Assistant Professor & Principal Investigator

Theo Hofman

About me

As a researcher and mechanical engineer, I am always interested in the ‘why-question’. Especially, my interests are in multi-disciplinary and multi-domain research (mainly, in the field of automotive). I am intrigued by complex problems that require a systems’ approach and trade-offs in the design space. Within its limits you may have infinite options… The choice of leaving Eindhoven with my family about five years ago and moving to a very small village (“De glimlach van Brabant”, Dries van Agt) with much more space, a warm community and quiet nature nearby appeared to be a very good decision. We have two young daughters, a cat and three (very productive) chicken.

Personal Motivation

My focus is on automated system design for automotive powertrains: translating requirements, functions automatically into system architectures using platform-based design methods and performing integrated system design using model-based approaches: cost (€), energy (J), performance (km/h, m/s2, …), etc. Ultimately, for example, in the context of NEON, making customer-tailored electrified vehicle concepts for fleet owners better, cheaper and faster to manufacture. I hope that we can learn how to extract engineering knowledge in an automated fashion so we can synthesize – based on this knowledge – automatically new and novel vehicle system architectures. This requires a new systems-designing-systems paradigm.

E-Powertrain design for zero emission energy and mobility

Vehicle fleet design optimization (PhD1): The automotive industry is in the middle of a turbulent shift from personally owned, gas powered cars to autonomous, shared, electric mobility solutions such as robot axis. This shift changes key assumptions in automotive design: usage cost becomes much more important and all the use cases that a single owned vehicle had to support can now be covered by a fleet of shared vehicles. Combined with digital manufacturing trends and faster development iterations this allows for more variety in fleets, with vehicles that are more optimized for a smaller set of use cases and hence exhibit lower operational costs. Based on the mobility demand of a region (e.g. The Netherlands) an ideal fleet composition of different, optimized vehicles can be determined. Therefore, it is necessary to accurately model the user demand, to be able to evaluate the convenience of the fleet for the user. Furthermore, it is necessary to develop accurate, parametric cost models for the development, production and use of vehicles with shared components. These models are then to be used to run a joint optimization to determine the specifications and relative share of the variants in the fleet.

Customer-tailored Product Family Design for Multiple Electric Vehicle Fleet Applications (PhD2): The automotive industry has generally focused on a platform strategy to minimize development and production costs and maximize the reuse of components in different vehicles. More variety in fleets due to the above trends calls for a re-evaluation of this strategy, where the component reuse is optimized for the total lifetime costs of the fleet instead of for the purchase cost, considering lifetime energy consumption, repair & maintenance and second life. The key performance indicators of the product family can be measured by jointly optimizing the sizing and the operation of the components for the whole family. To this end, it is important to devise mathematical models capturing the impact of the single components on the performance of the product family with adequate accuracy and in a computationally tractable fashion. The goal of this research is to devise computer-aided design tools to optimize the design of a modular platform (set of components or subsystems) for a product family of (solar) e-vehicles.

Architecture Design for Multiple Electric Vehicle Products (PhD3): Current optimization techniques often focus on the component level, while the biggest impact is often to be found on the systems’ level. The systems engineering process used in industry to derive architectures is often tedious and informal, resulting in suboptimal architectures. Formalizing and partially automating part of the concept design phase to enable fast creation and evaluation of architectures can lead to more optimal system architectures for vehicles. In this context, the topology of a system, namely the choice, placement and interconnection of its components, has a significant impact on its achievable performance and must be carefully studied. Therefore, the possibility to rapidly generate automatically optimized system architectures from a set of desired functionalities and a library of component technologies (platform) would ultimately greatly accelerate the deployment of electric vehicles and lead to better performance on the cost function (typically total-cost-of-ownership). The key questions relate to how to automatically extract engineering knowledge that supports the top-down design process by understanding which subsystems or components from the platform fulfil certain system-level functional requirements and how to connect them by formulating mathematical constraints as in a bottom-up design process. This research aims at devising discrete mathematical models and optimization models and methods for system-level powertrain design.

Multi-fidelity Modeling Methods for Electric Powertrain System Design (PhD4): The achievable performance of a topology-technology solution can be measured by jointly optimizing the sizing and the operation of its components. To this end, co-design at powertrain system design level is important to devise mathematical models capturing the impact of sizing choices on the components’ performance with adequate accuracy and in a computationally tractable fashion, using different heterogeneous models at different levels of aggregation. The research aims at devising computationally tractable optimization models for powertrain system design.

Link to other neon research

There are different “links” to the different WPs. In particular information to receive about real world cases, data to model our components (efficiency, cost, life cycle), vehicles in different usage conditions (geographically), yet we also to expect to obtain relevant data via our own WP partners Lightyear, TNO.