Constructing an EV Battery Monitoring answer with AWS IoT FleetWise (Half 1/2)

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 listing:

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/coverage.json

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

Please assessment the AWS IAM position coverage to study extra in regards to the essential permissions.

Preliminary AWS IoT FleetWise configuration

On this step, you configure AWS IoT FleetWise with the IAM position you created earlier and the Amazon Timestream database and desk names. It is advisable to carry out this configuration as soon as for the respective AWS area earlier than you need to use AWS IoT FleetWise.

aws iotfleetwise register-account 
    --cli-input-json file://1_setup/account_registration.json
# Examine if registration is successfull
aws iotfleetwise get-register-account-status

You possibly can study extra in regards to the preliminary configuration by reviewing the enter file account_registration.json . Please assessment to RegisterAccount API for particulars.

Create sign catalog

The sign catalog is a definition of standardized car indicators. To create a brand new sign catalog, please run the next command:

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

You possibly can study in regards to the construction of the sign catalog by reviewing the enter file create-signal-catalog.json . Please seek advice from the CreateSignalCatalog API for particulars.

Create car mannequin manifest

Let’s assessment the idea of a car mannequin in AWS IoT FleetWise. The aim of car mannequin is to implement constant knowledge construction throughout a number of autos of the identical sort, as a way to course of knowledge from fleets of autos. With AWS IoT FleetWise, we use the time period “sign” referring to the car knowledge.

For instance, a fleet operator could have a fleet of two autos sorts – EVs and electrical scooters. Each EVs and scooters could generate some frequent knowledge (additionally referred to as “car indicators”). An instance for that’s “present car velocity”. Nonetheless, the EVs could generate indicators which aren’t relevant for the scooters, for instance tire stress. To distinguish between knowledge schemas for EVs and electrical scooter, the fleet operator can create two car fashions in AWS IoT FleetWise.

The mannequin manifest formally describes the schema of the info to be collected from a particular sort of car. Please assessment the mannequin manifest examples in recordsdata vehicle-model1.json and vehicle-model2.json .

Please observe {that a} newly created mannequin manifest have to be activated earlier than additional use. As soon as activated, no additional adjustments of the mannequin manifest are attainable. To create and activate mannequin manifests for 2 car fashions, please run the next instructions.

# Mannequin 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
# Mannequin 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 seek advice from the CreateModelManifest documentation for particulars.

Please observe that whereas there may be overlap between the indicators for each fashions, some indicators are solely accessible for the particular mannequin. For instance, “EVBatterySample.BMS.Relay01.Standing” is just accessible for the first mannequin, not the second mannequin. That is to exhibit how one can mannequin heterogeneous car fleets whereas offering constant entry to the frequent indicators.

Create decoder manifest

The decoder manifest describes the decoding guidelines for indicators of a selected car. To create and activate decoder manifests, please run the next instructions:

# 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 possibly can study in regards to the mannequin manifest by reviewing the enter recordsdata decoder-manifest1.json and decoder-manifest2.json.

Please observe that encoding for the sign “EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a” is completely different between the primary and the second decoder manifest. That is to exhibit how AWS IoT FleetWise creates an abstraction of decoding from the car mannequin. This lets you deal with completely different encodings whereas offering constant entry to car indicators.

Please assessment CreateDecoderManifest API for particulars. Please observe that you would be able to additionally use the ImportDecoderManifest API to create a decoder manifest utilizing a DBC file.

Create a car

The car is an occasion of a car mannequin, representing a bodily car. To create autos, please run the next instructions:

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 possibly can study in regards to the car by reviewing the enter recordsdata vehicle01.json and vehicle02.json . Please assessment the CreateVehicle API for particulars.

Create a fleet and affiliate autos with the fleet

With AWS IoT FleetWise, you’ll be able to goal a marketing campaign both in the direction of a particular car or a fleet, i.e. a set of autos. To create an instance fleet and affiliate each autos with the fleet, please run the next instructions:

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 possibly can study in regards to the fleet by reviewing the enter file fleet.json . Please assessment the CreateFleet and AssociateVehicleFleet API for particulars.

Provoke knowledge assortment campaigns

On this part, you’ll implement two typical use circumstances for car knowledge assortment. For this function, you’ll create two knowledge assortment campaigns.

To provoke a knowledge assortment marketing campaign with AWS IoT FleetWise, create a brand new marketing campaign by calling CreateCampaign API after which approve the marketing campaign calling UpdateCampaign API . By approving the marketing campaign, you instruct AWS IoT FleetWise to ship the configuration for knowledge assortment campaigns and decoders to the autos. Based mostly on this configuration, the AWS IoT FleetWise Edge Agent begins accumulating, reworking, and transferring knowledge to the AWS.

Allow us to check out the person campaigns.

