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simulation of a car, a team viewing an airplane model and an engineer on the computer using software for HBK Technology Days 2021

2020 HBK Technology Days | Archive

This 6-part series of 90-minute virtual seminars focus 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.

Day 1 - Electric Vehicles

generic electric car with battery visible x-ray charging at a public charger in city parking lot with lens flare 3d render. Reliability of Electric Systems, session 5 from day 3 of the 2022 HBK Technology Days.

Session 1: Electric Vehicle Battery Testing

Dr. Peter Miller, Chief Engineer - Battery, Millbrook Proving Ground

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.

Dr. Matt Smith, Centre for Research into Electrical Energy Storage & Applications, University of Sheffield

The presentation details the results of a series of tests to determine the Dynamic Charge Acceptance (DCA) performance of small form-factor carbon-enhanced VRLA cells designed for use in Hybrid Electric Vehicle (HEV) applications, together with standard lead-acid and lithium iron phosphate (LFP) cells. The results demonstrate how varying the conditions and parameters of the standard DCA test regime can provide a superior evaluation of DCA performance and lead to a better understanding of cell behaviour under real-world conditions. It also demonstrates the importance of recognising the limitations of existing test procedures and how they should be considered before using results from such tests to make judgements about real-world battery performance.

Mr. Umesh Tiwari, SMM, Advanced Materials, Malvern Panalytical

The electrodes in a lithium-ion battery undergo reversible electrochemical reactions as lithium enters and leaves the atomic structure of the intercalated lithium compounds. Particle size, shape and crystal structure of electrode materials play an important role in Li ion diffusion and their transportation during charge-discharge cycling. Reversibility of these electrochemical reactions with cycling largely governs the lifetime of the batteries. With certain battery chemistries, these electrochemical reactions render the electrode materials unstable state, pushing them into irreversible physical or structural changes, ultimately leading to battery degradation with cycling. Understanding the intricacies of these electrochemical reactions is, therefore, an important step to improve battery performance. X-ray diffraction (XRD) and scattering is well-suited to study these atomic phase changes, as well as a tool to understand and optimize the pathways that lithium uses to move through the electrodes. However, XRD investigation of battery materials requires special considerations that are different form the routine powder diffraction measurements.

This presentation will review the techniques related to dimensional measurements in battery electrode materials and how these relate to the battery capacity. Special focus would be on X-ray diffraction and scattering methods including considerations for experimental design. Some examples will be shared on how these considerations are applied to cathode material analysis, including Rietveld refinement, to quantify phase mixtures and atomic structure. A case study on the analysis of NCM based batteries with in-operando XRD, to track phase changes and potentially the degradation mechanisms during charge-discharge cycling, will be shared.

Dr. Andrew Halfpenny, Chief Technologist, HBK Prenscia

This presentation offers a brief summary of battery durability and reliability design issues:

  • Mechanical and durability aspects
    Like their thermal-engine counterparts, electric vehicles are susceptible to structural fatigue failures. The mechanical complexity of the battery structure and its mountings also give rise to significant additional fatigue failure issues. Insights into these structural and vibration-induced failures enable engineers to eliminate the risk of fatigue failure, improve the durability of electric-engines, and increase vehicle reliability.

In this presentation we consider:

  • Fatigue design of battery packs;
  • Fatigue analysis of electric vehicle structures.
  • Accelerated vibration testing of battery packs;
  • Statistical and reliability aspects
    All electric vehicle batteries degrade overtime. Their performance, however, varies by model and external conditions such as usage, temperature, and charging methods. In order to improve the overall reliability of the battery system and avoid excessive warrantee exposure, it is important to understand both the mean life and the statistical distribution of lives for the battery. Furthermore, understanding how the battery degrades over time will lead to significant improvements in battery design, reliability and vehicle efficiency.

In this presentation we consider:

  • Battery life analysis;
  • Battery performance degradation modelling and analysis;
  • FMEA for new failure modes.

