A5103127693- Electric and Hybrid Vehicle Technologies
- Advanced Battery Technologies Research
- Electric Vehicles and Infrastructure
- Sensorless Control of Electric Motors
- Real-time simulation and control systems
- Multilevel Inverters and Converters
- Microgrid Control and Optimization
- Iterative Learning Control Systems
- Vehicle Dynamics and Control Systems
- Control Systems in Engineering
- Electric Motor Design and Analysis
Ghent University Hospital
2025
Ghent University
2017-2020
Flanders Make (Belgium)
2017-2020
<div class="section abstract"><div class="htmlview paragraph">The automotive industry is amidst an unprecedented multi-faceted transition striving for more sustainable passenger mobility and freight transportation. The rise of e-mobility coming along with energy efficiency improvements, greenhouse gas non-exhaust emission reductions, driving/propulsion technology innovations, a hardware-software-ratio shift in vehicle development road-based electric vehicles. Current R&amp;D...
All-electric drivetrains have been identified as a promising alternative to contemporary hybrid vehicle technology. Extending their operational range is key and can be achieved by means of design procedures based on high-fidelity models capturing the dynamical behavior electric drivetrain. This paper proposes dedicated power split embodying dual drive model-based strategy Advancements are required in that cope with complexity computationally expensive high-dimensional parametric problems. We...
This article proposes a real-time control strategy for induction motor drives, harmonizing accurate torque, and flux tracking with energy efficiency. Common problems related to lengthy horizons the associated computational burden are dealt by introducing value function approximation in model-predictive structure quantify impact of instantaneous decisions on future states. An augmented model drive is introduced determine this offline. The resulting optimization problem implemented environment...
Abstract High‐fidelity models capturing the dynamical behavior can be engaged for analysis of complex mechatronic systems. Determining optimal control parameters and design characteristics such systems necessitates solving multiple interconnected acting on their respective physical domains time scales. In this paper, high‐fidelity physics‐based are constructed several electrical subsystems. Loss mechanisms in various components inferred because these key when performing terms...
In contemporary mechatronic applications decision-making is often based on information about the underlying model governing dynamical evolution, in order to ensure optimal operation with respect a prioritized objective. Modeling errors stemming from parameter uncertainty or varying operational conditions result inevitable deviations theoretical estimate and consequently suboptimal operation. Intelligent systems need be equipped inherent means compensate for these priori unknown...
The limited operating range on a single charge can be seen as an important detriment to contemporary vehicular technology, necessitating regular charging of the battery pack. Due high load variability during driving, incorporating two different electric motors in drive provide significant improvements terms energy consumption. A data-driven approach towards optimal power flow management such configuration is proposed. Computationally expensive dynamic models are translated into equivalent...
Control topologies in electric drive applications commonly aim at minimizing the dissipated power system to guarantee energy-efficient operation. Especially vehicle electrification, loss minimization is main objective supervisory control loops as this directly related range of vehicle. Advanced systems are characterized by an elevated complexity but require nevertheless a real-time strategy be implemented. Appropriate model abstraction, enabling viability with reliable representation, found...
The need for frequent charging is perceived as a common inconvenience related to all-electric vehicles. A reinforcement learning-based strategy therefore introduced optimally exploit the possibilities of an electric dual-drive vehicle. Simplified subsystem models are defined describe inherent loss mechanisms, allowing problem be reformulated interconnection power flows. optimal flow management consequently casted into model-predictive structure. Inherent stochasticity coped with by...