HBM Prenscia Technology Day — Virtual Seminar Series

Join us November 3, 10, and 17 2020 for presentations about electric vehicle battery testing; electrical power testing & analysis; vehicle testing & simulation; vehicle fleet data analytics; and mathematical modelling for data analytics, durability, and reliability

HBM Prenscia Technology Day — Virtual Seminar Series

Join us November 3, 10, and 17 2020 for presentations about electric vehicle battery testing; electrical power testing & analysis; vehicle testing & simulation; vehicle fleet data analytics; and mathematical modelling for data analytics, durability, and reliability

Registration now open

HBM Prenscia invites you to attend the 2020 HBM Prenscia Technology Day – Virtual Seminar Series.

This series of 1½ hour virtual seminars focuses on the durability, reliability and associated data analytics challenges of in-field, proving ground and laboratory vehicle testing and analysis, with special sessions on electric vehicle batteries, and the challenges of measuring and calculating their electrical power. Presenting organizations include:

  • Millbrook Proving Ground
  • Malvern Panalytical
  • HBK
  • Cummins Turbo Technologies
  • VI-Grade
  • Prenscia Engineering Solutions
  • University of Sheffield
  • University of Bristol
  • HBM Prenscia
Session #1: Electric Vehicle Battery Testing
Automotive Battery Testing Capability and Best Practice
Dr. Peter Miller, Millbrook Proving Ground | Chief Engineer - Battery
This presentation will start by giving an overview of the various automotive battery applications (from SLI to EV) and the battery requirements for each. It will then look at test methods and standards for automotive batteries for xEV’s that cover abuse, performance and life and give some examples of these. Finally the split of testing between cells, modules and packs will be discussed.

About the presenter

Peter joined Millbrook in 2018 as the Chief Engineer for Battery. At Millbrook, Peter provides expert knowledge and experience in the technical and practical aspects of battery technology. Peter played a key role in the preparation, set-up and launch of a brand new Battery Test Facility at Millbrook, which opened in 2019.

Previously he worked for Johnson Matthey Battery Systems for 6 years as Chief Electronics Technologist, prior to this he was Director - Electrical/Electronic Engineering at Ricardo a large automotive consultancy where he specialised in batteries.
Experimental Analysis of Dynamic Charge Acceptance Test Conditions for HEV/EV Battery Cells
Dr. Matt Smith, University of Sheffield | Centre for Research into Electrical Energy Storage & Applications
Understanding Electrode Materials Stability with Charge Discharge Cycling
Mr. Umesh Tiwari, Malvern Panalytical | SMM, Advanced Materials
Structural Challenges for Battery Durability and Reliability
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #2: Electrical Power Testing & Analysis
To be confirmed
Test and Measurement of Electric Powertrain – A Complex Electromechanical Problem
Mr. Mitch Marks, HBK | Electrification
Analysis of Electric Powertrain Measurements – Torque, Power, Efficiency, Vibration
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #1: Electric Vehicle Battery Testing
Automotive Battery Testing Capability and Best Practice
Dr. Peter Miller, Millbrook Proving Ground | Chief Engineer - Battery
Experimental Analysis of Dynamic Charge Acceptance Test Conditions for HEV/EV Battery Cells
Dr. Matt Smith, University of Sheffield | Centre for Research into Electrical Energy Storage & Applications
Understanding Electrode Materials Stability with Charge Discharge Cycling
Mr. Umesh Tiwari, Malvern Panalytical | SMM, Advanced Materials
Structural Challenges for Battery Durability and Reliability
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #2: Electrical Power Testing & Analysis
To be confirmed
Test and Measurement of Electric Powertrain – A Complex Electromechanical Problem
Mr. Mitch Marks, HBK | Electrification
Analysis of Electric Powertrain Measurements – Torque, Power, Efficiency, Vibration
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #3: Vehicle Testing & Simulation
To be confirmed
Vehicle Test Simulation and WFT Loads Simulation
Mr. Dave Ewbank, VI-grade
Connected Vehicle Testing with 5G Technology
Mr. Chris Polmear, Millbrook Proving Ground | Principal Engineer
Data Analytics from Large Quantities of CAN Bus Data
Dr. Fred Kihm, HBM Prenscia | Data Analytics and Vibration Product Manager
Session #4: Vehicle Fleet Data Analytics
Analysis of Large In-Field Datasets to Understand Duty Cycle Variation and Refine Product Limits
Mr. David Brown, Cummins Turbo Technologies | Group Leader – Low Cycle Fatigue
Product limits have generally been established through a combination of testing, analysis and field experience. Obtaining field data that is representative of real world usage has, until recently, been expensive to collect.

