2022 HBK Technology Days | Archive

Dr. Matthias Simolka, Technical Solution Engineering, TWAICE

The change to electromobility continues to pick up speed, ensuring that one component is at the center of attention: the battery. It is both the enabler and Achilles heel of the transition from ICE to EV. Batteries are used in various applications with significantly different requirements and many diverse environmental conditions. Predicting the behavior of batteries with different cell chemistries and formats when impacted by these parameters is challenging. Hence, different individual approaches for battery simulations exist. Combining some of these individual approaches can have a strong impact on the overall model performance.

TWAICE enables the automotive industry to step up its battery game – generate more value with batteries by simulating before start of production and by enhancing after-sales services with our predictive battery analytics. We will shed some light on the benefits of semi-empirical and machine learning models as well as their combination – we call this a hybrid model.

Dr. Patrick (Peng) Xiao, Lead Engineer – Battery Cell Chemistry, Jaguar Land Rover

As the evolution of the cell is moving fast forward with advanced chemistry, formats, capturing the key characteristics of the cell’s performance becomes critical to cell integration into the Pack. Those key characteristics include cell’s fundamental performance such as capacity, DCIR, cycle life & storage life as well thermal properties, safeties under extreme use conditions or abuse conditions.

A suite of characterization is introduced how Jaguar Land Rover cell team are capturing those key characteristics as the cell testing & validation methods. Those key characteristics are providing a full spectrum of the information of cells’ interfaces in the Pack.

Mr. Thomas Kleckers, Product and Application Manager - Force Transducers, HBK 

Electric cars play an important role in the decarbonization strategy of many countries. The technology has been improved in the last years, but besides the range of the car, the time required to charge an electric vehicle is also important as shorter charge stops would make them much more attractive to a wider audience. 

A shorter time with the same capacity entails operating with higher currents. Current, temperature and number of cycles are the most important factors that influence capacity fading, meaning the decrease of the capacity of a lithium-ion battery. 

Traditionally, battery tests are performed by measuring voltage and current. A more innovative method is to measure the force of a lithium-ion pack while charging or discharging processes in a fixed position. Using a suitable load cell, the test can be performed at various temperatures, and long-term tests are possible as well.

The requirements the force transducer must meet are given by the nature of the testing procedure: The long duration requires a low drift, and possible harsh environments make a hermetically sealed load cell necessary. Recent load cells are extremely accurate sensors, but as with every measurement, a certain measurement uncertainty occurs with a battery test as well. 

This lecture will discuss the most important technical aspects for choosing a suitable load cell for a “punch cell test”, and you will also learn how to do an easy calculation to get a good estimation of the measurement uncertainty.

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

Electric motors and powertrains present new challenges for durability testing and understanding the physics of failure. Traditional acceleration methods of running at an increased temperature are often not possible because the motor and inverter cannot survive them.

Motors also introduce new failure modes like demagnetization, delamination, and turn-to-turn shorts amongst others. These failures will result in mechanical failure modes but can be more easily monitored and understood through electrical measurements.

This session will discuss the failure modes of electric motors, the benefits of measuring electrical values for durability testing and give real data from durability testing.

Dr. Holger Behme-Jahns, Head of Project Engineering and Acoustics, Discom GmbH

Acoustical quality has become an increasingly important topic over the last few years. Customers expect technology to be not only reliable but also sustainable, well-designed and quiet. Especially in the automotive industry, the change toward electric mobility shifted customer expectations from roaring engine power sound to silent gliding. This leads to ever-increasing requirements for acoustic quality testing in production, not only in R&D.

A well-designed acoustic analysis for end-of-line testing can do much more than simply find “loud” units. By using constructive information about the device under test, irregular noises can be attributed to specific parts and root causes, enabling efficient repair. Combining results from actual drive tests in cars with limits derived from production statistics, it is possible to identify units which would lead to customer complaints and as well units which have hidden production defects. Long-term statistical analysis of production data expands the scope from the single device under test towards the whole production process with trends and hidden correlations.

This presentation will show the current state of end-of-line acoustic production testing with a focus on automotive powertrain applications and on the Discom production test system.

