both vehicles with the fleet, please run the following commands:

aws iotfleetwise create-fleet --cli-input-json file://6_fleet/fleet.json
aws iotfleetwise associate-vehicle-fleet --fleet-id blog-fleet\
    --vehicle-name blog-vehicle-01
aws iotfleetwise associate-vehicle-fleet --fleet-id blog-fleet\
    --vehicle-name blog-vehicle-02

You can learn about the fleet by reviewing the input file fleet.json. Please review the CreateFleet and AssociateVehicleFleet API for details.

Initiate data collection campaigns

In this section, you will implement two typical use cases for vehicle data collection. For this purpose, you will create two data collection campaigns.

To initiate a data collection campaign with AWS IoT FleetWise, create a new campaign by calling CreateCampaign API and then approve the campaign calling UpdateCampaign API . By approving the campaign, you instruct AWS IoT FleetWise to send the configuration for data collection campaigns and decoders to the vehicles. Based on this configuration, the AWS IoT FleetWise Edge Agent starts collecting, transforming, and transferring data to the AWS.

Let us take a look at the individual campaigns.

Campaign 1: continuously collect battery pack current and cell temperature

This campaign will use a time-based collection scheme, i.e. the Edge Agent will continuously collect vehicle data and transfer it to the cloud in the specified time period. To create this campaign, please run the following commands:

aws iotfleetwise create-campaign \
    --cli-input-json file://7_campaign/continious-monitoring-campaign.json
# Please wait 15 seconds before running the next command to allow for campaign creation to complete
aws iotfleetwise update-campaign --action APPROVE\
    --name continious-monitoring-campaign

You can learn more about this campaign by reviewing continious-monitoring-campaign.

Campaign 2: conditionally collect a high-resolution (50 ms sampling rate) snapshot of multiple Battery Management System (BMS) signals.

An example of a use case for this campaign is the analysis of potential problems with the battery packs of a pre-production vehicle fleet. To perform an analysis, vehicle quality engineers may need a high-resolution snapshot for multiple signals (e.g., temperature development of specific battery cells). However, let us assume that the vehicle engineers are only interested in the data if overcurrent event occurs.

To implement this use case, you will create a campaign with a condition-based collection scheme. The Edge Agent will evaluate an expression that you can define (for example, $variable.`EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a` > 450.0). Only if this expression evaluates to true does the Edge Agent collect vehicle data and transfer it to the cloud.

To create this campaign, please run the following commands:

aws iotfleetwise create-campaign\
     --cli-input-json file://7_campaign/conditional-snapshot-campaign.json
# Please wait 15 seconds before running the next command to allow for campaign creation to complete
aws iotfleetwise update-campaign --action APPROVE\
    --name conditional-snapshot-campaign

You can learn more about this campaign by reviewing conditional-snapshot-campaign.json.

Testing the solution

You have successfully deployed a simulation and configured the AWS IoT FleetWise service. Now it’s time to see the overall solution in action. Please follow the instructions below.

Step 1: Connect to the simulated vehicle

In this step, you will connect to the Amazon EC2 instance that simulates the vehicle. You will then review the output of two software components: Edge Agent and the CAN bus simulation script. Please complete the following steps:

1. Get the output of the stack

aws cloudformation describe-stacks \
    --stack-name vehiclesimulation \
    --query "Stacks[0].Outputs[?OutputKey=='Ec2Instance1SSH'].OutputValue" \
    --output text

If you see “None” as output, it means that stack deployment has not completed yet. Please retry in few minutes. If you experience problems with the stack deployment, please consider the troubleshooting guidelines.

2. SSH into the Amazon EC2 instance
Please copy and paste the SSH command from the stack output above and run it. When asked if you want to connect to the instance, please confirm with “yes”:

ssh -i ~/fleetwiseblogec2key.pem ubuntu@<Value of Vehicle1EC2PublicIP>

3. View CAN messages

Please view the CAN data generated by the vehicle simulation script that runs as a system service.

candump vcan0

4. You can view Edge Agent output

sudo journalctl -fu fwe@0

You can safely ignore error message “Failed to receive VIN from Engine ECU”.

Step 2: Review collected vehicle data in Amazon Timestream

In this step, you will review the results of the data collection campaigns stored in the Amazon Timestream table.

1. Open the Amazon Timestream Console

Please navigate to “Amazon Timestream” service in AWS management console or click on this link. Next, select the Query editor menu item.

Amazon Timestream console

2. Review vehicle data collected by campaign 1 “Continuously collect battery pack current and cell temperature”

In the text field for the query, enter the following sample query and choose Run :

SELECT time, vehicleName, measure_name, measure_value::double, VehicleVIN
FROM "FleetWiseDatabase"."FleetWiseTable"
WHERE campaignName = 'continious-monitoring-campaign' AND
      time between ago(15m) and now()
ORDER BY time DESC LIMIT 10

You can now view the collected vehicle data as you see in example below:

In spite of the fact that both vehicles encode the CAN message for the signal EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a in different ways (see DBC files for vehicle 1 and vehicle 2), you can access and analyze the vehicle data under a uniform name!

We suggest to run your own experiments with visualizing the vehicle data by using Amazon Managed Grafana with example dashboards, as you can see in the example below:

3. Review vehicle data collected by campaign 2

Let’s query the statistics of a specific cell temperature. Enter the following query and select Run :

SELECT vehicleName, measure_name,
       hour(time) as hour, minute(time) as minute,
       max(measure_value::double) as MaxTemperature,
       min(measure_value::double) as MinTemperature
FROM "FleetWiseDatabase"."FleetWiseTable"
WHERE campaignName = 'conditional-snapshot-campaign'
      AND measure_name = 'EVBatterySample.BMS.BatteryPack01.Cell001.CellTemperature'
      AND (time between ago(10m) and now())
GROUP by vehicleName, measure_name, hour(time), minute(time)
ORDER by vehicleName,hour(time), minute(time)

You can now view the aggregated vehicle data as you see in example below. According to the campaign configuration , the data was transferred only when battery pack shunt current was over 450 amperes. For 19:42 and 19:43, shunt current was below 450 amperes, so no data was transferred.

We suggest to run your own experiments with the vehicle data by following Amazon Timestream Query Language reference guidelines or by creating Amazon Managed Grafana dashboards like below using provided examples:

Cleaning up

Please ensure to exit the SSH session to the Amazon EC2 instance before you continue. Please run the following commands in AWS CloudShell to remove the created resources from your AWS account:

aws cloudformation delete-stack --stack-name vehiclesimulation
sh ~/aws-iot-fleetwise-evbatterymonitoring/cloud/automation/fleetwise-cleanup.sh

Summary and next steps

You have learned how to use AWS IoT FleetWise to implement the collection, conversion, transmission, and storage of CAN bus signals and how to query these signals using Amazon Timestream. In the next part of this blog series, we will introduce you to how you can use AWS services to analyze stored data.

Visit the AWS IoT FleetWise webpage to learn more.