With the advent of the vehicle CAN bus and integration of sensors at the component level, large field datasets are now relatively inexpensive to obtain from in-service vehicles.

This presentation discusses some of the work done at Cummins Turbo Technologies to make use of these large in-service datasets to better understand usage variation and refine product limits to suit real world conditions.

About the presenter

David Brown has 24 years of experience working in the automotive industry with Cummins. His career includes development of CFD methods, turbocharger aerodynamic design and systems/reliability engineering.

He is currently a Group Leader in the Applied Mechanics function responsible for fatigue evaluation of turbocharger components.

Prior to joining Cummins, he worked in the aerospace industry as a Systems and Performance Engineer for Hurel-Dubois.

David holds an M.Eng in Aeronautical Engineering from the University of Glasgow.
VePRO - A Program Overview – Scalable CBM
Dr. Mark A. Pompetzki, Prenscia Engineering Solutions | Director – Engineering Solutions
Application of a Vehicle Data Simulator to Big Data CBM Systems
Mr. Iain J. Dodds, Prenscia Engineering Solutions | Head of Integrated Solutions / Technical Fellow
Session #3: Vehicle Testing & Simulation
To be confirmed
Vehicle Test Simulation and WFT Loads Simulation
Mr. Dave Ewbank, VI-grade
Connected Vehicle Testing with 5G Technology
Mr. Chris Polmear, Millbrook Proving Ground | Principal Engineer
Data Analytics from Large Quantities of CAN Bus Data
Dr. Fred Kihm, HBM Prenscia | Data Analytics and Vibration Product Manager
Session #4: Vehicle Fleet Data Analytics
Analysis of Large In-Field Datasets to Understand Duty Cycle Variation and Refine Product Limits
Mr. David Brown, Cummins Turbo Technologies | Group Leader – Low Cycle Fatigue
Product limits have generally been established through a combination of testing, analysis and field experience. Obtaining field data that is representative of real world usage has, until recently, been expensive to collect.

With the advent of the vehicle CAN bus and integration of sensors at the component level, large field datasets are now relatively inexpensive to obtain from in-service vehicles.

This presentation discusses some of the work done at Cummins Turbo Technologies to make use of these large in-service datasets to better understand usage variation and refine product limits to suit real world conditions.
VePRO - A Program Overview – Scalable CBM
Dr. Mark A. Pompetzki, Prenscia Engineering Solutions | Director – Engineering Solutions
Application of a Vehicle Data Simulator to Big Data CBM Systems
Mr. Iain J. Dodds, Prenscia Engineering Solutions | Head of Integrated Solutions / Technical Fellow
Session #5: Mathematical Modelling for Data Analytics, Durability, and Reliability #1
Overcoming Information Sparsity in Engineering Datasets - A Transfer Learning Viewpoint
Dr. Paul Gardner, University of Sheffield
In the age of big data many engineering companies are collecting large quantities of data from their systems in operation. However, much of this data represents normal, benign, operating conditions, and reveals little information about what data will look like when my system is damaged, or what will the system response be to an extreme event? In light of this problem, it is useful to be able transfer knowledge gained from one dataset or simulation to current operational data, aiding diagnosis of what the data represents. Transfer learning, a branch of machine learning, is designed for exactly these scenarios, allowing knowledge to be moved from one dataset to another. This presentation discusses exciting innovations in applying transfer learning to engineering problems, with motivating examples from non-destructive testing and aerospace applications.

About the presenter

Paul is a Research Associate at the University of Sheffield researching technologies relating to digital twins for improved dynamic design. He obtained his PhD titled 'On novel approaches to model-based structural health monitoring' from the University of Sheffield in 2019. His research interests focus on generating new simulations methods that combine machine learning and engineering modelling.
Exploiting Novel Data Sources in Probabilistic Design
Dr. Josh Hoole, University of Bristol
Uncertainty Quantification and Propagation through Measurement and Analysis
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #6: Mathematical Modelling for Data Analytics, Durability, and Reliability #2
Grey Box Modelling for Structural Health Monitoring
Prof. Elizabeth Cross, University of Sheffield | Dynamics Research Group
The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This talk will introduce the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. We will look at how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo.

