Connected Battery Management

Connected Battery Management

Remote battery monitoring for your electric fleet

Batteries are at the heart of electric traction and managing their health is critical

Unexpected faults in batteries, just like traditional propulsion can lead to reduced range, excessive ageing, and failures in service. This delivers delays, increased costs and loss of availability.

Ricardo's Connected Battery Management System platform is helping our customers to manage the health of the batteries in their fleets, allowing them to identify battery faults before they lead to an in-service breakdown.

We use advanced machine learning algorithms to accurately predict the health of electric vehicle batteries in service, which can be monitored from a centralised data platform.

Fleet vehicles are connected to the cloud enabling data capture and “over the air” updates for the battery health of each vehicle. Ricardo can also build a Digital Twin of the vehicles as a “virtual fleet”, which allows us to optimise data collection, and test the impact of “over the air” updates before they’re deployed.

By identifying anomalies or failures before they occur, these updates can be applied to improve the life of the batteries by up to 13% and prevent in-service failures.

As we learn to embrace battery technology in our fleets, our team is there to help you every step of the way. Contact us to discuss your battery project needs

If you'd like to watch a mode in-depth webinar on the topic for please click here

 

Virtual Fleet and Machine Learning Technology

 

Our Connected Battery Management System platform makes use of our Virtual Fleet technology and machine learning to help to predict when faults may occur even before there is actual data available from the physical vehicles creating a digital twin to evaluate before the vehicle ever leaves the depot.

We can use Virtual Fleet simulations to create input data for our machine learning algorithms for battery health estimation. This data can be used to establish which signals are most significant for monitoring and analytics when no historical fleet data is available so right from the outset you can have confidence you are measuring and monitoring the optimum set of parameters.

To start with, we train a model using our Virtual Fleet tool, running simulations over realistic duty cycles. Then, as real data becomes available, we retrain the model based on “over-the-air” fleet data and service interval data.

The goal of the machine learning platform is to identify where batteries are aging excessively within the fleet, enabling online adaptation of the BMS calibration to maximise battery life. It can also predict anomalies that may result in battery failures and coordinate actions with the vehicle owners and service centres before they occur.

When physical fleet data becomes available, this Virtual Fleet can be used as a “digital twin” of the fleet and enables the development and testing of calibration updates, schedules, and strategies to maximise the life, capacity and operational efficiency of the batteries within the fleet.

If you'd like to find out more then contact us