P.P.J. van den Bosch’s research while affiliated with Eindhoven University of Technology and other places

A short-stroke reluctance actuator linearization scheme that simultaneously achieves high linearity, high bandwidth, and low stiffness is demonstrated. These properties are required in high speed and high precision motion systems. They are achieved by combining various control schemes, namely flux feedforward and analog sensing coil feedback for high bandwidth, Hall probe feedback to stabilize the drift, and an air gap observer together with gain scheduling to reduce the remaining stiffness. Using the presented scheme, the attractive force of the actuator can be controlled with high precision without the need for a position or force sensor. Experiments indicate that a linearization error of 50mN for second-order 200 N force reference profiles is obtained. This translates into force predictability of 99.98%. Furthermore, absolute actuator stiffness below 500 N/m at force levels of 100 N is achieved, which is comparable to more linear Lorentz actuators.

This chapter presents a modelling approach for the air conditioning (AC) system in heavy duty trucks. The presented model entails two major elements: a mechanical compressor model and a thermal AC model. The compressor model describes the massflow of the refrigerant as well as the mechanical power requested from the combustion engine. The thermal AC model predicts how ambient air flow cools down when it passes the AC system. This model also includes the latent heat emerging from water condensation. Both elements of the model have been validated with experimental data. The compressor parameters follow from hardware-in-the-loop experiments where the AC compressor is measured under various load profiles. Validation of the thermal AC model is done by climate chamber testing with a DAF XF heavy duty truck on a roller dynamometer.

In this paper we formulate the problem of economically optimal and reliable spatial distribution of power balance ancillary services in electrical power networks. Furthermore, we propose a novel price-based (market-based) approach for scheduling of the control services in which we explicitly take into account limited capacity of transmission network. A novel notion of nodal pricing for power balance control services is introduced while constructive solution for calculation of nodal prices is presented.

Battery lifetime management plays an important role for successful commercializing hybrid electric vehicles. This paper aims at integrating the battery lifetime management into the energy management system of a heavy-duty hybrid electric truck. The developed strategy called Integrated Energy Management(IEM) is real-time implementable, and able to guarantee the battery lifetime requirement while allowing appropriate HEV operations. Moreover, by utilizing feedforward control, the IEM strategy handles the trade-off between the cost (for battery aging) and the benefit (from fuel reduction) when recovering regenerative braking energy in the battery: braking energy is absorbed without harming the battery life too much. The feedforward controller uses a (dynamic) battery lifetime model and the angular engine speed, power demand as well as the battery temperature. Simulations illustrate the capability of the IEM strategy in assuring the battery lifetime requirement and the benefit from using the proposed feedforward control algorithm.

Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS's operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.

The research focuses on a state estimation problem for the paper path and heat flow control of industrial printers. The dynamics of industrial printers can be modeled with a system with input-induced nonlinearities. This model of the system switches in time with a parameter vector which is externally triggered. An important issue in the design of the state estimator involves the question as to what extend the stability and the performance of the estimated dynamics are robust against perturbations and uncertainties in the parameters of the system. For this class of systems we propose the design of an estimator based on an affine parameter dependent representation of the system with parametric uncertainties. The performance of the estimator is analyzed and the stability proven based on Lyapunov theory. Simulation examples show the effectiveness of the proposed approach in the presence of several arbitrary switches between modes.

The research in this paper focuses on a state estimation problem for paper path and heat flow control of industrial printers. The nonlinear dynamics can be approximated, up to an arbitrary accuracy, with a piecewise linear system, and an arbitrary time driven switching law. The stability and performance of the estimators are analyzed and proven based on Lyapunov theory. Simulation examples show the effectiveness of the proposed approaches in the presence of several switches between modes.

Markets demand continuously for higher quality, higher speed, and more energy-efficient professional printers. Drop-on-Demand (DoD) inkjet printing is considered as one of the most promising printing technologies. It offers many advantages including high speed, quiet operation, and compatibility with a variety of printing media. Nowadays, it has been used as low-cost and efficient manufacturing technology in a wide variety of markets. Although the performance requirements, which are imposed by the current applications, are tight, the future performance requirements are expected to be even more challenging. These print requirements are related to the jetted drop properties, namely, drop velocity, drop volume, drop velocity consistency, productivity, and reliability. Meeting these performance requirements is restricted by several operational issues that are associated with the design and the operation of inkjet printheads. Major issues that are usually encountered are residual vibrations and crosstalk among ink channels. These result in a poor printing quality for high-speed printing. The main objective is to design a feedforward control strategy such that variations in the velocity and volume of the jetted drops are minimized. In this article, an experimental-based feedforward control scheme is proposed to improve the performance of a professional inkjet printer.

This paper discusses an integrated approach for energy and thermal management to minimize the fuel consumption of a hybrid electric heavy-duty truck. Conventional Energy Management Systems (EMS) operate separately from the Battery Thermal Management System (BTMS) in Hybrid Electric Vehicles (HEVs). Specifically, the latter system tries to keep the battery temperature always at a predefined level and requests a non-negligible amount of energy. The EMS then provides the energy demand from the BTMS without checking whether its action is fuel beneficial. In this paper, a fuel minimization control problem is defined to handle the trade off between the cost and benefit of the BTMS for the hybrid powertrain. A real-time implementable control strategy is derived and its robustness is tested with different driving cycles as well as ambient temperatures. Improvements up to 1:8% fuel savings compared to a non-integrated approach are shown by simulation results.