Session 2: Electrical Power Testing & Analysis

Illustration of an electric power integrated solution testing on automotive test bench in isometric perspective

Professor ZQ Zhu, Head of Electrical Machines and Drives Research Group, University of Sheffield

Permanent magnet (PM) machines exhibit high torque density and high efficiency, and are eminently suitable for electric vehicles (EVs). This presentation will start with an introduction of various electrical machine technologies including induction machines (used by Tesla), PM machines (used by Toyota/Nissan/BMW) and synchronous reluctance machines. Their system efficiencies for a typical driving cycle will then be compared. It will continue to overview PM machine topologies for EV applications. Finally, it will highlight the challenges and opportunities for the development of PM machines.

Mr. Mitch Marks, Business Development Manager - EPT, Electrification, HBK

Electrical powertrain is a complex system that has both electrical and mechanical elements. In the electric powertrain there are 5 main subsystems, batteries, inverters, motors, torque conversion, and control system. Each one of these systems has losses, dynamics, thermal limitations, and a control system. By simplifying a measurement chain to bring all these signals into one location and continuously recording the data there are many gains to the engineers. This streamlined system of testing allows for faster data collection, processing, and model correlation. This session will explore test and measurement of electric motors and inverters at a system level, including electrical power, mechanical power, temperature, NVH, and the dynamics of each of these systems. Some specific topics will include Torque ripple, NVH, efficiency, and Control system calibration.

Dr. Andrew Halfpenny, Chief Technologist, HBK Prenscia

The theoretical and real-world range of an electric vehicle may differ significantly. To maximize the range and overall efficiency of the vehicle, it is necessary to understand and characterize how the vehicle is used and determine through meticulous measurement and analysis where efficiency losses occur.

Quantifying AC power is particularly difficult. Unlike the conventual electricity grid, electric vehicles convert DC to AC using an electrical inverter. Using pulse-width modulation, these produce a frequency-modulated, non-sinusoidal, transient waveform.

This presentation introduces the concept of AC power analysis post-processing. Starting with steady-state sinusoidal waveforms, it explains the basic concepts of Active, Reactive, Apparent Power, and the Power Factor. It then considers the effect of non-sinusoidal and transient waveforms. Digital Signal Processing (DSP) techniques are introduced that take advantage of high-speed digitized data.

The presentation covers the following methods of AC power analysis:

Time-averaging methods:

  • Windowed-statistical method - time domain;
  • PSD/CSD method - frequency domain.

Instantaneous methods:

  • Clarke transform method - time domain;
  • Hilbert transform method - frequency domain.

A case study shows the advantages of each method based on real data from an electric vehicle.

Day 2 - Vehicle Test & Simulation

professional driving simulator from VI-GRADE, from HBK’s Virtual Test Division

Session 3: Vehicle Testing & Simulation

Dr.Ing. Prashant Khapane, Engineering Operations Manager, Jaguar Land Rover

Our products today are becoming complex, are becoming smart, our products are becoming hyperconnected. Thanks to smart, connected technology and the IoT, organizations are being buried under a flood of data from their products and processes. Digital Twin is often thrown at us as a solution to solve all the problems. This presentation will look at digital twin from automotive engineering perspective, discuss challenges involved and will ponder over the fact if we are asking the right question, does automotive engineering need a digital twin in its classical definition?

Mr. David Ewbank, Technical Director, VI-grade

Vehicle design for durability is a long-established discipline, affecting all aspects of vehicle engineering. However, despite well proven techniques and processes, the loads used for component and system design are measured on a mule or even a competitor vehicle at the beginning of a vehicle programme and may not be revisited until well into an advanced prototype stage. At this stage, any durability concerns due to the use of approximated load cases can be difficult to implement due to costly tooling updates.

This presentation demonstrates the use of advanced simulation methods to investigate the variability of a vehicle design on the wheel loads and to produce a set of load cases which are robust and encompass the loads that could be achieved considering all possible tuning variations. As well as using robot driver technology this process can be extended to introduce the human element through the use of VI-grades advanced driving simulators with a human driver to study the impact of human variability of predicted wheel forces.

Mr. Chris Polmear, Principal Engineer, Millbrook Proving Ground

The presentation explains how 5G technology is used for connected vehicle testing at Millbrook Proving Ground. It gives an overview of the onsite networks available, and an explanation of our approach to connectivity, vehicle data, and data processing. It includes examples of real-world use cases involving data acquisition from test vehicles.

