Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries

Petchjatuporn, P.; Khaehintung, N.; Sunat, Khamron; Sirisuk, P.; Kiranon, W.

doi:10.1109/ipemc.2006.4777963

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…In the second case an adaptation algorithm is required to adjust GRNN parameters. The second approach is shown in Fig Some of the applications of GRNN in control systems include dead-zone estimation and compensation in motion control of a traveling wave ultrasonic motor [15], fault diagnosis of power system [16], intelligent battery charger [17], microgrid hybrid power systems control [18], bipedal standing stabilization [19], air conditioning control [20], wind generation system [21], helicopter motion control [22], active vibration control [23], active noise cancellation [24], rat-like robot control [25], pipe climbing robot control [26], tracking-control for an optomechatronical Image derotator [27], tracking in marine navigational radars [28], factory monitoring [29], and flapping wing micro aerial vehicle control [3].…”

Section: Applications Of Grnn In Control Systemsmentioning

confidence: 99%

Applications of General Regression Neural Networks in Dynamic Systems

Al-Mahasneh¹,

Anavatti²,

Garratt³

et al. 2018

Digital Systems

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This paper depicts a brief revision of Generalized Regression Neural Networks (GRNN) applications in system identification and control of dynamic systems. In addition, a comparison study between the performance of back-propagation neural networks and GRNN is presented for system identification problems. The results of the comparison confirms that, GRNN has shorter training time and higher accuracy than the counterpart back-propagation neural networks.

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Section: Applications Of Grnn In Control Systemsmentioning

confidence: 99%

Applications of General Regression Neural Networks in Dynamic Systems

Al-Mahasneh¹,

Anavatti²,

Garratt³

et al. 2018

Digital Systems

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“…First, we look at the charging problem using an equivalent electrical circuit, more realistic than the model used in [1]. There are different approaches for charging batteries in the literature, including: traditional methods of charging such as the constant trickle current charge strategy, constant current strategy and the constant-current constant-voltage (CC-CV) [2], multi-step constant-current charging [3], Taguchi-based methods [4], [5], boost charging [6], pulse-charging [7], [8], [9], [10], ant-colony based optimization of multistage constant current strategy [11], optimal-control based approaches [12], neural network [13], Grey-predicted charging system [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal charging for general equivalent electrical battery model, and battery life management

Abdollahi

Han

Raghunathan

et al. 2017

Journal of Energy Storage

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The contributions of this paper are threefold. First, we present the linear quadratic solution to optimally charge a Li-ion battery in a general form. Although the battery model considered here is a circuit comprised of an open-circuit voltage (OCV), a series resistance, and an RC circuit, the methodology is applicable to any electrical circuit model of a battery. A combination of different cost functions is considered including: time-to-charge, energy loss, and temperature rise index. We discuss the effect of different weightings in the cost functions on the current and voltage profiles. Second, we present two models for normalized battery capacity as a function of the number of cycles and two charge control parameters, viz., maximum terminal voltage of the battery and maximum charge current. These models are compared to a bi-exponential capacity model. The effectiveness of the proposed models for forecasting the battery capacity is validated using the experimental data. Third, these models are used for battery life management by developing an optimal charging parameter selection method which provides the best setpoint values for the control variables to achieve a pre-specified desired "useful cycle life" while attaining the fastest possible time-to-charge. The proposed optimal charging parameter selection method is illustrated via numerical results.

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“…Recently, in [14], battery charging is considered as an optimization problem with cost function of time-to-charge and energy loss (as we do in this paper), but they have not solved the problem analytically; rather they have presented a numerical solution to the problem. Other approaches, such as genetic algorithm and neural network based strategies [20], data mining [4], [13], Grey-predicted charging system [11] have also been used for charging batteries.…”

Section: Introductionmentioning

confidence: 99%

Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

Abdollahi

Han

Balasingam

et al. 2016

Journal of Power Sources

100

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In this paper, the first of a series of papers on optimal battery charging, we present a closed-form solution to the problem of optimally charging a Li-ion battery. A combination of three cost functions is considered as the objective function: time-to-charge (TTC), energy losses (EL), and a temperature rise index (TRI). First, we consider the cost function of the optimization problem as a weighted sum of TTC and EL. We show that the optimal charging strategy in this case is the well-known Constant Current-Constant Voltage (CC-CV) policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery. Then, we extend the cost function to a weighted sum of TTC, EL and TRI and derive an analytical solution for the problem. It is shown that the analytical solution can be approximated by a CC-CV with the value of current in the CC stage being a function of ratio of weighting on TTC and EL, resistance of the battery and the effective thermal resistance. Index Terms battery charging, optimal charging, time to charge, open circuit voltage, state of charge.

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Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries

Cited by 8 publications

References 7 publications

Applications of General Regression Neural Networks in Dynamic Systems

Applications of General Regression Neural Networks in Dynamic Systems

Optimal charging for general equivalent electrical battery model, and battery life management

Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

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