Marketing campaign 1: repeatedly gather battery pack present and cell temperature

This marketing campaign will use a time-based assortment scheme, i.e. the Edge Agent will repeatedly gather car knowledge and switch it to the cloud within the specified time interval. To create this marketing campaign, please run the next instructions:

aws iotfleetwise create-campaign 
    --cli-input-json file://7_campaign/continious-monitoring-campaign.json
# Please wait 15 seconds earlier than operating the subsequent command to permit for marketing campaign creation to finish
aws iotfleetwise update-campaign --action APPROVE
    --name continious-monitoring-campaign

You possibly can study extra about this marketing campaign by reviewing continious-monitoring-campaign .

Marketing campaign 2: conditionally gather a high-resolution (50 ms sampling charge) snapshot of a number of Battery Administration System (BMS) indicators.

An instance of a use case for this marketing campaign is the evaluation of potential issues with the battery packs of a pre-production car fleet. To carry out an evaluation, car high quality engineers may have a high-resolution snapshot for a number of indicators (e.g., temperature improvement of particular battery cells). Nonetheless, allow us to assume that the car engineers are solely within the knowledge if overcurrent occasion happens.

To implement this use case, you’ll create a marketing campaign with a condition-based assortment scheme. The Edge Agent will consider an expression that you would be able to outline (for instance, $variable.`EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a` > 450.0). Provided that this expression evaluates to true does the Edge Agent gather car knowledge and switch it to the cloud.

To create this marketing campaign, please run the next instructions:

aws iotfleetwise create-campaign
     --cli-input-json file://7_campaign/conditional-snapshot-campaign.json
# Please wait 15 seconds earlier than operating the subsequent command to permit for marketing campaign creation to finish
aws iotfleetwise update-campaign --action APPROVE
    --name conditional-snapshot-campaign

You possibly can study extra about this marketing campaign by reviewing conditional-snapshot-campaign.json .

Testing the answer

You have got efficiently deployed a simulation and configured the AWS IoT FleetWise service. Now it’s time to see the general answer in motion. Please observe the directions beneath.

Step 1: Hook up with the simulated car

On this step, you’ll connect with the Amazon EC2 occasion that simulates the car. You’ll then assessment the output of two software program parts: Edge Agent and the CAN bus simulation script. Please full the next steps:

1. Get the output of the stack

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

In the event you see “None” as output, it implies that stack deployment has not accomplished but. Please retry in couple of minutes. In the event you expertise issues with the stack deployment, please take into account the troubleshooting pointers.

2. SSH into the Amazon EC2 occasion
Please copy and paste the SSH command from the stack output above and run it. When requested if you wish to connect with the occasion, please verify with “sure”:

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

3. View CAN messages

Please view the CAN knowledge generated by the car simulation script that runs as a system service .

candump vcan0

4. You possibly can view Edge Agent output

sudo journalctl -fu fwe@0

You possibly can safely ignore error message “Did not obtain VIN from Engine ECU”.

Step 2: Evaluation collected car knowledge in Amazon Timestream

On this step, you’ll assessment the outcomes of the info assortment campaigns saved within the Amazon Timestream desk.

1. Open the Amazon Timestream Console

Please navigate to “Amazon Timestream” service in AWS administration console or click on on this hyperlink. Subsequent, choose the Question editor menu merchandise.

Amazon Timestream console

2. Evaluation car knowledge collected by marketing campaign 1 “Repeatedly gather battery pack present and cell temperature”

Within the textual content discipline for the question, enter the next pattern question and select Run :

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

Now you can view the collected car knowledge as you see in instance beneath:

Despite the truth that each autos encode the CAN message for the sign EVBatterySample.BMS.BatteryPack01.ShuntPlusCurrent_a in numerous methods (see DBC recordsdata for car 1 and car 2), you’ll be able to entry and analyze the car knowledge beneath a uniform title!

We recommend to run your individual experiments with visualizing the car knowledge through the use of Amazon Managed Grafana with instance dashboards, as you’ll be able to see within the instance beneath:

3. Evaluation car knowledge collected by marketing campaign 2

Let’s question the statistics of a particular cell temperature. Enter the next question and choose 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 in the past(10m) and now())
GROUP by vehicleName, measure_name, hour(time), minute(time)
ORDER by vehicleName,hour(time), minute(time)

Now you can view the aggregated car knowledge as you see in instance beneath. In accordance with the marketing campaign configuration , the info was transferred solely when battery pack shunt present was over 450 amperes. For 19:42 and 19:43, shunt present was beneath 450 amperes, so no knowledge was transferred.

We recommend to run your individual experiments with the car knowledge by following Amazon Timestream Question Language reference pointers or by creating Amazon Managed Grafana dashboards like beneath utilizing offered examples:

Cleansing up

Please guarantee to exit the SSH session to the Amazon EC2 occasion earlier than you proceed. Please run the next instructions in AWS CloudShell to take away the created sources out of your AWS account:

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

Abstract and subsequent steps

You have got discovered use AWS IoT FleetWise to implement the gathering, conversion, transmission, and storage of CAN bus indicators and question these indicators utilizing Amazon Timestream. Within the subsequent a part of this weblog sequence, we’ll introduce you to how you need to use AWS companies to investigate saved knowledge.

Go to the AWS IoT FleetWise webpage to study extra.