http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems

The fuel economy and all-electric range (AER) of hybrid electric vehicles (HEVs) are highly dependent on the onboard energy-storage system (ESS) of the vehicle. Energy-storage devices charge during low power demands and discharge during high power demands, acting as catalysts to provide energy boost. Batteries are the primary energy-storage devices in ground vehicles. Increasing the AER of vehicles by 15% almost doubles the incremental cost of the ESS. This is due to the fact that the ESS of HEVs requires higher peak power while preserving high energy density. Ultracapacitors (UCs) are the options with higher power densities in comparison with batteries. A hybrid ESS composed of batteries, UCs, and/or fuel cells (FCs) could be a more appropriate option for advanced hybrid vehicular ESSs. This paper presents state-of-the-art energy-storage topologies for HEVs and plug-in HEVs (PHEVs). Battery, UC, and FC technologies are discussed and compared in this paper. In addition, various hybrid ESSs that combine two or more storage devices are addressed.

Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art

This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.

State of the Art of Finite Control Set Model Predictive Control in Power Electronics

Recent development of polymer electrolyte membranes for fuel cells.

Combining high-energy-density batteries and high-power-density ultracapacitors in fuel cell hybrid electric vehicles (FCHEVs) results in a high-performance, highly efficient, low-size, and light system. Often, the battery is rated with respect to its energy requirement to reduce its volume and mass. This does not prevent deep discharges of the battery, which are critical to the lifetime of the battery. In this paper, the ratings of the battery and ultracapacitors are investigated. Comparisons of the system volume, the system mass, and the lifetime of the battery due to the rating of the energy storage devices are presented. It is concluded that not only should the energy storage devices of a FCHEV be sized by their power and energy requirements, but the battery lifetime should also be considered. Two energy-management strategies, which sufficiently divide the load power between the fuel cell stack, the battery, and the ultracapacitors, are proposed. A charging strategy, which charges the energy-storage devices due to the conditions of the FCHEV, is also proposed. The analysis provides recommendations on the design of the battery and the ultracapacitor energy-storage systems for FCHEVs.

Influence of Battery/Ultracapacitor Energy-Storage Sizing on Battery Lifetime in a Fuel Cell Hybrid Electric Vehicle

In this paper, a novel current control approach called model predictive direct current control (MPDCC) is presented. The controller takes into account the discrete states of the voltage source inverter (VSI), and the current errors are predicted for each sampling period. Voltage vectors are selected by a graph algorithm, whereby the most appropriate vector is chosen based on an optimization criterion. However, this depends on whether the state of the system is transient or steady. In the first case, the current error should be minimized as fast as possible in order to obtain fast dynamics. In the latter one, the VSI switching behavior is optimized since the switching losses account for a large amount of the total converter losses in high-power drive systems. MPDCC has been developed for a general neutral-point isolated resistive-inductive load with an internal voltage source. For demonstration, the presented control strategy has been implemented on a small-scale permanent-magnet synchronous machine drive system with a two-level VSI. This new approach has several advantages. The most important one is that the switching frequency is reduced up to 70% compared to linear control combined with pulsewidth modulation. Second, MPDCC obtains fast dynamic responses, which are already known from, e.g., direct torque control.

Switching Frequency Reduction Using Model Predictive Direct Current Control for High-Power Voltage Source Inverters

The model predictive direct current control (MPDCC) is a promising control approach for high- power converters. It takes the discrete states of the voltage source inverter (VSI) into account, and the future converter behavior is predicted for each sampling period. Possible voltage vectors are selected by a graph algorithm, and the most appropriate one is chosen based on an optimization criterion in order to obtain high dynamics. Applying this method to sensorless MPDCC drive systems, special observer characteristics are required. For this reason, an angle and speed observer has been designed. The position of the internal voltage vector, i.e., the back electromotive force, is obtained with a phase-locked loop structure. Control strategy and observer have been developed for permanent-magnet synchronous machine drive systems, and they have been implemented on a small-scale system with two-level VSI for demonstration. This new observer has the advantages to combine high bandwidth with disturbance robustness and can be applied to round rotor and salient pole machines. Moreover, the possibility to combine MPDCC with a sensorless observer is shown.

Sensorless Model Predictive Direct Current Control Using Novel Second-Order PLL Observer for PMSM Drive Systems

The implementation of an accurate and low computational demanding state-of-health (SOH) estimation algorithm represents a key challenge for the battery management systems in electric vehicle (EV) applications. In this article, we investigate the suitability of the incremental capacity analysis (ICA) technique for estimating the capacity fade and subsequently the SOH of LMO/NMC-based EV lithium-ion batteries. Based on calendar aging results collected during 11 months of testing, we were able to relate the capacity fade of the studied batteries to the evolution of four metric points, which were obtained using the ICA. Furthermore, the accuracy of the proposed models for capacity fade and SOH estimation was successfully verified considering two different aging conditions.

/pdf/lithium-ion-battery-state-of-health-estimation-using-the-cq86by4qpv.pdf

Erik Schaltz

Papers

Influence of Battery/Ultracapacitor Energy-Storage Sizing on Battery Lifetime in a Fuel Cell Hybrid Electric Vehicle

Switching Frequency Reduction Using Model Predictive Direct Current Control for High-Power Voltage Source Inverters

Sensorless Model Predictive Direct Current Control Using Novel Second-Order PLL Observer for PMSM Drive Systems

Lithium-Ion Battery State-of-Health Estimation Using the Incremental Capacity Analysis Technique

Electrochemical characterization of a polybenzimidazole-based high temperature proton exchange membrane unit cell