18,422 research outputs found
Accuracy versus simplicity in online battery model identification
This paper presents a framework for battery
modeling in online, real-time applications where accuracy is
important but speed is the key. The framework allows users to
select model structures with the smallest number of parameters
that is consistent with the accuracy requirements of the target
application. The tradeoff between accuracy and speed in a battery
model identification process is explored using different model
structures and parameter-fitting algorithms. Pareto optimal sets
are obtained, allowing a designer to select an appropriate compromise
between accuracy and speed. In order to get a clearer
understanding of the battery model identification problem, “identification
surfaces” are presented. As an outcome of the battery
identification surfaces, a new analytical solution is derived for
battery model identification using a closed-form formula to obtain
a battery’s ohmic resistance and open circuit voltage from measurement
data. This analytical solution is used as a benchmark
for comparison of other fitting algorithms and it is also used in its
own right in a practical scenario for state-of-charge estimation.
A simulation study is performed to demonstrate the effectiveness
of the proposed framework and the simulation results are
verified by conducting experimental tests on a small NiMH
battery pack
The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle
The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable
Modeling and simulation enabled UAV electrical power system design
With the diversity of mission capability and the associated requirement for more advanced technologies, designing modern unmanned aerial vehicle (UAV) systems is an especially challenging task. In particular, the increasing reliance on the electrical power system for delivering key aircraft functions, both electrical and mechanical, requires that a systems-approach be employed in their development. A key factor in this process is the use of modeling and simulation to inform upon critical design choices made. However, effective systems-level simulation of complex UAV power systems presents many challenges, which must be addressed to maximize the value of such methods. This paper presents the initial stages of a power system design process for a medium altitude long endurance (MALE) UAV focusing particularly on the development of three full candidate architecture models and associated technologies. The unique challenges faced in developing such a suite of models and their ultimate role in the design process is explored, with case studies presented to reinforce key points. The role of the developed models in supporting the design process is then discussed
Avionics and controls research and technology
The workshop provided a forum for industry and universities to discuss the state-of-the-art, identify the technology needs and opportunities, and describe the role of NASA in avionics and controls research
The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management
Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation
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