Dr. Fred Kihm, Data Analytics and Vibration Product Manager, HBK Prenscia

Engineers in the fields of test & measurement and maintenance are facing the need to process a mixture of various data types, including low-cost digital data from communication buses and connected vehicles. Bus data is an inexpensive source of readily available parameters in a complex electronic system. Accessing bus data offers huge potential benefits such as understanding customer usage from connected vehicles in order to improve the product validation process and thereby reduce unexpected failures. However, bus data also raises a number of challenges in terms of its analysis because of the quality of the data, quantity of data, inconsistency of data, lack of certain data, etc.

The solution would be a dedicated digital bus data processing tool that would perform analytics from huge quantities of unevenly sampled, heterogenous sensor data in a scalable fashion and at high speed.

The main benefit for the engineers would be for them to convert overwhelming data volumes into actionable decisions without the intervention of data scientists.

Session 4: Vehicle Fleet Data Analysis

white truck from Navistar on the road

Mr. David Brown, Group Leader – Low Cycle Fatigue, Cummins Turbo Technologies

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.

Dr. Mark A. Pompetzki, Director – Engineering Solutions, HBK Prenscia Engineering Solutions

The U.S. Army is working to improve Reliability, Availability and Maintainability (RAM) as well as durability for its tactical wheeled vehicle fleet while reducing operating costs through the use of Reliability Centered Maintenance (RCM), Condition Based Maintenance (CBM) and Health and Usage Monitoring Systems (HUMS).

The Vehicle Performance, Reliability & Operations ‐ Analysis (VePRO‐A) program focuses on the scalability of a HUMS and CBM software system with the ultimate goal of reducing cost and extending equipment life. The objective of this program is to configure scalable and robust software system components along with the end-to-end integrated system for deployment to broaden the understanding of operational usage severity and deterioration as it relates to RAM, cost, readiness, durability, etc. The VePRO‐A system approach demonstrates a progressive increase in the ability to analyze operational usage data with a program goal of managing up to 20,000 vehicles and a scalable solution that can be used as a baseline towards achieving the U.S. Army’s long-term desired goal of monitoring up to 80,000 Tactical Wheeled Vehicles (TWVs). Overall system architecture and features will be discussed and presented.

Mr. Iain J. Dodds, Head of Integrated Solutions / Technical Fellow, HBK Prenscia Engineering Solutions

When designing and testing large Condition Base Monitoring (CBM) type systems (10,000+ assets) an important step is to ensure the system can handle the expected volumes of data and can sustain growth as more assets are added over time. When commissioning a new system, customers typically do not have 10,000+ assets instrumented in the field producing data on a daily basis, and the process of asset onboarding can be expensive and have long lead times. In order to design and build large usable CBM systems, alternative methods are needed to generate a suitable volume of asset data that allows engineers to configure and test the system prior to investing in large-scale “real” asset onboarding. This data needs to be representative of actual usage/conditions while having sufficient variability from asset to asset, channel to channel, day to day etc. Achieving this representative data volume is more than simply duplicating existing sample records.

This presentation discusses the implementation and usage of a Vehicle Data Simulator. This simulator allows for the generation of multiple vehicle operational data sets that contain sufficient data variability while being representative of real vehicle operating conditions. The data from the simulator draws from many external sources and is of suitable fidelity that it can be used within the CBM system for detailed calculations and derivations similar to those performed with real vehicle data. Although this simulator focuses on the creation of wheeled vehicle operational data, the methods described in this presentation could easily be adapted and applied to alternative asset groups such as tracked vehicles, rail vehicles or aircraft. The Vehicle Data Simulator design and usage scenarios will be presented.

Day 3 - Machine Learning

Modern Factory Office Meeting Room: Diverse Team of Engineers, Managers and Investors Talking at Conference Table, Use Interactive TV, Analyze Sustainable Energy Engine Blueprints. High-Tech Facility

Session 5: Mathematical Modelling for Data Analytics, Durability, and Reliability #1

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.

Dr. Josh Hoole, University of Bristol

Probabilistic design approaches provide a route to assessing the reliability of safety-critical structures, potentially yielding more optimum designs, increased service lives or a quantification of the level of safety within a structure. However, one of the inhibiting factors that routinely prevents the implementation of probabilistic approaches is the lack of available data to statistically characterise the variability present within engineering parameters. Fortunately, the rapidly growing field of ‘big-data’ now provides greater opportunities for capturing engineering design datasets than ever before. This presentation will demonstrate a novel data source, in the form of real-time aircraft tracking, which has been exploited within the development of a probabilistic fatigue methodology for aircraft landing gear structures.

