1,524 research outputs found
Model migration neural network for predicting battery aging trajectories
Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction
Flexible operation of shared energy storage at households to facilitate PV penetration
This paper proposes a new methodology to enable high penetration of photovoltaic (PV) generation in low voltage (LV) distribution networks by using shared battery storage and variable tariffs. The battery installed at customer premises is shared between customers and local distribution network operators (DNOs) to achieve two goals-minimizing energy costs for customers and releasing distribution network constraints for DNOs. The two objectives are realised through a new concept - “charging envelope”, which dynamically allocates storage capacity between customers and the DNO. Charging envelope first reserves a portion of storage capacity for network operator's priority to mitigate network problems caused by either thermal or voltage limit violation in order to defer or even reduce network investment. Then, the remaining capacity is used by customers to respond to energy price variations to facilitate in-home PV penetration. Case study results show that the concept can provide an attractive solution to realise the dual conflicting objectives for network operators and customers. The proposed concept has been adopted by the Western Power Distribution (UK) in a smart grid demonstration project SoLa Bristol.</p
Research on RBF neural network model reference adaptive control system based on nonlinear U – model
The overall objective of this study is to design the nonlinear U-model-based radial basis function neural network model reference adaptive control system, through research into a class of complex time-varying nonlinear plants. First, the ideal nonlinear plant is adopted as the reference model and transformed into the U-model representation. In the process, the authors establish the corresponding relationship between the degrees of the reference nonlinear model and the controlled nonlinear plants, and carry out research into the corresponding coefficient relationship between the reference nonlinear model and the controlled nonlinear plants. Also, the impact of the adjusting amplitude and tracking speed of the model on the system control accuracy is analyzed. Then, according to the learning error index of the neural network, the paper designs the adaptive algorithm of the radial basis function neural network, and trains the network by the error variety. With the weight coefficients and network parameters automatically updated and the adaptive controller adjusted, the output of controlled nonlinear plants can track the ideal output completely. The simulation results show that the model reference adaptive control system based on RBF neural network has better control effect than the nonlinear U-model adaptive control system based on the gradient descent method
LAWS AND CHARACTERISTICS OF THROWING POWER CHANGES FOR DIFFERENT WOMEN DISCUS THROWERS
Throwing power means that rate of muscle do work when throwers do throwing movements. It depends on strength and speed of throwers. It is a sensitive index to mirror explosive force and fast strength Tl1is paper adopts experiment and video analysis methods. The purpose was to research the laws and characteristics of throwing power changes for different women’s discus throwers through measuring results of throwing various weights to deferent Chinese women’s discus throwers (master:55m, n1=13; first grade: 51m, n2=17; second grade: 39m, n3=30). The results show that following: 1.Throwing weight of women[s discus throwers is closely related to throwing power. With increasing of the weight, the power also raise gradually (r1 =0. 905, r2= o 862, r3=0.900) But when the weight comes up to a certain extent, the power not only don’t raise but also reduce obviously if the weight is continued increasing (r1 =0.996, r2=-0.964, r3= -0.933). It is various that different women’s discus throwers show the greatest throwing power and its corresponding throwing weight. Generally speaking, the higher thrower’s performance level is, the greater the greatest throwing power and its corresponding throwing weight show. Even if the level of throwers is same, the weight of the greatest throwing power is not completely sam
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given
optimization problem is an important task in evolutionary computation. We
compare in this work the results of four different hyperparameter tuning
approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean
optimization problems. More precisely, we compare previously obtained results
from a grid search with those obtained from three automated configuration
techniques: iterated racing, mixed-integer parallel efficient global
optimization, and mixed-integer evolutionary strategies.
Using two different cost metrics, expected running time and the area under
the empirical cumulative distribution function curve, we find that in several
cases the best configurations with respect to expected running time are
obtained when using the area under the empirical cumulative distribution
function curve as the cost metric during the configuration process. Our results
suggest that even when interested in expected running time performance, it
might be preferable to use anytime performance measures for the configuration
task. We also observe that tuning for expected running time is much more
sensitive with respect to the budget that is allocated to the target
algorithms
Benchmarking a Genetic Algorithm with Configurable Crossover Probability
We investigate a family of Genetic Algorithms (GAs) which
creates offspring either from mutation or by recombining two randomly chosen
parents. By scaling the crossover probability, we can thus interpolate from a
fully mutation-only algorithm towards a fully crossover-based GA. We analyze,
by empirical means, how the performance depends on the interplay of population
size and the crossover probability.
Our comparison on 25 pseudo-Boolean optimization problems reveals an
advantage of crossover-based configurations on several easy optimization tasks,
whereas the picture for more complex optimization problems is rather mixed.
Moreover, we observe that the ``fast'' mutation scheme with its are power-law
distributed mutation strengths outperforms standard bit mutation on complex
optimization tasks when it is combined with crossover, but performs worse in
the absence of crossover.
We then take a closer look at the surprisingly good performance of the
crossover-based GAs on the well-known LeadingOnes benchmark
problem. We observe that the optimal crossover probability increases with
increasing population size . At the same time, it decreases with
increasing problem dimension, indicating that the advantages of the crossover
are not visible in the asymptotic view classically applied in runtime analysis.
We therefore argue that a mathematical investigation for fixed dimensions might
help us observe effects which are not visible when focusing exclusively on
asymptotic performance bounds
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