About the presenter

Lizzy Cross is a Professor in the Dynamics Research Group at the University of Sheffield. Before starting her lectureship in 2012, she completed a Bachelors in Mathematics (1st class), and Masters and PhD in Mechanical Engineering.

She currently holds an EPRSC Innovation Fellowship on the development of grey-box models for assessing the health of structures in operation (grey-box models combine physics-based models with machine learning technology).

Lizzy is a co-director of the Laboratory for Verification and Validation, a state-of-the-art dynamic testing facility (lvv.ac.uk).
(TBC) Machine Learning
Prenscia Engineering Solutions
(TBC) Modelling Operational Reliability
Prenscia Engineering Solutions
Session #5: Mathematical Modelling for Data Analytics, Durability, and Reliability #1
Overcoming Information Sparsity in Engineering Datasets - A Transfer Learning Viewpoint
Dr. Paul Gardner, University of Sheffield
In the age of big data many engineering companies are collecting large quantities of data from their systems in operation. However, much of this data represents normal, benign, operating conditions, and reveals little information about what data will look like when my system is damaged, or what will the system response be to an extreme event? In light of this problem, it is useful to be able transfer knowledge gained from one dataset or simulation to current operational data, aiding diagnosis of what the data represents. Transfer learning, a branch of machine learning, is designed for exactly these scenarios, allowing knowledge to be moved from one dataset to another. This presentation discusses exciting innovations in applying transfer learning to engineering problems, with motivating examples from non-destructive testing and aerospace applications.

About the presenter

Paul is a Research Associate at the University of Sheffield researching technologies relating to digital twins for improved dynamic design. He obtained his PhD titled 'On novel approaches to model-based structural health monitoring' from the University of Sheffield in 2019. His research interests focus on generating new simulations methods that combine machine learning and engineering modelling.
Exploiting Novel Data Sources in Probabilistic Design
Dr. Josh Hoole, University of Bristol
Uncertainty Quantification and Propagation through Measurement and Analysis
Dr. Andrew Halfpenny, HBM Prenscia | Chief Technologist
Session #6: Mathematical Modelling for Data Analytics, Durability, and Reliability #2
Grey Box Modelling for Structural Health Monitoring
Prof. Elizabeth Cross, University of Sheffield | Dynamics Research Group
The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This talk will introduce the concept of physics-informed machine learning, where one adapts ML algorithms to account for the physical insight an engineer will often have of the structure they are attempting to model or assess. We will look at how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability in an SHM setting. A particular strength of the approach demonstrated here is the capacity of the models to generalise with enhanced predictive capability in different regimes. This is a key issue when life-time assessment is a requirement, or when monitoring data do not span the operational conditions a structure will undergo.

About the presenter

Lizzy Cross is a Professor in the Dynamics Research Group at the University of Sheffield. Before starting her lectureship in 2012, she completed a Bachelors in Mathematics (1st class), and Masters and PhD in Mechanical Engineering.

She currently holds an EPRSC Innovation Fellowship on the development of grey-box models for assessing the health of structures in operation (grey-box models combine physics-based models with machine learning technology).

Lizzy is a co-director of the Laboratory for Verification and Validation, a state-of-the-art dynamic testing facility (lvv.ac.uk).
(TBC) Machine Learning
Prenscia Engineering Solutions
(TBC) Modelling Operational Reliability
Prenscia Engineering Solutions

Event Details

Session times

Each day of the virtual event is comprised of two 1.5 hour sessions. Below are the start times of the two sessions:

London: 10 AM & 3 PM
Paris / Berlin: 11 AM & 4 PM
Detroit: 5 AM & 10 AM
San Francisco: 2 AM & 7 AM
Singapore: 5 PM & 10 PM
Mumbai: 2:30 PM & 7:30 PM 

Location

Webex (links will be provided via email)

Registration

This Virtual Seminar Series is free to attend. Please complete this form to register your details and select the sessions you wish to attend. You will receive a registration confirmation email from info@hbmprenscia.com shortly after registering; approximately one business day after registering, you will receive a "registration acceptance" email from Webex for each session you selected.