Prof. Maria Pina Limongelli, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano

The management of civil infrastructures in the aftermath of a disruptive event is a concern for decision-makers, which have to choose quickly among alternative actions with limited knowledge of the actual structural conditions. Structural Health Monitoring (SHM) information can support these decisions. However, information comes with a cost and the relevant benefit must be assessed before the acquisition of the SHM system is decided.

A powerful tool to estimate the benefits and optimize the SHM system design for specific applications is the Value of Information (VoI) analysis based on pre-posterior Bayesian analysis.  In the presentation, the theory will be shortly described and demonstrated through a couple of exemplary case studies.

Dr. David Hester, Senior Lecturer in Structural Engineering, School of Natural and Built Environment, Queen’s University Belfast 

Bridges are an interesting set of structures from the point of view of infrastructure management. Firstly, they are a diverse set of structures, and secondly, they individually experience significant variation in the environmental conditions and loading.

The aim of this presentation is to provide an overview of how bridges are managed, the kind of sensing/monitoring that is sometimes undertaken and the trajectory of research in this area.

Initially, we look at how short to medium-span bridges are typically managed via periodic visual inspections. Subsequently, the presentation gives a sense of the kind of monitoring that has been used on bridges, as well as some of the sensing and data processing challenges that exist. Finally, the presentation looks at some of the very latest research in particular the idea of looking at bridges, or subsets of bridges, as a population of structures.

Cristina Barbosa, Product Manager Optical Business, HBK FiberSensing

Structural Health Monitoring systems aim to control the integrity of a structure throughout its service life so that planned maintenance and serviceability extension safely maximize this asset's profitability. Monitoring systems are expected to reliably operate through long periods and resist extreme events.

The use of optical sensors based on Fiber Bragg Grating technology is becoming an interesting choice for Structural Monitoring Systems. In this presentation, you will learn about the technology, product possibilities and current state of the art.

The main challenges we are facing when monitoring civil structures will be identified and we will show how using optical technology can support overcoming them with concrete application examples. 

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

Structural Health Monitoring as a research field began by studying physical models for systems, comparing modelled and monitored response to understand observed behaviours. As sensing technology advanced and monitoring data became more readily available, many practitioners and researchers adopted a purely data-driven approach capitalising also on technology enhancement from the machine learning field.

Today, where we would like to be able to automatically assess the health of our structures across their operational envelope, we find that despite these advances, we often lack data that represent all behaviours of interest, thereby precluding an entirely data-driven approach.

In this talk, we will discuss the development of a physics-informed approach to machine learning for structural health monitoring. This fast-growing area of research attempts to build our engineering knowledge of a system, alleviating some burden on data acquisition and increasing model interpretability.

Haroun El Mir, PhD Researcher, Transport Systems, Cranfield University

Landing gear systems on Aircraft undergo a multitude of forces during their life cycle, leading to the eventual replacement of this system based on a ‘safe life’ approach that certain circumstances underestimate the component’s remaining useful life. 

The efficacy of fatigue life approximation methodologies is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system’s specific life and withstanding abilities, ranging from creating a digital twin to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure. Explainable Artificial Intelligence (AI) allows for the ease of integration of Deep Neural Network (DNN) data into predictive maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensor-based data.

Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself, requiring the use of DNN models for damage assessment and failure anticipation, where compliance to standards is a major question raised, as the EASA AI roadmap is followed, as well as the ICAO and FAA. 

This presentation additionally discusses the challenges faced with respect to standardizing the explainable AI methodologies and their parameters specifically for the case of landing gear.

Dietmar Maicz, Railway and Infrastructure Monitoring Expert, HBK

With the help of HBK's TSI-Spot® (Single Point Of Truth) concept, railroad companies are enabled to obtain a holistic view of vehicles and infrastructure. New measurement methods and data processing methods allow the early detection of unnecessary wear and deviations on vehicles (wheels and pantograph) and infrastructure (superstructure and overhead line) in real-time and, thanks to the high data quality, to derive reliable, fully automated forecasts for future plannable maintenance interventions.