Dr. Andrew Halfpenny, Chief Technologist, HBK Prenscia

"Fatigue is the progressive weakening of a material caused by cyclic or otherwise varying loads, even though the resulting stresses are well within the static strength limits. The art of fatigue simulation is to be approximately right rather than exactly wrong." - Prof. Keith Miller

The fatigue design of mechanical systems has historically followed a 'deterministic' process. That means, for a given set of inputs they will return a consistent set of fatigue life results with no scatter. In reality the inputs are statistically uncertain -- they have an expected value and a variability. Deterministic design methods take no implicit account of uncertainty. In practice, the designer applies a safety factor to each input parameter along with an additional safety factor to the final result to allow for 'modelling errors'. In most cases, the engineer is fairly certain that the simulation results are conservative, but cannot state with any confidence what the final safety margin, reliability or failure rate will be. Furthermore, it is almost impossible to qualify the simulation by experimental testing because the test lives are significantly higher than the conservative simulations would suggest.

In comparison, a 'Probabilistic Fatigue Simulation' method is 'stochastic' in nature. In this presentation we review the concepts of 'Stochastic Design' under the following headings:

  1. Uncertainty sources: including Epistemic (or reducible) uncertainties, and Aleatoric (or irreducible) uncertainties;
  2. Uncertainty propagation: including 'Analytical methods' and 'Numerical simulation methods' covering:
  • Monte Carlo Simulation;
  • Design of Experiments (DOE);
  • Response Surface Analysis;
  • Reduced Order Modelling (ROM).

3. Uncertainty quantification: including Design for 'Reliability', and Design for 'Robustness'.


Probabilistic Fatigue Simulation offers many significant advantages over the traditional deterministic design approach:

  1. Calculate life & variability in life;
  2. Identify the most critical input uncertainties:
    Optimize for design uncertainty;
    Avoid large safety factors;
    Minimize risk exposure.
  3. Design for Reliability:
    Understanding the shape of the statistical life curve;
    Design for an acceptable reliability target;
    Quantify warrantee exposure;
    Manage maintenance schedules;
    Cost vs. risk vs. benefit analysis;
    Maintain good brand reputation.
  4. Design for Robustness:
    Understanding the edge cases;
    Design for the worst-case scenarios.
  5. Validate simulation against physical test data:
    Validation includes mean time to failure and the statistical distribution.

A case study is presented.

Session 6: Mathematical Modelling for Data Analytics, Durability, and Reliability #2

Work of trucks and the excavator in an open pit on gold mining, soft focus

Prof. Elizabeth Cross, Dynamics Research Group, University of Sheffield

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.

Guga Gugaratshan, Head of Partner Solutions & Analytics, HBK Prenscia Engineering Solutions

This presentation outlines how combining multiple data sources can be used to get key information about an asset’s health and to improve operational efficiency. Avoiding failure of the asset during operation is very critical especially if the asset is very hard to access by the maintenance team. Every time an asset fails, production efficiency is affected. An effective maintenance plan will help improve the efficiency. Management of effective maintenance plan requires reliable data that provides information about the asset. Getting access to such data can be very challenging. Prenscia Engineering Solutions is working with multiple industry and academic partners to provide a complete solution that can be implemented without spending years of planning and execution. The approach combines both operational, sensor, and maintenance data along with the digital twin of the asset to improve the data quality, capture and verify failure events, and perform analysis to help improve operational efficiency.

Adi Dhora, Reliability Solutions Consultant, HBK Prenscia Engineer Solutions

This presentation describes practical approaches for mining and process industries to overcome data quality issues and pursue more data-driven reliability practices. Like most industries, mining and process industries are constantly looking to improve equipment reliability and institute new, best work practices. However, a clear understanding of the requirements regarding data, systems, and work processes is often not widely known and accessible. It describes the use of proven techniques, including life data analysis, ‘reliability-twins’, and new predictive analysis methods, and how they build upon foundations like ‘reliability-centered maintenance’ for more data-driven reliability.