Refine
Publication Type
- Conference Proceeding (12)
- Article (10)
- Part of a Book (2)
- Report (1)
Keywords
- Analysis (1)
- Battery State Estimation (1)
- Deep Learning (1)
- Development and implementation (1)
- Diamond (1)
- End of charge (1)
- GNSS (1)
- Highly Sensitive (1)
- Hochintegrierte Mikro- und Nanosysteme (1)
- Interval analysis · Particle filtering · Kalman filtering · Bayesian filtering · Sequential Monte Carlo simulation · Bounded-error estimation (1)
Faculty
The main task of battery management systems is to keep the working area of the battery in a safe state. Estimation of the state of charge and the state of health is therefore essential. The traditional way uses the voltage level of a battery to determine those values. Modern metal air batteries provide a flat voltage characteristic which necessitates new approaches. One promising technique is the electrochemical impedance spectroscopy, which measures the AC resistance for a set of different frequencies. Previous approaches match the measured impedances with a nonlinear equivalent circuit, which needs a lot of time to solve a nonlinear least-squares problem. This paper combines the electrochemical impedance spectroscopy with neural networks to speed up the state estimation using the example of zinc air batteries. Moreover, these networks are trained with different subsets of the spectra as input data in order to determine the required number of frequencies.
Oxygen consumption of zinc-air batteries and theirperformance at low oxygen concentration levels
(2018)
Already existing primary Zinc-air batteries providea high energy density. Due to new secondary cells, its tech-nology can become an alternative for energy storage. Sincethese applications require a big amount of storable energy, theoxygen consumption has to be taken into account. This articledetermines the oxygen consumption of zinc-air batteries duringdischarging. Furthermore the performance of zinc-air batteries atlow oxygen concentrations is analyzed. Both aspects are validatedby practical experiments.
Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.
State of Charge estimation of zinc air batteries using electrochemical impedance spectroscopy
(2018)
The subject of this paper is the analysis of various switching electronics for batteries with separate electrodes for charging and discharging. The aim is to find a switching method that is energy-efficient on the one hand, but also economically viable on the other. Both relays and MOSFETs are suitable for switching between the electrodes. Both variants have advantages and disadvantages. The results show that a solution with MOSFETs is generally more energy-efficient, but requires a large number of cycles to be economically viable compared to the relay.
This work describes the setup of an ultrawideband (UWB)
realtime localization system (RTLS) for tracking of particles.We describe
how the RTLS obtains distances and positions through radio waves and
the setup and evaluation of a real world system is stated in detail. In
the proposed system the particles track a subtrates surface
ow inside a
biogas plant for verication of agitation processes.
Magnetic field sensors based on quantum mechanic effects are often
susceptible to misalignments of the magnetic field or need advanced
procedures to compensate for these. Also, the record breaking sensitivities reported for superconducting quantum interference devices and alkali vapor magnetometers come along with large and complex experimental setups. The nitrogen vacancy center in diamond can be used to design a simple, small, and robust sensor without employing microwave radiation. By using compressed nanodiamond particles, it is possible to eliminate the need of an alignment of the magnetic field and still obtain the absolute magnetic flux density in a single measurement. In order to demonstrate the capabilities of this approach, a centimeter-sized modified automotive demo board is employed as a complete sensor with a sensitivity of 78 µT/Wurzel Hz.
Magnetometry with nitrogen–vacancy (NV) defects in diamond has been extensively stud-ied in the past [1]. While most approaches in-clude the use of microwaves (MW) for the de-tection of electron spin resonance, only few investigate the sensitivity of the photolumines-cence (PL) from NV centers to an external magnetic field without MW [2, 3, 4]. This work aims to utilize this effect to build a highly sensi-tive and compact room temperature magne-tometer. The avoidance of MW serves the re-duction of production costs and allows a com-mercialization at the current patent situation.
Die wachsenden Anteile fluktuierender regenerativer Energien in der Energieversorgung (bis 2020 sollen 30 % und 2050 sogar So % des Stroms aus regenerativen Energiequellen stammen) sowie die Steigerung der Elektromobilität machen deutlich: Das Thema der Zwischenspeicherung elektrischer Energie ist von höchster gesellschaftlicher Relevanz und verlangt zwingend nach einer Lösung. Neue Technologien, die umweltfreundlich, sicher, leistungsfähig und bezahlbar zugleich sind, müssen deshalb entwickelt werden.