• Journal of Power Electronics
• /
• v.18 no.3
• /
• pp.802-816
• /
• 2018

A digital direct phase-shift control (DDPSC) method based on the phase-shifted full-bridge LLC (PSFB-LLC) converter is presented. This work combines DDPSC with the conventional linear control to obtain a hybrid control strategy that has the advantages of linear control and DDPSC control. The strategy is easy to realize and has good dynamic responses. The PSFB-LLC circuit structure is simple and works in the fixed frequency mode, which is beneficial to magnetic component design; it can realize the ZVS of the switch and the ZCS of the rectifier diode in a wide load range. In this work, the PSFB-LLC converter resonator is analyzed in detail, and the concrete realization scheme of the hybrid control strategy is provided by analyzing the state-plane trajectory and the time-domain model. Finally, a 3 kW prototype is developed, and the feasibility and effectiveness of the DDPSC controller and the hybrid strategy are verified by experimental results.

• Journal of Drive and Control
• /
• v.12 no.3
• /
• pp.18-24
• /
• 2015

The plug-in hybrid electric vehicle has a high fuel economy and can be driven long distances. Its different modes include the electric vehicle, hybrid electric vehicle, and only engine operating mode. A power management strategy is important to determine which mode should be selected. The strategy makes the vehicle more efficient using appropriate power sources for driving. However, the strategy usually needs a driving speed profile which is future driving cycle. If the profile is known, the strategy easily determines which mode is driven efficiently. However, it is difficult to estimate the speed profile for a real system. To address this problem, this paper proposes a new power distribution strategy using a neural network. The average speed and driving range are used as input parameters to train the neural network system. The strategy determines a limit for the use of the battery and the desired power is distributed between the engine and the motor simultaneously. Its fuel economy can increase by improving the basic strategy.

• Journal of Power Electronics
• /
• v.19 no.2
• /
• pp.549-559
• /
• 2019

This paper proposes an adaptive sliding mode control (ASMC) strategy with an enhanced optimal reaching law (EORL) for the robust current tracking control of the boost converter based hybrid power source (HPS) in an electric vehicle (EV). A conventional ASMC strategy based on state observers and the hysteresis control method is used to realize the current tracking control for the boost converter based HPS. Then a novel enhanced exponential reaching law is proposed to improve the ASMC. Moreover, an enhanced exponential reaching law is optimized by particle swarm optimization. Finally, the adaptive control factor is redesigned based on the EORL. Simulations and experiments are established to validate the ASMC strategy with the EORL. Results show that the ASMC strategy with the EORL has an excellent current tracking control effect for the boost converter based HPS. When compared with the conventional ASMC strategy, the convergence time of the ASMC strategy with the EORL can be effectively improved. In EV applications, the ASMC strategy with the EORL can achieve robust current tracking control of the boost converter based HPS. It can guarantee the active and stable power distribution for boost converter based HPS.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1684-1692
• /
• 2016

In order to adapt the fast dynamic performances of Buck DC/DC converter, and reduce the influence on converter performance owing to uncertain factors such as the disturbances of parameters and load, a control strategy based on two-dimensional hybrid cloud model is proposed. Firstly, two cloud models corresponding to the specific control inputs are determined by maximum determination approach, respectively, and then a control rule decided by the two cloud models is selected by a rule selector, finally, according to the reasoning structure of the rule, the control increment is calculated out by a two-dimensional hybrid cloud decision module. Both the simulation and experiment results show that the strategy can dramatically improve the dynamic performances of the converter, and enhance the adaptive ability to resist the random disturbances, and its control effect is superior to that of the current-mode control.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.23 no.2
• /
• pp.239-245
• /
• 2015

In order to control the hybrid power system efficiently, the knowledge for the required load of the system is important. The agricultural tractor performs various farm works such as plow, rotary, and baler. When it performs rotary tillage and baler operation, the generated work load is analyzed. To analyze trend of work load, moving average technique is applied to the measurement data. Optimal control inputs for the two works are obtained from simulation using the dynamic programming. The novel fundamental control strategy for parallel hybrid tractor called Max. SOC is proposed.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.6
• /
• pp.46-51
• /
• 2012

Fuel Cell Hybrid Vehicles (FCHVs) have become a major topic of interest in the automotive industry owing to recent energy supply and environmental problems. Several types of power management strategies have been developed to improve the fuel economy of FCHVs including optimal control strategy based on optimal control theory, rule-based strategy, and equivalent consumption minimization strategy (ECMS). The ECMS is applied in this study. This strategy is based on the heuristic concept that the usage of the electric energy can be exchanged to equivalent fuel consumption. This strategy is known as one of the promising solutions for real-time control of hybrid vehicles. The ECMS for an FCHV is introduced in this paper as well as the equivalent fuel consumption parameter. The relationship between the battery final state of charge (SOC) and the fuel consumption while changing the equivalent fuel consumption parameter is obtained for three different driving cycles. The function of the equivalent fuel consumption parameter is also discussed.

• Journal of Computing Science and Engineering
• /
• v.8 no.4
• /
• pp.199-206
• /
• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.198-200
• /
• 2006

In this paper, three types of power control strategies for controlling a Fuel Cell Hybrid Electric Vehicle(FCHEV) are studied in view of fuel economy. The FCHEV has become one of alternatives for future vehicles since it does emit water only without any exhaust gas while it has a high well-to-wheel efficiency together with an energy saving due to regenerative braking. However, it has also several disadvantages such as the complexity of vehicle system, the increased weight and the extra battery cost. Among various power control strategies, a static power control strategy, a power assist control strategy and a fuzzy logic-based power control strategy are simulated and compared to show the effectiveness of each method.

• Proceedings of the KIPE Conference
• /
• 2013.07a
• /
• pp.467-468
• /
• 2013

In this paper, a switching control strategy of bidirectional converter for energy storage system in photovoltaic hybrid modules is proposed. The bidirectional converter for energy storage system (ESS) with battery is connected with DC link in parallel which is located between current source flyback converters(CSFC) and unfolding bridge. Because CSFC generates rectified sinusoidal current, the bidirectional converter requires suitable control strategy. Therefore, a theoretical analysis of the proposed switching control strategy is presented. And, validity is confirmed through simulation results.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.1-11
• /
• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.