Maintenance planning receives daily updated information about the track condition and the individual reaction of a vehicle type in real-time. HBK's high-precision, compact and cost-effective measurement technology, combined with state-of-the-art software solutions, enables fully autonomous measurement of the relevant permanent way and overhead line parameters in real-time.

Andrew Brown, Reliability & Validation Methods Chapter Lead, Jaguar Land Rover

The transition from ICE to EV powertrains has been rapid and the array of configurations including Mild-Hybrid (MHEV), Plug-In Hybrid (PHEV) and fully Electric (BEV) creates a multitude of customer use cases that need to be accounted for in the validation plan.

The relative cost of failure has more than doubled for the electrified powertrain, and the warranty period of high-cost systems (such as the battery) extends beyond the conventional 3 years, putting more emphasis on reliability improvements of the powertrain.

Dr. Suresh Perinpanayagam, Integrated Vehicle Health Management (IVHM) Centre, Cranfield University

Prognostics-enabled electrification should lead to high reliability, low maintenance (via only repair or replacement during scheduled maintenance) and uptake of more electrical systems for primary controls.

This presentation proposes a radically new approach to reliable system design to improve the robustness of complex electrical and electronics systems in aerospace and automotive applications by means of intelligent prognostics for extending the usable life.

More robustness is mostly required in applications where reliability and system health management are important or critical, such as automotive industries and aeronautics. It will provide a new source of an advanced supervisory unit to quantify the practicality of implementing prognostics tools in powertrains to detect the degradation and estimate their remaining life-time.

Dr. Andrew Halfpenny, Director of Technology, HBK nCode Products

The automotive industry is mobilizing at a rapid pace to meet the demands and challenges presented by the shift to more widely accepted battery-powered electric vehicles. The rapid pace of innovation can expose manufacturers to potentially expensive warranty claims.

This presentation addresses techniques to quantify and minimize reliability exposure for structural EV battery systems. It covers the following topics:

• Understanding the impact of mechanical shock, vibration, and thermal stresses, on the durability and reliability of EV batteries.
• Obtaining statistically representative loading spectra, and deriving vibration test profiles.
• Using a combination of physical vibration testing with CAE simulation to better understand component reliability and the impact of uncertainty on risk exposure.

Mr. Chris Wynn-Jones, Application Engineer – Reliability, HBK

A fault tree is used to identify the critical path/s in a process, system or service. This introduction will focus on two different views, top down and bottom up.

Top Down

• This view is read downwards, from undesirable events down to the lowest known fault/cause branch item.
• All the undesirable events that can result from your Process, System or Service, are driven by something that is no longer working correctly. 
• Engineers use this mentality to discover weaknesses in their Process, System or Service to calculate probability of failure and occurrence. 

Bottom Up

• This view is read upwards, from the fault/cause up to their end effect. 
• All known faults/causes have an undesirable end effect on the Process, System or Service. 
• Using mixed modelling it is possible to combine faults/causes, build in redundancy and/or identify standbys (a mitigating action) enabling reliability and probability of failure to be calculated for known end effects.

Mr. Sam Eisenberg, Product Manager, HBK ReliaSoft Products

This presentation will explore how best to use fault tree analyses to uncover and surface your primary unreliability drivers in your system.

We will explore the methodologies and next steps once you have identified the bad actors in your system.

Mr. Adi Dhora, Engineering Solutions, Reliability Solutions Consultant, HBK & Mr. Eric Fritts, Engineering Solutions, Project Engineer, HBK

This presentation describes a practical approach to use automation for creating reliability models, simulations, and insights for a large number of assets. Most industries are looking to pursue more data-driven reliability practices. Most reliability practitioners highly value the insights they gain from fault tree analysis and reliability block diagrams.

However, often a limiting factor is data access, quality, and personnel time to process large quantities of data. This is where automation can help to enable reliability practitioners to focus majority of their time on analysis and decisions.

This presentation will showcase how best to merge automation with reliability-know-how to scale in-depth analysis like reliability predictions and event probability for tens of thousands of assets in a short timeframe.