van den Ppj Paul Bosch

Energy management strategies for vehicular electric power systems

In the near future, a significant increase in electric power consumption in vehicles is expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of electric power will be used. Inspired by the research on energy management for hybrid electric vehicles (HEVs), this paper presents an extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments. For this purpose, both off-line optimization methods using knowledge of the driving pattern and on-line implementable ones are developed and tested in a simulation environment. Results show a reduction in fuel use of 2%, even without a prediction of the driving cycle being used. Simultaneously, even larger reductions of the emissions are obtained. The strategies can also be applied to a mild HEV with an integrated starter alternator (ISA), without modifications, or to other types of HEVs with slight changes in the formulation.

Online Energy Management for Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the drivers power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off.

On the identifiability of pharmacokinetic parameters in dynamic contrast‐enhanced imaging

The so‐called “Kety model” is a two‐compartment pharmacokinetic model describing tumor perfusion kinetics. Its parameters, the transendothelial transfer constant (Ktrans), extravascular extracellular volume fraction (υe), and microvascular plasma volume fraction (υp), can be estimated with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). However, the results obtained by current methods show large variation in predictability and reliability. Here, the aim was to examine which experimental conditions have to be fulfilled to avoid large uncertainties and mutual dependencies of the parameters. Using frequency response analysis and simulation, the identifiability of the model was examined. The requirements and influence of contrast enhancement measurements, such as temporal resolution, signal to noise ratio, and contrast injection rate, on the accuracy of the parameters were analyzed. Tissue response characteristics revealed a low‐frequency system with a cutoff frequency equal to Ktrans/υe, which confines the required temporal resolution. For malignant tissue with hyperpermeable vasculature (high Ktrans) a higher sampling frequency is required to accurately estimate Ktrans than for normal tissue. Too low sampling rates or too low injection rates resulted in inaccurate Ktrans values and hereby unreliable classification of malignant tissue. Magn Reson Med 58:425–429, 2007.

Prediction of Battery Behavior Subject to High-Rate Partial State of Charge

An online optimization procedure provides the parameters of a nonlinear battery model by taking into account a few minutes of measured current-voltage data. Within a defined range in terms of charge current, state of charge (SOC), and duration of charge and discharge events, the model is able to capture the relevant battery dynamics and predict the behavior for the next few minutes. From the battery behavior during specific events, the state of the battery can be revealed, which is defined as the state of function. Validation, which is carried out on measured current-voltage profiles, shows the accuracy of prediction during the high-rate partial SOC operation. Even with the data measured during a city drive within a microhybrid electrical vehicle, the method is able to predict the voltage level during high-rate discharge pulses (cranking).cranking

Independent of delay stability criteria for uncertain linear state space models

New stability robustness criteria are presented for linear state space models with multiple uncertain time delays and structured parameter uncertainty using the Lyapunov equation approach. The proposed stability criteria are independent of the size of time delays. Some previous quadratic stability criteria for linear uncertain systems without time delays are included as special cases. Examples are used to illustrate the less conservative results of the proposed methods compared with existing ones in the literature.

Energy Management for the Electric Powernet in Vehicles With a Conventional Drivetrain

The electric power demand in road vehicles increases rapidly. Energy management (EM) turns out to be a viable solution for supplying all electric loads efficiently. The EM strategies developed in this paper focus on vehicles with a conventional drivetrain. By exploiting the storage capacity of the battery, the production, and distribution of electric power is rescheduled to more economic moments. In addition, this paper explores the advantages of electric loads with a flexible power demand. Based on optimization techniques, an optimal offline strategy as well as a causal online strategy are presented. Simulations illustrate the benefits of the EM strategies in terms of fuel economy. The online strategy has also been implemented in a series-production vehicle. Real-world experiments on a roller dynamometer test-bench validate the strategy, but also reveal additional fuel benefits due to unexpected side-effects from the engine control unit and the driver. Measured profits in fuel economy are as large as 2.6%, with only minimal changes to the vehicle hardware

Integrated energy & emission management for hybrid electric truck with SCR aftertreatment

Energy management in hybrid vehicles typically relates to the vehicle powertrain, whereas emission management is associated with the combustion engine and aftertreatment system. To achieve maximum performance in fuel economy and regulated pollutants, the concept of (model-based) Integrated Powertrain Control (IPC) is proposed.

On Constrained Steady-State Regulation: Dynamic KKT Controllers

This technical note presents a solution to the problem of regulating a general nonlinear dynamical system to an economically optimal operating point. The system is characterized by a set of exogenous inputs as an abstraction of time-varying loads and disturbances. The economically optimal operating point is implicitly defined as a solution to a given constrained convex optimization problem, which is related to steady-state operation. The system outputs and the exogenous inputs represent respectively the decision variables and the parameters in the optimization problem. The proposed solution is based on a specific dynamic extension of the Karush-Kuhn-Tucker optimality conditions for the steady-state related optimization problem, which is conceptually related to the continuous-time Arrow-Hurwicz-Uzawa algorithm. Furthermore, it can be interpreted as a generalization of the standard output regulation problem with respect to a constant reference signal.

An overview of non-centralized Kalman filters

The usage of wireless sensor networks (WSNs) for state-estimation has recently gained increasing attention due to its cost effectiveness and feasibility. One of the major challenges of state-estimation via WSNs is the distribution of the centralized state-estimator among the nodes in the network. Significant emphasis has been on developing non-centralized state-estimators considering communication, processing-demand and estimation-error. This survey paper presents different methodologies to obtain non-centralized state-estimators and focuses on the estimation algorithms and their implementation. The temperature distribution of a bar is used as a benchmark to assess the non-centralized state-estimators in terms of estimation-error and communication requirements.

Delay-independent robust stability of uncertain linear systems

In this paper we present some new sufficient conditions for robust stability of linear systems with uncertain time delay as well as structured parameter uncertainty. The stability robustness bounds obtained from the new sufficient conditions are independent of the size of time delay. Some illustrative examples are given.